Applied System Innovation Latest open access articles published in Appl. Syst. Innov. at https://www.mdpi.com/journal/asi https://www.mdpi.com/journal/asi MDPI en Creative Commons Attribution (CC-BY) MDPI support@mdpi.com ASI, Vol. 9, Pages 99: AI-Driven Decision Support Beneath Uncertainty: A Hybrid Bayesian–PLS Model for Systemic Sustainability Innovation https://www.mdpi.com/2571-5577/9/5/99 This study examines Responsible Decision-Making (RADM) in AI-enabled sustainability within tertiary education under conditions of uncertainty and complex interdependence. Conventional analytical approaches are limited in such settings because they typically explain behavioural relationships without adequately modelling uncertainty. To address this limitation, the study proposes an AI-driven Decision Support System (DSS) based on a hybrid probabilistic framework integrating PLS-SEM with Bayesian Network (BN) inference. The framework combines structural analysis with probabilistic reasoning in a unified, interpretable system capable of modelling conditional dependencies among decision variables. Data were collected from 713 academic leaders in tertiary education institutions in Saudi Arabia. The model examines the effects of AI-Driven Sustainable Value (AISV), Responsible AI Ease of Use (RAIU), Institutional Sustainability Support (ISS), Ethical Leadership Norms (ELN), Responsible AI Competence (RAC), and AI Risk and Hallucination Awareness (ARHA) on Responsible Decision-Making and Sustainability Impact Performance (GGIP). The results indicate that ELN and ARHA have significant positive effects on RADM, while AISV and RAIU also contribute positively to decision quality. In contrast, ISS and RAC do not demonstrate significant direct effects on RADM. However, ISS shows indirect effects through contextual and cognitive pathways. The findings further suggest that awareness of uncertainty and AI-related risks plays a more influential role in decision quality than technical competence alone. The model demonstrates strong explanatory power (R2 = 0.64) and acceptable predictive capability (R2 = 0.48). Bayesian inference further indicates that sustainability outcomes improve under favourable institutional and cognitive conditions. Overall, the framework provides an interpretable and scalable DSS that supports scenario-based evaluation and probabilistic decision analysis under uncertainty. The findings are specific to the institutional context examined in this study. Although the framework may have relevance to other organisational environments characterised by uncertainty and complex decision structures, no external or cross-contextual validation was conducted. Therefore, the findings should be interpreted with appropriate contextual caution. 2026-05-12 ASI, Vol. 9, Pages 99: AI-Driven Decision Support Beneath Uncertainty: A Hybrid Bayesian–PLS Model for Systemic Sustainability Innovation

Applied System Innovation doi: 10.3390/asi9050099

Authors: Mostafa Aboulnour Salem

This study examines Responsible Decision-Making (RADM) in AI-enabled sustainability within tertiary education under conditions of uncertainty and complex interdependence. Conventional analytical approaches are limited in such settings because they typically explain behavioural relationships without adequately modelling uncertainty. To address this limitation, the study proposes an AI-driven Decision Support System (DSS) based on a hybrid probabilistic framework integrating PLS-SEM with Bayesian Network (BN) inference. The framework combines structural analysis with probabilistic reasoning in a unified, interpretable system capable of modelling conditional dependencies among decision variables. Data were collected from 713 academic leaders in tertiary education institutions in Saudi Arabia. The model examines the effects of AI-Driven Sustainable Value (AISV), Responsible AI Ease of Use (RAIU), Institutional Sustainability Support (ISS), Ethical Leadership Norms (ELN), Responsible AI Competence (RAC), and AI Risk and Hallucination Awareness (ARHA) on Responsible Decision-Making and Sustainability Impact Performance (GGIP). The results indicate that ELN and ARHA have significant positive effects on RADM, while AISV and RAIU also contribute positively to decision quality. In contrast, ISS and RAC do not demonstrate significant direct effects on RADM. However, ISS shows indirect effects through contextual and cognitive pathways. The findings further suggest that awareness of uncertainty and AI-related risks plays a more influential role in decision quality than technical competence alone. The model demonstrates strong explanatory power (R2 = 0.64) and acceptable predictive capability (R2 = 0.48). Bayesian inference further indicates that sustainability outcomes improve under favourable institutional and cognitive conditions. Overall, the framework provides an interpretable and scalable DSS that supports scenario-based evaluation and probabilistic decision analysis under uncertainty. The findings are specific to the institutional context examined in this study. Although the framework may have relevance to other organisational environments characterised by uncertainty and complex decision structures, no external or cross-contextual validation was conducted. Therefore, the findings should be interpreted with appropriate contextual caution.

]]> AI-Driven Decision Support Beneath Uncertainty: A Hybrid Bayesian–PLS Model for Systemic Sustainability Innovation Mostafa Aboulnour Salem doi: 10.3390/asi9050099 Applied System Innovation 2026-05-12 Applied System Innovation 2026-05-12 9 5 Article 99 10.3390/asi9050099 https://www.mdpi.com/2571-5577/9/5/99 ASI, Vol. 9, Pages 98: Maritime Integrated Systems Architecture in the Digital Era: A Systematic Review of Model-Based Approaches, Interoperability, and Resilience https://www.mdpi.com/2571-5577/9/5/98 Background: Maritime operations increasingly rely on integrated, secure, and resilient architectures, yet the associated body of knowledge remains fragmented across conceptual, operational, logical, methodological, and governance-oriented perspectives. Objective: Our aim is to systematically review the literature on maritime integrated systems architecture in order to identify dominant themes, methodological tendencies, enabling technologies, and unresolved research gaps. Eligibility criteria: Peer-reviewed studies published in English were included when they addressed integrated systems architecture, or closely related architectural approaches, in maritime or naval contexts. Studies centred exclusively on isolated components, non-maritime settings without clear architectural transferability, or insufficient technical or methodological detail were excluded. Information sources: ACM Digital Library, IEEE Xplore, SpringerLink, ScienceDirect, MDPI, and IMarEST. Searches were carried out between January and March 2025, with the final search update for all sources completed in March 2025. Methods: The review was conducted and reported in accordance with PRISMA 2020. Three reviewers independently screened titles, abstracts, and full texts. Two reviewers independently extracted data, assessed methodological limitations and risk of bias using a review-specific qualitative appraisal framework, and evaluated the risk of bias due to missing results at the synthesis level. Disagreements were resolved through discussion and consensus, with third-reviewer arbitration when necessary. The synthesis combined qualitative thematic analysis across eleven predefined analytical categories with descriptive bibliometric and thematic mapping procedures. Results: Of 300 identified records, 60 studies met the inclusion criteria. Across non-mutually exclusive analytical categories, the literature was concentrated in Integrated Systems Architecture (52 studies), Development Processes (42), and Conceptual Models (37), whereas Zachman-based Methodology (4) and Maturity Models (3) were only marginally represented. Three recurrent patterns were observed across the corpus: the central role of cybersecurity and risk governance in architectural design; the growing importance of information technology and operational technology convergence for resilient monitoring, coordination, and decision support; and the increasing use of model-based and model-driven approaches to address architectural complexity. Overall confidence in the principal synthesized findings was judged to be moderate. Limitations: The review was limited to six databases and English-language publications, and the included studies varied in reporting depth, methodological transparency, and degree of empirical validation. Conclusions: The review organizes the field into a multilevel taxonomy spanning conceptual and operational models, logical and layered views, development processes, reference architectures, enabling technologies, and maturity-related perspectives. Taken together, the findings suggest that research in this area has progressed more clearly in architectural representation and integration than in long-term evaluation, particularly with regard to the practical operationalization of Zachman-based approaches and the development of maritime-specific maturity assessment frameworks. Funding: This review received no external funding. Registration: The review was not prospectively registered, and no publicly accessible protocol was prepared. 2026-05-12 ASI, Vol. 9, Pages 98: Maritime Integrated Systems Architecture in the Digital Era: A Systematic Review of Model-Based Approaches, Interoperability, and Resilience

Applied System Innovation doi: 10.3390/asi9050098

Authors: Ernesto José García Fernández de Castro Leonardo Lizcano Daladier Jabba Miguel Jimeno Wilson Nieto Bernal Andrés Pedraza

Background: Maritime operations increasingly rely on integrated, secure, and resilient architectures, yet the associated body of knowledge remains fragmented across conceptual, operational, logical, methodological, and governance-oriented perspectives. Objective: Our aim is to systematically review the literature on maritime integrated systems architecture in order to identify dominant themes, methodological tendencies, enabling technologies, and unresolved research gaps. Eligibility criteria: Peer-reviewed studies published in English were included when they addressed integrated systems architecture, or closely related architectural approaches, in maritime or naval contexts. Studies centred exclusively on isolated components, non-maritime settings without clear architectural transferability, or insufficient technical or methodological detail were excluded. Information sources: ACM Digital Library, IEEE Xplore, SpringerLink, ScienceDirect, MDPI, and IMarEST. Searches were carried out between January and March 2025, with the final search update for all sources completed in March 2025. Methods: The review was conducted and reported in accordance with PRISMA 2020. Three reviewers independently screened titles, abstracts, and full texts. Two reviewers independently extracted data, assessed methodological limitations and risk of bias using a review-specific qualitative appraisal framework, and evaluated the risk of bias due to missing results at the synthesis level. Disagreements were resolved through discussion and consensus, with third-reviewer arbitration when necessary. The synthesis combined qualitative thematic analysis across eleven predefined analytical categories with descriptive bibliometric and thematic mapping procedures. Results: Of 300 identified records, 60 studies met the inclusion criteria. Across non-mutually exclusive analytical categories, the literature was concentrated in Integrated Systems Architecture (52 studies), Development Processes (42), and Conceptual Models (37), whereas Zachman-based Methodology (4) and Maturity Models (3) were only marginally represented. Three recurrent patterns were observed across the corpus: the central role of cybersecurity and risk governance in architectural design; the growing importance of information technology and operational technology convergence for resilient monitoring, coordination, and decision support; and the increasing use of model-based and model-driven approaches to address architectural complexity. Overall confidence in the principal synthesized findings was judged to be moderate. Limitations: The review was limited to six databases and English-language publications, and the included studies varied in reporting depth, methodological transparency, and degree of empirical validation. Conclusions: The review organizes the field into a multilevel taxonomy spanning conceptual and operational models, logical and layered views, development processes, reference architectures, enabling technologies, and maturity-related perspectives. Taken together, the findings suggest that research in this area has progressed more clearly in architectural representation and integration than in long-term evaluation, particularly with regard to the practical operationalization of Zachman-based approaches and the development of maritime-specific maturity assessment frameworks. Funding: This review received no external funding. Registration: The review was not prospectively registered, and no publicly accessible protocol was prepared.

]]> Maritime Integrated Systems Architecture in the Digital Era: A Systematic Review of Model-Based Approaches, Interoperability, and Resilience Ernesto José García Fernández de Castro Leonardo Lizcano Daladier Jabba Miguel Jimeno Wilson Nieto Bernal Andrés Pedraza doi: 10.3390/asi9050098 Applied System Innovation 2026-05-12 Applied System Innovation 2026-05-12 9 5 Systematic Review 98 10.3390/asi9050098 https://www.mdpi.com/2571-5577/9/5/98 ASI, Vol. 9, Pages 97: Total Ionizing Dose Effects Investigation on the Performance of MEMS Microphone Irradiated by γ-Ray https://www.mdpi.com/2571-5577/9/5/97 Data collected by sensors plays a critical role in system decision-making. Microphone arrays enable distance measurement and fault localization, which is particularly critical in the radiation environments of nuclear facilities. Acoustic localization based on microphone arrays can effectively fulfill this requirement. This study experimentally evaluates the Total Ionizing Dose (TID) effects of 60Co γ-ray radiation on commercial MEMS (micro-electro-mechanical systems) silicon microphones. Five identical microphone units were simultaneously irradiated at a dose rate of 0.0342 Gy(Si)/s while continuously monitoring operating current and spectral response. Experimental results show that the commercial MEMS silicon microphones exhibit an average TID failure threshold of 932.6 ± 62.8 Gy(Si), with a 95% confidence interval of [875.5, 989.7] Gy(Si). Three degradation/failure levels are clearly defined: channel degradation, channel failure, and full system failure. Radiation exposure causes a progressive increase in operating current (up to 6.7 times the initial value), severe spectral distortion, and ultimately complete loss of localization function. This indicated that standard commercial MEMS silicon microphones possess a certain degree of tolerance to TID radiation. Subsequently, an annealing test was performed. However, Post-irradiation annealing restored the operating current but not the acoustic performance, indicating irreversible radiation-induced damage. 2026-05-09 ASI, Vol. 9, Pages 97: Total Ionizing Dose Effects Investigation on the Performance of MEMS Microphone Irradiated by γ-Ray

Applied System Innovation doi: 10.3390/asi9050097

Authors: Panfeng Zhang Xuecheng Du Chao Ma Yiran Wu Zhenya Li Hao Yun Jiajun Wei Zhirui Zheng

Data collected by sensors plays a critical role in system decision-making. Microphone arrays enable distance measurement and fault localization, which is particularly critical in the radiation environments of nuclear facilities. Acoustic localization based on microphone arrays can effectively fulfill this requirement. This study experimentally evaluates the Total Ionizing Dose (TID) effects of 60Co γ-ray radiation on commercial MEMS (micro-electro-mechanical systems) silicon microphones. Five identical microphone units were simultaneously irradiated at a dose rate of 0.0342 Gy(Si)/s while continuously monitoring operating current and spectral response. Experimental results show that the commercial MEMS silicon microphones exhibit an average TID failure threshold of 932.6 ± 62.8 Gy(Si), with a 95% confidence interval of [875.5, 989.7] Gy(Si). Three degradation/failure levels are clearly defined: channel degradation, channel failure, and full system failure. Radiation exposure causes a progressive increase in operating current (up to 6.7 times the initial value), severe spectral distortion, and ultimately complete loss of localization function. This indicated that standard commercial MEMS silicon microphones possess a certain degree of tolerance to TID radiation. Subsequently, an annealing test was performed. However, Post-irradiation annealing restored the operating current but not the acoustic performance, indicating irreversible radiation-induced damage.

]]> Total Ionizing Dose Effects Investigation on the Performance of MEMS Microphone Irradiated by γ-Ray Panfeng Zhang Xuecheng Du Chao Ma Yiran Wu Zhenya Li Hao Yun Jiajun Wei Zhirui Zheng doi: 10.3390/asi9050097 Applied System Innovation 2026-05-09 Applied System Innovation 2026-05-09 9 5 Article 97 10.3390/asi9050097 https://www.mdpi.com/2571-5577/9/5/97 ASI, Vol. 9, Pages 96: A Systematic Review of Eco-Adaptive Cruise Control for Electric Vehicles: Control Strategies, Computational Challenges, and the Simulation-to-Reality Gap https://www.mdpi.com/2571-5577/9/5/96 Energy-aware Adaptive Cruise Control (Eco-ACC) has become an essential approach for enhancing the energy efficiency of electric vehicles while ensuring safe and comfortable driving. This paper presents a systematic review, following the PRISMA methodology, of 60 recent studies published between 2021 and 2025. The review provides a structured analysis of control strategies, validation approaches, computational demands, and battery-related considerations in Eco-ACC systems. The results indicate that Model Predictive Control (MPC) remains the most widely adopted technique (41.7%), primarily due to its ability to handle system constraints and address multi-objective optimization problems. Reinforcement Learning (RL) approaches (33.3%) are increasingly explored for their capability to adapt to uncertain and dynamic driving conditions. In addition, hybrid MPC–AI methods (16.7%) show strong potential for balancing optimal control performance with real-time implementation requirements. A key observation is the clear imbalance in validation practices: more than 73% of the studies rely on simulation-based evaluation, whereas only 10% include real-world experiments, revealing a pronounced simulation-to-reality (sim2real) gap. Furthermore, two critical research gaps are identified. First, the computational energy paradox highlights the trade-off between improved control performance and increased computational cost. Second, battery-aware control remains insufficiently addressed, as most existing methods overlook long-term battery degradation effects. Based on these findings, this review proposes a deployment-oriented research framework that prioritizes hybrid control architectures, real-time feasibility, and robust validation strategies, including Hardware-in-the-Loop and field testing. The presented insights aim to support the development of practical and energy-efficient Eco-ACC systems suitable for real-world deployment in next-generation electric vehicles. 2026-04-30 ASI, Vol. 9, Pages 96: A Systematic Review of Eco-Adaptive Cruise Control for Electric Vehicles: Control Strategies, Computational Challenges, and the Simulation-to-Reality Gap

Applied System Innovation doi: 10.3390/asi9050096

Authors: Mostafa A. Mahdy A. Abdellatif Mohamed Fawzy El-Khatib

Energy-aware Adaptive Cruise Control (Eco-ACC) has become an essential approach for enhancing the energy efficiency of electric vehicles while ensuring safe and comfortable driving. This paper presents a systematic review, following the PRISMA methodology, of 60 recent studies published between 2021 and 2025. The review provides a structured analysis of control strategies, validation approaches, computational demands, and battery-related considerations in Eco-ACC systems. The results indicate that Model Predictive Control (MPC) remains the most widely adopted technique (41.7%), primarily due to its ability to handle system constraints and address multi-objective optimization problems. Reinforcement Learning (RL) approaches (33.3%) are increasingly explored for their capability to adapt to uncertain and dynamic driving conditions. In addition, hybrid MPC–AI methods (16.7%) show strong potential for balancing optimal control performance with real-time implementation requirements. A key observation is the clear imbalance in validation practices: more than 73% of the studies rely on simulation-based evaluation, whereas only 10% include real-world experiments, revealing a pronounced simulation-to-reality (sim2real) gap. Furthermore, two critical research gaps are identified. First, the computational energy paradox highlights the trade-off between improved control performance and increased computational cost. Second, battery-aware control remains insufficiently addressed, as most existing methods overlook long-term battery degradation effects. Based on these findings, this review proposes a deployment-oriented research framework that prioritizes hybrid control architectures, real-time feasibility, and robust validation strategies, including Hardware-in-the-Loop and field testing. The presented insights aim to support the development of practical and energy-efficient Eco-ACC systems suitable for real-world deployment in next-generation electric vehicles.

]]> A Systematic Review of Eco-Adaptive Cruise Control for Electric Vehicles: Control Strategies, Computational Challenges, and the Simulation-to-Reality Gap Mostafa A. Mahdy A. Abdellatif Mohamed Fawzy El-Khatib doi: 10.3390/asi9050096 Applied System Innovation 2026-04-30 Applied System Innovation 2026-04-30 9 5 Review 96 10.3390/asi9050096 https://www.mdpi.com/2571-5577/9/5/96 ASI, Vol. 9, Pages 95: Agricultural Intelligence: A Technical Review Within the Perception–Decision–Execution Framework https://www.mdpi.com/2571-5577/9/5/95 Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems. 2026-04-30 ASI, Vol. 9, Pages 95: Agricultural Intelligence: A Technical Review Within the Perception–Decision–Execution Framework

Applied System Innovation doi: 10.3390/asi9050095

Authors: Shaode Yu Xinyi Li Songnan Zhao Qian Liu

Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems.

]]> Agricultural Intelligence: A Technical Review Within the Perception–Decision–Execution Framework Shaode Yu Xinyi Li Songnan Zhao Qian Liu doi: 10.3390/asi9050095 Applied System Innovation 2026-04-30 Applied System Innovation 2026-04-30 9 5 Review 95 10.3390/asi9050095 https://www.mdpi.com/2571-5577/9/5/95 ASI, Vol. 9, Pages 94: Impact of Wind Speed Variations on Frequency Control in Grid-Forming PMSG-Based Wind Turbines https://www.mdpi.com/2571-5577/9/5/94 With the fast penetration of renewable energy resources (RERs) in modern power grids, system inertia is gradually decreasing, whereby threatening frequency stability. Grid-forming (GFM) permanent magnet synchronous generator (PMSG) wind turbines have emerged as a promising solution for supporting and maintaining power system stability. Nevertheless, many studies neglect the inherent intermittency and limited power capability of RERs. As a result, the dynamic interactions between machine-side and grid-side converters are often neglected, and the DC link is commonly modeled as either an ideal voltage source or a controlled current source, which may lead to inaccurate representations of system dynamics. As a solution, this paper investigates the influence of RER intermittency and power constraints on DC-link dynamics and their impact on the frequency support performance of GFM PMSGs. First, the overall system is configured using back-to-back voltage source converters, and the system’s dynamic equations are presented. Afterwards, the impact of wind speed variations is thoroughly discussed, alongside a critical examination of the requirements specified in IEEE Standard 2800-2022. Furthermore, a supervisory curtailment strategy is proposed to ensure overall system stability under severe load disturbances when the PMSG is unable to supply the required power. Finally, detailed case studies are conducted to: (1) assess the influence of variable wind speed and DC-link voltage control on the dynamic response of PMSGs, and (2) compare the performance of the accurate DC-link dynamic model with conventional idealized and simplified models. 2026-04-30 ASI, Vol. 9, Pages 94: Impact of Wind Speed Variations on Frequency Control in Grid-Forming PMSG-Based Wind Turbines

Applied System Innovation doi: 10.3390/asi9050094

Authors: Masood Mottaghizadeh Shayan Soltani Innocent Kamwa Abbas Rabiee Seyed Masoud Mohseni-Bonab

With the fast penetration of renewable energy resources (RERs) in modern power grids, system inertia is gradually decreasing, whereby threatening frequency stability. Grid-forming (GFM) permanent magnet synchronous generator (PMSG) wind turbines have emerged as a promising solution for supporting and maintaining power system stability. Nevertheless, many studies neglect the inherent intermittency and limited power capability of RERs. As a result, the dynamic interactions between machine-side and grid-side converters are often neglected, and the DC link is commonly modeled as either an ideal voltage source or a controlled current source, which may lead to inaccurate representations of system dynamics. As a solution, this paper investigates the influence of RER intermittency and power constraints on DC-link dynamics and their impact on the frequency support performance of GFM PMSGs. First, the overall system is configured using back-to-back voltage source converters, and the system’s dynamic equations are presented. Afterwards, the impact of wind speed variations is thoroughly discussed, alongside a critical examination of the requirements specified in IEEE Standard 2800-2022. Furthermore, a supervisory curtailment strategy is proposed to ensure overall system stability under severe load disturbances when the PMSG is unable to supply the required power. Finally, detailed case studies are conducted to: (1) assess the influence of variable wind speed and DC-link voltage control on the dynamic response of PMSGs, and (2) compare the performance of the accurate DC-link dynamic model with conventional idealized and simplified models.

]]> Impact of Wind Speed Variations on Frequency Control in Grid-Forming PMSG-Based Wind Turbines Masood Mottaghizadeh Shayan Soltani Innocent Kamwa Abbas Rabiee Seyed Masoud Mohseni-Bonab doi: 10.3390/asi9050094 Applied System Innovation 2026-04-30 Applied System Innovation 2026-04-30 9 5 Article 94 10.3390/asi9050094 https://www.mdpi.com/2571-5577/9/5/94 ASI, Vol. 9, Pages 93: Agentic AI for Price-Only 15 min SDAC Market Diagnostics in Central and Eastern Europe https://www.mdpi.com/2571-5577/9/5/93 The shift to 15 min market time units (MTUs) in single-day-ahead coupling (SDAC) increases temporal granularity, but complicates the interpretation of intra-hour electricity price spikes and rapid ramps. This paper examines whether architectural decomposition improves the reliability of large language model (LLM)-based diagnostics in price-only settings, rather than causal market analytics, under severe information constraints. We compare a proposed agentic workflow featuring structured context extraction, spike/ramp detection, hypothesis generation, consistency checks, and explicit uncertainty calibration against non-agentic baselines. The paper contributes: (i) a reproducible benchmark for 15 min diagnostic question answering in day-ahead markets, (ii) an agentic architecture tailored to structured time-series reasoning with explicit uncertainty handling, and (iii) empirical evidence that decomposition and verification improve evidence grounding and trustworthiness in market analytics. The evaluation includes 360 price-only cases sampled across autumn 2025, winter 2025–2026, and early spring 2026, balanced by bidding zone, temporal period, event type, and impact tier, comprising 180 spike and 180 ramp cases from six Central and Eastern European bidding zones (Bulgaria, Czechia, Hungary, Poland, Romania, and Slovakia). Using identical inputs, we assess automatic reliability metrics and human ratings. The agentic workflow improves reliability (∆ = +0.067, 95% CI [+0.049, +0.085]) and significantly increases calibrated price-only disclaimers (∆ = +0.500) relative to the monolithic LLM baseline. Human evaluation confirms higher overall quality (+0.74), helpfulness (+1.06), and correctness (+0.94), with a 65.5% pairwise win rate. Overall, the results support a narrower conclusion: structured decomposition and verification improve calibration and perceived explanation quality relative to a simple monolithic LLM baseline, but their advantages are not uniform across stronger non-agentic baselines and remain limited by the absence of exogenous market data. 2026-04-29 ASI, Vol. 9, Pages 93: Agentic AI for Price-Only 15 min SDAC Market Diagnostics in Central and Eastern Europe

Applied System Innovation doi: 10.3390/asi9050093

Authors: Șener Ali Simona-Vasilica Oprea Adela Bâra

The shift to 15 min market time units (MTUs) in single-day-ahead coupling (SDAC) increases temporal granularity, but complicates the interpretation of intra-hour electricity price spikes and rapid ramps. This paper examines whether architectural decomposition improves the reliability of large language model (LLM)-based diagnostics in price-only settings, rather than causal market analytics, under severe information constraints. We compare a proposed agentic workflow featuring structured context extraction, spike/ramp detection, hypothesis generation, consistency checks, and explicit uncertainty calibration against non-agentic baselines. The paper contributes: (i) a reproducible benchmark for 15 min diagnostic question answering in day-ahead markets, (ii) an agentic architecture tailored to structured time-series reasoning with explicit uncertainty handling, and (iii) empirical evidence that decomposition and verification improve evidence grounding and trustworthiness in market analytics. The evaluation includes 360 price-only cases sampled across autumn 2025, winter 2025–2026, and early spring 2026, balanced by bidding zone, temporal period, event type, and impact tier, comprising 180 spike and 180 ramp cases from six Central and Eastern European bidding zones (Bulgaria, Czechia, Hungary, Poland, Romania, and Slovakia). Using identical inputs, we assess automatic reliability metrics and human ratings. The agentic workflow improves reliability (∆ = +0.067, 95% CI [+0.049, +0.085]) and significantly increases calibrated price-only disclaimers (∆ = +0.500) relative to the monolithic LLM baseline. Human evaluation confirms higher overall quality (+0.74), helpfulness (+1.06), and correctness (+0.94), with a 65.5% pairwise win rate. Overall, the results support a narrower conclusion: structured decomposition and verification improve calibration and perceived explanation quality relative to a simple monolithic LLM baseline, but their advantages are not uniform across stronger non-agentic baselines and remain limited by the absence of exogenous market data.

]]> Agentic AI for Price-Only 15 min SDAC Market Diagnostics in Central and Eastern Europe Șener Ali Simona-Vasilica Oprea Adela Bâra doi: 10.3390/asi9050093 Applied System Innovation 2026-04-29 Applied System Innovation 2026-04-29 9 5 Article 93 10.3390/asi9050093 https://www.mdpi.com/2571-5577/9/5/93 ASI, Vol. 9, Pages 92: Applications of Distribution Phasor Measurement Units for the Integration of Distributed Energy Resources in Modern Distribution Networks https://www.mdpi.com/2571-5577/9/5/92 The rapid growth of Distributed Energy Resources (DERs) has intensified operational challenges in modern distribution networks, especially with respect to observability, bidirectional power flow, feeder model accuracy, and fast event detection. This review critically examines the role of Distribution Phasor Measurement Units (D-PMUs) in this transition. Rather than only listing reported applications, the paper evaluates the technical and practical conditions under which D-PMUs provide meaningful value beyond conventional monitoring technologies. Particular attention is given to state estimation, event detection, ancillary operation, communication latency, synchronization vulnerability, economic viability, and the limited evidence from field deployment. The review shows that D-PMUs are especially attractive at feeder heads, DER interconnection points, switching locations, and microgrid boundaries, where synchronized phase-angle measurements improve visibility of dynamic and unbalanced phenomena. However, widespread deployment is still constrained by cost, communication infrastructure, interoperability, timing security, and the scarcity of publicly documented utility-scale results. The paper concludes by identifying the most promising research directions, including physics-aware learning, graph-based analytics, edge processing, and application-driven placement strategies for DER-rich distribution systems. 2026-04-29 ASI, Vol. 9, Pages 92: Applications of Distribution Phasor Measurement Units for the Integration of Distributed Energy Resources in Modern Distribution Networks

Applied System Innovation doi: 10.3390/asi9050092

Authors: John Steven Fierro-Rincón Carlos Arturo Lozano-Moncada Eduardo Gómez-Luna Luis Fernando Grisales-Noreña Daniel Sanin-Villa

The rapid growth of Distributed Energy Resources (DERs) has intensified operational challenges in modern distribution networks, especially with respect to observability, bidirectional power flow, feeder model accuracy, and fast event detection. This review critically examines the role of Distribution Phasor Measurement Units (D-PMUs) in this transition. Rather than only listing reported applications, the paper evaluates the technical and practical conditions under which D-PMUs provide meaningful value beyond conventional monitoring technologies. Particular attention is given to state estimation, event detection, ancillary operation, communication latency, synchronization vulnerability, economic viability, and the limited evidence from field deployment. The review shows that D-PMUs are especially attractive at feeder heads, DER interconnection points, switching locations, and microgrid boundaries, where synchronized phase-angle measurements improve visibility of dynamic and unbalanced phenomena. However, widespread deployment is still constrained by cost, communication infrastructure, interoperability, timing security, and the scarcity of publicly documented utility-scale results. The paper concludes by identifying the most promising research directions, including physics-aware learning, graph-based analytics, edge processing, and application-driven placement strategies for DER-rich distribution systems.

]]> Applications of Distribution Phasor Measurement Units for the Integration of Distributed Energy Resources in Modern Distribution Networks John Steven Fierro-Rincón Carlos Arturo Lozano-Moncada Eduardo Gómez-Luna Luis Fernando Grisales-Noreña Daniel Sanin-Villa doi: 10.3390/asi9050092 Applied System Innovation 2026-04-29 Applied System Innovation 2026-04-29 9 5 Review 92 10.3390/asi9050092 https://www.mdpi.com/2571-5577/9/5/92 ASI, Vol. 9, Pages 91: Experimental and Multiphysics Analysis of Graphene Oxide Paper-Based Ionic Thermoelectric Cell https://www.mdpi.com/2571-5577/9/5/91 Approximately 60% of the world’s primary energy is dissipated as waste heat, representing a critical opportunity for energy recovery in sectors such as electro-mobility and fuel cells. Commercial thermoelectric generators (TEGs), predominantly based on bismuth telluride (Bi2Te3), face limitations due to mechanical rigidity, toxicity, and high production costs. This study proposes graphene oxide (GO) as an emerging alternative thanks to its oxygenated functional groups and layered structure as well as GO paper facilitates’ thermal and electrical transport. However, the effective integration of this nanomaterial into solid-state systems under real operating conditions remains a technical challenge. Therefore, this work presents the development, multiphysics modeling, and experimental validation of an innovative TEG cell using GO paper as an active layer. The results demonstrate that the proposed GO-ITC achieves an average of 2.75 times higher generated voltage with a lower thermal gradient as well as an improved equivalent figure of merit (ZT) compared to Bi2Te3-based TEGs. This work contributes to the evaluation of GO-doped materials for voltage generation under specific thermal gradients, providing a lightweight and flexible solution for waste heat harvesting in modern power systems. 2026-04-29 ASI, Vol. 9, Pages 91: Experimental and Multiphysics Analysis of Graphene Oxide Paper-Based Ionic Thermoelectric Cell

Applied System Innovation doi: 10.3390/asi9050091

Authors: Iván Abel Hernández-Robles Xiomara González-Ramírez Aldo Elizarraraz-Perez Luis Ramón Merchan-Villalba Jesús Martínez-Patiño

Approximately 60% of the world’s primary energy is dissipated as waste heat, representing a critical opportunity for energy recovery in sectors such as electro-mobility and fuel cells. Commercial thermoelectric generators (TEGs), predominantly based on bismuth telluride (Bi2Te3), face limitations due to mechanical rigidity, toxicity, and high production costs. This study proposes graphene oxide (GO) as an emerging alternative thanks to its oxygenated functional groups and layered structure as well as GO paper facilitates’ thermal and electrical transport. However, the effective integration of this nanomaterial into solid-state systems under real operating conditions remains a technical challenge. Therefore, this work presents the development, multiphysics modeling, and experimental validation of an innovative TEG cell using GO paper as an active layer. The results demonstrate that the proposed GO-ITC achieves an average of 2.75 times higher generated voltage with a lower thermal gradient as well as an improved equivalent figure of merit (ZT) compared to Bi2Te3-based TEGs. This work contributes to the evaluation of GO-doped materials for voltage generation under specific thermal gradients, providing a lightweight and flexible solution for waste heat harvesting in modern power systems.

]]> Experimental and Multiphysics Analysis of Graphene Oxide Paper-Based Ionic Thermoelectric Cell Iván Abel Hernández-Robles Xiomara González-Ramírez Aldo Elizarraraz-Perez Luis Ramón Merchan-Villalba Jesús Martínez-Patiño doi: 10.3390/asi9050091 Applied System Innovation 2026-04-29 Applied System Innovation 2026-04-29 9 5 Article 91 10.3390/asi9050091 https://www.mdpi.com/2571-5577/9/5/91 ASI, Vol. 9, Pages 90: Hybrid Approaches of Machine Learning Algorithms in Predictive Maintenance: A Systematic Literature Review https://www.mdpi.com/2571-5577/9/5/90 The advent of Industry 4.0 has precipitated the digitization of myriad industrial processes, a feat attributable to the implementation of sophisticated digital enablers such as artificial intelligence (AI) and the Internet of Things (IoT). These technological advances have facilitated the implementation of various innovative applications, especially in the field of predictive maintenance. This approach facilitates more precise estimation of the remaining useful life (RUL) of equipment, determination of the health index (HI) of machinery, and planning of effective maintenance schedules that circumvent unexpected and costly shutdowns in industrial operations. The employment of hybrid approaches founded on machine learning algorithms in the domain of predictive maintenance signifies a perpetually evolving field of research, wherein novel techniques, methodologies, and strategies are proposed to enhance maintenance efficiency and reliability. In order to furnish a substantial and exhaustive compendium of information, a methodical literature review is hereby presented, offering a meticulous survey of the hybrid approaches utilized within this domain. The study analyzed 77 papers from the 914 papers found on the topic, to find and organize the body of knowledge, and presents a lucid taxonomy, the primary algorithms employed in hybrid approaches, the most prevalent datasets, the applicable technology architectures, and the maturity level of these solutions. This study provides a robust conceptual foundation for future research, underscoring the significance of hybrid approaches as a promising field of study, with considerable potential for advancement in the realm of industrial predictive maintenance. 2026-04-29 ASI, Vol. 9, Pages 90: Hybrid Approaches of Machine Learning Algorithms in Predictive Maintenance: A Systematic Literature Review

Applied System Innovation doi: 10.3390/asi9050090

Authors: Jorge Paredes Danilo Chavez Ramiro Isa-Jara Diego Vargas

The advent of Industry 4.0 has precipitated the digitization of myriad industrial processes, a feat attributable to the implementation of sophisticated digital enablers such as artificial intelligence (AI) and the Internet of Things (IoT). These technological advances have facilitated the implementation of various innovative applications, especially in the field of predictive maintenance. This approach facilitates more precise estimation of the remaining useful life (RUL) of equipment, determination of the health index (HI) of machinery, and planning of effective maintenance schedules that circumvent unexpected and costly shutdowns in industrial operations. The employment of hybrid approaches founded on machine learning algorithms in the domain of predictive maintenance signifies a perpetually evolving field of research, wherein novel techniques, methodologies, and strategies are proposed to enhance maintenance efficiency and reliability. In order to furnish a substantial and exhaustive compendium of information, a methodical literature review is hereby presented, offering a meticulous survey of the hybrid approaches utilized within this domain. The study analyzed 77 papers from the 914 papers found on the topic, to find and organize the body of knowledge, and presents a lucid taxonomy, the primary algorithms employed in hybrid approaches, the most prevalent datasets, the applicable technology architectures, and the maturity level of these solutions. This study provides a robust conceptual foundation for future research, underscoring the significance of hybrid approaches as a promising field of study, with considerable potential for advancement in the realm of industrial predictive maintenance.

]]> Hybrid Approaches of Machine Learning Algorithms in Predictive Maintenance: A Systematic Literature Review Jorge Paredes Danilo Chavez Ramiro Isa-Jara Diego Vargas doi: 10.3390/asi9050090 Applied System Innovation 2026-04-29 Applied System Innovation 2026-04-29 9 5 Systematic Review 90 10.3390/asi9050090 https://www.mdpi.com/2571-5577/9/5/90 ASI, Vol. 9, Pages 89: Automated Identification from CT Using Sphenoid Sinus Geometry as an Anatomical Biometric https://www.mdpi.com/2571-5577/9/5/89 Reliable identification of deceased individuals may be difficult when conventional biometric methods such as facial recognition, fingerprint analysis, or DNA profiling cannot be applied. In such cases, medical imaging records acquired during a person’s lifetime may serve as an alternative source of identifying information. Certain anatomical structures visible in computed tomography (CT), including the sphenoid sinus, exhibit considerable inter-individual variability while remaining relatively stable within the same individual. This study investigates the feasibility of using sphenoid sinus morphology as an anatomical biometric for automated identification from head CT scans. Identification is formulated as a ranking problem in which a query CT examination is compared with a reference database using geometric descriptors derived from segmentation masks, reducing dependence on CT intensity values. The dataset consisted of CT scans from 816 individuals acquired in two patient positioning modes: Head First Supine (HFS) and Head First Prone (HFP). Several deep learning architectures, including YOLOv8 variants, YOLO11L-seg, UNet++, DeepLabV3+, HRNet, and SegFormer-B2, were evaluated for sphenoid sinus segmentation. Based on F1-score performance and cross-mode stability, YOLO11L-seg was selected and further trained to construct a database of binary masks representing individual sphenoid sinus anatomy. Identification was performed using pairwise mask comparison based on the Intersection over Union (IoU) metric. To reduce the influence of segmentation artifacts and slice-level variability, the final similarity score for each candidate was computed as the average of the four highest IoU values across slice comparisons. Individuals were ranked according to similarity, and identification was considered successful if the correct subject appeared among the top five candidates and exceeded a predefined similarity threshold. The proposed approach achieved Top-5 identification accuracies of 97.27% for HFP and 87.67% for HFS acquisitions. These results demonstrate the feasibility of using sphenoid sinus geometry as a stable anatomical biometric for automated identification. The key contribution of this study is the introduction of a ranking-based identification framework that utilizes anatomical biometrics derived from CT data for reliable patient matching. 2026-04-29 ASI, Vol. 9, Pages 89: Automated Identification from CT Using Sphenoid Sinus Geometry as an Anatomical Biometric

Applied System Innovation doi: 10.3390/asi9050089

Authors: Nataliya Bilous Vladyslav Malko Dmytro Tkachenko Marcus Frohme

Reliable identification of deceased individuals may be difficult when conventional biometric methods such as facial recognition, fingerprint analysis, or DNA profiling cannot be applied. In such cases, medical imaging records acquired during a person’s lifetime may serve as an alternative source of identifying information. Certain anatomical structures visible in computed tomography (CT), including the sphenoid sinus, exhibit considerable inter-individual variability while remaining relatively stable within the same individual. This study investigates the feasibility of using sphenoid sinus morphology as an anatomical biometric for automated identification from head CT scans. Identification is formulated as a ranking problem in which a query CT examination is compared with a reference database using geometric descriptors derived from segmentation masks, reducing dependence on CT intensity values. The dataset consisted of CT scans from 816 individuals acquired in two patient positioning modes: Head First Supine (HFS) and Head First Prone (HFP). Several deep learning architectures, including YOLOv8 variants, YOLO11L-seg, UNet++, DeepLabV3+, HRNet, and SegFormer-B2, were evaluated for sphenoid sinus segmentation. Based on F1-score performance and cross-mode stability, YOLO11L-seg was selected and further trained to construct a database of binary masks representing individual sphenoid sinus anatomy. Identification was performed using pairwise mask comparison based on the Intersection over Union (IoU) metric. To reduce the influence of segmentation artifacts and slice-level variability, the final similarity score for each candidate was computed as the average of the four highest IoU values across slice comparisons. Individuals were ranked according to similarity, and identification was considered successful if the correct subject appeared among the top five candidates and exceeded a predefined similarity threshold. The proposed approach achieved Top-5 identification accuracies of 97.27% for HFP and 87.67% for HFS acquisitions. These results demonstrate the feasibility of using sphenoid sinus geometry as a stable anatomical biometric for automated identification. The key contribution of this study is the introduction of a ranking-based identification framework that utilizes anatomical biometrics derived from CT data for reliable patient matching.

]]> Automated Identification from CT Using Sphenoid Sinus Geometry as an Anatomical Biometric Nataliya Bilous Vladyslav Malko Dmytro Tkachenko Marcus Frohme doi: 10.3390/asi9050089 Applied System Innovation 2026-04-29 Applied System Innovation 2026-04-29 9 5 Article 89 10.3390/asi9050089 https://www.mdpi.com/2571-5577/9/5/89 ASI, Vol. 9, Pages 88: Adaptive Underwater Image Enhancement Techniques Using Deep Learning https://www.mdpi.com/2571-5577/9/5/88 Underwater images often suffer from degradations, including color distortion, reduced visibility, and low contrast due to light absorption and scatter in water. Numerous enhancement techniques have been proposed to improve visual quality and address these challenges. However, no single method consistently performs best across all underwater scenes. This work introduces a novel deep learning framework for the automatic selection of the most suitable enhancement technique for underwater images. A novel fused objective metric, combining the Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), and Underwater Image Fidelity (UIF) metrics is introduced to assess image quality effectively. The metric is then utilized to train a Shifted Window (Swin) transformer model, which predicts the best enhancement method for each image. This approach advances automatic underwater image enhancement by addressing varying image conditions with a data-driven, adaptive process. Experimental results show that the proposed model achieves an F1 score of 87.88% in selecting the optimal enhancement technique, effectively determining the best enhancement based on the characteristics of the input image. 2026-04-28 ASI, Vol. 9, Pages 88: Adaptive Underwater Image Enhancement Techniques Using Deep Learning

Applied System Innovation doi: 10.3390/asi9050088

Authors: Alexandros Vrochidis Stelios Krinidis

Underwater images often suffer from degradations, including color distortion, reduced visibility, and low contrast due to light absorption and scatter in water. Numerous enhancement techniques have been proposed to improve visual quality and address these challenges. However, no single method consistently performs best across all underwater scenes. This work introduces a novel deep learning framework for the automatic selection of the most suitable enhancement technique for underwater images. A novel fused objective metric, combining the Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), and Underwater Image Fidelity (UIF) metrics is introduced to assess image quality effectively. The metric is then utilized to train a Shifted Window (Swin) transformer model, which predicts the best enhancement method for each image. This approach advances automatic underwater image enhancement by addressing varying image conditions with a data-driven, adaptive process. Experimental results show that the proposed model achieves an F1 score of 87.88% in selecting the optimal enhancement technique, effectively determining the best enhancement based on the characteristics of the input image.

]]> Adaptive Underwater Image Enhancement Techniques Using Deep Learning Alexandros Vrochidis Stelios Krinidis doi: 10.3390/asi9050088 Applied System Innovation 2026-04-28 Applied System Innovation 2026-04-28 9 5 Article 88 10.3390/asi9050088 https://www.mdpi.com/2571-5577/9/5/88 ASI, Vol. 9, Pages 87: Profit Maximization in a Retrial Queueing-Inventory System: A Hybrid Algorithm https://www.mdpi.com/2571-5577/9/5/87 This study investigates the problem of profit maximization in a retrial queueing-inventory system. Customers who arrive at the system when there is no stock enter a retrial orbit and are treated as retrial demands. We consider two strategies for inventory replenishment: the base stock policy and the (s, S) policy. For each strategy, we first formulate the fundamental equations needed to determine the rate matrix and the steady-state probabilities. Then, we compute the system’s performance metrics and profit function. Moreover, by leveraging particle swarm optimization (PSO) and genetic algorithm (GA), we introduce an improved hybrid optimization algorithm, Improved Hybrid Particle Swarm optimization (IHPSO), to solve the profit maximization problem. This algorithm initially uses PSO, followed by GA crossover and mutation to improve performance. In comparison to the canonical PSO algorithm (CPSO), our algorithm exhibits superior global search capabilities. Finally, we conduct a numerical analysis on the optimal decision variables and the corresponding profits utilizing the IHPSO algorithm and present several interesting findings. 2026-04-28 ASI, Vol. 9, Pages 87: Profit Maximization in a Retrial Queueing-Inventory System: A Hybrid Algorithm

Applied System Innovation doi: 10.3390/asi9050087

Authors: Xiao-Li Cai Yong Qin

This study investigates the problem of profit maximization in a retrial queueing-inventory system. Customers who arrive at the system when there is no stock enter a retrial orbit and are treated as retrial demands. We consider two strategies for inventory replenishment: the base stock policy and the (s, S) policy. For each strategy, we first formulate the fundamental equations needed to determine the rate matrix and the steady-state probabilities. Then, we compute the system’s performance metrics and profit function. Moreover, by leveraging particle swarm optimization (PSO) and genetic algorithm (GA), we introduce an improved hybrid optimization algorithm, Improved Hybrid Particle Swarm optimization (IHPSO), to solve the profit maximization problem. This algorithm initially uses PSO, followed by GA crossover and mutation to improve performance. In comparison to the canonical PSO algorithm (CPSO), our algorithm exhibits superior global search capabilities. Finally, we conduct a numerical analysis on the optimal decision variables and the corresponding profits utilizing the IHPSO algorithm and present several interesting findings.

]]> Profit Maximization in a Retrial Queueing-Inventory System: A Hybrid Algorithm Xiao-Li Cai Yong Qin doi: 10.3390/asi9050087 Applied System Innovation 2026-04-28 Applied System Innovation 2026-04-28 9 5 Article 87 10.3390/asi9050087 https://www.mdpi.com/2571-5577/9/5/87 ASI, Vol. 9, Pages 86: Using Natural Language and Health Ontologies in Hope Recommender System: Evaluation of Use in Medicine https://www.mdpi.com/2571-5577/9/5/86 Objectives: Despite the widespread availability of digital clinical information, timely access to relevant biomedical evidence during routine consultations remains limited in practice. Primary care clinicians, in particular, face significant time constraints that make it difficult to integrate comprehensive literature searches into everyday workflows. This study evaluates whether an ontology-based recommender system can support routine clinical workflows by reducing information retrieval time while preserving the clinically acceptable usefulness of retrieved evidence. We assessed the performance of the HOPE (Health Operation for Personalised Evidence) system compared with realistic manual PubMed searches conducted by physicians. Materials and Methods: We conducted an observational evaluation involving 50 primary care physicians, who independently assessed 30 anonymised, rewritten clinical cases representative of common primary care scenarios. HOPE automatically extracted biomedical concepts from case descriptions using natural language processing and mapped them to Unified Medical Language System (UMLS) ontologies to generate ranked PubMed recommendations. A subset of 10 physicians also conducted manual PubMed searches in line with their usual clinical practice. Article relevance was assessed using a predefined binary criterion, and a reference relevance set was established by consensus among three senior physicians using a pooled document set. Retrieval performance was evaluated using Precision@k, relative Recall@k, and Normalised Discounted Cumulative Gain (NDCG@k). Manual search time was measured using a standardised stopwatch protocol, whereas HOPE response time was logged automatically by the system. Results: Inter-physician agreement in relevance assessment was substantial (Fleiss’ κ = 0.66; 95% CI: 0.61–0.70). HOPE achieved moderate-to-high precision within the top-ranked results (Precision@3 = 0.72), with relative recall increasing as additional documents were considered. Ranking metrics indicated that relevant articles were generally positioned early in the result lists. The mean total retrieval time for manual PubMed searches was 13.3 ± 1.7 min per case, compared with 17.4 ± 2.1 s for HOPE-assisted retrieval (p < 0.001). Conclusions: In a controlled, workflow-oriented evaluation using synthetic clinical cases, HOPE substantially reduced information retrieval time while maintaining clinically acceptable relevance in the retrieved literature. These findings support the use of ontology-based, AI-assisted systems as workflow-support tools to facilitate timely access to biomedical evidence, without replacing clinical judgment. 2026-04-27 ASI, Vol. 9, Pages 86: Using Natural Language and Health Ontologies in Hope Recommender System: Evaluation of Use in Medicine

Applied System Innovation doi: 10.3390/asi9050086

Authors: Hans Eguia Carlos Sánchez-Bocanegra Carlos Fernandez Llatas Fernando Alvarez López Francesc Saigí-Rubió

Objectives: Despite the widespread availability of digital clinical information, timely access to relevant biomedical evidence during routine consultations remains limited in practice. Primary care clinicians, in particular, face significant time constraints that make it difficult to integrate comprehensive literature searches into everyday workflows. This study evaluates whether an ontology-based recommender system can support routine clinical workflows by reducing information retrieval time while preserving the clinically acceptable usefulness of retrieved evidence. We assessed the performance of the HOPE (Health Operation for Personalised Evidence) system compared with realistic manual PubMed searches conducted by physicians. Materials and Methods: We conducted an observational evaluation involving 50 primary care physicians, who independently assessed 30 anonymised, rewritten clinical cases representative of common primary care scenarios. HOPE automatically extracted biomedical concepts from case descriptions using natural language processing and mapped them to Unified Medical Language System (UMLS) ontologies to generate ranked PubMed recommendations. A subset of 10 physicians also conducted manual PubMed searches in line with their usual clinical practice. Article relevance was assessed using a predefined binary criterion, and a reference relevance set was established by consensus among three senior physicians using a pooled document set. Retrieval performance was evaluated using Precision@k, relative Recall@k, and Normalised Discounted Cumulative Gain (NDCG@k). Manual search time was measured using a standardised stopwatch protocol, whereas HOPE response time was logged automatically by the system. Results: Inter-physician agreement in relevance assessment was substantial (Fleiss’ κ = 0.66; 95% CI: 0.61–0.70). HOPE achieved moderate-to-high precision within the top-ranked results (Precision@3 = 0.72), with relative recall increasing as additional documents were considered. Ranking metrics indicated that relevant articles were generally positioned early in the result lists. The mean total retrieval time for manual PubMed searches was 13.3 ± 1.7 min per case, compared with 17.4 ± 2.1 s for HOPE-assisted retrieval (p < 0.001). Conclusions: In a controlled, workflow-oriented evaluation using synthetic clinical cases, HOPE substantially reduced information retrieval time while maintaining clinically acceptable relevance in the retrieved literature. These findings support the use of ontology-based, AI-assisted systems as workflow-support tools to facilitate timely access to biomedical evidence, without replacing clinical judgment.

]]> Using Natural Language and Health Ontologies in Hope Recommender System: Evaluation of Use in Medicine Hans Eguia Carlos Sánchez-Bocanegra Carlos Fernandez Llatas Fernando Alvarez López Francesc Saigí-Rubió doi: 10.3390/asi9050086 Applied System Innovation 2026-04-27 Applied System Innovation 2026-04-27 9 5 Article 86 10.3390/asi9050086 https://www.mdpi.com/2571-5577/9/5/86 ASI, Vol. 9, Pages 85: Experimental Investigation of Manufacturing Constrained Induction Motor to PMSM Conversion for Direct-Drive Agricultural Ventilation Systems https://www.mdpi.com/2571-5577/9/5/85 Large-diameter axial ventilation fans are widely used in poultry houses to regulate ai flow, temperature, and air quality. However, conventional induction motors driving these fans typically operate at fixed speed and suffer efficiency degradation under low-speed, high-torque conditions due to slip-induced rotor copper losses. This study presents an experimental investigation of a manufacturing constrained conversion of a commercial induction motor platform into a direct-drive surface permanent magnet synchronous motor (PMSM). Instead of developing a completely new motor design, the proposed approach reuses the existing stator lamination, housing structure, and winding production process while redesigning the rotor electromagnetic structure to incorporate surface-mounted permanent magnets. Experimental testing was conducted using a dynamo meter-based measurement system to evaluate the performance of both the commercial induction motor and the converted PMSM prototype. The results show that the commercial induction motor exhibits significant efficiency degradation at high torque due to increased slip, whereas the PMSM eliminates slip-dependent rotor copper losses and maintains efficiencies above 88% within the typical ventilation operating range of 650–750 rpm. This study further relates airflow demand to rotational speed using fan affinity laws, highlighting the cubic relationship between speed and input power and demonstrating the energy-saving potential of variable-speed PMSM drives. The proposed conversion framework therefore provides a practical pathway for improving the energy efficiency of agricultural ventilation systems while maintaining compatibility with existing motor manufacturing infrastructure. 2026-04-25 ASI, Vol. 9, Pages 85: Experimental Investigation of Manufacturing Constrained Induction Motor to PMSM Conversion for Direct-Drive Agricultural Ventilation Systems

Applied System Innovation doi: 10.3390/asi9050085

Authors: Ritthichai Ratchapan Wanwinit Wijittemee Surasak Noituptim Theerapol Muankhaw Sawek Pratummet Boonyang Plangklang

Large-diameter axial ventilation fans are widely used in poultry houses to regulate ai flow, temperature, and air quality. However, conventional induction motors driving these fans typically operate at fixed speed and suffer efficiency degradation under low-speed, high-torque conditions due to slip-induced rotor copper losses. This study presents an experimental investigation of a manufacturing constrained conversion of a commercial induction motor platform into a direct-drive surface permanent magnet synchronous motor (PMSM). Instead of developing a completely new motor design, the proposed approach reuses the existing stator lamination, housing structure, and winding production process while redesigning the rotor electromagnetic structure to incorporate surface-mounted permanent magnets. Experimental testing was conducted using a dynamo meter-based measurement system to evaluate the performance of both the commercial induction motor and the converted PMSM prototype. The results show that the commercial induction motor exhibits significant efficiency degradation at high torque due to increased slip, whereas the PMSM eliminates slip-dependent rotor copper losses and maintains efficiencies above 88% within the typical ventilation operating range of 650–750 rpm. This study further relates airflow demand to rotational speed using fan affinity laws, highlighting the cubic relationship between speed and input power and demonstrating the energy-saving potential of variable-speed PMSM drives. The proposed conversion framework therefore provides a practical pathway for improving the energy efficiency of agricultural ventilation systems while maintaining compatibility with existing motor manufacturing infrastructure.

]]> Experimental Investigation of Manufacturing Constrained Induction Motor to PMSM Conversion for Direct-Drive Agricultural Ventilation Systems Ritthichai Ratchapan Wanwinit Wijittemee Surasak Noituptim Theerapol Muankhaw Sawek Pratummet Boonyang Plangklang doi: 10.3390/asi9050085 Applied System Innovation 2026-04-25 Applied System Innovation 2026-04-25 9 5 Article 85 10.3390/asi9050085 https://www.mdpi.com/2571-5577/9/5/85 ASI, Vol. 9, Pages 84: A Constrained-Aware Genetic Algorithm for Coverage Optimization in Range-Free Sensor Networks https://www.mdpi.com/2571-5577/9/5/84 Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes K-out-of-N sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration. 2026-04-23 ASI, Vol. 9, Pages 84: A Constrained-Aware Genetic Algorithm for Coverage Optimization in Range-Free Sensor Networks

Applied System Innovation doi: 10.3390/asi9050084

Authors: Ioannis S. Barbounakis Ioannis V. Saradopoulos Nikolaos E. Antonidakis Erietta Vasilaki Maria S. Zakynthinaki

Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes K-out-of-N sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration.

]]> A Constrained-Aware Genetic Algorithm for Coverage Optimization in Range-Free Sensor Networks Ioannis S. Barbounakis Ioannis V. Saradopoulos Nikolaos E. Antonidakis Erietta Vasilaki Maria S. Zakynthinaki doi: 10.3390/asi9050084 Applied System Innovation 2026-04-23 Applied System Innovation 2026-04-23 9 5 Article 84 10.3390/asi9050084 https://www.mdpi.com/2571-5577/9/5/84 ASI, Vol. 9, Pages 83: An Overview of the Application of Modern Statistical Techniques in Semiconductor Manufacturing https://www.mdpi.com/2571-5577/9/4/83 The semiconductor industry has long relied on Statistical Process Control (SPC) for yield and reliability management. In early technology nodes, classic univariate tools such as Shewhart charts, cumulative sums (CUSUM), exponentially weighted moving averages (EWMA), and the Cp/Cpk exponent could effectively monitor a finite set of key variables. However, sub-5nm and emerging 3 nm technologies have fundamentally changed the statistical environment. Advanced patterning, high-aspect-ratio etching, atomic layer deposition (ALD), chemical-mechanical polishing (CMP), and novel materials have drastically narrowed the process window. At these scales, nanometer-level deviations in critical dimensions (CD), overlay, or surface roughness can significantly impact yield. Simultaneously, modern wafer fabs generate massive amounts of high-frequency sensor data and high-dimensional metrology data. Traditional SPC assumptions—such as independence, normality, low dimensionality, and stationarity—often do not hold. Semiconductor data exhibits: (i) extremely high-dimensionality and strong intervariate correlations; (ii) a hierarchical structure encompassing fab → tooling → chamber → recipe → batch → wafer → field; and (iii) metrological delays and sampling limitations leading to incomplete and asynchronous observations. To address these challenges, this paper reviews advanced statistical methods applicable to wafer fabrication. These methods include multivariate statistical process control (MSPC) approaches such as Hotelling T2 statistics, PCA/PLS combining T2 and Q statistics, contribution diagnostics, time-series drift and change point detection, and Bayesian hierarchical modeling for uncertainty-aware monitoring in data-limited scenarios. Furthermore, we discuss how to integrate these methods with fault detection and classification (FDC), line-to-line monitoring (R2R), advanced process control (APC), and manufacturing execution systems (MES). This paper focuses on scalable, interpretable, and maintainable implementations that transform statistical analysis from a passive monitoring tool into an active component of data-driven fab control. 2026-04-21 ASI, Vol. 9, Pages 83: An Overview of the Application of Modern Statistical Techniques in Semiconductor Manufacturing

Applied System Innovation doi: 10.3390/asi9040083

Authors: Hsuan-Yu Chen Chiachung Chen

The semiconductor industry has long relied on Statistical Process Control (SPC) for yield and reliability management. In early technology nodes, classic univariate tools such as Shewhart charts, cumulative sums (CUSUM), exponentially weighted moving averages (EWMA), and the Cp/Cpk exponent could effectively monitor a finite set of key variables. However, sub-5nm and emerging 3 nm technologies have fundamentally changed the statistical environment. Advanced patterning, high-aspect-ratio etching, atomic layer deposition (ALD), chemical-mechanical polishing (CMP), and novel materials have drastically narrowed the process window. At these scales, nanometer-level deviations in critical dimensions (CD), overlay, or surface roughness can significantly impact yield. Simultaneously, modern wafer fabs generate massive amounts of high-frequency sensor data and high-dimensional metrology data. Traditional SPC assumptions—such as independence, normality, low dimensionality, and stationarity—often do not hold. Semiconductor data exhibits: (i) extremely high-dimensionality and strong intervariate correlations; (ii) a hierarchical structure encompassing fab → tooling → chamber → recipe → batch → wafer → field; and (iii) metrological delays and sampling limitations leading to incomplete and asynchronous observations. To address these challenges, this paper reviews advanced statistical methods applicable to wafer fabrication. These methods include multivariate statistical process control (MSPC) approaches such as Hotelling T2 statistics, PCA/PLS combining T2 and Q statistics, contribution diagnostics, time-series drift and change point detection, and Bayesian hierarchical modeling for uncertainty-aware monitoring in data-limited scenarios. Furthermore, we discuss how to integrate these methods with fault detection and classification (FDC), line-to-line monitoring (R2R), advanced process control (APC), and manufacturing execution systems (MES). This paper focuses on scalable, interpretable, and maintainable implementations that transform statistical analysis from a passive monitoring tool into an active component of data-driven fab control.

]]> An Overview of the Application of Modern Statistical Techniques in Semiconductor Manufacturing Hsuan-Yu Chen Chiachung Chen doi: 10.3390/asi9040083 Applied System Innovation 2026-04-21 Applied System Innovation 2026-04-21 9 4 Review 83 10.3390/asi9040083 https://www.mdpi.com/2571-5577/9/4/83 ASI, Vol. 9, Pages 82: Process-Oriented Framework for Reliability and Life-Cycle Engineering of Railway Systems https://www.mdpi.com/2571-5577/9/4/82 Modern standards and requirements for ensuring the reliability and safety of transport infrastructure are aimed at shifting from routine maintenance to preventive maintenance, focused on predicting technical conditions and lifecycle management. Modern engineering approaches are based on the logic of state assessment and ensuring structural strength and dimensional stability. Therefore, they focus on recording defects or deviations from acceptable values without revealing the failure mechanism, which limits the ability to identify degradation processes and predict failures. The purpose of this article is to develop a formal conceptual framework for operationalizing process-oriented reliability analysis. Within this methodological framework, state is viewed as a snapshot of a dynamic process, while process stability is defined as the ability of a system to maintain its key behavioral characteristics under changing operating conditions and the geometric and physical–mechanical properties of system elements. The proposed framework expands on classical state-based diagnostics by introducing process invariants as prognostic indicators. The transition to trajectory-based behavior analysis allows monitoring systems to evolve into lifecycle management tools. 2026-04-21 ASI, Vol. 9, Pages 82: Process-Oriented Framework for Reliability and Life-Cycle Engineering of Railway Systems

Applied System Innovation doi: 10.3390/asi9040082

Authors: Iryna Bondarenko

Modern standards and requirements for ensuring the reliability and safety of transport infrastructure are aimed at shifting from routine maintenance to preventive maintenance, focused on predicting technical conditions and lifecycle management. Modern engineering approaches are based on the logic of state assessment and ensuring structural strength and dimensional stability. Therefore, they focus on recording defects or deviations from acceptable values without revealing the failure mechanism, which limits the ability to identify degradation processes and predict failures. The purpose of this article is to develop a formal conceptual framework for operationalizing process-oriented reliability analysis. Within this methodological framework, state is viewed as a snapshot of a dynamic process, while process stability is defined as the ability of a system to maintain its key behavioral characteristics under changing operating conditions and the geometric and physical–mechanical properties of system elements. The proposed framework expands on classical state-based diagnostics by introducing process invariants as prognostic indicators. The transition to trajectory-based behavior analysis allows monitoring systems to evolve into lifecycle management tools.

]]> Process-Oriented Framework for Reliability and Life-Cycle Engineering of Railway Systems Iryna Bondarenko doi: 10.3390/asi9040082 Applied System Innovation 2026-04-21 Applied System Innovation 2026-04-21 9 4 Article 82 10.3390/asi9040082 https://www.mdpi.com/2571-5577/9/4/82 ASI, Vol. 9, Pages 81: An Adaptive Traffic Signal Control Framework Integrating Regime-Aware LSTM Forecasting and Signal Optimization Under Socio-Temporal Demand Shifts https://www.mdpi.com/2571-5577/9/4/81 Recurring socio-temporal events, such as Ramadan in Middle Eastern cities, introduce pronounced non-stationarity in urban traffic demand. During these periods, daytime traffic volumes typically decline, while congestion becomes more severe in the evening around the Iftar (fast-breaking) period and persists into late-night hours, making conventional fixed-time signal plans less effective. An additional challenge is that demand is not only time-varying, but also unevenly distributed across competing movements: attempts to prioritize high-volume phases can inadvertently cause excessive delays—or even starvation—on lower-demand approaches. To address these issues, this study presents an adaptive, regime-aware traffic signal control framework that combines predictive modeling with constrained optimization. Short-term phase-level delays are forecast using Long Short-Term Memory (LSTM) models, and a Model Predictive Control (MPC) scheme then determines the green time allocation at each control cycle through a receding-horizon strategy. The optimization explicitly represents phase interactions by including constraints that prevent excessive delay in competing movements, thereby yielding a balanced and operationally realistic control policy. The approach is validated with one-minute-resolution TomTom delay data from a signalized intersection in Jeddah, Saudi Arabia, covering both Normal and Ramadan conditions. The LSTM models show stable predictive performance, achieving root mean square errors (RMSEs) of 19.8 s under Normal conditions and 17.1 s during Ramadan. In general, the results show that the proposed framework cuts total intersection delay by about 0.3% to 2.8% compared to standard control strategies. Even though these total-delay improvements are small, they come with big drops in delay for lower-demand phases (about 12–20%) and keep the delay increases for higher-demand phases under control. This shows that the method makes the whole process more efficient by fairly spreading out the delay instead of just making one phase better on its own. The results show that combining forecasting with constrained optimization is a strong and useful way to handle changing traffic demand. This is especially true during times of high demand when flexibility, stability, and fairness across movements are all important. 2026-04-20 ASI, Vol. 9, Pages 81: An Adaptive Traffic Signal Control Framework Integrating Regime-Aware LSTM Forecasting and Signal Optimization Under Socio-Temporal Demand Shifts

Applied System Innovation doi: 10.3390/asi9040081

Authors: Sara Atef Ahmed Karam

Recurring socio-temporal events, such as Ramadan in Middle Eastern cities, introduce pronounced non-stationarity in urban traffic demand. During these periods, daytime traffic volumes typically decline, while congestion becomes more severe in the evening around the Iftar (fast-breaking) period and persists into late-night hours, making conventional fixed-time signal plans less effective. An additional challenge is that demand is not only time-varying, but also unevenly distributed across competing movements: attempts to prioritize high-volume phases can inadvertently cause excessive delays—or even starvation—on lower-demand approaches. To address these issues, this study presents an adaptive, regime-aware traffic signal control framework that combines predictive modeling with constrained optimization. Short-term phase-level delays are forecast using Long Short-Term Memory (LSTM) models, and a Model Predictive Control (MPC) scheme then determines the green time allocation at each control cycle through a receding-horizon strategy. The optimization explicitly represents phase interactions by including constraints that prevent excessive delay in competing movements, thereby yielding a balanced and operationally realistic control policy. The approach is validated with one-minute-resolution TomTom delay data from a signalized intersection in Jeddah, Saudi Arabia, covering both Normal and Ramadan conditions. The LSTM models show stable predictive performance, achieving root mean square errors (RMSEs) of 19.8 s under Normal conditions and 17.1 s during Ramadan. In general, the results show that the proposed framework cuts total intersection delay by about 0.3% to 2.8% compared to standard control strategies. Even though these total-delay improvements are small, they come with big drops in delay for lower-demand phases (about 12–20%) and keep the delay increases for higher-demand phases under control. This shows that the method makes the whole process more efficient by fairly spreading out the delay instead of just making one phase better on its own. The results show that combining forecasting with constrained optimization is a strong and useful way to handle changing traffic demand. This is especially true during times of high demand when flexibility, stability, and fairness across movements are all important.

]]> An Adaptive Traffic Signal Control Framework Integrating Regime-Aware LSTM Forecasting and Signal Optimization Under Socio-Temporal Demand Shifts Sara Atef Ahmed Karam doi: 10.3390/asi9040081 Applied System Innovation 2026-04-20 Applied System Innovation 2026-04-20 9 4 Article 81 10.3390/asi9040081 https://www.mdpi.com/2571-5577/9/4/81 ASI, Vol. 9, Pages 80: LLM-Driven Modeling and Decision Support Methods for Cross-Domain Collaborative Mission Systems https://www.mdpi.com/2571-5577/9/4/80 Cross-domain formations composed of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vessels (USVs) are critical for maritime defense but face significant challenges in countering complex aerial threats and developing flexible, collaborative strategies. Addressing the limitations of traditional decision support systems in semantic understanding and dynamic adaptation, this paper proposes a novel Large Language Model (LLM)-driven decision support framework grounded in the Department of Defense Architecture Framework (DoDAF). By integrating Retrieval-Augmented Generation (RAG) with a domain-specific knowledge base, the framework enhances the LLM’s ability to align natural-language directives with standardized DoDAF view models, effectively mitigating hallucinations in tactical generation. The proposed framework coordinates a closed-loop process, using Petri net-based static logic verification to ensure structural consistency and Monte Carlo-based dynamic effectiveness evaluation to optimize the selection of kill chains. Experimental validations in a simulated UAV-USV maritime defense scenario demonstrate that the framework achieves 96.6% entity accuracy and 100% format compliance in model generation. In comparison, the generated cooperative kill chains significantly outperform non-cooperative methods by improving interception efficacy by approximately 26.08% under saturation attack conditions. This study develops an automated, interpretable workflow that transforms unstructured situational understanding into decision reporting, significantly enhancing the efficiency and reliability of cross-domain collaborative mission planning. 2026-04-17 ASI, Vol. 9, Pages 80: LLM-Driven Modeling and Decision Support Methods for Cross-Domain Collaborative Mission Systems

Applied System Innovation doi: 10.3390/asi9040080

Authors: Han Li Dongji Li Yunxiao Liu Jinyu Ma Guangyao Wang Jianliang Ai

Cross-domain formations composed of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vessels (USVs) are critical for maritime defense but face significant challenges in countering complex aerial threats and developing flexible, collaborative strategies. Addressing the limitations of traditional decision support systems in semantic understanding and dynamic adaptation, this paper proposes a novel Large Language Model (LLM)-driven decision support framework grounded in the Department of Defense Architecture Framework (DoDAF). By integrating Retrieval-Augmented Generation (RAG) with a domain-specific knowledge base, the framework enhances the LLM’s ability to align natural-language directives with standardized DoDAF view models, effectively mitigating hallucinations in tactical generation. The proposed framework coordinates a closed-loop process, using Petri net-based static logic verification to ensure structural consistency and Monte Carlo-based dynamic effectiveness evaluation to optimize the selection of kill chains. Experimental validations in a simulated UAV-USV maritime defense scenario demonstrate that the framework achieves 96.6% entity accuracy and 100% format compliance in model generation. In comparison, the generated cooperative kill chains significantly outperform non-cooperative methods by improving interception efficacy by approximately 26.08% under saturation attack conditions. This study develops an automated, interpretable workflow that transforms unstructured situational understanding into decision reporting, significantly enhancing the efficiency and reliability of cross-domain collaborative mission planning.

]]> LLM-Driven Modeling and Decision Support Methods for Cross-Domain Collaborative Mission Systems Han Li Dongji Li Yunxiao Liu Jinyu Ma Guangyao Wang Jianliang Ai doi: 10.3390/asi9040080 Applied System Innovation 2026-04-17 Applied System Innovation 2026-04-17 9 4 Article 80 10.3390/asi9040080 https://www.mdpi.com/2571-5577/9/4/80 ASI, Vol. 9, Pages 79: Optimized Lyapunov-Theory-Based Filter for MIMO Time-Varying Uncertain Nonlinear Systems with Measurement Noises Using Multi-Dimensional Taylor Network https://www.mdpi.com/2571-5577/9/4/79 Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments. 2026-04-16 ASI, Vol. 9, Pages 79: Optimized Lyapunov-Theory-Based Filter for MIMO Time-Varying Uncertain Nonlinear Systems with Measurement Noises Using Multi-Dimensional Taylor Network

Applied System Innovation doi: 10.3390/asi9040079

Authors: Chao Zhang Zhimeng Li Ziao Li

Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments.

]]> Optimized Lyapunov-Theory-Based Filter for MIMO Time-Varying Uncertain Nonlinear Systems with Measurement Noises Using Multi-Dimensional Taylor Network Chao Zhang Zhimeng Li Ziao Li doi: 10.3390/asi9040079 Applied System Innovation 2026-04-16 Applied System Innovation 2026-04-16 9 4 Article 79 10.3390/asi9040079 https://www.mdpi.com/2571-5577/9/4/79 ASI, Vol. 9, Pages 78: A Dual Approach to the A* Algorithm to Generate Consistent Trajectories for the Leader–Follower Scheme https://www.mdpi.com/2571-5577/9/4/78 Path planning and formation control in leader–follower robotic systems are active areas of research, as both are highly relevant to the proper execution of the assigned task. In this work, a dual approach to the A* algorithm is applied to generate consistent trajectories for a multi-agent robotic system with a leader–follower scheme. The conventional A* algorithm aims to minimize the cost of finding the best path by minimizing distances. In this case, a modified A* algorithm is used because, although decision-making also involves choosing among eight options or cells, the goal is not to minimize distance; instead, the focus is on analyzing the direction of acceleration. The proposed algorithm is robust regarding the initial and relative pose of the leader with respect to the followers. The leader is tracked using a digital accelerometer. The algorithm is tested by simulating various patterns and implemented in two experimental test scenarios: the first with differential mobile robots, and the second with an Ackerman-type mobile robot. In both scenarios, the trajectories were achieved with deviations in x and y between the follower’s path and the leader’s path of less than 0.03, and the leader’s pose independence was maintained. 2026-04-16 ASI, Vol. 9, Pages 78: A Dual Approach to the A* Algorithm to Generate Consistent Trajectories for the Leader–Follower Scheme

Applied System Innovation doi: 10.3390/asi9040078

Authors: Griselda Stephany Abarca-Jiménez Manuel Vladimir Vega-Blanco Jesús Mares-Carreño Juan Cruz-Castro Yunuén López-Grijalba

Path planning and formation control in leader–follower robotic systems are active areas of research, as both are highly relevant to the proper execution of the assigned task. In this work, a dual approach to the A* algorithm is applied to generate consistent trajectories for a multi-agent robotic system with a leader–follower scheme. The conventional A* algorithm aims to minimize the cost of finding the best path by minimizing distances. In this case, a modified A* algorithm is used because, although decision-making also involves choosing among eight options or cells, the goal is not to minimize distance; instead, the focus is on analyzing the direction of acceleration. The proposed algorithm is robust regarding the initial and relative pose of the leader with respect to the followers. The leader is tracked using a digital accelerometer. The algorithm is tested by simulating various patterns and implemented in two experimental test scenarios: the first with differential mobile robots, and the second with an Ackerman-type mobile robot. In both scenarios, the trajectories were achieved with deviations in x and y between the follower’s path and the leader’s path of less than 0.03, and the leader’s pose independence was maintained.

]]> A Dual Approach to the A* Algorithm to Generate Consistent Trajectories for the Leader–Follower Scheme Griselda Stephany Abarca-Jiménez Manuel Vladimir Vega-Blanco Jesús Mares-Carreño Juan Cruz-Castro Yunuén López-Grijalba doi: 10.3390/asi9040078 Applied System Innovation 2026-04-16 Applied System Innovation 2026-04-16 9 4 Article 78 10.3390/asi9040078 https://www.mdpi.com/2571-5577/9/4/78 ASI, Vol. 9, Pages 77: SMART-CROWD: A System Architecture for Intelligent Assessment of Crowdsourcing Maturity in Urban Mobility Governance https://www.mdpi.com/2571-5577/9/4/77 Urban mobility has undergone a significant transformation in recent years, caused by rapid urbanization, environmental pressures, and technological innovation. Even though digital tools and mobility platforms are increasingly used to address transportation challenges, these challenges remain complex and multidimensional, concerning not only infrastructure, but also user behavior, institutional coordination, trust, and social acceptance. Crowdsourcing has proven effective in leveraging distributed knowledge and accelerating innovation in business and public sectors. However, its application in urban mobility contexts has not yet been sufficiently synthesized in a framework-oriented manner. To address this, the study first conducted a comprehensive literature review of existing crowdsourcing assessment frameworks and their applicability to mobility systems. The results show that current implementations in urban mobility often remain fragmented and limited to unidirectional data extraction, lacking comprehensive approaches that integrate technological, social, and organizational dimensions. In response to this, the authors developed the SMART-CROWD framework for assessing cities’ maturity in using crowdsourcing across six dimensions: Strategy & Leadership (S), Methods & Tools (M), Engagement & Representativeness (A), Responsiveness & Impact (R), Technology & Data (T), and Civic Capital & Sustainability (CROWD). Each dimension includes measurable indicators, providing a structured basis of diagnosing disparities between technological capabilities and socio-institutional readiness. The SMART-CROWD framework is intended to support a transition from one-way data acquisition toward more scalable, reciprocal, and citizen-focused innovation ecosystems. This work contributes to the field of applied systems innovation by proposing a structured framework for assessing and guiding the use of distributed intelligence in smart urban mobility. 2026-03-31 ASI, Vol. 9, Pages 77: SMART-CROWD: A System Architecture for Intelligent Assessment of Crowdsourcing Maturity in Urban Mobility Governance

Applied System Innovation doi: 10.3390/asi9040077

Authors: Katarzyna Turoń Andrzej Kubik

Urban mobility has undergone a significant transformation in recent years, caused by rapid urbanization, environmental pressures, and technological innovation. Even though digital tools and mobility platforms are increasingly used to address transportation challenges, these challenges remain complex and multidimensional, concerning not only infrastructure, but also user behavior, institutional coordination, trust, and social acceptance. Crowdsourcing has proven effective in leveraging distributed knowledge and accelerating innovation in business and public sectors. However, its application in urban mobility contexts has not yet been sufficiently synthesized in a framework-oriented manner. To address this, the study first conducted a comprehensive literature review of existing crowdsourcing assessment frameworks and their applicability to mobility systems. The results show that current implementations in urban mobility often remain fragmented and limited to unidirectional data extraction, lacking comprehensive approaches that integrate technological, social, and organizational dimensions. In response to this, the authors developed the SMART-CROWD framework for assessing cities’ maturity in using crowdsourcing across six dimensions: Strategy & Leadership (S), Methods & Tools (M), Engagement & Representativeness (A), Responsiveness & Impact (R), Technology & Data (T), and Civic Capital & Sustainability (CROWD). Each dimension includes measurable indicators, providing a structured basis of diagnosing disparities between technological capabilities and socio-institutional readiness. The SMART-CROWD framework is intended to support a transition from one-way data acquisition toward more scalable, reciprocal, and citizen-focused innovation ecosystems. This work contributes to the field of applied systems innovation by proposing a structured framework for assessing and guiding the use of distributed intelligence in smart urban mobility.

]]> SMART-CROWD: A System Architecture for Intelligent Assessment of Crowdsourcing Maturity in Urban Mobility Governance Katarzyna Turoń Andrzej Kubik doi: 10.3390/asi9040077 Applied System Innovation 2026-03-31 Applied System Innovation 2026-03-31 9 4 Article 77 10.3390/asi9040077 https://www.mdpi.com/2571-5577/9/4/77 ASI, Vol. 9, Pages 76: Design and Implementation of a Remote Water Level Control and Monitoring System in Rural Community Tanks Using LoRa and SMS Technology https://www.mdpi.com/2571-5577/9/4/76 This paper presents the design and implementation of a low-profile remote monitoring and control system for water level management in storage tanks located in rural communities. The system was developed to ensure a reliable water supply, prevent spills, reduce electrical energy consumption, and mitigate theft and vandalism risks posed by a previously installed, highly exposed commercial system. The proposed system employs LoRa technology to transmit water level data from the storage tank to a receiver located 6 km from the water well. When the water level drops below a predefined threshold, the system transmits an activation signal through the LoRa network to start the well pump and trigger tank refilling. In addition, an SMS monitoring module enables users to remotely verify water level and pump operational status at any time. System notifications and operational data are automatically delivered via SMS to predefined phone numbers, enabling continuous supervision without requiring internet connectivity. The implementation of the proposed system thus provides an efficient and reliable solution for water resource management in rural environments, ensuring continuous water availability and preventing supply shortages. LoRa communication enables robust long-range data transmission, while SMS-based monitoring offers real-time operational awareness for end users. The system was validated through field testing in a pilot rural community, demonstrating operational robustness, improved water management efficiency, and measurable positive impacts on residents’ water service continuity. The low-profile physical design significantly reduced theft and vandalism incidents reported by the local water authority. Experimental results showed an average monthly reduction of 41.2% in electrical energy consumption while maintaining high system reliability, physical security, and real-time monitoring capability. 2026-03-31 ASI, Vol. 9, Pages 76: Design and Implementation of a Remote Water Level Control and Monitoring System in Rural Community Tanks Using LoRa and SMS Technology

Applied System Innovation doi: 10.3390/asi9040076

Authors: Ulises Balderrama-Rey Rafael Verdugo-Miranda Miguel Martínez-Gil Joel Carvajal-Soto Frank Romo-García Luis Medina-Zazueta Edgar Espinoza-Zallas Rolando Flores-Ochoa

This paper presents the design and implementation of a low-profile remote monitoring and control system for water level management in storage tanks located in rural communities. The system was developed to ensure a reliable water supply, prevent spills, reduce electrical energy consumption, and mitigate theft and vandalism risks posed by a previously installed, highly exposed commercial system. The proposed system employs LoRa technology to transmit water level data from the storage tank to a receiver located 6 km from the water well. When the water level drops below a predefined threshold, the system transmits an activation signal through the LoRa network to start the well pump and trigger tank refilling. In addition, an SMS monitoring module enables users to remotely verify water level and pump operational status at any time. System notifications and operational data are automatically delivered via SMS to predefined phone numbers, enabling continuous supervision without requiring internet connectivity. The implementation of the proposed system thus provides an efficient and reliable solution for water resource management in rural environments, ensuring continuous water availability and preventing supply shortages. LoRa communication enables robust long-range data transmission, while SMS-based monitoring offers real-time operational awareness for end users. The system was validated through field testing in a pilot rural community, demonstrating operational robustness, improved water management efficiency, and measurable positive impacts on residents’ water service continuity. The low-profile physical design significantly reduced theft and vandalism incidents reported by the local water authority. Experimental results showed an average monthly reduction of 41.2% in electrical energy consumption while maintaining high system reliability, physical security, and real-time monitoring capability.

]]> Design and Implementation of a Remote Water Level Control and Monitoring System in Rural Community Tanks Using LoRa and SMS Technology Ulises Balderrama-Rey Rafael Verdugo-Miranda Miguel Martínez-Gil Joel Carvajal-Soto Frank Romo-García Luis Medina-Zazueta Edgar Espinoza-Zallas Rolando Flores-Ochoa doi: 10.3390/asi9040076 Applied System Innovation 2026-03-31 Applied System Innovation 2026-03-31 9 4 Article 76 10.3390/asi9040076 https://www.mdpi.com/2571-5577/9/4/76 ASI, Vol. 9, Pages 75: A CNN–LSTM Framework for Player-Specific Baseball Pitch Type Prediction from Video Sequences https://www.mdpi.com/2571-5577/9/4/75 The performance of the pitcher is the cornerstone of baseball, often determining the flow and ultimate outcome of a game. Given this centrality, understanding the mechanics of an elite pitcher and decoding their strategies are paramount for both internal optimization and competitive scouting. This study proposes an end-to-end deep learning pipeline for automatically classifying five distinct pitch types from raw broadcast footage of MLB pitcher Max Scherzer between 2015 and 2020. By formulating pitch delivery as a time-series classification problem tailored to the unique biomechanics of an elite athlete, the proposed CNN–LSTM framework integrates per-frame spatial feature extraction using an advanced CNN backbone (YOLOv8s-cls) with a two-layer long short-term memory (LSTM) network to capture subtle biomechanical cues across a standardized 20-frame delivery sequence. While skeletal pose estimation primarily focuses on tracking major joints to analyze standard pitching mechanics, the proposed pixel-based method preserves fine-grained visual cues—such as finger grip and wrist rotation—that are critical for distinguishing pitch variations. The proposed framework achieved an accuracy of 91.8% under a standard Random Split and, importantly, 84.5% under a strict Chronological Split across different seasons, validating the feasibility of automated pitch “tell” detection from broadcast video. The resulting system provides coaches and analysts with an objective, data-driven tool for generating personalized scouting reports, identifying mechanical inconsistencies, and refining pitching strategies. 2026-03-30 ASI, Vol. 9, Pages 75: A CNN–LSTM Framework for Player-Specific Baseball Pitch Type Prediction from Video Sequences

Applied System Innovation doi: 10.3390/asi9040075

Authors: Chin-Chih Chang Chi-Hung Wei Hao-Chen Li Sean Hsiao

The performance of the pitcher is the cornerstone of baseball, often determining the flow and ultimate outcome of a game. Given this centrality, understanding the mechanics of an elite pitcher and decoding their strategies are paramount for both internal optimization and competitive scouting. This study proposes an end-to-end deep learning pipeline for automatically classifying five distinct pitch types from raw broadcast footage of MLB pitcher Max Scherzer between 2015 and 2020. By formulating pitch delivery as a time-series classification problem tailored to the unique biomechanics of an elite athlete, the proposed CNN–LSTM framework integrates per-frame spatial feature extraction using an advanced CNN backbone (YOLOv8s-cls) with a two-layer long short-term memory (LSTM) network to capture subtle biomechanical cues across a standardized 20-frame delivery sequence. While skeletal pose estimation primarily focuses on tracking major joints to analyze standard pitching mechanics, the proposed pixel-based method preserves fine-grained visual cues—such as finger grip and wrist rotation—that are critical for distinguishing pitch variations. The proposed framework achieved an accuracy of 91.8% under a standard Random Split and, importantly, 84.5% under a strict Chronological Split across different seasons, validating the feasibility of automated pitch “tell” detection from broadcast video. The resulting system provides coaches and analysts with an objective, data-driven tool for generating personalized scouting reports, identifying mechanical inconsistencies, and refining pitching strategies.

]]> A CNN–LSTM Framework for Player-Specific Baseball Pitch Type Prediction from Video Sequences Chin-Chih Chang Chi-Hung Wei Hao-Chen Li Sean Hsiao doi: 10.3390/asi9040075 Applied System Innovation 2026-03-30 Applied System Innovation 2026-03-30 9 4 Article 75 10.3390/asi9040075 https://www.mdpi.com/2571-5577/9/4/75 ASI, Vol. 9, Pages 74: ABC Classification as Business Intelligence Method Based on a Novel Sales Segmentation and Feature Extraction Proposal https://www.mdpi.com/2571-5577/9/4/74 Daily, monthly, and annual multi-product sales records are stored in databases, but due to the massive amounts of data, they are not used for decision-making when updating product catalogs. Meanwhile, the use of artificial intelligence in business is increasing across all sectors of the economy. Large-scale data handling can be achieved using artificial intelligence techniques. Specifically, ABC inventory classification currently employs artificial intelligence techniques, including neural networks, fuzzy systems, and genetic algorithms. However, a state-of-the-art review has not found any research using vision techniques to classify ABC inventories. To address this gap, this research presents a novel approach to the intelligent classification of a company’s multiple products, using ABC. Recent vision system research often uses the Otsu method or its variants to determine the optimum threshold for binary image segmentation. Unlike this approach, our research does not use a single threshold value; instead, it uses the full binary frequency histogram as an image representation. From this, eight invariant characteristics are extracted from translation, rotation, and scale. The results show that the classification is accurate, clear, and simple as a decision-making tool. The proposed method is general and can be used in any production sector and at any enterprise size. 2026-03-30 ASI, Vol. 9, Pages 74: ABC Classification as Business Intelligence Method Based on a Novel Sales Segmentation and Feature Extraction Proposal

Applied System Innovation doi: 10.3390/asi9040074

Authors: Roberto Baeza-Serrato Jorge Manuel Barrios-Sánchez

Daily, monthly, and annual multi-product sales records are stored in databases, but due to the massive amounts of data, they are not used for decision-making when updating product catalogs. Meanwhile, the use of artificial intelligence in business is increasing across all sectors of the economy. Large-scale data handling can be achieved using artificial intelligence techniques. Specifically, ABC inventory classification currently employs artificial intelligence techniques, including neural networks, fuzzy systems, and genetic algorithms. However, a state-of-the-art review has not found any research using vision techniques to classify ABC inventories. To address this gap, this research presents a novel approach to the intelligent classification of a company’s multiple products, using ABC. Recent vision system research often uses the Otsu method or its variants to determine the optimum threshold for binary image segmentation. Unlike this approach, our research does not use a single threshold value; instead, it uses the full binary frequency histogram as an image representation. From this, eight invariant characteristics are extracted from translation, rotation, and scale. The results show that the classification is accurate, clear, and simple as a decision-making tool. The proposed method is general and can be used in any production sector and at any enterprise size.

]]> ABC Classification as Business Intelligence Method Based on a Novel Sales Segmentation and Feature Extraction Proposal Roberto Baeza-Serrato Jorge Manuel Barrios-Sánchez doi: 10.3390/asi9040074 Applied System Innovation 2026-03-30 Applied System Innovation 2026-03-30 9 4 Article 74 10.3390/asi9040074 https://www.mdpi.com/2571-5577/9/4/74 ASI, Vol. 9, Pages 73: A Lawful Metadata-Driven Framework for Linking Encrypted Communication Behavior and Cryptocurrency Wallet Activity in Digital Investigations https://www.mdpi.com/2571-5577/9/4/73 End-to-end encrypted (E2EE) messaging and the growing use of cryptocurrency create an attribution gap for digital investigators because message content is unavailable and wallet activity is often decoupled from subscriber identities, which makes it difficult to link communication behaviors with wallet activity. We propose a lawful and metadata-driven forensic attribution framework called the Data-Source Association Framework (DSAF). The DSAF links encrypted communication behavior with cryptocurrency wallet activity by correlating only legally obtainable network metadata that are observable under lawful interception (LI) with on-chain traces. By integrating information from communication behaviors and wallet activity, the framework aims to narrow the person–application–wallet attribution gap. The framework integrates two components, where one performs encrypted-application classification using transport-layer signals and flow-level features and the other conducts wallet–identity association by applying controlled decoding to intercepted traffic and extracting relevant transaction traces. Both components operate under a minimum-field schema that is aligned with Taiwanese LI procedures. We implemented the workflow and evaluated it using controlled experiments across multiple wallets and assets, reporting Wilson 95% confidence intervals (CIs). We achieved 91.4% accuracy (181/198) in end-to-end association under a confidence threshold, with high performance across wallet types, including Monero and TronLink. 2026-03-30 ASI, Vol. 9, Pages 73: A Lawful Metadata-Driven Framework for Linking Encrypted Communication Behavior and Cryptocurrency Wallet Activity in Digital Investigations

Applied System Innovation doi: 10.3390/asi9040073

Authors: Wei-Hsiang Lin Che-Yen Wen

End-to-end encrypted (E2EE) messaging and the growing use of cryptocurrency create an attribution gap for digital investigators because message content is unavailable and wallet activity is often decoupled from subscriber identities, which makes it difficult to link communication behaviors with wallet activity. We propose a lawful and metadata-driven forensic attribution framework called the Data-Source Association Framework (DSAF). The DSAF links encrypted communication behavior with cryptocurrency wallet activity by correlating only legally obtainable network metadata that are observable under lawful interception (LI) with on-chain traces. By integrating information from communication behaviors and wallet activity, the framework aims to narrow the person–application–wallet attribution gap. The framework integrates two components, where one performs encrypted-application classification using transport-layer signals and flow-level features and the other conducts wallet–identity association by applying controlled decoding to intercepted traffic and extracting relevant transaction traces. Both components operate under a minimum-field schema that is aligned with Taiwanese LI procedures. We implemented the workflow and evaluated it using controlled experiments across multiple wallets and assets, reporting Wilson 95% confidence intervals (CIs). We achieved 91.4% accuracy (181/198) in end-to-end association under a confidence threshold, with high performance across wallet types, including Monero and TronLink.

]]> A Lawful Metadata-Driven Framework for Linking Encrypted Communication Behavior and Cryptocurrency Wallet Activity in Digital Investigations Wei-Hsiang Lin Che-Yen Wen doi: 10.3390/asi9040073 Applied System Innovation 2026-03-30 Applied System Innovation 2026-03-30 9 4 Article 73 10.3390/asi9040073 https://www.mdpi.com/2571-5577/9/4/73 ASI, Vol. 9, Pages 72: A Controllability-Based Reliability Framework for Mechanical Systems with Scenario-Driven Performance Evaluation https://www.mdpi.com/2571-5577/9/4/72 In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor α(t), and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold ε. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at TCRC=17.0 s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. 2026-03-27 ASI, Vol. 9, Pages 72: A Controllability-Based Reliability Framework for Mechanical Systems with Scenario-Driven Performance Evaluation

Applied System Innovation doi: 10.3390/asi9040072

Authors: Daniel Osezua Aikhuele Shahryar Sorooshian

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor α(t), and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold ε. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at TCRC=17.0 s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system.

]]> A Controllability-Based Reliability Framework for Mechanical Systems with Scenario-Driven Performance Evaluation Daniel Osezua Aikhuele Shahryar Sorooshian doi: 10.3390/asi9040072 Applied System Innovation 2026-03-27 Applied System Innovation 2026-03-27 9 4 Article 72 10.3390/asi9040072 https://www.mdpi.com/2571-5577/9/4/72 ASI, Vol. 9, Pages 71: Machine Learning Operations on ZYNQ FPGA Board for Real-Time Face Recognition https://www.mdpi.com/2571-5577/9/4/71 Nowadays, MLOps approaches are gaining popularity thanks to their ability to apply DevOps best practices to machine learning models. They enable the automation and optimization of model training, deployment, and monitoring in various environments, while ensuring effective Continuous Integration/Continuous Deployment (CI/CD). These approaches thus promote real-time applications that can react quickly and improve continuously. This paper examines the feasibility of implementing MLOps practices in embedded systems, specifically on the Zynq-7000 FPGA board. We present a comprehensive MLOps architecture that enables the automated deployment and monitoring of a convolutional neural network model for face recognition on an embedded hardware platform for datacenter physical access control scenarios. This architecture integrates GitLab CI/CD for version control and pipeline automation, MLflow for experiment tracking and model lifecycles management, Prometheus and Grafana for monitoring, and data storage in an S3 Bucket cloud connected to DVC for dataset versioning. The results demonstrate that the proposed pipeline can be effectively deployed on a Zynq-7000 FPGA board enabling automated model retraining, redeployment, and performance monitoring. This approach reduces operational complexity and supports faster adaptation to dataset changes. 2026-03-26 ASI, Vol. 9, Pages 71: Machine Learning Operations on ZYNQ FPGA Board for Real-Time Face Recognition

Applied System Innovation doi: 10.3390/asi9040071

Authors: Bouchra Kouach Mohcin Mekhfioui Rachid El Gouri

Nowadays, MLOps approaches are gaining popularity thanks to their ability to apply DevOps best practices to machine learning models. They enable the automation and optimization of model training, deployment, and monitoring in various environments, while ensuring effective Continuous Integration/Continuous Deployment (CI/CD). These approaches thus promote real-time applications that can react quickly and improve continuously. This paper examines the feasibility of implementing MLOps practices in embedded systems, specifically on the Zynq-7000 FPGA board. We present a comprehensive MLOps architecture that enables the automated deployment and monitoring of a convolutional neural network model for face recognition on an embedded hardware platform for datacenter physical access control scenarios. This architecture integrates GitLab CI/CD for version control and pipeline automation, MLflow for experiment tracking and model lifecycles management, Prometheus and Grafana for monitoring, and data storage in an S3 Bucket cloud connected to DVC for dataset versioning. The results demonstrate that the proposed pipeline can be effectively deployed on a Zynq-7000 FPGA board enabling automated model retraining, redeployment, and performance monitoring. This approach reduces operational complexity and supports faster adaptation to dataset changes.

]]> Machine Learning Operations on ZYNQ FPGA Board for Real-Time Face Recognition Bouchra Kouach Mohcin Mekhfioui Rachid El Gouri doi: 10.3390/asi9040071 Applied System Innovation 2026-03-26 Applied System Innovation 2026-03-26 9 4 Article 71 10.3390/asi9040071 https://www.mdpi.com/2571-5577/9/4/71 ASI, Vol. 9, Pages 70: A Systematic Review of Wearable Assistive Technologies for Hearing Impairment: Current Landscape, User Experience, and Future Directions https://www.mdpi.com/2571-5577/9/4/70 Background: Hearing impairment affects a significant portion of the global population. The development of assistive technologies, particularly wearable devices, has been pivotal in mitigating these challenges. Methods: We present a systematic literature review on wearable assistive technologies for individuals with hearing impairment, analyzing 106 scientific articles identified from diverse sources (IEEE Xplore, ACM Digital Library, and Web of Science). Our comprehensive analysis is structured around device types, body locations, user study methodologies, sensory modalities, and application domains. Results: Findings reveal a strong emphasis on auditory and visual feedback, a mix of traditional hearing aids complemented by smart wearable devices, and experimental evaluations focusing on speech comprehension and usability. Visual analysis highlights a significant anatomical shift towards body-worn and wrist-worn haptic devices. While speech accuracy is rigorously reported, user-centric metrics like comfort and battery life are frequently neglected. Conclusions: Addressing these disparities, we propose the HEAR framework (Hybrid Architectures, Engaging Experiences, Adaptive Systems, Real-world Validation). This strategic roadmap advocates for a diversification of sensory outputs, more extensive longitudinal user studies, and the development of adaptive, multi-modal solutions that seamlessly integrate into users’ everyday lives. 2026-03-25 ASI, Vol. 9, Pages 70: A Systematic Review of Wearable Assistive Technologies for Hearing Impairment: Current Landscape, User Experience, and Future Directions

Applied System Innovation doi: 10.3390/asi9040070

Authors: Mihai Emanuel Spiţă Ovidiu Andrei Schipor

Background: Hearing impairment affects a significant portion of the global population. The development of assistive technologies, particularly wearable devices, has been pivotal in mitigating these challenges. Methods: We present a systematic literature review on wearable assistive technologies for individuals with hearing impairment, analyzing 106 scientific articles identified from diverse sources (IEEE Xplore, ACM Digital Library, and Web of Science). Our comprehensive analysis is structured around device types, body locations, user study methodologies, sensory modalities, and application domains. Results: Findings reveal a strong emphasis on auditory and visual feedback, a mix of traditional hearing aids complemented by smart wearable devices, and experimental evaluations focusing on speech comprehension and usability. Visual analysis highlights a significant anatomical shift towards body-worn and wrist-worn haptic devices. While speech accuracy is rigorously reported, user-centric metrics like comfort and battery life are frequently neglected. Conclusions: Addressing these disparities, we propose the HEAR framework (Hybrid Architectures, Engaging Experiences, Adaptive Systems, Real-world Validation). This strategic roadmap advocates for a diversification of sensory outputs, more extensive longitudinal user studies, and the development of adaptive, multi-modal solutions that seamlessly integrate into users’ everyday lives.

]]> A Systematic Review of Wearable Assistive Technologies for Hearing Impairment: Current Landscape, User Experience, and Future Directions Mihai Emanuel Spiţă Ovidiu Andrei Schipor doi: 10.3390/asi9040070 Applied System Innovation 2026-03-25 Applied System Innovation 2026-03-25 9 4 Systematic Review 70 10.3390/asi9040070 https://www.mdpi.com/2571-5577/9/4/70 ASI, Vol. 9, Pages 69: Recursive Weight Sharing for Parameter-Efficient Deep Convolutional Networks: Application to Skin Lesion Classification https://www.mdpi.com/2571-5577/9/4/69 Modern deep convolutional neural networks achieve remarkable performance but require substantial computational resources due to their large parameter counts, limiting their suitability for resource-constrained environments. We propose Tiny Recursive ResNet-50, a parameter-efficient architecture that reduces model complexity through recursive feature refinement with weight sharing across reasoning cycles. The proposed design combines lightweight bottleneck blocks, iterative latent state accumulation, and deep supervision to enhance representation quality without increasing parameter count. Extensive experiments are conducted on melanoma classification using the HAM10000 dataset as the primary training and evaluation benchmark. Results demonstrate that the proposed recursive architecture maintains competitive accuracy while reducing parameters by approximately 49%, confirming its efficiency under constrained settings. To assess robustness under limited data and acquisition variability, we additionally validate on the PH2 dataset (200 images). Due to the small dataset size and class imbalance, evaluation is performed using 5-fold stratified cross-validation, and performance metrics are reported as mean ± standard deviation. This validation confirms that recursive refinement with moderate cycle depth improves stability and generalization in small-data regimes. 2026-03-25 ASI, Vol. 9, Pages 69: Recursive Weight Sharing for Parameter-Efficient Deep Convolutional Networks: Application to Skin Lesion Classification

Applied System Innovation doi: 10.3390/asi9040069

Authors: Ali Belkhiri My Abdelouahed Sabri Abdellah Aarab

Modern deep convolutional neural networks achieve remarkable performance but require substantial computational resources due to their large parameter counts, limiting their suitability for resource-constrained environments. We propose Tiny Recursive ResNet-50, a parameter-efficient architecture that reduces model complexity through recursive feature refinement with weight sharing across reasoning cycles. The proposed design combines lightweight bottleneck blocks, iterative latent state accumulation, and deep supervision to enhance representation quality without increasing parameter count. Extensive experiments are conducted on melanoma classification using the HAM10000 dataset as the primary training and evaluation benchmark. Results demonstrate that the proposed recursive architecture maintains competitive accuracy while reducing parameters by approximately 49%, confirming its efficiency under constrained settings. To assess robustness under limited data and acquisition variability, we additionally validate on the PH2 dataset (200 images). Due to the small dataset size and class imbalance, evaluation is performed using 5-fold stratified cross-validation, and performance metrics are reported as mean ± standard deviation. This validation confirms that recursive refinement with moderate cycle depth improves stability and generalization in small-data regimes.

]]> Recursive Weight Sharing for Parameter-Efficient Deep Convolutional Networks: Application to Skin Lesion Classification Ali Belkhiri My Abdelouahed Sabri Abdellah Aarab doi: 10.3390/asi9040069 Applied System Innovation 2026-03-25 Applied System Innovation 2026-03-25 9 4 Article 69 10.3390/asi9040069 https://www.mdpi.com/2571-5577/9/4/69 ASI, Vol. 9, Pages 68: Systemic Integration of Artificial Intelligence in Financial Project Management: A Systematic Literature Review and BERTopic-Based Analysis https://www.mdpi.com/2571-5577/9/4/68 Artificial Intelligence (AI) is increasingly embedded in project management within the financial sector, yet existing research remains fragmented and largely focused on isolated technical applications. A systemic understanding of how AI reshapes financial project management as an integrated socio-technical capability is still lacking. This study addresses this gap through a systematic literature review of 62 peer-reviewed articles (2022–2025), combined with BERTopic-based thematic analysis supported by large language model-assisted topic representation. The findings reveal the emergence of Agentic AI as a dominant theme, marking a shift from analytical support tools toward autonomous and collaborative agents embedded in project processes. While predictive analytics and automation are relatively mature, governance-oriented and human-centric dimensions remain underdeveloped and weakly integrated. This study contributes by: (1) presenting a computationally enhanced systematic mapping study that integrates a systematic literature review with BERTopic-based topic modelling to map the evolving research landscape; (2) identifying Agentic AI as a pivotal interface between technical execution and strategic governance; and (3) proposing a socio-technical target architecture that offers a structured roadmap for AI-enabled transformation in financial project management systems. 2026-03-24 ASI, Vol. 9, Pages 68: Systemic Integration of Artificial Intelligence in Financial Project Management: A Systematic Literature Review and BERTopic-Based Analysis

Applied System Innovation doi: 10.3390/asi9040068

Authors: Styve L. Ndjonkin Simen Simon P. Philbin Gordon Hunter

Artificial Intelligence (AI) is increasingly embedded in project management within the financial sector, yet existing research remains fragmented and largely focused on isolated technical applications. A systemic understanding of how AI reshapes financial project management as an integrated socio-technical capability is still lacking. This study addresses this gap through a systematic literature review of 62 peer-reviewed articles (2022–2025), combined with BERTopic-based thematic analysis supported by large language model-assisted topic representation. The findings reveal the emergence of Agentic AI as a dominant theme, marking a shift from analytical support tools toward autonomous and collaborative agents embedded in project processes. While predictive analytics and automation are relatively mature, governance-oriented and human-centric dimensions remain underdeveloped and weakly integrated. This study contributes by: (1) presenting a computationally enhanced systematic mapping study that integrates a systematic literature review with BERTopic-based topic modelling to map the evolving research landscape; (2) identifying Agentic AI as a pivotal interface between technical execution and strategic governance; and (3) proposing a socio-technical target architecture that offers a structured roadmap for AI-enabled transformation in financial project management systems.

]]> Systemic Integration of Artificial Intelligence in Financial Project Management: A Systematic Literature Review and BERTopic-Based Analysis Styve L. Ndjonkin Simen Simon P. Philbin Gordon Hunter doi: 10.3390/asi9040068 Applied System Innovation 2026-03-24 Applied System Innovation 2026-03-24 9 4 Review 68 10.3390/asi9040068 https://www.mdpi.com/2571-5577/9/4/68 ASI, Vol. 9, Pages 67: Spatiotemporal Ergonomic Fatigue Analysis in Seated Postures Using a Multimodal Smart-Skin System: A Comparative Study Between Mannequin and Human Measurements https://www.mdpi.com/2571-5577/9/4/67 Continuous monitoring of sitting posture is crucial for ergonomic assessment and fatigue prevention, yet many existing approaches rely on vision-based systems or single-modality sensing that are limited in capturing spatial and temporal biomechanical dynamics. This paper presents a multimodal smart-skin sensing system for spatial and temporal ergonomic fatigue analysis in sitting postures. The proposed platform integrates 42 distributed pressure, temperature, and vibration sensors arranged in 14 trimodal sensing nodes embedded across anatomical seating and back regions to enable real-time multimodal acquisition of human–chair interaction patterns. The study introduces an analytical framework combining anatomical heatmap visualization, temporal evolution analysis, delta pressure mapping, fatigue intensity estimation, and hotspot detection to characterize dynamic pressure redistribution during prolonged sitting. Experimental evaluations were conducted using a biomechanical mannequin and a single human participant with identical anthropometric characteristics (165 cm height and 62 kg body mass) across nine seated conditions, including neutral sitting, reclining, leaning, periodic shifting, and vibration-induced motion. Each posture condition was recorded as a time-series session and segmented into temporal phases to analyze fatigue evolution during prolonged sitting. Statistical analysis of pressure redistribution dynamics indicates significantly higher pressure drift in human measurements compared with the mechanically stable mannequin baseline (p < 0.001). The proposed framework provides a scalable sensing approach for ergonomic monitoring, intelligent seating systems, and human–machine interface applications. 2026-03-24 ASI, Vol. 9, Pages 67: Spatiotemporal Ergonomic Fatigue Analysis in Seated Postures Using a Multimodal Smart-Skin System: A Comparative Study Between Mannequin and Human Measurements

Applied System Innovation doi: 10.3390/asi9040067

Authors: Giva Andriana Mutiara Muhammad Rizqy Alfarisi Paramita Mayadewi Lisda Meisaroh Periyadi

Continuous monitoring of sitting posture is crucial for ergonomic assessment and fatigue prevention, yet many existing approaches rely on vision-based systems or single-modality sensing that are limited in capturing spatial and temporal biomechanical dynamics. This paper presents a multimodal smart-skin sensing system for spatial and temporal ergonomic fatigue analysis in sitting postures. The proposed platform integrates 42 distributed pressure, temperature, and vibration sensors arranged in 14 trimodal sensing nodes embedded across anatomical seating and back regions to enable real-time multimodal acquisition of human–chair interaction patterns. The study introduces an analytical framework combining anatomical heatmap visualization, temporal evolution analysis, delta pressure mapping, fatigue intensity estimation, and hotspot detection to characterize dynamic pressure redistribution during prolonged sitting. Experimental evaluations were conducted using a biomechanical mannequin and a single human participant with identical anthropometric characteristics (165 cm height and 62 kg body mass) across nine seated conditions, including neutral sitting, reclining, leaning, periodic shifting, and vibration-induced motion. Each posture condition was recorded as a time-series session and segmented into temporal phases to analyze fatigue evolution during prolonged sitting. Statistical analysis of pressure redistribution dynamics indicates significantly higher pressure drift in human measurements compared with the mechanically stable mannequin baseline (p < 0.001). The proposed framework provides a scalable sensing approach for ergonomic monitoring, intelligent seating systems, and human–machine interface applications.

]]> Spatiotemporal Ergonomic Fatigue Analysis in Seated Postures Using a Multimodal Smart-Skin System: A Comparative Study Between Mannequin and Human Measurements Giva Andriana Mutiara Muhammad Rizqy Alfarisi Paramita Mayadewi Lisda Meisaroh Periyadi doi: 10.3390/asi9040067 Applied System Innovation 2026-03-24 Applied System Innovation 2026-03-24 9 4 Article 67 10.3390/asi9040067 https://www.mdpi.com/2571-5577/9/4/67 ASI, Vol. 9, Pages 66: Signal Processing Techniques for Enhancing an Areal Density in Two-Reader/Three-Track Detection of Staggered Bit-Patterned Magnetic Recording Systems https://www.mdpi.com/2571-5577/9/3/66 As the demand for digital storage capacity continues to grow, bit-patterned magnetic recording (BPMR) has emerged as a promising technology to overcome the superparamagnetic limit of conventional recording methods. Nevertheless, the extremely close spacing of magnetic islands in BPMR can result in significant signal corruption, particularly due to inter-track interference. This paper presents robust signal-processing schemes for a two-reader, three-track detection system in a staggered BPMR configuration to address these challenges. The first proposed method employs a sum-soft-information technique, which combines log-likelihood ratios from two detectors to maximize mutual information. This approach significantly improves the reliability of middle-track detection. We also propose the inter-track interference subtraction technique, in which the highly reliable data recovered from the middle track are used to reconstruct the interference signal, which is then subtracted from the upper and lower tracks using an optimized weighting factor. Simulation results at an areal density of 3.0 Tb/in2 demonstrate that an optimized weighting factor of 1.78 effectively cancels interference. Moreover, the results indicate that our proposed scheme achieves a bit-error rate (BER) comparable to that of the three-reader, one-track detection BPMR systems. Furthermore, our method also demonstrates a lower BER for both adjacent tracks when compared to the conventional single-reader, two-track reading system, even in the presence of 10% media noise. 2026-03-23 ASI, Vol. 9, Pages 66: Signal Processing Techniques for Enhancing an Areal Density in Two-Reader/Three-Track Detection of Staggered Bit-Patterned Magnetic Recording Systems

Applied System Innovation doi: 10.3390/asi9030066

Authors: Natthakan Rueangnetr Satra Tor. Wattanaphol Kittipon Kankhunthod Simon J. Greaves Chanon Warisarn

As the demand for digital storage capacity continues to grow, bit-patterned magnetic recording (BPMR) has emerged as a promising technology to overcome the superparamagnetic limit of conventional recording methods. Nevertheless, the extremely close spacing of magnetic islands in BPMR can result in significant signal corruption, particularly due to inter-track interference. This paper presents robust signal-processing schemes for a two-reader, three-track detection system in a staggered BPMR configuration to address these challenges. The first proposed method employs a sum-soft-information technique, which combines log-likelihood ratios from two detectors to maximize mutual information. This approach significantly improves the reliability of middle-track detection. We also propose the inter-track interference subtraction technique, in which the highly reliable data recovered from the middle track are used to reconstruct the interference signal, which is then subtracted from the upper and lower tracks using an optimized weighting factor. Simulation results at an areal density of 3.0 Tb/in2 demonstrate that an optimized weighting factor of 1.78 effectively cancels interference. Moreover, the results indicate that our proposed scheme achieves a bit-error rate (BER) comparable to that of the three-reader, one-track detection BPMR systems. Furthermore, our method also demonstrates a lower BER for both adjacent tracks when compared to the conventional single-reader, two-track reading system, even in the presence of 10% media noise.

]]> Signal Processing Techniques for Enhancing an Areal Density in Two-Reader/Three-Track Detection of Staggered Bit-Patterned Magnetic Recording Systems Natthakan Rueangnetr Satra Tor. Wattanaphol Kittipon Kankhunthod Simon J. Greaves Chanon Warisarn doi: 10.3390/asi9030066 Applied System Innovation 2026-03-23 Applied System Innovation 2026-03-23 9 3 Article 66 10.3390/asi9030066 https://www.mdpi.com/2571-5577/9/3/66 ASI, Vol. 9, Pages 65: Modular Linear Fresnel Solar Concentrator for Integrated Photovoltaic Thermal Energy Systems: A Comprehensive Design and Numerical Analysis https://www.mdpi.com/2571-5577/9/3/65 Photovoltaic thermal concentration has emerged as a method to enhance the energy efficiency and performance of photovoltaic installations. This approach addresses the growing demand for renewable energy aimed at reducing emissions and mitigating climate change. It represents a significant solution for applications requiring both thermal and electrical energy under constraints of a limited available area for solar energy harvesting. However, currently developed devices rely on expensive photovoltaic cells, incorporate complex geometries that are difficult to manufacture and maintain, and employ tracking systems that complicate interconnection with similar units. The objective of this study is to design and numerically evaluate a hybrid thermal–photovoltaic modular linear Fresnel solar concentrator (H-MLFRC) based on commercial silicon cells. The proposed system allows series and parallel interconnection and is suitable for both islanded and grid-connected configurations. Its development was guided by integrated optical, photovoltaic, and thermal analyses, which defined the system geometry, characteristic parameters, and operating conditions. The results indicate that the maximum operating temperature of the device is 70 °C under a nominal operating mass flow rate of 0.45 kg/s. Additionally, the thermal and photovoltaic efficiencies are 49% and 16%, respectively, resulting in a combined efficiency of 65%. 2026-03-23 ASI, Vol. 9, Pages 65: Modular Linear Fresnel Solar Concentrator for Integrated Photovoltaic Thermal Energy Systems: A Comprehensive Design and Numerical Analysis

Applied System Innovation doi: 10.3390/asi9030065

Authors: Juan Carlos Castro-Dominguez Oscar Alejandro López-Núñez Jorge O. Aguilar Karla G. Cedano-Villavicencio Oscar A. Jaramillo

Photovoltaic thermal concentration has emerged as a method to enhance the energy efficiency and performance of photovoltaic installations. This approach addresses the growing demand for renewable energy aimed at reducing emissions and mitigating climate change. It represents a significant solution for applications requiring both thermal and electrical energy under constraints of a limited available area for solar energy harvesting. However, currently developed devices rely on expensive photovoltaic cells, incorporate complex geometries that are difficult to manufacture and maintain, and employ tracking systems that complicate interconnection with similar units. The objective of this study is to design and numerically evaluate a hybrid thermal–photovoltaic modular linear Fresnel solar concentrator (H-MLFRC) based on commercial silicon cells. The proposed system allows series and parallel interconnection and is suitable for both islanded and grid-connected configurations. Its development was guided by integrated optical, photovoltaic, and thermal analyses, which defined the system geometry, characteristic parameters, and operating conditions. The results indicate that the maximum operating temperature of the device is 70 °C under a nominal operating mass flow rate of 0.45 kg/s. Additionally, the thermal and photovoltaic efficiencies are 49% and 16%, respectively, resulting in a combined efficiency of 65%.

]]> Modular Linear Fresnel Solar Concentrator for Integrated Photovoltaic Thermal Energy Systems: A Comprehensive Design and Numerical Analysis Juan Carlos Castro-Dominguez Oscar Alejandro López-Núñez Jorge O. Aguilar Karla G. Cedano-Villavicencio Oscar A. Jaramillo doi: 10.3390/asi9030065 Applied System Innovation 2026-03-23 Applied System Innovation 2026-03-23 9 3 Article 65 10.3390/asi9030065 https://www.mdpi.com/2571-5577/9/3/65 ASI, Vol. 9, Pages 64: Integration of AI Content Generation-Enabled Virtual Museums into University History Education https://www.mdpi.com/2571-5577/9/3/64 Traditional approaches to university-level history education often fail to provide immersive and interactive environments that foster deep cognitive engagement. To address these limitations, we developed an AI-enabled virtual museum system that integrates AI-generated content with knowledge graphs through a multi-layered architecture. The system architecture follows a three-tier framework: a front-end interaction layer (Unity/Unreal Engine) for real-time user engagement, a core service layer for intelligent event scheduling and response control (Chat General Language Model/Stable Diffusion), and a data and model layer (My Structured Query Language/MongoDB) to provide structured knowledge. To evaluate the system’s effectiveness, a four-week controlled experiment was conducted with 83 university students. The experimental group using the AI virtual museum showed a significantly higher mean post-test score (84.5 ± 6.8) than that of the control group (71.6 ± 7.9), with statistical significance at p < 0.001, starting from nearly identical baseline scores (61.2 and 60.4 for the experimental and control groups). Correlation analysis was conducted to identify scenario simulations (r = 0.59) and deep inquiry tasks (r = 0.54) as key drivers of learning mastery. By aligning advanced system engineering with educational theory, the results of this study offer a solution for high-fidelity, intelligent digital educational platforms, proposing a validated model for integrated system innovation in education. 2026-03-18 ASI, Vol. 9, Pages 64: Integration of AI Content Generation-Enabled Virtual Museums into University History Education

Applied System Innovation doi: 10.3390/asi9030064

Authors: Shirong Tan Yuchun Liu Lei Wang

Traditional approaches to university-level history education often fail to provide immersive and interactive environments that foster deep cognitive engagement. To address these limitations, we developed an AI-enabled virtual museum system that integrates AI-generated content with knowledge graphs through a multi-layered architecture. The system architecture follows a three-tier framework: a front-end interaction layer (Unity/Unreal Engine) for real-time user engagement, a core service layer for intelligent event scheduling and response control (Chat General Language Model/Stable Diffusion), and a data and model layer (My Structured Query Language/MongoDB) to provide structured knowledge. To evaluate the system’s effectiveness, a four-week controlled experiment was conducted with 83 university students. The experimental group using the AI virtual museum showed a significantly higher mean post-test score (84.5 ± 6.8) than that of the control group (71.6 ± 7.9), with statistical significance at p < 0.001, starting from nearly identical baseline scores (61.2 and 60.4 for the experimental and control groups). Correlation analysis was conducted to identify scenario simulations (r = 0.59) and deep inquiry tasks (r = 0.54) as key drivers of learning mastery. By aligning advanced system engineering with educational theory, the results of this study offer a solution for high-fidelity, intelligent digital educational platforms, proposing a validated model for integrated system innovation in education.

]]> Integration of AI Content Generation-Enabled Virtual Museums into University History Education Shirong Tan Yuchun Liu Lei Wang doi: 10.3390/asi9030064 Applied System Innovation 2026-03-18 Applied System Innovation 2026-03-18 9 3 Article 64 10.3390/asi9030064 https://www.mdpi.com/2571-5577/9/3/64 ASI, Vol. 9, Pages 63: Data-Driven Fleet Optimization Using ML Algorithms and a Decision-Making Grid Framework https://www.mdpi.com/2571-5577/9/3/63 The most impactful factors for the cost of fleet management are maintenance expenses and fuel consumption. Traditional ways of monitoring fleet performance fail to connect raw operational data with driving habits. The current study addresses this challenge by developing an architecture of frameworks, consisting of unsupervised and supervised machine learning algorithms, statistical testing, simulation and survival analysis to discover insights that lead to key behavioral predictors. The nucleus of this complex architecture is the decision-making grid (DMG), a two-dimensional matrix that groups vehicles based on their frequency of entering the service and the cost of their repairs. It is the first integration of DMG with ML for prescriptive fleet management. The objective of the study is twofold: firstly, to build a system that classifies vehicles according to their risk profile, and secondly, to offer clear directions for changing driver patterns that most affect vehicle costs or for keeping good practices. The framework proposed by this study not only drives the optimization of operational efficiency but also contributes to a methodology that links driver profiles to costs, offering a scalable methodology for similar business contexts. 2026-03-17 ASI, Vol. 9, Pages 63: Data-Driven Fleet Optimization Using ML Algorithms and a Decision-Making Grid Framework

Applied System Innovation doi: 10.3390/asi9030063

Authors: Ashraf Labib Coralia Tǎnǎsuicǎ (Zotic) Turuna S. Seecharan Mihai-Daniel Roman

The most impactful factors for the cost of fleet management are maintenance expenses and fuel consumption. Traditional ways of monitoring fleet performance fail to connect raw operational data with driving habits. The current study addresses this challenge by developing an architecture of frameworks, consisting of unsupervised and supervised machine learning algorithms, statistical testing, simulation and survival analysis to discover insights that lead to key behavioral predictors. The nucleus of this complex architecture is the decision-making grid (DMG), a two-dimensional matrix that groups vehicles based on their frequency of entering the service and the cost of their repairs. It is the first integration of DMG with ML for prescriptive fleet management. The objective of the study is twofold: firstly, to build a system that classifies vehicles according to their risk profile, and secondly, to offer clear directions for changing driver patterns that most affect vehicle costs or for keeping good practices. The framework proposed by this study not only drives the optimization of operational efficiency but also contributes to a methodology that links driver profiles to costs, offering a scalable methodology for similar business contexts.

]]> Data-Driven Fleet Optimization Using ML Algorithms and a Decision-Making Grid Framework Ashraf Labib Coralia Tǎnǎsuicǎ (Zotic) Turuna S. Seecharan Mihai-Daniel Roman doi: 10.3390/asi9030063 Applied System Innovation 2026-03-17 Applied System Innovation 2026-03-17 9 3 Article 63 10.3390/asi9030063 https://www.mdpi.com/2571-5577/9/3/63 ASI, Vol. 9, Pages 62: EKA—Enterprise Knowledge Assistant: Collaborative Multi-Agent AI for Large Claims Handling https://www.mdpi.com/2571-5577/9/3/62 Large insurance claims handling is a complex, knowledge-intensive process that requires the analysis of heterogeneous information sources and the reuse of past experience distributed across multiple organizational data sources. Consequently, a significant portion of decision-making knowledge is embedded in historical claims records and internal documents, making systematic access and reuse challenging. This paper presents Enterprise Knowledge Assistant (EKA), a collaborative multi-agent AI system designed to act as a sparring partner for large claims handlers. EKA integrates claims structured and unstructured data with an archive of more than five thousand historical cases related to claims management, enabling retrieval, interpretation, and synthesis of relevant past cases and decision patterns. The system is organized as a set of specialized AI agents, each responsible for distinct tasks including claim context analysis, knowledge extraction, document synthesis, and interaction with human users. Through agent collaboration, EKA provides decision support by analyzing comparable historical cases, uncovering hidden correlations, and extracting insurance wisdom, while keeping the human expert firmly in control. The paper describes the system architecture and reports an industrial case study evaluating EKA in a real insurance environment. Results indicate improved knowledge reuse and reduced analysis effort in large claims handling. 2026-03-17 ASI, Vol. 9, Pages 62: EKA—Enterprise Knowledge Assistant: Collaborative Multi-Agent AI for Large Claims Handling

Applied System Innovation doi: 10.3390/asi9030062

Authors: Alberto Loffredo Yunting Liu Zhengdao Chen Yifei Fu Joerg Ahrens Yifeng Lu Dong Chen

Large insurance claims handling is a complex, knowledge-intensive process that requires the analysis of heterogeneous information sources and the reuse of past experience distributed across multiple organizational data sources. Consequently, a significant portion of decision-making knowledge is embedded in historical claims records and internal documents, making systematic access and reuse challenging. This paper presents Enterprise Knowledge Assistant (EKA), a collaborative multi-agent AI system designed to act as a sparring partner for large claims handlers. EKA integrates claims structured and unstructured data with an archive of more than five thousand historical cases related to claims management, enabling retrieval, interpretation, and synthesis of relevant past cases and decision patterns. The system is organized as a set of specialized AI agents, each responsible for distinct tasks including claim context analysis, knowledge extraction, document synthesis, and interaction with human users. Through agent collaboration, EKA provides decision support by analyzing comparable historical cases, uncovering hidden correlations, and extracting insurance wisdom, while keeping the human expert firmly in control. The paper describes the system architecture and reports an industrial case study evaluating EKA in a real insurance environment. Results indicate improved knowledge reuse and reduced analysis effort in large claims handling.

]]> EKA—Enterprise Knowledge Assistant: Collaborative Multi-Agent AI for Large Claims Handling Alberto Loffredo Yunting Liu Zhengdao Chen Yifei Fu Joerg Ahrens Yifeng Lu Dong Chen doi: 10.3390/asi9030062 Applied System Innovation 2026-03-17 Applied System Innovation 2026-03-17 9 3 Article 62 10.3390/asi9030062 https://www.mdpi.com/2571-5577/9/3/62 ASI, Vol. 9, Pages 61: INTELLECTUM: A Hybrid AR-VR Metaverse Framework for Smart Cities https://www.mdpi.com/2571-5577/9/3/61 This work presents INTELLECTUM as a reference architecture and design-time evaluation framework for multi-entity XR–AI–digital twin systems. Rather than optimizing a specific implementation, the paper formalizes architectural invariants, event semantics, and coordination mechanisms that precede and inform system realization. INTELLECTUM provides a conceptual framework for structuring interactions across physical and virtual environments, emphasizing human-centered design, immersive digital twins, and collaborative extended-reality workspaces. The technical specification defines core architectural components, human integration modalities via WebXR and heterogeneous sensor networks, and representative usage scenarios within smart city ecosystems. By enabling AI-assisted urban planning, interactive simulation, and multi-actor coordination, INTELLECTUM positions itself as an XR-based architectural foundation for next-generation smart city platforms. 2026-03-17 ASI, Vol. 9, Pages 61: INTELLECTUM: A Hybrid AR-VR Metaverse Framework for Smart Cities

Applied System Innovation doi: 10.3390/asi9030061

Authors: Andrey Nechesov Janne Ruponen

This work presents INTELLECTUM as a reference architecture and design-time evaluation framework for multi-entity XR–AI–digital twin systems. Rather than optimizing a specific implementation, the paper formalizes architectural invariants, event semantics, and coordination mechanisms that precede and inform system realization. INTELLECTUM provides a conceptual framework for structuring interactions across physical and virtual environments, emphasizing human-centered design, immersive digital twins, and collaborative extended-reality workspaces. The technical specification defines core architectural components, human integration modalities via WebXR and heterogeneous sensor networks, and representative usage scenarios within smart city ecosystems. By enabling AI-assisted urban planning, interactive simulation, and multi-actor coordination, INTELLECTUM positions itself as an XR-based architectural foundation for next-generation smart city platforms.

]]> INTELLECTUM: A Hybrid AR-VR Metaverse Framework for Smart Cities Andrey Nechesov Janne Ruponen doi: 10.3390/asi9030061 Applied System Innovation 2026-03-17 Applied System Innovation 2026-03-17 9 3 Article 61 10.3390/asi9030061 https://www.mdpi.com/2571-5577/9/3/61 ASI, Vol. 9, Pages 60: Design of the Electric Power Control System for a Hydrogen-Fed AEMFC Polymeric Fuel Cell Generator to Power a 0.75 KW DC Motor https://www.mdpi.com/2571-5577/9/3/60 Mitigating pollution in cities where transportation powered by fossil fuels has a significant impact on human health is a public health priority. Although electric vehicles are one solution to this problem, their high acquisition and maintenance costs have limited their rapid adoption; therefore, other solutions may be useful in supporting reduction efforts. Therefore, this paper proposes a power control system for an Anion Exchange Membrane Fuel Cell (AEMFC) generator powered by hydrogen with the capacity to supply a direct current (DC) motor of 0.75 kW. A mathematical model of the AEMFC was proposed, and the parameters were adjusted to obtain polarization and power curves defining safe operating ranges (12.45–17.9 V). A boost converter was designed to increase the voltage of the cell output to 48 V to meet the requirements of the DC motor. The performance of the power converter was studied by analyzing its small-signal ripple, operating modes, and efficiency. The models and simulations were implemented using MATLAB and PSIM. A cascaded control system with proportional–integral (PI) and proportional–integral–derivative (PID) controllers was implemented to maintain voltage stability in the presence of input and load variation. The results show that the AEMFC is reliable and that the boost converter presents an efficiency higher than 98% in continuous mode. The robustness of the model was validated through simulations and using a prototype. 2026-03-16 ASI, Vol. 9, Pages 60: Design of the Electric Power Control System for a Hydrogen-Fed AEMFC Polymeric Fuel Cell Generator to Power a 0.75 KW DC Motor

Applied System Innovation doi: 10.3390/asi9030060

Authors: Mario Alejandro Benavides Álvarez Fredy E. Hoyos John E. Candelo-Becerra

Mitigating pollution in cities where transportation powered by fossil fuels has a significant impact on human health is a public health priority. Although electric vehicles are one solution to this problem, their high acquisition and maintenance costs have limited their rapid adoption; therefore, other solutions may be useful in supporting reduction efforts. Therefore, this paper proposes a power control system for an Anion Exchange Membrane Fuel Cell (AEMFC) generator powered by hydrogen with the capacity to supply a direct current (DC) motor of 0.75 kW. A mathematical model of the AEMFC was proposed, and the parameters were adjusted to obtain polarization and power curves defining safe operating ranges (12.45–17.9 V). A boost converter was designed to increase the voltage of the cell output to 48 V to meet the requirements of the DC motor. The performance of the power converter was studied by analyzing its small-signal ripple, operating modes, and efficiency. The models and simulations were implemented using MATLAB and PSIM. A cascaded control system with proportional–integral (PI) and proportional–integral–derivative (PID) controllers was implemented to maintain voltage stability in the presence of input and load variation. The results show that the AEMFC is reliable and that the boost converter presents an efficiency higher than 98% in continuous mode. The robustness of the model was validated through simulations and using a prototype.

]]> Design of the Electric Power Control System for a Hydrogen-Fed AEMFC Polymeric Fuel Cell Generator to Power a 0.75 KW DC Motor Mario Alejandro Benavides Álvarez Fredy E. Hoyos John E. Candelo-Becerra doi: 10.3390/asi9030060 Applied System Innovation 2026-03-16 Applied System Innovation 2026-03-16 9 3 Article 60 10.3390/asi9030060 https://www.mdpi.com/2571-5577/9/3/60 ASI, Vol. 9, Pages 59: Load Frequency Control in Multi-Area Power Systems Using Incremental Proportional–Integral–Derivative and Model-Free Adaptive Control https://www.mdpi.com/2571-5577/9/3/59 Maintaining frequency stability in modern multi-area interconnected power systems has become increasingly challenging due to the stochastic nature of wind power and reduced effective system inertia. Under these dynamic conditions, traditional fixed-gain PID controllers frequently fail to provide robust regulation. To address this limitation, this study proposes and evaluates a practical model-free secondary control strategy for multi-area Load Frequency Control (LFC). The proposed hybrid MFAC–PID framework integrates an incremental model-free adaptive control (MFAC) law with a low-gain incremental PID damping term. This combination leverages real-time input–output data to determine primary control actions without relying on an explicit plant model, while the PID component supplies supplementary damping based on recent control errors. Furthermore, the controller utilizes online pseudo-gradient estimation to dynamically adapt to stochastic wind fluctuations and ±5% parametric uncertainty. Simulation results demonstrate that the hybrid design substantially enhances Area Control Error (ACE) regulation. Under wind-disturbed conditions, it reduces the aggregated Integral Absolute Error (IAEtotal) from 92.76 to 41.10, representing an improvement of over 50% compared with the fixed-gain PID baseline. Additionally, the controller maintains a low computational overhead of 0.306 milliseconds per control cycle. These findings indicate that the hybrid MFAC–PID structure provides a robust, computationally efficient solution for real-time Automatic Generation Control (AGC) in renewable-integrated multi-area power grids. 2026-03-16 ASI, Vol. 9, Pages 59: Load Frequency Control in Multi-Area Power Systems Using Incremental Proportional–Integral–Derivative and Model-Free Adaptive Control

Applied System Innovation doi: 10.3390/asi9030059

Authors: Md Asif Shaharear Chengyu Zhou Shahin Shaikh Md Mehedy Hasan Faruk

Maintaining frequency stability in modern multi-area interconnected power systems has become increasingly challenging due to the stochastic nature of wind power and reduced effective system inertia. Under these dynamic conditions, traditional fixed-gain PID controllers frequently fail to provide robust regulation. To address this limitation, this study proposes and evaluates a practical model-free secondary control strategy for multi-area Load Frequency Control (LFC). The proposed hybrid MFAC–PID framework integrates an incremental model-free adaptive control (MFAC) law with a low-gain incremental PID damping term. This combination leverages real-time input–output data to determine primary control actions without relying on an explicit plant model, while the PID component supplies supplementary damping based on recent control errors. Furthermore, the controller utilizes online pseudo-gradient estimation to dynamically adapt to stochastic wind fluctuations and ±5% parametric uncertainty. Simulation results demonstrate that the hybrid design substantially enhances Area Control Error (ACE) regulation. Under wind-disturbed conditions, it reduces the aggregated Integral Absolute Error (IAEtotal) from 92.76 to 41.10, representing an improvement of over 50% compared with the fixed-gain PID baseline. Additionally, the controller maintains a low computational overhead of 0.306 milliseconds per control cycle. These findings indicate that the hybrid MFAC–PID structure provides a robust, computationally efficient solution for real-time Automatic Generation Control (AGC) in renewable-integrated multi-area power grids.

]]> Load Frequency Control in Multi-Area Power Systems Using Incremental Proportional–Integral–Derivative and Model-Free Adaptive Control Md Asif Shaharear Chengyu Zhou Shahin Shaikh Md Mehedy Hasan Faruk doi: 10.3390/asi9030059 Applied System Innovation 2026-03-16 Applied System Innovation 2026-03-16 9 3 Article 59 10.3390/asi9030059 https://www.mdpi.com/2571-5577/9/3/59 ASI, Vol. 9, Pages 58: Automated Real-Time Detection and Correction of Children’s Kinesthetic Learning Using Expert-User Performance and Smartphones as Wearables https://www.mdpi.com/2571-5577/9/3/58 More than 80% of young people (11–17 years) do not meet recommended levels of physical activity, while excessive sedentary smartphone use increases rapidly, highlighting the need for accessible tools that promote active and kinesthetic learning. This study investigates whether smartphones can function as wearable devices capable of tracking movement, detecting biomechanical errors, and providing real-time corrective feedback. Using a user-centered design approach, we developed a gamified Exertion Trainer in which children practiced a straight punch (boxing jab) while wearing a smartphone on their wrist. Embedded accelerometer data were processed on board to deliver immediate, task-specific feedback on arm orientation, using gravity as a fixed reference frame. A randomized crossover trial was conducted with 40 children, comparing a feedback condition with a no-feedback control across two test orders. Quantitative results showed that real-time feedback produced a statistically significant improvement in punch accuracy (p < 0.001) and reduced performance variability, with the strongest effects observed after initial practice and partial retention following feedback removal. Qualitative findings indicated higher engagement and stronger perceptions of kinesthetic learning when feedback was available. These results demonstrate that smartphones can serve as practical wearable devices for delivering biomechanical guidance and supporting movement skill acquisition in children. 2026-03-12 ASI, Vol. 9, Pages 58: Automated Real-Time Detection and Correction of Children’s Kinesthetic Learning Using Expert-User Performance and Smartphones as Wearables

Applied System Innovation doi: 10.3390/asi9030058

Authors: Carla Gómez-Monroy Alejandro C. Ramírez-Reivich Vicente Borja José Luis Jimenez-Corona Victor Gonzalez

More than 80% of young people (11–17 years) do not meet recommended levels of physical activity, while excessive sedentary smartphone use increases rapidly, highlighting the need for accessible tools that promote active and kinesthetic learning. This study investigates whether smartphones can function as wearable devices capable of tracking movement, detecting biomechanical errors, and providing real-time corrective feedback. Using a user-centered design approach, we developed a gamified Exertion Trainer in which children practiced a straight punch (boxing jab) while wearing a smartphone on their wrist. Embedded accelerometer data were processed on board to deliver immediate, task-specific feedback on arm orientation, using gravity as a fixed reference frame. A randomized crossover trial was conducted with 40 children, comparing a feedback condition with a no-feedback control across two test orders. Quantitative results showed that real-time feedback produced a statistically significant improvement in punch accuracy (p < 0.001) and reduced performance variability, with the strongest effects observed after initial practice and partial retention following feedback removal. Qualitative findings indicated higher engagement and stronger perceptions of kinesthetic learning when feedback was available. These results demonstrate that smartphones can serve as practical wearable devices for delivering biomechanical guidance and supporting movement skill acquisition in children.

]]> Automated Real-Time Detection and Correction of Children’s Kinesthetic Learning Using Expert-User Performance and Smartphones as Wearables Carla Gómez-Monroy Alejandro C. Ramírez-Reivich Vicente Borja José Luis Jimenez-Corona Victor Gonzalez doi: 10.3390/asi9030058 Applied System Innovation 2026-03-12 Applied System Innovation 2026-03-12 9 3 Article 58 10.3390/asi9030058 https://www.mdpi.com/2571-5577/9/3/58 ASI, Vol. 9, Pages 57: Bayesian Hyperparameter Optimization of GRU and LSTM Models for Short-Term Traffic Flow Prediction: A Case Study of Globe Roundabout in Saudi Arabia https://www.mdpi.com/2571-5577/9/3/57 Accurate short-term traffic flow prediction is vital for effective signal control and sustainable urban mobility. Deep learning models, such as the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, have demonstrated strong capability in modelling temporal traffic dynamics. However, the influence of their architectural and hyperparameter configurations remains underexplored. This study proposes a systematic methodology to assess the impact of hyperparameter optimization on GRU and LSTM models for predicting traffic flow at a signalized intersection. The methodology is evaluated using minute-level traffic data from the Globe Roundabout in Jeddah, Saudi Arabia. Bayesian optimization is applied to identify the best-performing hyperparameters. The results show that the optimized GRU model achieves a Root Mean Square Error (RMSE) of 0.0953, representing a 90.2% improvement compared to the baseline GRU (RMSE ≈ 0.969). Likewise, the optimized LSTM model attains an RMSE of 0.0960, corresponding to an 85.2% improvement relative to its baseline (RMSE ≈ 0.648). Similar gains are observed for the Mean Absolute Error. Visual analysis further shows that optimized models reduce smoothing bias, enhance the tracking of transient fluctuations, and produce stable, low-variance residuals. The findings demonstrate that hyperparameter optimization substantially improves predictive accuracy while preserving computational efficiency, enabling lightweight recurrent architectures to perform at a level comparable to more complex models. 2026-03-10 ASI, Vol. 9, Pages 57: Bayesian Hyperparameter Optimization of GRU and LSTM Models for Short-Term Traffic Flow Prediction: A Case Study of Globe Roundabout in Saudi Arabia

Applied System Innovation doi: 10.3390/asi9030057

Authors: Sara Atef Siraj Zahran Ahmed Karam

Accurate short-term traffic flow prediction is vital for effective signal control and sustainable urban mobility. Deep learning models, such as the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, have demonstrated strong capability in modelling temporal traffic dynamics. However, the influence of their architectural and hyperparameter configurations remains underexplored. This study proposes a systematic methodology to assess the impact of hyperparameter optimization on GRU and LSTM models for predicting traffic flow at a signalized intersection. The methodology is evaluated using minute-level traffic data from the Globe Roundabout in Jeddah, Saudi Arabia. Bayesian optimization is applied to identify the best-performing hyperparameters. The results show that the optimized GRU model achieves a Root Mean Square Error (RMSE) of 0.0953, representing a 90.2% improvement compared to the baseline GRU (RMSE ≈ 0.969). Likewise, the optimized LSTM model attains an RMSE of 0.0960, corresponding to an 85.2% improvement relative to its baseline (RMSE ≈ 0.648). Similar gains are observed for the Mean Absolute Error. Visual analysis further shows that optimized models reduce smoothing bias, enhance the tracking of transient fluctuations, and produce stable, low-variance residuals. The findings demonstrate that hyperparameter optimization substantially improves predictive accuracy while preserving computational efficiency, enabling lightweight recurrent architectures to perform at a level comparable to more complex models.

]]> Bayesian Hyperparameter Optimization of GRU and LSTM Models for Short-Term Traffic Flow Prediction: A Case Study of Globe Roundabout in Saudi Arabia Sara Atef Siraj Zahran Ahmed Karam doi: 10.3390/asi9030057 Applied System Innovation 2026-03-10 Applied System Innovation 2026-03-10 9 3 Article 57 10.3390/asi9030057 https://www.mdpi.com/2571-5577/9/3/57 ASI, Vol. 9, Pages 56: Evaluating Maintainable Asset Criticality in Production Systems via a Network-Level, Consequence-Based Profitability Framework Enabled by Complex Repairable Flow Network Simulation https://www.mdpi.com/2571-5577/9/3/56 This paper presents a simulation-based methodology for evaluating maintainable asset criticality in production systems modelled as complex repairable flow networks (CRFNs). The proposed Flow-Based Asset Criticality Evaluation Methodology (FACE) adopts a consequence-based perspective, assessing criticality according to network-level economic impact rather than probability-weighted risk. FACE introduces two profitability-oriented metrics, the Minimum Consequence of Failure (MCoF) at the maintainable item (MI) and failure mode (FM) levels, computed using multilayered network simulation integrating topology, capacity, failure behaviour, and profitability-driven flow allocation. By directly linking asset unavailability to system-wide gross profitability, the methodology enables objective, data-driven criticality assessment without reliance on subjective inputs, such as guided scoring processes. The approach supports both strategic and operational maintenance decisions by identifying assets and failure modes most consequential to production throughput and profitability. 2026-03-06 ASI, Vol. 9, Pages 56: Evaluating Maintainable Asset Criticality in Production Systems via a Network-Level, Consequence-Based Profitability Framework Enabled by Complex Repairable Flow Network Simulation

Applied System Innovation doi: 10.3390/asi9030056

Authors: Nicholas Kaliszewski Romeo Marian Javaan Chahl

This paper presents a simulation-based methodology for evaluating maintainable asset criticality in production systems modelled as complex repairable flow networks (CRFNs). The proposed Flow-Based Asset Criticality Evaluation Methodology (FACE) adopts a consequence-based perspective, assessing criticality according to network-level economic impact rather than probability-weighted risk. FACE introduces two profitability-oriented metrics, the Minimum Consequence of Failure (MCoF) at the maintainable item (MI) and failure mode (FM) levels, computed using multilayered network simulation integrating topology, capacity, failure behaviour, and profitability-driven flow allocation. By directly linking asset unavailability to system-wide gross profitability, the methodology enables objective, data-driven criticality assessment without reliance on subjective inputs, such as guided scoring processes. The approach supports both strategic and operational maintenance decisions by identifying assets and failure modes most consequential to production throughput and profitability.

]]> Evaluating Maintainable Asset Criticality in Production Systems via a Network-Level, Consequence-Based Profitability Framework Enabled by Complex Repairable Flow Network Simulation Nicholas Kaliszewski Romeo Marian Javaan Chahl doi: 10.3390/asi9030056 Applied System Innovation 2026-03-06 Applied System Innovation 2026-03-06 9 3 Article 56 10.3390/asi9030056 https://www.mdpi.com/2571-5577/9/3/56 ASI, Vol. 9, Pages 55: Artificial Neural Network Applications in Supply Chain Management: A Literature Review and Classification https://www.mdpi.com/2571-5577/9/3/55 Supply Chain Management (SCM) has received considerable attention from the industrial community in recent decades. SCM continues to be an interesting and relevant research topic in many business areas such as revealing supply chain integration benefits, uncertainty and risk mitigation methods, decision-making and optimization methodologies, etc. In current supply chain management, huge volumes of data are being developed each second, and emerging technologies such as Radio Frequency Identification (RFID) have amplified the availability of online data. Using Artificial Intelligence (AI) methods that go beyond simply using the huge volume of online data enables Supply Chain (SC) managers to monitor everything in a timely fashion. There are several aspects of an SC that AI—and specifically Artificial Neural Networks (ANNs)—can be applied to better help them manage and optimize. This study aims to review state-of-the-art ANNs and Deep Neural Networks (DNNs) in the field of supply chain management. One hundred high-quality research studies that applied ANNs in supply chain management are reviewed and categorized into four classes: performance optimization, supplier selection, forecasting, and inventory management studies. Our study shows that there is a significant possibility that we could use ANNs and DNNs to better manage supply chains. Across the reviewed studies, neural networks are frequently reported to improve predictive performance and support monitoring/control in complex, nonlinear supply chain settings, often complementing traditional operations research approaches. Finally, the limitations of ANN models and the possibilities for future studies are presented at the end of this study. 2026-02-28 ASI, Vol. 9, Pages 55: Artificial Neural Network Applications in Supply Chain Management: A Literature Review and Classification

Applied System Innovation doi: 10.3390/asi9030055

Authors: Iman Ghalehkhondabi

Supply Chain Management (SCM) has received considerable attention from the industrial community in recent decades. SCM continues to be an interesting and relevant research topic in many business areas such as revealing supply chain integration benefits, uncertainty and risk mitigation methods, decision-making and optimization methodologies, etc. In current supply chain management, huge volumes of data are being developed each second, and emerging technologies such as Radio Frequency Identification (RFID) have amplified the availability of online data. Using Artificial Intelligence (AI) methods that go beyond simply using the huge volume of online data enables Supply Chain (SC) managers to monitor everything in a timely fashion. There are several aspects of an SC that AI—and specifically Artificial Neural Networks (ANNs)—can be applied to better help them manage and optimize. This study aims to review state-of-the-art ANNs and Deep Neural Networks (DNNs) in the field of supply chain management. One hundred high-quality research studies that applied ANNs in supply chain management are reviewed and categorized into four classes: performance optimization, supplier selection, forecasting, and inventory management studies. Our study shows that there is a significant possibility that we could use ANNs and DNNs to better manage supply chains. Across the reviewed studies, neural networks are frequently reported to improve predictive performance and support monitoring/control in complex, nonlinear supply chain settings, often complementing traditional operations research approaches. Finally, the limitations of ANN models and the possibilities for future studies are presented at the end of this study.

]]> Artificial Neural Network Applications in Supply Chain Management: A Literature Review and Classification Iman Ghalehkhondabi doi: 10.3390/asi9030055 Applied System Innovation 2026-02-28 Applied System Innovation 2026-02-28 9 3 Review 55 10.3390/asi9030055 https://www.mdpi.com/2571-5577/9/3/55 ASI, Vol. 9, Pages 54: Mechatronic Reference Model for Innovation: Connecting Complex Design to Business Issues Through the Concepts of Cycles and Revisions https://www.mdpi.com/2571-5577/9/3/54 This article presents a study that combined theoretical and empirical methods in a longitudinal approach to develop and validate the Mechatronic Reference Model for Innovation (MRM4i), a detailed framework for designing and developing mechatronic products. The text aims to present the model in terms of cycles and revisions and to compare it with the V- and W-models for mechatronic design, as well as with previous reference models in new product development (NPD). The primary characteristic of the MRM4i is to connect traditional concepts of new product development reference models—such as phases, decisions, documents, and prototypes—with the core principles of mechatronic design, as outlined in the V-Model and W-Model. The concepts and their implementation were exemplified through a longitudinal case study at a company, in which technical artifacts for four mechatronic products were presented and discussed, and compared to V/W-Models. Validation issues are outlined, and future research directions are presented. 2026-02-28 ASI, Vol. 9, Pages 54: Mechatronic Reference Model for Innovation: Connecting Complex Design to Business Issues Through the Concepts of Cycles and Revisions

Applied System Innovation doi: 10.3390/asi9030054

Authors: Sanderson Barbalho Mariannys Rodríguez-Gasca

This article presents a study that combined theoretical and empirical methods in a longitudinal approach to develop and validate the Mechatronic Reference Model for Innovation (MRM4i), a detailed framework for designing and developing mechatronic products. The text aims to present the model in terms of cycles and revisions and to compare it with the V- and W-models for mechatronic design, as well as with previous reference models in new product development (NPD). The primary characteristic of the MRM4i is to connect traditional concepts of new product development reference models—such as phases, decisions, documents, and prototypes—with the core principles of mechatronic design, as outlined in the V-Model and W-Model. The concepts and their implementation were exemplified through a longitudinal case study at a company, in which technical artifacts for four mechatronic products were presented and discussed, and compared to V/W-Models. Validation issues are outlined, and future research directions are presented.

]]> Mechatronic Reference Model for Innovation: Connecting Complex Design to Business Issues Through the Concepts of Cycles and Revisions Sanderson Barbalho Mariannys Rodríguez-Gasca doi: 10.3390/asi9030054 Applied System Innovation 2026-02-28 Applied System Innovation 2026-02-28 9 3 Article 54 10.3390/asi9030054 https://www.mdpi.com/2571-5577/9/3/54 ASI, Vol. 9, Pages 52: Leveraging Machine Learning to Evaluate the ESG Performance of Listed and OTC Firms in a Small Open Economy https://www.mdpi.com/2571-5577/9/3/52 This study investigates the predictability of Environmental, Social, and Governance (ESG) performance using financial fundamentals within the context of Taiwan, a prominent small open economy integrated into global value chains. As global markets transition toward mandatory sustainability reporting, identifying the financial ante-cedents of ESG outcomes is critical for risk management and regulatory oversight. Uti-lizing a decade of firm-level data (2014–2023) from the Taiwan Economic Journal (TEJ), we employ supervised machine learning (ML) architectures-including Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost)-to classify firms into ESG performance tiers based on indicators such as profitability, valuation, and scale. Our empirical results provide robust support for the Slack Resources Hypothesis, identifying Return on Assets (ROA) and Firm Size (SIZE) as the most consistent predictors of ESG excellence across the semiconductor, cement, and steel sectors. Conversely, mar-ket-based indicators (Tobin’s Q) dominate predictive models for the financial industry. Methodologically, XGBoost delivers superior predictive calibration for the financial sector, while Decision Trees offer highly interpretable threshold-based logic for risk screening. Our study contributes a transparent “early-warning” framework, enabling investors and regulators to identify sustainability risks through auditable financial benchmarks. The findings suggest that while financial latitude is a structural prerequisite for ESG engagement, it is not its sole determinant, pointing toward a “virtuous circle” of financial health and managerial quality. 2026-02-27 ASI, Vol. 9, Pages 52: Leveraging Machine Learning to Evaluate the ESG Performance of Listed and OTC Firms in a Small Open Economy

Applied System Innovation doi: 10.3390/asi9030052

Authors: Hui-Juan Xiao Tsung-Nan Chou Jian-Fa Li Kuei-Kuei Lai

This study investigates the predictability of Environmental, Social, and Governance (ESG) performance using financial fundamentals within the context of Taiwan, a prominent small open economy integrated into global value chains. As global markets transition toward mandatory sustainability reporting, identifying the financial ante-cedents of ESG outcomes is critical for risk management and regulatory oversight. Uti-lizing a decade of firm-level data (2014–2023) from the Taiwan Economic Journal (TEJ), we employ supervised machine learning (ML) architectures-including Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost)-to classify firms into ESG performance tiers based on indicators such as profitability, valuation, and scale. Our empirical results provide robust support for the Slack Resources Hypothesis, identifying Return on Assets (ROA) and Firm Size (SIZE) as the most consistent predictors of ESG excellence across the semiconductor, cement, and steel sectors. Conversely, mar-ket-based indicators (Tobin’s Q) dominate predictive models for the financial industry. Methodologically, XGBoost delivers superior predictive calibration for the financial sector, while Decision Trees offer highly interpretable threshold-based logic for risk screening. Our study contributes a transparent “early-warning” framework, enabling investors and regulators to identify sustainability risks through auditable financial benchmarks. The findings suggest that while financial latitude is a structural prerequisite for ESG engagement, it is not its sole determinant, pointing toward a “virtuous circle” of financial health and managerial quality.

]]> Leveraging Machine Learning to Evaluate the ESG Performance of Listed and OTC Firms in a Small Open Economy Hui-Juan Xiao Tsung-Nan Chou Jian-Fa Li Kuei-Kuei Lai doi: 10.3390/asi9030052 Applied System Innovation 2026-02-27 Applied System Innovation 2026-02-27 9 3 Article 52 10.3390/asi9030052 https://www.mdpi.com/2571-5577/9/3/52 ASI, Vol. 9, Pages 53: Systematized Literature Review: Model-Based Test Case Generation for Requirements Verification at the Subsystem Level https://www.mdpi.com/2571-5577/9/3/53 This study examines model-based systems engineering (MBSE) within the context of vehicle development at the subsystem level. The investigation encompasses the examination of the transfer of requirements from the overarching system level—the vehicle level—to its constituent subsystems, the subsequent implementation of these requirements within the subsystems, and the generation of model-based test cases for the purpose of verification. A systematized literature review according to the key principles of PRISMA 2020 was conducted to address this research question. To this end, a set of criteria for a systematic analysis were developed and applied to the identified studies. 2026-02-27 ASI, Vol. 9, Pages 53: Systematized Literature Review: Model-Based Test Case Generation for Requirements Verification at the Subsystem Level

Applied System Innovation doi: 10.3390/asi9030053

Authors: Jana Wendt Umut Volkan Kizgin Dirk Clasen Thomas Vietor

This study examines model-based systems engineering (MBSE) within the context of vehicle development at the subsystem level. The investigation encompasses the examination of the transfer of requirements from the overarching system level—the vehicle level—to its constituent subsystems, the subsequent implementation of these requirements within the subsystems, and the generation of model-based test cases for the purpose of verification. A systematized literature review according to the key principles of PRISMA 2020 was conducted to address this research question. To this end, a set of criteria for a systematic analysis were developed and applied to the identified studies.

]]> Systematized Literature Review: Model-Based Test Case Generation for Requirements Verification at the Subsystem Level Jana Wendt Umut Volkan Kizgin Dirk Clasen Thomas Vietor doi: 10.3390/asi9030053 Applied System Innovation 2026-02-27 Applied System Innovation 2026-02-27 9 3 Systematic Review 53 10.3390/asi9030053 https://www.mdpi.com/2571-5577/9/3/53 ASI, Vol. 9, Pages 51: ZernikeViewer: An Open-Source Framework for Fast Simulation and Real-Time Reconstruction of Phase, Fringe, and PSF Maps https://www.mdpi.com/2571-5577/9/3/51 Zernike polynomials constitute an essential mathematical basis for representing functions defined over the unit disk. They are widely used in a diverse range of scientific and engineering disciplines, including adaptive optics for characterizing atmospheric distortions, ophthalmology for quantifying ocular aberrations, microscopy for instrument characterization and aberration correction, and optical metrology for surface profiling. This paper introduces ZernikeViewer, a software framework developed for the rapid calculation and visualization of fringe, phase, and point spread function (PSF) maps from Zernike coefficients. The framework leverages CPU multicore and multithreading capabilities through the .NET Task Parallel Library (TPL), augmented by codebase optimizations and the preloading of precomputed Zernike polynomial matrices. These optimizations reduce computation time by a factor of 7 to 10 compared to a conventional approach; for instance, from 1 ms to 0.1 ms for a radial order of n = 10 and from 700 ms to 80 ms for n = 100. Numerical error analysis confirms the accuracy of the computation, with an average root-mean-square (RMS) error of 0.11 ms observed in the timing measurements. Furthermore, it is demonstrated that implementing Jacobi recursion relations could potentially reduce the numerical calculation error by up to 5 orders of magnitude. 2026-02-26 ASI, Vol. 9, Pages 51: ZernikeViewer: An Open-Source Framework for Fast Simulation and Real-Time Reconstruction of Phase, Fringe, and PSF Maps

Applied System Innovation doi: 10.3390/asi9030051

Authors: Ilya Galaktionov

Zernike polynomials constitute an essential mathematical basis for representing functions defined over the unit disk. They are widely used in a diverse range of scientific and engineering disciplines, including adaptive optics for characterizing atmospheric distortions, ophthalmology for quantifying ocular aberrations, microscopy for instrument characterization and aberration correction, and optical metrology for surface profiling. This paper introduces ZernikeViewer, a software framework developed for the rapid calculation and visualization of fringe, phase, and point spread function (PSF) maps from Zernike coefficients. The framework leverages CPU multicore and multithreading capabilities through the .NET Task Parallel Library (TPL), augmented by codebase optimizations and the preloading of precomputed Zernike polynomial matrices. These optimizations reduce computation time by a factor of 7 to 10 compared to a conventional approach; for instance, from 1 ms to 0.1 ms for a radial order of n = 10 and from 700 ms to 80 ms for n = 100. Numerical error analysis confirms the accuracy of the computation, with an average root-mean-square (RMS) error of 0.11 ms observed in the timing measurements. Furthermore, it is demonstrated that implementing Jacobi recursion relations could potentially reduce the numerical calculation error by up to 5 orders of magnitude.

]]> ZernikeViewer: An Open-Source Framework for Fast Simulation and Real-Time Reconstruction of Phase, Fringe, and PSF Maps Ilya Galaktionov doi: 10.3390/asi9030051 Applied System Innovation 2026-02-26 Applied System Innovation 2026-02-26 9 3 Article 51 10.3390/asi9030051 https://www.mdpi.com/2571-5577/9/3/51 ASI, Vol. 9, Pages 50: Predictive Thermal Management for Dual PWM Fans in High-Power Audio Amplifiers https://www.mdpi.com/2571-5577/9/3/50 This paper presents the design and implementation of a low-cost microcontroller-based dual-channel fan controller optimized for high-power audio amplifiers, yet adaptable to power supplies, electronic loads, and other thermally intensive systems. Unlike conventional designs that drive all fans uniformly, the proposed solution provides fully independent cooling via dual I2C temperature sensors, predictive trend analysis, and multi-stage hysteresis. The controller incorporates advanced features including an anti-dust startup sequence, predictive boost with latching, active cross-cooling, anti-heat-soak protection, and stall detection via tachometer monitoring, complemented by LED-based fault signaling and automatic channel muting during overheating or fan failure. Hardware support for 12 V and 24 V fans, dual power-input options, and a compact PCB layout enhance integration flexibility. The firmware employs temperature-driven PWM mapping with EMA filtering and multi-level hysteresis. The experimental results confirm that all implemented features operate as intended, with each function demonstrating clear practical relevance, whether in improving responsiveness, preventing heat accumulation, or enhancing system reliability under a wide range of operating conditions. 2026-02-26 ASI, Vol. 9, Pages 50: Predictive Thermal Management for Dual PWM Fans in High-Power Audio Amplifiers

Applied System Innovation doi: 10.3390/asi9030050

Authors: Andrei Militaru Emanuel-Valentin Buica Horia Andrei

This paper presents the design and implementation of a low-cost microcontroller-based dual-channel fan controller optimized for high-power audio amplifiers, yet adaptable to power supplies, electronic loads, and other thermally intensive systems. Unlike conventional designs that drive all fans uniformly, the proposed solution provides fully independent cooling via dual I2C temperature sensors, predictive trend analysis, and multi-stage hysteresis. The controller incorporates advanced features including an anti-dust startup sequence, predictive boost with latching, active cross-cooling, anti-heat-soak protection, and stall detection via tachometer monitoring, complemented by LED-based fault signaling and automatic channel muting during overheating or fan failure. Hardware support for 12 V and 24 V fans, dual power-input options, and a compact PCB layout enhance integration flexibility. The firmware employs temperature-driven PWM mapping with EMA filtering and multi-level hysteresis. The experimental results confirm that all implemented features operate as intended, with each function demonstrating clear practical relevance, whether in improving responsiveness, preventing heat accumulation, or enhancing system reliability under a wide range of operating conditions.

]]> Predictive Thermal Management for Dual PWM Fans in High-Power Audio Amplifiers Andrei Militaru Emanuel-Valentin Buica Horia Andrei doi: 10.3390/asi9030050 Applied System Innovation 2026-02-26 Applied System Innovation 2026-02-26 9 3 Article 50 10.3390/asi9030050 https://www.mdpi.com/2571-5577/9/3/50 ASI, Vol. 9, Pages 49: Explainable Hybrid CNN–XGBoost Framework for Multi-Class IoT Intrusion Detection with Leakage-Aware Feature Selection https://www.mdpi.com/2571-5577/9/3/49 The rapid deployment of Internet of Things (IoT) devices has increased exposure to a diverse array of evolving cyberattacks, motivating the need for accurate and interpretable intrusion detection systems (IDS). In this work, we develop an explainable hybrid Convolutional Neural Network–Extreme Gradient Boosting (CNN–XGBoost) framework for multi-class IoT attack classification using the CIC IoT-DIAD 2024 dataset. Network-traffic records are preprocessed and standardized using a scalable, chunk-wise workflow, after which a compact top-k subset of features is selected via Random Forest importance ranking. To reduce selection bias, a leakage-prone feature-ranking strategy is compared with a leakage-aware strategy in which features are ranked using only the training data within each split. Subsequently, a one-dimensional Convolutional Neural Network (CNN) learns a 128-dimensional representation from the selected predictors, and XGBoost performs the final multi-class classification. Under the leakage-aware protocol, the proposed model achieves 0.9324 accuracy with 0.5910 macro-F1. Results indicate that leakage-aware selection provides a more defensible estimate of generalization while maintaining competitive detection performance. Finally, SHapley Additive exPlanations (SHAP) is used to interpret the model’s decisions in the learned latent space. The analysis shows that only a small number of embedding dimensions contribute most of the decision evidence, which can aid analyst triage, although the explanations remain indirect with respect to the original traffic features. 2026-02-26 ASI, Vol. 9, Pages 49: Explainable Hybrid CNN–XGBoost Framework for Multi-Class IoT Intrusion Detection with Leakage-Aware Feature Selection

Applied System Innovation doi: 10.3390/asi9030049

Authors: Deemah AlFuraih Lotfi Mhamdi Abdullah S. Karar

The rapid deployment of Internet of Things (IoT) devices has increased exposure to a diverse array of evolving cyberattacks, motivating the need for accurate and interpretable intrusion detection systems (IDS). In this work, we develop an explainable hybrid Convolutional Neural Network–Extreme Gradient Boosting (CNN–XGBoost) framework for multi-class IoT attack classification using the CIC IoT-DIAD 2024 dataset. Network-traffic records are preprocessed and standardized using a scalable, chunk-wise workflow, after which a compact top-k subset of features is selected via Random Forest importance ranking. To reduce selection bias, a leakage-prone feature-ranking strategy is compared with a leakage-aware strategy in which features are ranked using only the training data within each split. Subsequently, a one-dimensional Convolutional Neural Network (CNN) learns a 128-dimensional representation from the selected predictors, and XGBoost performs the final multi-class classification. Under the leakage-aware protocol, the proposed model achieves 0.9324 accuracy with 0.5910 macro-F1. Results indicate that leakage-aware selection provides a more defensible estimate of generalization while maintaining competitive detection performance. Finally, SHapley Additive exPlanations (SHAP) is used to interpret the model’s decisions in the learned latent space. The analysis shows that only a small number of embedding dimensions contribute most of the decision evidence, which can aid analyst triage, although the explanations remain indirect with respect to the original traffic features.

]]> Explainable Hybrid CNN–XGBoost Framework for Multi-Class IoT Intrusion Detection with Leakage-Aware Feature Selection Deemah AlFuraih Lotfi Mhamdi Abdullah S. Karar doi: 10.3390/asi9030049 Applied System Innovation 2026-02-26 Applied System Innovation 2026-02-26 9 3 Article 49 10.3390/asi9030049 https://www.mdpi.com/2571-5577/9/3/49 ASI, Vol. 9, Pages 48: Adaptive Trajectory Control of a Hydraulic Excavator Based on RBF Sliding-Mode Control Method https://www.mdpi.com/2571-5577/9/3/48 In this paper, a nonlinear integral sliding-mode controller (SMC) based on a radial basis function (RBF) neural network is proposed to address the challenges of high nonlinearity, parameter uncertainty, and unmodeled dynamics in the electro-hydraulic servo system of a robotic excavator. The controller design incorporates adaptive RBF neural networks to compensate for system perturbations and uncertain nonlinearities, while an integral sliding surface is employed to eliminate steady-state error. This approach not only compensates for uncertainties but also reduces the traditional SMC’s high dependency on precise system parameters. The mathematical model of the bucket electro-hydraulic servo system is established without linear approximation. Based on this model, the sliding-mode controller with RBF neural networks (SMC-RBF) is designed, and its asymptotic stability is proven using the Lyapunov method. Simulation and experimental results are compared with a traditional PID controller to verify the proposed controller’s superiority. The simulations show that the SMC-RBF controller meets the requirements for tracking performance and demonstrates robustness, improving sinusoidal tracking performance by 46% compared to the PID controller. Experimental results further demonstrate that the SMC-RBF controller improves the trajectory accuracy for a two-meter straight line by 52.46% in comparison to the traditional PID controller. 2026-02-25 ASI, Vol. 9, Pages 48: Adaptive Trajectory Control of a Hydraulic Excavator Based on RBF Sliding-Mode Control Method

Applied System Innovation doi: 10.3390/asi9030048

Authors: Linyu Tao Changchun Hua Wei Ma Gang Lu Zhenhua Wei Shijia Wei

In this paper, a nonlinear integral sliding-mode controller (SMC) based on a radial basis function (RBF) neural network is proposed to address the challenges of high nonlinearity, parameter uncertainty, and unmodeled dynamics in the electro-hydraulic servo system of a robotic excavator. The controller design incorporates adaptive RBF neural networks to compensate for system perturbations and uncertain nonlinearities, while an integral sliding surface is employed to eliminate steady-state error. This approach not only compensates for uncertainties but also reduces the traditional SMC’s high dependency on precise system parameters. The mathematical model of the bucket electro-hydraulic servo system is established without linear approximation. Based on this model, the sliding-mode controller with RBF neural networks (SMC-RBF) is designed, and its asymptotic stability is proven using the Lyapunov method. Simulation and experimental results are compared with a traditional PID controller to verify the proposed controller’s superiority. The simulations show that the SMC-RBF controller meets the requirements for tracking performance and demonstrates robustness, improving sinusoidal tracking performance by 46% compared to the PID controller. Experimental results further demonstrate that the SMC-RBF controller improves the trajectory accuracy for a two-meter straight line by 52.46% in comparison to the traditional PID controller.

]]> Adaptive Trajectory Control of a Hydraulic Excavator Based on RBF Sliding-Mode Control Method Linyu Tao Changchun Hua Wei Ma Gang Lu Zhenhua Wei Shijia Wei doi: 10.3390/asi9030048 Applied System Innovation 2026-02-25 Applied System Innovation 2026-02-25 9 3 Article 48 10.3390/asi9030048 https://www.mdpi.com/2571-5577/9/3/48 ASI, Vol. 9, Pages 47: Multi-Class Leak Detection in Water Pipelines Using a Wavelet-Guided Frequency-Informed Transformer https://www.mdpi.com/2571-5577/9/2/47 Water utilities continue to lose a lot of Non-Revenue Water (NRW) because of leaks that go undetected. This makes it necessary to find accurate but easy-to-use monitoring solutions. This paper presents FiT-WST+, a wavelet-guided Frequency-Informed Transformer (FiT) designed for the classification of five distinct leak types utilising accelerometer measurements. The proposed architecture combines the spectral modelling ability of a FIT with the stable translation-invariant representation of the Wavelet Scattering Transform (WST). The model uses a guided attention mechanism to combine spectral and scattering cues that work well together to make classes more distinct, especially for fault types that are similar. On the held-out test set, FiT-WST+ achieves 99.6% accuracy, 99.6% balanced accuracy, and a 99.6% macro-averaged F1-score. Comparative benchmarking against recent methods tested on the same dataset shows that this method works at a low sampling rate (1 kHz), which greatly lowers bandwidth needs and allows for scalable deployment on edge devices with limited resources for real-time monitoring of important water infrastructure. 2026-02-23 ASI, Vol. 9, Pages 47: Multi-Class Leak Detection in Water Pipelines Using a Wavelet-Guided Frequency-Informed Transformer

Applied System Innovation doi: 10.3390/asi9020047

Authors: Mohammed Essouabni Jamal El Mhamdi Abdelilah Jilbab

Water utilities continue to lose a lot of Non-Revenue Water (NRW) because of leaks that go undetected. This makes it necessary to find accurate but easy-to-use monitoring solutions. This paper presents FiT-WST+, a wavelet-guided Frequency-Informed Transformer (FiT) designed for the classification of five distinct leak types utilising accelerometer measurements. The proposed architecture combines the spectral modelling ability of a FIT with the stable translation-invariant representation of the Wavelet Scattering Transform (WST). The model uses a guided attention mechanism to combine spectral and scattering cues that work well together to make classes more distinct, especially for fault types that are similar. On the held-out test set, FiT-WST+ achieves 99.6% accuracy, 99.6% balanced accuracy, and a 99.6% macro-averaged F1-score. Comparative benchmarking against recent methods tested on the same dataset shows that this method works at a low sampling rate (1 kHz), which greatly lowers bandwidth needs and allows for scalable deployment on edge devices with limited resources for real-time monitoring of important water infrastructure.

]]> Multi-Class Leak Detection in Water Pipelines Using a Wavelet-Guided Frequency-Informed Transformer Mohammed Essouabni Jamal El Mhamdi Abdelilah Jilbab doi: 10.3390/asi9020047 Applied System Innovation 2026-02-23 Applied System Innovation 2026-02-23 9 2 Article 47 10.3390/asi9020047 https://www.mdpi.com/2571-5577/9/2/47 ASI, Vol. 9, Pages 46: Smart Farming Innovation: Automated Biomechanical Monitoring of Broilers Using a Hybrid YOLO-SAM Pipeline https://www.mdpi.com/2571-5577/9/2/46 Precision Livestock Farming (PLF) relies on accurate, high-frequency data to optimize production efficiency. Traditional assessments of feeding behavior remain manual and invasive, lacking the kinematic resolution required for automated control systems. This study developed and validated a novel computer vision framework integrating YOLOv8 and the Segment Anything Model (SAM) to address this gap. The objective was to engineer a non-invasive, automated pipeline to quantify high-speed broiler biomechanics in real time. The system was validated using video data from broilers across three growth stages and varying feed granulometries (fine mash, coarse mash, and pellets) to test its robustness in detecting subtle kinematic variations. The hybrid YOLO-SAM pipeline achieved high performance, with a precision of 0.95 and a recall of 0.91, confirming its reliability as a scalable sensor for smart farming platforms. Biomechanical analysis demonstrated the system’s sensitivity, showing that larger feed particles induce greater beak gape and displacement while significantly improving ingestion efficiency (0.6 effort ratio for pellets vs. 3.0 for mash). This research provides a validated technical foundation for digital phenotyping in poultry, offering a hands-free, quantitative tool that supports data-driven decision-making in feed formulation and production management. 2026-02-20 ASI, Vol. 9, Pages 46: Smart Farming Innovation: Automated Biomechanical Monitoring of Broilers Using a Hybrid YOLO-SAM Pipeline

Applied System Innovation doi: 10.3390/asi9020046

Authors: Victória Fernanda Dionizio Marcelo Tsuguio Okano Irenilza de Alencar Nääs

Precision Livestock Farming (PLF) relies on accurate, high-frequency data to optimize production efficiency. Traditional assessments of feeding behavior remain manual and invasive, lacking the kinematic resolution required for automated control systems. This study developed and validated a novel computer vision framework integrating YOLOv8 and the Segment Anything Model (SAM) to address this gap. The objective was to engineer a non-invasive, automated pipeline to quantify high-speed broiler biomechanics in real time. The system was validated using video data from broilers across three growth stages and varying feed granulometries (fine mash, coarse mash, and pellets) to test its robustness in detecting subtle kinematic variations. The hybrid YOLO-SAM pipeline achieved high performance, with a precision of 0.95 and a recall of 0.91, confirming its reliability as a scalable sensor for smart farming platforms. Biomechanical analysis demonstrated the system’s sensitivity, showing that larger feed particles induce greater beak gape and displacement while significantly improving ingestion efficiency (0.6 effort ratio for pellets vs. 3.0 for mash). This research provides a validated technical foundation for digital phenotyping in poultry, offering a hands-free, quantitative tool that supports data-driven decision-making in feed formulation and production management.

]]> Smart Farming Innovation: Automated Biomechanical Monitoring of Broilers Using a Hybrid YOLO-SAM Pipeline Victória Fernanda Dionizio Marcelo Tsuguio Okano Irenilza de Alencar Nääs doi: 10.3390/asi9020046 Applied System Innovation 2026-02-20 Applied System Innovation 2026-02-20 9 2 Article 46 10.3390/asi9020046 https://www.mdpi.com/2571-5577/9/2/46 ASI, Vol. 9, Pages 45: TRM-ViT: A Tiny Recursive Vision Transformer for Efficient Melanoma Detection https://www.mdpi.com/2571-5577/9/2/45 Melanoma remains one of the most aggressive forms of skin cancer, and its early detection is critical for improving patient survival. Vision Transformers (ViTs) have recently shown strong performance in dermoscopic image analysis; however, their effectiveness often relies on stacking multiple transformer encoder blocks, resulting in large numbers of trainable parameters and increased model complexity. In this study, we propose TRM-ViT, a parameter-efficient recursive Vision Transformer designed for binary melanoma classification. Instead of using multiple independent encoder blocks, TRM-ViT applies a single transformer encoder block recursively with shared weights, enabling effective depth while substantially reducing the number of trainable parameters. Experiments conducted on the HAM10000 dataset demonstrate that TRM-ViT achieves a ROC–AUC of 0.7952, comparable to a standard Vision Transformer (0.7951), while using approximately seven times fewer parameters (2.15 M vs. 14.57 M). Notably, the proposed model maintains high melanoma sensitivity, making it particularly suitable for screening-oriented applications. These results indicate that recursive weight sharing can provide an effective trade-off between diagnostic performance and model compactness, supporting the development of efficient decision-support tools for melanoma screening in resource-constrained environments. 2026-02-19 ASI, Vol. 9, Pages 45: TRM-ViT: A Tiny Recursive Vision Transformer for Efficient Melanoma Detection

Applied System Innovation doi: 10.3390/asi9020045

Authors: My Abdelouahed Sabri Ali Belkhiri Abla Rahmouni Abdellah Aarab

Melanoma remains one of the most aggressive forms of skin cancer, and its early detection is critical for improving patient survival. Vision Transformers (ViTs) have recently shown strong performance in dermoscopic image analysis; however, their effectiveness often relies on stacking multiple transformer encoder blocks, resulting in large numbers of trainable parameters and increased model complexity. In this study, we propose TRM-ViT, a parameter-efficient recursive Vision Transformer designed for binary melanoma classification. Instead of using multiple independent encoder blocks, TRM-ViT applies a single transformer encoder block recursively with shared weights, enabling effective depth while substantially reducing the number of trainable parameters. Experiments conducted on the HAM10000 dataset demonstrate that TRM-ViT achieves a ROC–AUC of 0.7952, comparable to a standard Vision Transformer (0.7951), while using approximately seven times fewer parameters (2.15 M vs. 14.57 M). Notably, the proposed model maintains high melanoma sensitivity, making it particularly suitable for screening-oriented applications. These results indicate that recursive weight sharing can provide an effective trade-off between diagnostic performance and model compactness, supporting the development of efficient decision-support tools for melanoma screening in resource-constrained environments.

]]> TRM-ViT: A Tiny Recursive Vision Transformer for Efficient Melanoma Detection My Abdelouahed Sabri Ali Belkhiri Abla Rahmouni Abdellah Aarab doi: 10.3390/asi9020045 Applied System Innovation 2026-02-19 Applied System Innovation 2026-02-19 9 2 Article 45 10.3390/asi9020045 https://www.mdpi.com/2571-5577/9/2/45 ASI, Vol. 9, Pages 44: Pipeline Curvature Detection Using a Pipeline Inspection Gauge Equipped with Multiple Odometry https://www.mdpi.com/2571-5577/9/2/44 Pipeline integrity is crucial for ensuring the safe and efficient transportation of hydrocarbons. One of the essential methods for maintaining pipeline integrity is periodic inspection using Pipeline Inspection Gauges (PIGs). These PIGs traverse extensive pipeline networks, collecting critical data related to inertial navigation and inspection technologies, such as geometric, ultrasonic, or magnetic flux inspection. Following an inspection, data is downloaded for post-processing to identify and accurately locate pipeline anomalies. Accurate positioning of indications is crucial for effective repair or maintenance of the identified pipeline section. Thus, ongoing efforts aim to improve the precision of indication positioning. This study introduces an innovative method and model for deriving pipeline trajectory characteristics to enhance positioning accuracy. The method is based on distance sampling of odometers, improving the PIG displacement measurement by implementing multiple odometries. Using the method described in this work can compensate for odometer slip, since the distance measurement error was reduced from 15.67% to 1.38%. The model simulates (three and four) odometer trajectories in curvature and calculates the curvature along the pipeline based on odometer data. The curvature model is evaluated with real data obtained from a test circuit, demonstrating that the proposed method and model technique can yield trajectory characteristics such as curvature detection; we can differentiate linear sections from bend sections in the test circuit. However, the curvature measurement error remains considerable due to odometer slippage. Therefore, future work proposes using additional odometers to improve measurement accuracy. 2026-02-19 ASI, Vol. 9, Pages 44: Pipeline Curvature Detection Using a Pipeline Inspection Gauge Equipped with Multiple Odometry

Applied System Innovation doi: 10.3390/asi9020044

Authors: Eloina Lugo-del-Real Jorge A. Soto-Cajiga Antonio Ramirez-Martinez Edmundo Guerra Paradas Antoni Grau

Pipeline integrity is crucial for ensuring the safe and efficient transportation of hydrocarbons. One of the essential methods for maintaining pipeline integrity is periodic inspection using Pipeline Inspection Gauges (PIGs). These PIGs traverse extensive pipeline networks, collecting critical data related to inertial navigation and inspection technologies, such as geometric, ultrasonic, or magnetic flux inspection. Following an inspection, data is downloaded for post-processing to identify and accurately locate pipeline anomalies. Accurate positioning of indications is crucial for effective repair or maintenance of the identified pipeline section. Thus, ongoing efforts aim to improve the precision of indication positioning. This study introduces an innovative method and model for deriving pipeline trajectory characteristics to enhance positioning accuracy. The method is based on distance sampling of odometers, improving the PIG displacement measurement by implementing multiple odometries. Using the method described in this work can compensate for odometer slip, since the distance measurement error was reduced from 15.67% to 1.38%. The model simulates (three and four) odometer trajectories in curvature and calculates the curvature along the pipeline based on odometer data. The curvature model is evaluated with real data obtained from a test circuit, demonstrating that the proposed method and model technique can yield trajectory characteristics such as curvature detection; we can differentiate linear sections from bend sections in the test circuit. However, the curvature measurement error remains considerable due to odometer slippage. Therefore, future work proposes using additional odometers to improve measurement accuracy.

]]> Pipeline Curvature Detection Using a Pipeline Inspection Gauge Equipped with Multiple Odometry Eloina Lugo-del-Real Jorge A. Soto-Cajiga Antonio Ramirez-Martinez Edmundo Guerra Paradas Antoni Grau doi: 10.3390/asi9020044 Applied System Innovation 2026-02-19 Applied System Innovation 2026-02-19 9 2 Article 44 10.3390/asi9020044 https://www.mdpi.com/2571-5577/9/2/44 ASI, Vol. 9, Pages 43: A Multi-Domain Collaborative Framework for Practical Application of Causal Knowledge Discovery from Public Data in Elite Sports https://www.mdpi.com/2571-5577/9/2/43 In elite sports, discovering interdisciplinary causal relationships from public data is critical for gaining a competitive edge. However, the causal knowledge required for these practices is difficult to obtain through either existing intervention-based sports science methods or computational techniques focused on statistical association. This paper formalizes a multi-domain collaborative framework, which involves three roles: (1) the elite sports team; (2) the sport science expert; and (3) the causal inference expert. Our nine-step workflow, which processes three core elements of problem, data, and computing, guides these experts through a cycle that systematically transforms practical problems into computational models and, crucially, translates complex analytical outputs back into actionable strategies. The framework also introduces a dual-dimensional “field evaluation” method, encompassing both process and outcome, to quantify the trustworthiness of knowledge in practical settings where a “gold standard” is absent. This framework was applied in an illustrative case study prior to the Paris 2024 Olympics, providing one additional evidence-informed input for the national team. The success was observed and interpreted as contextual consistency rather than causal validation. This framework ensures the practical application of causal discovery in elite sports, offering a repeatable and explainable pathway for generating credible, evidence-based insights from public data for elite sports decision-making. 2026-02-14 ASI, Vol. 9, Pages 43: A Multi-Domain Collaborative Framework for Practical Application of Causal Knowledge Discovery from Public Data in Elite Sports

Applied System Innovation doi: 10.3390/asi9020043

Authors: Dandan Cui Zili Jiang Xiangning Zhang Wenchao Yang Zihong He

In elite sports, discovering interdisciplinary causal relationships from public data is critical for gaining a competitive edge. However, the causal knowledge required for these practices is difficult to obtain through either existing intervention-based sports science methods or computational techniques focused on statistical association. This paper formalizes a multi-domain collaborative framework, which involves three roles: (1) the elite sports team; (2) the sport science expert; and (3) the causal inference expert. Our nine-step workflow, which processes three core elements of problem, data, and computing, guides these experts through a cycle that systematically transforms practical problems into computational models and, crucially, translates complex analytical outputs back into actionable strategies. The framework also introduces a dual-dimensional “field evaluation” method, encompassing both process and outcome, to quantify the trustworthiness of knowledge in practical settings where a “gold standard” is absent. This framework was applied in an illustrative case study prior to the Paris 2024 Olympics, providing one additional evidence-informed input for the national team. The success was observed and interpreted as contextual consistency rather than causal validation. This framework ensures the practical application of causal discovery in elite sports, offering a repeatable and explainable pathway for generating credible, evidence-based insights from public data for elite sports decision-making.

]]> A Multi-Domain Collaborative Framework for Practical Application of Causal Knowledge Discovery from Public Data in Elite Sports Dandan Cui Zili Jiang Xiangning Zhang Wenchao Yang Zihong He doi: 10.3390/asi9020043 Applied System Innovation 2026-02-14 Applied System Innovation 2026-02-14 9 2 Article 43 10.3390/asi9020043 https://www.mdpi.com/2571-5577/9/2/43 ASI, Vol. 9, Pages 42: Deep Learning for Classification of Internal Defects in Fused Filament Fabrication Using Optical Coherence Tomography https://www.mdpi.com/2571-5577/9/2/42 Additive manufacturing is increasingly adopted for the industrial production of small series of functional components, particularly in thermoplastic strand extrusion processes such as Fused Filament Fabrication. This transition relies on technological advances addressing key process limitations, including dimensional instability, weak interlayer bonding, extrusion defects, moisture sensitivity, and insufficient melting. Process monitoring therefore focuses on early defect detection to minimize failed builds and costs, while ultimately enabling process optimization and adaptive control to mitigate defects during fabrication. For this purpose, a data processing pipeline for monitoring Optical Coherence Tomography images acquired in Fused Filament Fabrication is introduced. Convolutional neural networks are used for the automatic classification of tomographic cross-sections. A dataset of tomographic images passes semi-automatic labeling, preprocessing, model training and evaluation. A sliding window detects outlier regions in the tomographic cross-sections, while masks suppress peripheral noise, enabling label generation based on outlier ratios. Data are split into training, validation, and test sets using block-based partitioning to limit leakage. The classification model employs a ResNet-V2 architecture with BottleneckV2 modules. Hyperparameters are optimized, with N = 2, K = 2, dropout 0.5, and learning rate 0.001 yielding best performance. The model achieves 0.9446 accuracy and outperforms EfficientNet-B0 and VGG16 in accuracy and efficiency. 2026-02-14 ASI, Vol. 9, Pages 42: Deep Learning for Classification of Internal Defects in Fused Filament Fabrication Using Optical Coherence Tomography

Applied System Innovation doi: 10.3390/asi9020042

Authors: Valentin Lang Qichen Zhu Malgorzata Kopycinska-Müller Steffen Ihlenfeldt

Additive manufacturing is increasingly adopted for the industrial production of small series of functional components, particularly in thermoplastic strand extrusion processes such as Fused Filament Fabrication. This transition relies on technological advances addressing key process limitations, including dimensional instability, weak interlayer bonding, extrusion defects, moisture sensitivity, and insufficient melting. Process monitoring therefore focuses on early defect detection to minimize failed builds and costs, while ultimately enabling process optimization and adaptive control to mitigate defects during fabrication. For this purpose, a data processing pipeline for monitoring Optical Coherence Tomography images acquired in Fused Filament Fabrication is introduced. Convolutional neural networks are used for the automatic classification of tomographic cross-sections. A dataset of tomographic images passes semi-automatic labeling, preprocessing, model training and evaluation. A sliding window detects outlier regions in the tomographic cross-sections, while masks suppress peripheral noise, enabling label generation based on outlier ratios. Data are split into training, validation, and test sets using block-based partitioning to limit leakage. The classification model employs a ResNet-V2 architecture with BottleneckV2 modules. Hyperparameters are optimized, with N = 2, K = 2, dropout 0.5, and learning rate 0.001 yielding best performance. The model achieves 0.9446 accuracy and outperforms EfficientNet-B0 and VGG16 in accuracy and efficiency.

]]> Deep Learning for Classification of Internal Defects in Fused Filament Fabrication Using Optical Coherence Tomography Valentin Lang Qichen Zhu Malgorzata Kopycinska-Müller Steffen Ihlenfeldt doi: 10.3390/asi9020042 Applied System Innovation 2026-02-14 Applied System Innovation 2026-02-14 9 2 Article 42 10.3390/asi9020042 https://www.mdpi.com/2571-5577/9/2/42 ASI, Vol. 9, Pages 41: 3D Finite Element Models of Zigzag Grounding Transformer for Zero-Sequence Impedance Calculation https://www.mdpi.com/2571-5577/9/2/41 Accurate prediction of the zero-sequence impedance (Z0) of three-legged zigzag grounding transformers is essential for ground-fault protection and power-quality performance, yet manufacturer analytical estimations often have limited accuracy. This paper investigates how accurately Z0 can be predicted using 3D finite element method (FEM) models based on the stored magnetic energy approach and how modeling the metallic tank and nonlinear core B–H behavior affects Z0 relative to analytical calculations and laboratory measurements. Two 3D FEM models are developed for a three-legged zigzag grounding transformer, incorporating the nonlinear core characteristic; impedance boundary conditions are used to efficiently account for tank-induced currents while reducing computational cost. The FEM results are compared with laboratory tests and with the analytical method used by manufacturers. The proposed models achieve errors below 4% with respect to the nominal Z0 and outperform the analytical approach. The contributions are a validated 3D FEM methodology that resolves zero-sequence flux paths under fault conditions and a practical modeling tool that improves grounding transformer design and ground-fault protection settings in modern power systems. 2026-02-13 ASI, Vol. 9, Pages 41: 3D Finite Element Models of Zigzag Grounding Transformer for Zero-Sequence Impedance Calculation

Applied System Innovation doi: 10.3390/asi9020041

Authors: Juan C. Olivares-Galvan Manuel A. Corona-Sánchez Rodrigo Ocon-Valdez Jose L. Hernandez-Avila Rafael Escarela-Perez David A. Aragon-Verduzco

Accurate prediction of the zero-sequence impedance (Z0) of three-legged zigzag grounding transformers is essential for ground-fault protection and power-quality performance, yet manufacturer analytical estimations often have limited accuracy. This paper investigates how accurately Z0 can be predicted using 3D finite element method (FEM) models based on the stored magnetic energy approach and how modeling the metallic tank and nonlinear core B–H behavior affects Z0 relative to analytical calculations and laboratory measurements. Two 3D FEM models are developed for a three-legged zigzag grounding transformer, incorporating the nonlinear core characteristic; impedance boundary conditions are used to efficiently account for tank-induced currents while reducing computational cost. The FEM results are compared with laboratory tests and with the analytical method used by manufacturers. The proposed models achieve errors below 4% with respect to the nominal Z0 and outperform the analytical approach. The contributions are a validated 3D FEM methodology that resolves zero-sequence flux paths under fault conditions and a practical modeling tool that improves grounding transformer design and ground-fault protection settings in modern power systems.

]]> 3D Finite Element Models of Zigzag Grounding Transformer for Zero-Sequence Impedance Calculation Juan C. Olivares-Galvan Manuel A. Corona-Sánchez Rodrigo Ocon-Valdez Jose L. Hernandez-Avila Rafael Escarela-Perez David A. Aragon-Verduzco doi: 10.3390/asi9020041 Applied System Innovation 2026-02-13 Applied System Innovation 2026-02-13 9 2 Article 41 10.3390/asi9020041 https://www.mdpi.com/2571-5577/9/2/41 ASI, Vol. 9, Pages 40: A Novel Hybrid Neural Network with Optimized Feature Selection for Spindle Thermal Error Prediction https://www.mdpi.com/2571-5577/9/2/40 In modern intelligent manufacturing, spindle thermal errors are critical to machining accuracy. To address this, we propose a two-stage prediction framework. First, for feature selection, an enhanced Red-Billed Magpie Optimization algorithm (RBMO-X) optimizes the parameters of a hybrid convolutional neural network (DLTK). Concurrently, PSO-optimized HDBSCAN clustering combined with Pearson correlation selects optimal temperature-sensitive points. The DLTK network integrates LSTM, deformable convolution, Transformer, and Fourier KAN modules for robust spatiotemporal feature extraction. The experimental results demonstrate significant improvements. The proposed feature selection method improves the Silhouette index by 32.39% and increases BWP by 49.16%. Using the selected points reduces prediction RMSE by 31.89% compared to random selection. The final RBMO-X-DLTK model achieves an RMSE of 0.181 μm, an MAE of 0.128 μm, and an R2 score of 0.9978, outperforming seven benchmark models (e.g., BP, LSTM, CNN-LSTM). In practical validation, the model enabled an average thermal error reduction of 89%. This integrated approach provides a robust and accurate solution for spindle thermal error prediction, demonstrating strong generalization capability. 2026-02-05 ASI, Vol. 9, Pages 40: A Novel Hybrid Neural Network with Optimized Feature Selection for Spindle Thermal Error Prediction

Applied System Innovation doi: 10.3390/asi9020040

Authors: Lifeng Yin Chenglong Li Yaohan Peng Hao Tang Ningruo Wang Huayue Chen

In modern intelligent manufacturing, spindle thermal errors are critical to machining accuracy. To address this, we propose a two-stage prediction framework. First, for feature selection, an enhanced Red-Billed Magpie Optimization algorithm (RBMO-X) optimizes the parameters of a hybrid convolutional neural network (DLTK). Concurrently, PSO-optimized HDBSCAN clustering combined with Pearson correlation selects optimal temperature-sensitive points. The DLTK network integrates LSTM, deformable convolution, Transformer, and Fourier KAN modules for robust spatiotemporal feature extraction. The experimental results demonstrate significant improvements. The proposed feature selection method improves the Silhouette index by 32.39% and increases BWP by 49.16%. Using the selected points reduces prediction RMSE by 31.89% compared to random selection. The final RBMO-X-DLTK model achieves an RMSE of 0.181 μm, an MAE of 0.128 μm, and an R2 score of 0.9978, outperforming seven benchmark models (e.g., BP, LSTM, CNN-LSTM). In practical validation, the model enabled an average thermal error reduction of 89%. This integrated approach provides a robust and accurate solution for spindle thermal error prediction, demonstrating strong generalization capability.

]]> A Novel Hybrid Neural Network with Optimized Feature Selection for Spindle Thermal Error Prediction Lifeng Yin Chenglong Li Yaohan Peng Hao Tang Ningruo Wang Huayue Chen doi: 10.3390/asi9020040 Applied System Innovation 2026-02-05 Applied System Innovation 2026-02-05 9 2 Article 40 10.3390/asi9020040 https://www.mdpi.com/2571-5577/9/2/40 ASI, Vol. 9, Pages 39: Dynamic Logarithmic Quantized Stabilization of Switched Systems Subject to Denial-of-Service Attacks https://www.mdpi.com/2571-5577/9/2/39 The problem of the dynamic quantization stabilization of the networked switched systems affected by denial-of-service (DoS) attacks is investigated. Firstly, a quasi-periodic logarithmic quantization strategy is proposed, which ensures the quantization accuracy of the quantizer under the premise of limited quantization levels. Secondly, the adjustment time and the update period of the quantizer are designed to avoid the saturation of the quantizer under DoS attacks. Subsequently, a quantized feedback controller is designed for the switched system under the influence of DoS attacks, and the sufficient conditions are obtained to ensure the global asymptotic stability of the closed-loop system. Finally, the effectiveness of the theoretical analysis is verified through a dual-tank system. 2026-02-03 ASI, Vol. 9, Pages 39: Dynamic Logarithmic Quantized Stabilization of Switched Systems Subject to Denial-of-Service Attacks

Applied System Innovation doi: 10.3390/asi9020039

Authors: Yunhui Gu Jingjing Yan Yunliang Ma

The problem of the dynamic quantization stabilization of the networked switched systems affected by denial-of-service (DoS) attacks is investigated. Firstly, a quasi-periodic logarithmic quantization strategy is proposed, which ensures the quantization accuracy of the quantizer under the premise of limited quantization levels. Secondly, the adjustment time and the update period of the quantizer are designed to avoid the saturation of the quantizer under DoS attacks. Subsequently, a quantized feedback controller is designed for the switched system under the influence of DoS attacks, and the sufficient conditions are obtained to ensure the global asymptotic stability of the closed-loop system. Finally, the effectiveness of the theoretical analysis is verified through a dual-tank system.

]]> Dynamic Logarithmic Quantized Stabilization of Switched Systems Subject to Denial-of-Service Attacks Yunhui Gu Jingjing Yan Yunliang Ma doi: 10.3390/asi9020039 Applied System Innovation 2026-02-03 Applied System Innovation 2026-02-03 9 2 Article 39 10.3390/asi9020039 https://www.mdpi.com/2571-5577/9/2/39 ASI, Vol. 9, Pages 38: Exploring Problem-Solving Strategies in Gifted and Regular Students: Education Insights from Eye-Tracking Analysis https://www.mdpi.com/2571-5577/9/2/38 This study investigated how gifted and regular high school students employ different cognitive strategies and integrate information during scientific problem solving, using eye-tracking techniques. Eighteen multiple-choice items were selected from the Investigating Scientific Thinking and Reasoning (iSTAR) assessment developed at The Ohio State University, including nine text-only questions (tMCQs) and nine picture-embedded questions (pMCQs). The items were chosen to ensure clear spatial separation among text, image, and answer areas, allowing reliable region-based eye-movement analysis. Eye-tracking data were analyzed using two indices: fixation time ratio (FTR), reflecting relative attention allocation, and saccade count ratio (SCR), capturing cross-region information integration. The results revealed clear group differences. Gifted students devoted a larger proportion of attention to pictorial information (0.38 vs. 0.32) and showed more frequent transitions between picture and answer regions (0.15 vs. 0.12), indicating more integrative processing and mental model construction. In contrast, regular students spent more time focusing on textual regions and exhibited higher within-text saccade activity, consistent with a direct translation strategy. Furthermore, SCR-based machine learning classification using a Random Forest model demonstrated meaningful discriminative capability between the two groups, particularly for picture-embedded questions, achieving an accuracy of 77.5%. Overall, the findings provide empirical evidence that question format influences students’ cognitive strategies during scientific reasoning. Methodologically, this study combines a validated reasoning assessment, a carefully defined ROI-based eye-tracking design, and interpretable behavioral indicators, offering practical implications for differentiated science instruction. 2026-02-01 ASI, Vol. 9, Pages 38: Exploring Problem-Solving Strategies in Gifted and Regular Students: Education Insights from Eye-Tracking Analysis

Applied System Innovation doi: 10.3390/asi9020038

Authors: Po-Lei Lee Shih-Ting Hung Pao-Hsin Chang Chun-Yen Chang Lei Bao Ting-Kuang Yeh Li-Ching Lee

This study investigated how gifted and regular high school students employ different cognitive strategies and integrate information during scientific problem solving, using eye-tracking techniques. Eighteen multiple-choice items were selected from the Investigating Scientific Thinking and Reasoning (iSTAR) assessment developed at The Ohio State University, including nine text-only questions (tMCQs) and nine picture-embedded questions (pMCQs). The items were chosen to ensure clear spatial separation among text, image, and answer areas, allowing reliable region-based eye-movement analysis. Eye-tracking data were analyzed using two indices: fixation time ratio (FTR), reflecting relative attention allocation, and saccade count ratio (SCR), capturing cross-region information integration. The results revealed clear group differences. Gifted students devoted a larger proportion of attention to pictorial information (0.38 vs. 0.32) and showed more frequent transitions between picture and answer regions (0.15 vs. 0.12), indicating more integrative processing and mental model construction. In contrast, regular students spent more time focusing on textual regions and exhibited higher within-text saccade activity, consistent with a direct translation strategy. Furthermore, SCR-based machine learning classification using a Random Forest model demonstrated meaningful discriminative capability between the two groups, particularly for picture-embedded questions, achieving an accuracy of 77.5%. Overall, the findings provide empirical evidence that question format influences students’ cognitive strategies during scientific reasoning. Methodologically, this study combines a validated reasoning assessment, a carefully defined ROI-based eye-tracking design, and interpretable behavioral indicators, offering practical implications for differentiated science instruction.

]]> Exploring Problem-Solving Strategies in Gifted and Regular Students: Education Insights from Eye-Tracking Analysis Po-Lei Lee Shih-Ting Hung Pao-Hsin Chang Chun-Yen Chang Lei Bao Ting-Kuang Yeh Li-Ching Lee doi: 10.3390/asi9020038 Applied System Innovation 2026-02-01 Applied System Innovation 2026-02-01 9 2 Article 38 10.3390/asi9020038 https://www.mdpi.com/2571-5577/9/2/38 ASI, Vol. 9, Pages 37: Remote Laboratory Based on FPGA Devices Using the E-Learning Approach https://www.mdpi.com/2571-5577/9/2/37 Laboratories across educational levels have traditionally required in-person attendance, limiting practical activities to specific times and physical spaces. This paper presents a technological architecture based on a system-on-chip (SoC) and a connectivist model, grounded in Connectivism Learning Theory, for implementing a remote laboratory in digital logic design using FPGA devices. The architecture leverages an Internet-of-Things (IoT) environment to provide applications and servers that enable remote access, programming, manipulation, and visualization of FPGA-based development boards located in the institution’s laboratory, from anywhere and at any time. The connectivist model allows learners to interact with multiple nodes for attending synchronous classes, performing laboratory exercises, managing the remote laboratory, and accessing educational resources asynchronously. This approach aims to enhance learning, knowledge transfer, and skills development. A four-year evaluation was conducted, including one experimental group using an e-learning approach and three in-person control groups from a Digital Logic Design course. The experimental group achieved an average performance score of 9.777, surpassing the control groups, suggesting improved academic outcomes with the proposed system. Additionally, a Technology Acceptance Model-based survey showed very high acceptance among learners. This paper presents a novel connectivist model, which we call the Massive Open Online Laboratory. 2026-01-31 ASI, Vol. 9, Pages 37: Remote Laboratory Based on FPGA Devices Using the E-Learning Approach

Applied System Innovation doi: 10.3390/asi9020037

Authors: Victor H. García Ortega Josefina Bárcenas López Enrique Ruiz-Velasco Sánchez

Laboratories across educational levels have traditionally required in-person attendance, limiting practical activities to specific times and physical spaces. This paper presents a technological architecture based on a system-on-chip (SoC) and a connectivist model, grounded in Connectivism Learning Theory, for implementing a remote laboratory in digital logic design using FPGA devices. The architecture leverages an Internet-of-Things (IoT) environment to provide applications and servers that enable remote access, programming, manipulation, and visualization of FPGA-based development boards located in the institution’s laboratory, from anywhere and at any time. The connectivist model allows learners to interact with multiple nodes for attending synchronous classes, performing laboratory exercises, managing the remote laboratory, and accessing educational resources asynchronously. This approach aims to enhance learning, knowledge transfer, and skills development. A four-year evaluation was conducted, including one experimental group using an e-learning approach and three in-person control groups from a Digital Logic Design course. The experimental group achieved an average performance score of 9.777, surpassing the control groups, suggesting improved academic outcomes with the proposed system. Additionally, a Technology Acceptance Model-based survey showed very high acceptance among learners. This paper presents a novel connectivist model, which we call the Massive Open Online Laboratory.

]]> Remote Laboratory Based on FPGA Devices Using the E-Learning Approach Victor H. García Ortega Josefina Bárcenas López Enrique Ruiz-Velasco Sánchez doi: 10.3390/asi9020037 Applied System Innovation 2026-01-31 Applied System Innovation 2026-01-31 9 2 Article 37 10.3390/asi9020037 https://www.mdpi.com/2571-5577/9/2/37 ASI, Vol. 9, Pages 36: ADAEN: Adaptive Diffusion Adversarial Evolutionary Network for Unsupervised Anomaly Detection in Tabular Data https://www.mdpi.com/2571-5577/9/2/36 Existing unsupervised anomaly detection methods suffer from insufficient parameter precision, poor robustness to noise, and limited generalization capability. To address these issues, this paper proposes an Adaptive Diffusion Adversarial Evolutionary Network (ADAEN) for unsupervised anomaly detection in tabular data. The proposed network employs an adaptive hierarchical feature evolution generator that captures multi-scale feature representations at different abstraction levels through learnable attribute encoding and a three-layer Transformer encoder, effectively mitigating the gradient vanishing problem and the difficulty of modeling complex feature relationships that are commonly observed in conventional generators. ADAEN incorporates a multi-scale adaptive diffusion-augmented discriminator, which preserves scale-specific features across different diffusion stages via cosine-scheduled adaptive noise injection, thereby endowing the discriminator with diffusion-stage awareness. Furthermore, ADAEN introduces a multi-scale robust adversarial gradient loss function that ensures training stability through a diffusion-step-conditional Wasserstein loss combined with gradient penalty. The method has been evaluated on 14 UCI benchmark datasets and achieves state-of-the-art performance in anomaly detection compared to existing advanced algorithms, with an average improvement of 8.3% in AUC, an 11.2% increase in F1-Score, and a 15.7% reduction in false positive rate. 2026-01-30 ASI, Vol. 9, Pages 36: ADAEN: Adaptive Diffusion Adversarial Evolutionary Network for Unsupervised Anomaly Detection in Tabular Data

Applied System Innovation doi: 10.3390/asi9020036

Authors: Yong Lu Sen Wang Lingjun Kong Wenju Wang

Existing unsupervised anomaly detection methods suffer from insufficient parameter precision, poor robustness to noise, and limited generalization capability. To address these issues, this paper proposes an Adaptive Diffusion Adversarial Evolutionary Network (ADAEN) for unsupervised anomaly detection in tabular data. The proposed network employs an adaptive hierarchical feature evolution generator that captures multi-scale feature representations at different abstraction levels through learnable attribute encoding and a three-layer Transformer encoder, effectively mitigating the gradient vanishing problem and the difficulty of modeling complex feature relationships that are commonly observed in conventional generators. ADAEN incorporates a multi-scale adaptive diffusion-augmented discriminator, which preserves scale-specific features across different diffusion stages via cosine-scheduled adaptive noise injection, thereby endowing the discriminator with diffusion-stage awareness. Furthermore, ADAEN introduces a multi-scale robust adversarial gradient loss function that ensures training stability through a diffusion-step-conditional Wasserstein loss combined with gradient penalty. The method has been evaluated on 14 UCI benchmark datasets and achieves state-of-the-art performance in anomaly detection compared to existing advanced algorithms, with an average improvement of 8.3% in AUC, an 11.2% increase in F1-Score, and a 15.7% reduction in false positive rate.

]]> ADAEN: Adaptive Diffusion Adversarial Evolutionary Network for Unsupervised Anomaly Detection in Tabular Data Yong Lu Sen Wang Lingjun Kong Wenju Wang doi: 10.3390/asi9020036 Applied System Innovation 2026-01-30 Applied System Innovation 2026-01-30 9 2 Article 36 10.3390/asi9020036 https://www.mdpi.com/2571-5577/9/2/36 ASI, Vol. 9, Pages 35: The Rise of Foundation Models: Opportunities, Technology, Applications, Challenges, Recent Trends, and Future Directions https://www.mdpi.com/2571-5577/9/2/35 Foundation models (FMs) have become a paradigm shift in the field of artificial intelligence, allowing one large-scale pretrained model to be customized for a broad set of downstream tasks using very little task-specific data. These models, which include GPT, CLIP, BERT, and vision transformers, have altered the scope of transfer learning and multimodal understanding and are built on top of enormous datasets and self-supervised learning. The paper provides a broad view of the modern state of foundation models, with an emphasis on their technological foundation, training, and cross-domain use in fields like natural language processing, computer vision, healthcare, robotics and scientific discovery. We also explore the main opportunities that FMs offer, as well as state-of-the-art methods and techniques for the development of foundation models. we discuss their applications in natural language processing, computer vision, healthcare, etc. Furthermore, their limitations and challenges are also investigated. Lastly, future prospects are discussed so that professionals and scientists obtain a better understanding of the importance of foundation models for addressing their research goals. 2026-01-30 ASI, Vol. 9, Pages 35: The Rise of Foundation Models: Opportunities, Technology, Applications, Challenges, Recent Trends, and Future Directions

Applied System Innovation doi: 10.3390/asi9020035

Authors: Ali Hussain Umm E. Farwa Sikandar Ali Hee-Cheol Kim

Foundation models (FMs) have become a paradigm shift in the field of artificial intelligence, allowing one large-scale pretrained model to be customized for a broad set of downstream tasks using very little task-specific data. These models, which include GPT, CLIP, BERT, and vision transformers, have altered the scope of transfer learning and multimodal understanding and are built on top of enormous datasets and self-supervised learning. The paper provides a broad view of the modern state of foundation models, with an emphasis on their technological foundation, training, and cross-domain use in fields like natural language processing, computer vision, healthcare, robotics and scientific discovery. We also explore the main opportunities that FMs offer, as well as state-of-the-art methods and techniques for the development of foundation models. we discuss their applications in natural language processing, computer vision, healthcare, etc. Furthermore, their limitations and challenges are also investigated. Lastly, future prospects are discussed so that professionals and scientists obtain a better understanding of the importance of foundation models for addressing their research goals.

]]> The Rise of Foundation Models: Opportunities, Technology, Applications, Challenges, Recent Trends, and Future Directions Ali Hussain Umm E. Farwa Sikandar Ali Hee-Cheol Kim doi: 10.3390/asi9020035 Applied System Innovation 2026-01-30 Applied System Innovation 2026-01-30 9 2 Review 35 10.3390/asi9020035 https://www.mdpi.com/2571-5577/9/2/35 ASI, Vol. 9, Pages 34: Hybrid Fuzzy–Rough MCDM Framework and Decision Support Application for Sustainable Evaluation of Virtualization Technologies https://www.mdpi.com/2571-5577/9/2/34 Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. 2026-01-30 ASI, Vol. 9, Pages 34: Hybrid Fuzzy–Rough MCDM Framework and Decision Support Application for Sustainable Evaluation of Virtualization Technologies

Applied System Innovation doi: 10.3390/asi9020034

Authors: Seren Başaran

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives.

]]> Hybrid Fuzzy–Rough MCDM Framework and Decision Support Application for Sustainable Evaluation of Virtualization Technologies Seren Başaran doi: 10.3390/asi9020034 Applied System Innovation 2026-01-30 Applied System Innovation 2026-01-30 9 2 Article 34 10.3390/asi9020034 https://www.mdpi.com/2571-5577/9/2/34 ASI, Vol. 9, Pages 33: Planning Product Upgrades: A Method for Defining Release Types and Their Strategies for Software-Intensive Products https://www.mdpi.com/2571-5577/9/2/33 The environment of today’s companies is marked by increasing dynamism. Rapid technological developments, strong innovation impulses, and continual market entry of new competitors create volatile conditions that make the delivery of valuable products challenging. Long-term corporate success therefore depends on offering a product portfolio consistently aligned with evolving market needs. Customers expect products that show continuous improvements in performance and functionality over time, making systematic product upgrading a key success factor. Release planning addresses this need by enabling continuous product evolution through planned product upgrades. It focuses on selecting and combining functional units for structured publication within releases. This proactive management of product value offers substantial potential but also demands comprehensive know-how, particularly given rising product complexity and the interplay of multiple technologies. The objective of this work is to develop a methodology that supports effective planning of product upgrades. The method assists in the product-specific selection of release types and the derivation of suitable release strategies. It yields release units defined by product structure and provides recommendations for appropriate release strategies. The methodology is demonstrated through its application to an electric vehicle, illustrating its practical relevance for software-intensive products. 2026-01-28 ASI, Vol. 9, Pages 33: Planning Product Upgrades: A Method for Defining Release Types and Their Strategies for Software-Intensive Products

Applied System Innovation doi: 10.3390/asi9020033

Authors: Armin Stein Umut Volkan Kizgin Mohammad Albittar Thomas Vietor

The environment of today’s companies is marked by increasing dynamism. Rapid technological developments, strong innovation impulses, and continual market entry of new competitors create volatile conditions that make the delivery of valuable products challenging. Long-term corporate success therefore depends on offering a product portfolio consistently aligned with evolving market needs. Customers expect products that show continuous improvements in performance and functionality over time, making systematic product upgrading a key success factor. Release planning addresses this need by enabling continuous product evolution through planned product upgrades. It focuses on selecting and combining functional units for structured publication within releases. This proactive management of product value offers substantial potential but also demands comprehensive know-how, particularly given rising product complexity and the interplay of multiple technologies. The objective of this work is to develop a methodology that supports effective planning of product upgrades. The method assists in the product-specific selection of release types and the derivation of suitable release strategies. It yields release units defined by product structure and provides recommendations for appropriate release strategies. The methodology is demonstrated through its application to an electric vehicle, illustrating its practical relevance for software-intensive products.

]]> Planning Product Upgrades: A Method for Defining Release Types and Their Strategies for Software-Intensive Products Armin Stein Umut Volkan Kizgin Mohammad Albittar Thomas Vietor doi: 10.3390/asi9020033 Applied System Innovation 2026-01-28 Applied System Innovation 2026-01-28 9 2 Article 33 10.3390/asi9020033 https://www.mdpi.com/2571-5577/9/2/33 ASI, Vol. 9, Pages 32: A Data-Driven Two-Phase Energy Consumption Prediction Method for Injection Compressor Systems in Underground Gas Storage https://www.mdpi.com/2571-5577/9/2/32 Since the compressor system in underground gas storage (UGS) facilities operates under highly dynamic and complex injection conditions, traditional rule-based operation and mechanism-based modeling approaches prove inadequate for meeting the stringent requirements of high-accuracy prediction under such variable conditions. To address this, a data-driven two-phase prediction framework for compressor energy consumption is proposed. In the first phase, a convolutional neural network with efficient channel attention (CNN-ECA) is developed to accurately forecast key operating condition parameters. Based on these outputs, the second phase employs a compressor performance prediction model to estimate unit energy consumption with improved precision. In addition, a hybrid prediction strategy integrating a Transformer architecture is introduced to capture long-range temporal dependencies, thereby enhancing both single-step and multi-step forecasting performance. The proposed method is evaluated using operational data from eight compressors at the Xiangguosi underground gas storage. Experimental results show that the framework achieves high prediction accuracy, with a MAPE of 4.0779% (single-step) and 4.2449% (multi-step), outperforming advanced benchmark models. 2026-01-28 ASI, Vol. 9, Pages 32: A Data-Driven Two-Phase Energy Consumption Prediction Method for Injection Compressor Systems in Underground Gas Storage

Applied System Innovation doi: 10.3390/asi9020032

Authors: Ying Yang De Tang Guicheng Yu Junchi Zhou Jinsong Yang Tingting Jiang Zixu Huang Jianguo Miao

Since the compressor system in underground gas storage (UGS) facilities operates under highly dynamic and complex injection conditions, traditional rule-based operation and mechanism-based modeling approaches prove inadequate for meeting the stringent requirements of high-accuracy prediction under such variable conditions. To address this, a data-driven two-phase prediction framework for compressor energy consumption is proposed. In the first phase, a convolutional neural network with efficient channel attention (CNN-ECA) is developed to accurately forecast key operating condition parameters. Based on these outputs, the second phase employs a compressor performance prediction model to estimate unit energy consumption with improved precision. In addition, a hybrid prediction strategy integrating a Transformer architecture is introduced to capture long-range temporal dependencies, thereby enhancing both single-step and multi-step forecasting performance. The proposed method is evaluated using operational data from eight compressors at the Xiangguosi underground gas storage. Experimental results show that the framework achieves high prediction accuracy, with a MAPE of 4.0779% (single-step) and 4.2449% (multi-step), outperforming advanced benchmark models.

]]> A Data-Driven Two-Phase Energy Consumption Prediction Method for Injection Compressor Systems in Underground Gas Storage Ying Yang De Tang Guicheng Yu Junchi Zhou Jinsong Yang Tingting Jiang Zixu Huang Jianguo Miao doi: 10.3390/asi9020032 Applied System Innovation 2026-01-28 Applied System Innovation 2026-01-28 9 2 Article 32 10.3390/asi9020032 https://www.mdpi.com/2571-5577/9/2/32 ASI, Vol. 9, Pages 31: A Multi-Stage Algorithm of Fringe Map Reconstruction for Fiber-End Surface Analysis and Non-Phase-Shifting Interferometry https://www.mdpi.com/2571-5577/9/2/31 Interferometers are essential tools for quality control of optical surfaces. While interferometric techniques like phase-shifting interferometry offer high accuracy, they involve complex setups, require stringent calibration, and are sensitive to phase shift errors, noise, and surface inhomogeneities. In this research, we introduce an alternative algorithm that integrates Moving Average and Fast Fourier Transform (MAFFT) techniques with Polynomial Fitting. The proposed method achieves results comparable to a Zygo interferometer under standard conditions, with an error margin under 2%. It also maintains measurement stability in noisy environments and in the presence of significant local inhomogeneities, operating in real-time to enable wavefront measurements at 30 Hz. We have validated the algorithm through simulations assessing noise-induced errors and through experimental comparisons with a Zygo interferometer. 2026-01-27 ASI, Vol. 9, Pages 31: A Multi-Stage Algorithm of Fringe Map Reconstruction for Fiber-End Surface Analysis and Non-Phase-Shifting Interferometry

Applied System Innovation doi: 10.3390/asi9020031

Authors: Ilya Galaktionov Vladimir Toporovsky

Interferometers are essential tools for quality control of optical surfaces. While interferometric techniques like phase-shifting interferometry offer high accuracy, they involve complex setups, require stringent calibration, and are sensitive to phase shift errors, noise, and surface inhomogeneities. In this research, we introduce an alternative algorithm that integrates Moving Average and Fast Fourier Transform (MAFFT) techniques with Polynomial Fitting. The proposed method achieves results comparable to a Zygo interferometer under standard conditions, with an error margin under 2%. It also maintains measurement stability in noisy environments and in the presence of significant local inhomogeneities, operating in real-time to enable wavefront measurements at 30 Hz. We have validated the algorithm through simulations assessing noise-induced errors and through experimental comparisons with a Zygo interferometer.

]]> A Multi-Stage Algorithm of Fringe Map Reconstruction for Fiber-End Surface Analysis and Non-Phase-Shifting Interferometry Ilya Galaktionov Vladimir Toporovsky doi: 10.3390/asi9020031 Applied System Innovation 2026-01-27 Applied System Innovation 2026-01-27 9 2 Article 31 10.3390/asi9020031 https://www.mdpi.com/2571-5577/9/2/31 ASI, Vol. 9, Pages 30: A System-Level Decision-Support Framework for Integrated Operating Room and Bed Capacity Planning Under Emergency Uncertainty https://www.mdpi.com/2571-5577/9/2/30 Coordinating operating room schedules with downstream inpatient bed availability remains a critical challenge for hospitals, particularly under emergency-driven uncertainty. Emergency arrivals introduce variability that propagates congestion across surgical and inpatient systems, reducing elective surgery throughput and resource utilization. Existing approaches often treat operating rooms and inpatient beds as isolated planning problems, limiting the ability to anticipate system-wide congestion effects. This study proposes a system-level decision-support framework that integrates elective operating room scheduling, emergency arrivals, and inpatient bed capacity within a unified stochastic optimization model. Uncertainty in surgical duration and patient length of stay is represented through scenario-based stochastic modeling. Computational experiments examine system performance under varying levels of emergency demand and bed availability. The results identify critical congestion thresholds beyond which elective throughput deteriorates rapidly, highlighting the role of downstream bed constraints in governing system capacity under uncertainty. The proposed framework provides hospital managers with practical insights for coordinated surgical and inpatient capacity planning, bridging operations research optimization with operations management principles at the system level. 2026-01-27 ASI, Vol. 9, Pages 30: A System-Level Decision-Support Framework for Integrated Operating Room and Bed Capacity Planning Under Emergency Uncertainty

Applied System Innovation doi: 10.3390/asi9020030

Authors: Beshoy Botros Mohamed Gheith Amr Eltawil

Coordinating operating room schedules with downstream inpatient bed availability remains a critical challenge for hospitals, particularly under emergency-driven uncertainty. Emergency arrivals introduce variability that propagates congestion across surgical and inpatient systems, reducing elective surgery throughput and resource utilization. Existing approaches often treat operating rooms and inpatient beds as isolated planning problems, limiting the ability to anticipate system-wide congestion effects. This study proposes a system-level decision-support framework that integrates elective operating room scheduling, emergency arrivals, and inpatient bed capacity within a unified stochastic optimization model. Uncertainty in surgical duration and patient length of stay is represented through scenario-based stochastic modeling. Computational experiments examine system performance under varying levels of emergency demand and bed availability. The results identify critical congestion thresholds beyond which elective throughput deteriorates rapidly, highlighting the role of downstream bed constraints in governing system capacity under uncertainty. The proposed framework provides hospital managers with practical insights for coordinated surgical and inpatient capacity planning, bridging operations research optimization with operations management principles at the system level.

]]> A System-Level Decision-Support Framework for Integrated Operating Room and Bed Capacity Planning Under Emergency Uncertainty Beshoy Botros Mohamed Gheith Amr Eltawil doi: 10.3390/asi9020030 Applied System Innovation 2026-01-27 Applied System Innovation 2026-01-27 9 2 Article 30 10.3390/asi9020030 https://www.mdpi.com/2571-5577/9/2/30 ASI, Vol. 9, Pages 29: Visual Navigation Using Depth Estimation Based on Hybrid Deep Learning in Sparsely Connected Path Networks for Robustness and Low Complexity https://www.mdpi.com/2571-5577/9/2/29 Robot navigation refers to a robot’s ability to determine its position within a reference frame and plan a path to a target location. Visual navigation, which relies on visual sensors such as cameras, is one approach to this problem. Among visual navigation methods, Visual Teach and Repeat (VT&R) techniques are commonly used. To develop an effective robot navigation framework based on the VT&R method, accurate and fast depth estimation of the scene is essential. In recent years, event cameras have garnered significant interest from machine vision researchers due to their numerous advantages and applicability in various environments, including robotics and drones. However, the main gap is how these cameras are used in a navigation system. The current research uses the attention-based UNET neural network to estimate the depth of a scene using an event camera. The attention-based UNET structure leads to accurate depth detection of the scene. This depth information is then used, together with a hybrid deep neural network consisting of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), for robot navigation. Simulation results on the DENSE dataset yield an RMSE of 8.15, which is an acceptable result compared to other similar methods. This method not only provides good accuracy but also operates at high speed, making it suitable for real-time applications and visual navigation methods based on VT&R. 2026-01-27 ASI, Vol. 9, Pages 29: Visual Navigation Using Depth Estimation Based on Hybrid Deep Learning in Sparsely Connected Path Networks for Robustness and Low Complexity

Applied System Innovation doi: 10.3390/asi9020029

Authors: Huda Al-Saedi Pedram Salehpour Seyyed Hadi Aghdasi

Robot navigation refers to a robot’s ability to determine its position within a reference frame and plan a path to a target location. Visual navigation, which relies on visual sensors such as cameras, is one approach to this problem. Among visual navigation methods, Visual Teach and Repeat (VT&R) techniques are commonly used. To develop an effective robot navigation framework based on the VT&R method, accurate and fast depth estimation of the scene is essential. In recent years, event cameras have garnered significant interest from machine vision researchers due to their numerous advantages and applicability in various environments, including robotics and drones. However, the main gap is how these cameras are used in a navigation system. The current research uses the attention-based UNET neural network to estimate the depth of a scene using an event camera. The attention-based UNET structure leads to accurate depth detection of the scene. This depth information is then used, together with a hybrid deep neural network consisting of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), for robot navigation. Simulation results on the DENSE dataset yield an RMSE of 8.15, which is an acceptable result compared to other similar methods. This method not only provides good accuracy but also operates at high speed, making it suitable for real-time applications and visual navigation methods based on VT&R.

]]> Visual Navigation Using Depth Estimation Based on Hybrid Deep Learning in Sparsely Connected Path Networks for Robustness and Low Complexity Huda Al-Saedi Pedram Salehpour Seyyed Hadi Aghdasi doi: 10.3390/asi9020029 Applied System Innovation 2026-01-27 Applied System Innovation 2026-01-27 9 2 Article 29 10.3390/asi9020029 https://www.mdpi.com/2571-5577/9/2/29 ASI, Vol. 9, Pages 28: Scenario-Based Analysis of the Future Technological Trends in the Automotive Sector in Southeast Lower-Saxony https://www.mdpi.com/2571-5577/9/2/28 The automotive industry faces radical technological change, driven by the adoption of electrification, automation, and digitalization. As a leading industrial hub with key OEMs and suppliers, such as Volkswagen, Southeast Lower Saxony is disproportionately impacted by this structural transformation. As a consequence of these trends, the region’s automotive base faces economic uncertainties, local regulatory lag, and technological disruptions. In this study a scenario planning methodology is conducted, to identify three potential mobility futures for 2035: a Best-Case scenario, where innovation and favorable policies enable a stable growth environment for the local automotive industry; a Trend scenario, marked by incremental yet uneven progress, while maintaining the current status quo; and a Worst-Case scenario, defined by economic stagnation and regulatory impediments, leading to a slow degradation of the regional automotive industry. The scenarios are then evaluated based upon their impact and probability of occurrence, while individual impact factors were also prepared and categorized to support future decision-making on a topical basis. This study offers an overview of potential scenarios for the Southeast Lower Saxon automotive industry, supporting the strategic decision-making. 2026-01-26 ASI, Vol. 9, Pages 28: Scenario-Based Analysis of the Future Technological Trends in the Automotive Sector in Southeast Lower-Saxony

Applied System Innovation doi: 10.3390/asi9020028

Authors: Armin Stein Lars Everding Henrik Münchhausen Björn Krüger Bassem Hichri Maximilian Flormann Axel Wolfgang Sturm Thomas Vietor

The automotive industry faces radical technological change, driven by the adoption of electrification, automation, and digitalization. As a leading industrial hub with key OEMs and suppliers, such as Volkswagen, Southeast Lower Saxony is disproportionately impacted by this structural transformation. As a consequence of these trends, the region’s automotive base faces economic uncertainties, local regulatory lag, and technological disruptions. In this study a scenario planning methodology is conducted, to identify three potential mobility futures for 2035: a Best-Case scenario, where innovation and favorable policies enable a stable growth environment for the local automotive industry; a Trend scenario, marked by incremental yet uneven progress, while maintaining the current status quo; and a Worst-Case scenario, defined by economic stagnation and regulatory impediments, leading to a slow degradation of the regional automotive industry. The scenarios are then evaluated based upon their impact and probability of occurrence, while individual impact factors were also prepared and categorized to support future decision-making on a topical basis. This study offers an overview of potential scenarios for the Southeast Lower Saxon automotive industry, supporting the strategic decision-making.

]]> Scenario-Based Analysis of the Future Technological Trends in the Automotive Sector in Southeast Lower-Saxony Armin Stein Lars Everding Henrik Münchhausen Björn Krüger Bassem Hichri Maximilian Flormann Axel Wolfgang Sturm Thomas Vietor doi: 10.3390/asi9020028 Applied System Innovation 2026-01-26 Applied System Innovation 2026-01-26 9 2 Article 28 10.3390/asi9020028 https://www.mdpi.com/2571-5577/9/2/28 ASI, Vol. 9, Pages 27: Modeling a Reliable Intermodal Routing Problem for Emergency Materials in the Early Stage of Post-Disaster Recovery Under Uncertainty of Demand and Capacity https://www.mdpi.com/2571-5577/9/2/27 This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the uncertainty of both emergency materials’ demand and the network capacity by LR triangular fuzzy numbers, and thus explores a reliable routing problem for transporting emergency materials that is further formulated by a fuzzy linear programming model. Considering the decision makers’ cautious attitude on the transportation of emergency materials to avoid transportation interruption, this study adopts chance-constrained programming based on necessity measure to build a solvable reformulation of the proposed model. A numerical case study is carried out to reveal the conflicting relationship between improving the reliability and reducing the time of transporting emergency materials. The decision-makers of the emergency materials transportation organization should select a reasonable confidence level based on the actual decision-making scenario to plan the reliable intermodal route for emergency materials. By comparing with deterministic modeling, this study verifies the feasibility of the modeling the uncertainty of both demand and capacity in avoiding unreliable transportation and enhancing the flexibility of the intermodal routing for emergency materials. By comparing with chance-constrained programming using possibility measure, this study demonstrates the feasibility of the necessity measure in planning the reliable intermodal route. This study further analyzes how the capacity level of the intermodal network, demand level of the emergency materials and stability of the LR triangular fuzzy parameters influence the optimization results. Accordingly, this study emphasizes the importance of objectively evaluating the uncertain demand for emergency materials, and reveals that the enhancement of the capacity level of the intermodal network and stability of LR triangular fuzzy parameters is able to reduce the transportation time of emergency materials and meanwhile maintain a high reliability. 2026-01-23 ASI, Vol. 9, Pages 27: Modeling a Reliable Intermodal Routing Problem for Emergency Materials in the Early Stage of Post-Disaster Recovery Under Uncertainty of Demand and Capacity

Applied System Innovation doi: 10.3390/asi9020027

Authors: Yu Huang Haochu Cui Yue Lu Yan Sun

This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the uncertainty of both emergency materials’ demand and the network capacity by LR triangular fuzzy numbers, and thus explores a reliable routing problem for transporting emergency materials that is further formulated by a fuzzy linear programming model. Considering the decision makers’ cautious attitude on the transportation of emergency materials to avoid transportation interruption, this study adopts chance-constrained programming based on necessity measure to build a solvable reformulation of the proposed model. A numerical case study is carried out to reveal the conflicting relationship between improving the reliability and reducing the time of transporting emergency materials. The decision-makers of the emergency materials transportation organization should select a reasonable confidence level based on the actual decision-making scenario to plan the reliable intermodal route for emergency materials. By comparing with deterministic modeling, this study verifies the feasibility of the modeling the uncertainty of both demand and capacity in avoiding unreliable transportation and enhancing the flexibility of the intermodal routing for emergency materials. By comparing with chance-constrained programming using possibility measure, this study demonstrates the feasibility of the necessity measure in planning the reliable intermodal route. This study further analyzes how the capacity level of the intermodal network, demand level of the emergency materials and stability of the LR triangular fuzzy parameters influence the optimization results. Accordingly, this study emphasizes the importance of objectively evaluating the uncertain demand for emergency materials, and reveals that the enhancement of the capacity level of the intermodal network and stability of LR triangular fuzzy parameters is able to reduce the transportation time of emergency materials and meanwhile maintain a high reliability.

]]> Modeling a Reliable Intermodal Routing Problem for Emergency Materials in the Early Stage of Post-Disaster Recovery Under Uncertainty of Demand and Capacity Yu Huang Haochu Cui Yue Lu Yan Sun doi: 10.3390/asi9020027 Applied System Innovation 2026-01-23 Applied System Innovation 2026-01-23 9 2 Article 27 10.3390/asi9020027 https://www.mdpi.com/2571-5577/9/2/27 ASI, Vol. 9, Pages 26: Enhancing Solar Cell Performance: Atan-Sinc Optimization Algorithm for Precise Parameter Extraction in the Three-Diode Model https://www.mdpi.com/2571-5577/9/1/26 This study focuses on estimating the nine parameters of the three-diode model (3DM) for photovoltaic (PV) cells by integrating the Atan-Sinc Optimization Algorithm (ASOA) with the Newton–Raphson (NR) method. The ASOA, a population-based metaheuristic approach inspired by the behaviors of the Sech and Tanh functions, systematically generates candidate solutions for the complete set of parameters in the 3DM. For each of these solutions, the NR method is employed to solve the transcendental equation governing the solar cell model, facilitating a precise evaluation of the associated objective function. To guide the parameter estimation process, experimental current-voltage (I-V) and voltage-power (V-P) curves are utilized. The robustness of the proposed methodology is validated through studies on both monocrystalline and polycrystalline solar cells. Computational results reveal that the ASOA effectively navigates the parameter space, while the NR method provides accurate evaluations, resulting in reliable and precise parameter estimations. All numerical validations were conducted using MATLAB software, version 2024b. 2026-01-22 ASI, Vol. 9, Pages 26: Enhancing Solar Cell Performance: Atan-Sinc Optimization Algorithm for Precise Parameter Extraction in the Three-Diode Model

Applied System Innovation doi: 10.3390/asi9010026

Authors: Diego Fernando Muñoz-Torres Oscar Danilo Montoya Jesús C. Hernández Walter Gil-González Luis Fernando Grisales-Noreña

This study focuses on estimating the nine parameters of the three-diode model (3DM) for photovoltaic (PV) cells by integrating the Atan-Sinc Optimization Algorithm (ASOA) with the Newton–Raphson (NR) method. The ASOA, a population-based metaheuristic approach inspired by the behaviors of the Sech and Tanh functions, systematically generates candidate solutions for the complete set of parameters in the 3DM. For each of these solutions, the NR method is employed to solve the transcendental equation governing the solar cell model, facilitating a precise evaluation of the associated objective function. To guide the parameter estimation process, experimental current-voltage (I-V) and voltage-power (V-P) curves are utilized. The robustness of the proposed methodology is validated through studies on both monocrystalline and polycrystalline solar cells. Computational results reveal that the ASOA effectively navigates the parameter space, while the NR method provides accurate evaluations, resulting in reliable and precise parameter estimations. All numerical validations were conducted using MATLAB software, version 2024b.

]]> Enhancing Solar Cell Performance: Atan-Sinc Optimization Algorithm for Precise Parameter Extraction in the Three-Diode Model Diego Fernando Muñoz-Torres Oscar Danilo Montoya Jesús C. Hernández Walter Gil-González Luis Fernando Grisales-Noreña doi: 10.3390/asi9010026 Applied System Innovation 2026-01-22 Applied System Innovation 2026-01-22 9 1 Article 26 10.3390/asi9010026 https://www.mdpi.com/2571-5577/9/1/26 ASI, Vol. 9, Pages 25: Semantic Segmentation-Based and Task-Aware Elastic Compression of Sequential Data for Aluminum Heating Furnaces https://www.mdpi.com/2571-5577/9/1/25 To address the challenges of compressing large-scale, multi-channel temperature data from aluminum alloy heating furnaces—and the limitations of traditional methods in preserving fidelity for critical tasks like energy accounting and process playback—this paper proposes an elastic, task-aware time-series compression method based on semantic segmentation. The method automatically segments data and annotates anchor points according to key process stages and significant operational events. Data are grouped by furnace number and alloy grade into segment-level buckets. Within this structure, an enhanced PCA model is built using channel-specific weights and amplified anchor points. The optimal principal component dimension is selected automatically under explained variance constraints, with channel-wise DCT used as a fallback for small samples. Compression accuracy is evaluated using combined rRMSE metrics (overall and per temperature channel) and key event recall rate. Experiments show the method achieves an average overall rRMSE of 0.11624, a temperature channel rRMSE of 0.08860, and a compression ratio of 1.18, outperforming Standard-PCA, PAA, and RP-Gauss. Notably, the proposed method achieves 100% recall for key events during heat preservation, demonstrating superior performance. Further analysis shows performance varies significantly across process stages, furnace IDs, and alloy grades, offering valuable insights for fine-grained evaluation and real-world deployment. 2026-01-22 ASI, Vol. 9, Pages 25: Semantic Segmentation-Based and Task-Aware Elastic Compression of Sequential Data for Aluminum Heating Furnaces

Applied System Innovation doi: 10.3390/asi9010025

Authors: Jie Hou Xiaoxuan Huang Jianping Tan Jianqiao Liu Xiaojie Jia Ruining Xie

To address the challenges of compressing large-scale, multi-channel temperature data from aluminum alloy heating furnaces—and the limitations of traditional methods in preserving fidelity for critical tasks like energy accounting and process playback—this paper proposes an elastic, task-aware time-series compression method based on semantic segmentation. The method automatically segments data and annotates anchor points according to key process stages and significant operational events. Data are grouped by furnace number and alloy grade into segment-level buckets. Within this structure, an enhanced PCA model is built using channel-specific weights and amplified anchor points. The optimal principal component dimension is selected automatically under explained variance constraints, with channel-wise DCT used as a fallback for small samples. Compression accuracy is evaluated using combined rRMSE metrics (overall and per temperature channel) and key event recall rate. Experiments show the method achieves an average overall rRMSE of 0.11624, a temperature channel rRMSE of 0.08860, and a compression ratio of 1.18, outperforming Standard-PCA, PAA, and RP-Gauss. Notably, the proposed method achieves 100% recall for key events during heat preservation, demonstrating superior performance. Further analysis shows performance varies significantly across process stages, furnace IDs, and alloy grades, offering valuable insights for fine-grained evaluation and real-world deployment.

]]> Semantic Segmentation-Based and Task-Aware Elastic Compression of Sequential Data for Aluminum Heating Furnaces Jie Hou Xiaoxuan Huang Jianping Tan Jianqiao Liu Xiaojie Jia Ruining Xie doi: 10.3390/asi9010025 Applied System Innovation 2026-01-22 Applied System Innovation 2026-01-22 9 1 Article 25 10.3390/asi9010025 https://www.mdpi.com/2571-5577/9/1/25 ASI, Vol. 9, Pages 24: Multi-Objective Optimization for the Location and Sizing of Capacitor Banks in Distribution Grids: An Approach Based on the Sine and Cosine Algorithm https://www.mdpi.com/2571-5577/9/1/24 This article presents a hybrid optimization model designed to determine the optimal location and operation of capacitor banks in medium-voltage distribution networks, aiming to reduce energy losses and enhance the system’s economic efficiency. The use of reactive power compensation through fixed-step capacitor banks is highlighted as an effective and cost-efficient solution; however, their optimal placement and sizing pose a mixed-integer nonlinear programming optimization challenge of a combinatorial nature. To address this issue, a multi-objective optimization methodology based on the Sine Cosine Algorithm (SCA) is proposed to identify the ideal location and capacity of capacitor banks within distribution networks. This model simultaneously focuses on minimizing technical losses while reducing both investment and operational costs, thereby producing a Pareto front that facilitates the analysis of trade-offs between technical performance and economic viability. The methodology is validated through comprehensive testing on the 33- and 69-bus reference systems. The results demonstrate that the proposed SCA-based approach is computationally efficient, easy to implement, and capable of effectively exploring the search space to identify high-quality Pareto-optimal solutions. These characteristics render the approach a valuable tool for the planning and operation of efficient and resilient distribution networks. 2026-01-21 ASI, Vol. 9, Pages 24: Multi-Objective Optimization for the Location and Sizing of Capacitor Banks in Distribution Grids: An Approach Based on the Sine and Cosine Algorithm

Applied System Innovation doi: 10.3390/asi9010024

Authors: Laura Camila Garzón-Perdomo Brayan David Duque-Chavarro Carlos Andrés Torres-Pinzón Oscar Danilo Montoya

This article presents a hybrid optimization model designed to determine the optimal location and operation of capacitor banks in medium-voltage distribution networks, aiming to reduce energy losses and enhance the system’s economic efficiency. The use of reactive power compensation through fixed-step capacitor banks is highlighted as an effective and cost-efficient solution; however, their optimal placement and sizing pose a mixed-integer nonlinear programming optimization challenge of a combinatorial nature. To address this issue, a multi-objective optimization methodology based on the Sine Cosine Algorithm (SCA) is proposed to identify the ideal location and capacity of capacitor banks within distribution networks. This model simultaneously focuses on minimizing technical losses while reducing both investment and operational costs, thereby producing a Pareto front that facilitates the analysis of trade-offs between technical performance and economic viability. The methodology is validated through comprehensive testing on the 33- and 69-bus reference systems. The results demonstrate that the proposed SCA-based approach is computationally efficient, easy to implement, and capable of effectively exploring the search space to identify high-quality Pareto-optimal solutions. These characteristics render the approach a valuable tool for the planning and operation of efficient and resilient distribution networks.

]]> Multi-Objective Optimization for the Location and Sizing of Capacitor Banks in Distribution Grids: An Approach Based on the Sine and Cosine Algorithm Laura Camila Garzón-Perdomo Brayan David Duque-Chavarro Carlos Andrés Torres-Pinzón Oscar Danilo Montoya doi: 10.3390/asi9010024 Applied System Innovation 2026-01-21 Applied System Innovation 2026-01-21 9 1 Article 24 10.3390/asi9010024 https://www.mdpi.com/2571-5577/9/1/24 ASI, Vol. 9, Pages 23: Autonomous Frequency–Voltage Regulation Strategy for Weak-Grid Renewable-Energy Stations Based on Hybrid Supercapacitors and Cascaded H-Bridge Converters https://www.mdpi.com/2571-5577/9/1/23 Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable energy stations. Based on this system, in this paper, a novel automatic frequency–voltage regulation strategy is proposed. First, a fast fault severity detection method is proposed. It evaluates the system’s fault condition by monitoring the voltage response and generates auxiliary signals to enable subsequent rapid compensation of voltage and frequency. Subsequently, fast automatic voltage and frequency regulation strategies are developed. These strategies leverage real-time fault assessment to deliver immediate power support to weak-grid renewable stations following a disturbance, thereby effectively stabilizing the terminal voltage magnitude and system frequency. The effectiveness of the proposed method is validated through simulations. A grid-connected model of a weak-grid renewable energy station is established in MATLAB (2023b)/Simulink. Tests under various fault scenarios with different short-circuit ratios and voltage sag depths demonstrate that the proposed strategy can rapidly stabilize both voltage and frequency after large disturbances. 2026-01-21 ASI, Vol. 9, Pages 23: Autonomous Frequency–Voltage Regulation Strategy for Weak-Grid Renewable-Energy Stations Based on Hybrid Supercapacitors and Cascaded H-Bridge Converters

Applied System Innovation doi: 10.3390/asi9010023

Authors: Geng Niu Yu Ji Ming Wu Nan Zheng Yongmei Liu Xiangwu Yan Yibo Gan

Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable energy stations. Based on this system, in this paper, a novel automatic frequency–voltage regulation strategy is proposed. First, a fast fault severity detection method is proposed. It evaluates the system’s fault condition by monitoring the voltage response and generates auxiliary signals to enable subsequent rapid compensation of voltage and frequency. Subsequently, fast automatic voltage and frequency regulation strategies are developed. These strategies leverage real-time fault assessment to deliver immediate power support to weak-grid renewable stations following a disturbance, thereby effectively stabilizing the terminal voltage magnitude and system frequency. The effectiveness of the proposed method is validated through simulations. A grid-connected model of a weak-grid renewable energy station is established in MATLAB (2023b)/Simulink. Tests under various fault scenarios with different short-circuit ratios and voltage sag depths demonstrate that the proposed strategy can rapidly stabilize both voltage and frequency after large disturbances.

]]> Autonomous Frequency–Voltage Regulation Strategy for Weak-Grid Renewable-Energy Stations Based on Hybrid Supercapacitors and Cascaded H-Bridge Converters Geng Niu Yu Ji Ming Wu Nan Zheng Yongmei Liu Xiangwu Yan Yibo Gan doi: 10.3390/asi9010023 Applied System Innovation 2026-01-21 Applied System Innovation 2026-01-21 9 1 Article 23 10.3390/asi9010023 https://www.mdpi.com/2571-5577/9/1/23 ASI, Vol. 9, Pages 22: Optimizing Energy Storage Systems with PSO: Improving Economics and Operations of PMGD—A Chilean Case Study https://www.mdpi.com/2571-5577/9/1/22 This work develops a methodology for operating Battery Energy Storage Systems (BESSs) in distribution networks, connected in parallel with a medium- and small-scale photovoltaic Distributed Generator (PMGD), focusing on a real project located in the O’Higgins region of Chile. The objective is to increase energy sales by the PMGD while ensuring compliance with operational constraints related to the grid, PMGD, and BESSs, and optimizing renewable energy use. A real distribution network from Compañía General de Electricidad (CGE) comprising 627 nodes was simplified into a validated three-node, two-line equivalent model to reduce computational complexity while maintaining accuracy. A mathematical model was designed to maximize economic benefits through optimal energy dispatch, considering solar generation variability, demand curves, and seasonal energy sales and purchasing prices. An energy management system was proposed based on a master–slave methodology composed of Particle Swarm Optimization (PSO) and an hourly power flow using the successive approximation method. Advanced optimization techniques such as Monte Carlo (MC) and the Genetic Algorithm (GAP) were employed as comparison methods, supported by a statistical analysis evaluating the best and average solutions, repeatability, and processing times to select the most effective optimization approach. Results demonstrate that BESS integration efficiently manages solar generation surpluses, injecting energy during peak demand and high-price periods to maximize revenue, alleviate grid congestion, and improve operational stability, with PSO proving particularly efficient. This work underscores the potential of BESS in PMGD to support a more sustainable and efficient energy matrix in Chile, despite regulatory and technical challenges that warrant further investigation. 2026-01-14 ASI, Vol. 9, Pages 22: Optimizing Energy Storage Systems with PSO: Improving Economics and Operations of PMGD—A Chilean Case Study

Applied System Innovation doi: 10.3390/asi9010022

Authors: Juan Tapia-Aguilera Luis Fernando Grisales-Noreña Roberto Eduardo Quintal-Palomo Oscar Danilo Montoya Daniel Sanin-Villa

This work develops a methodology for operating Battery Energy Storage Systems (BESSs) in distribution networks, connected in parallel with a medium- and small-scale photovoltaic Distributed Generator (PMGD), focusing on a real project located in the O’Higgins region of Chile. The objective is to increase energy sales by the PMGD while ensuring compliance with operational constraints related to the grid, PMGD, and BESSs, and optimizing renewable energy use. A real distribution network from Compañía General de Electricidad (CGE) comprising 627 nodes was simplified into a validated three-node, two-line equivalent model to reduce computational complexity while maintaining accuracy. A mathematical model was designed to maximize economic benefits through optimal energy dispatch, considering solar generation variability, demand curves, and seasonal energy sales and purchasing prices. An energy management system was proposed based on a master–slave methodology composed of Particle Swarm Optimization (PSO) and an hourly power flow using the successive approximation method. Advanced optimization techniques such as Monte Carlo (MC) and the Genetic Algorithm (GAP) were employed as comparison methods, supported by a statistical analysis evaluating the best and average solutions, repeatability, and processing times to select the most effective optimization approach. Results demonstrate that BESS integration efficiently manages solar generation surpluses, injecting energy during peak demand and high-price periods to maximize revenue, alleviate grid congestion, and improve operational stability, with PSO proving particularly efficient. This work underscores the potential of BESS in PMGD to support a more sustainable and efficient energy matrix in Chile, despite regulatory and technical challenges that warrant further investigation.

]]> Optimizing Energy Storage Systems with PSO: Improving Economics and Operations of PMGD—A Chilean Case Study Juan Tapia-Aguilera Luis Fernando Grisales-Noreña Roberto Eduardo Quintal-Palomo Oscar Danilo Montoya Daniel Sanin-Villa doi: 10.3390/asi9010022 Applied System Innovation 2026-01-14 Applied System Innovation 2026-01-14 9 1 Article 22 10.3390/asi9010022 https://www.mdpi.com/2571-5577/9/1/22 ASI, Vol. 9, Pages 21: Proactive Cooling Control Algorithm for Data Centers Based on LSTM-Driven Predictive Thermal Analysis https://www.mdpi.com/2571-5577/9/1/21 The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that integrates distributed sensor arrays for predictive analysis. By deploying high-density temperature and humidity sensors both inside and outside server racks, a real-time, high-fidelity three-dimensional digital twin of the data center’s thermal environment is constructed. Time-series analysis combined with Long Short-Term Memory algorithms is employed to forecast temperature and humidity based on the extensive environmental data collected, achieving high predictive accuracy with a root mean square error of 0.25 and an R2 value of 0.985. Building on these predictions, a proactive cooling control strategy is formulated to dynamically adjust fan speeds and the opening degree of chilled-water valves in computer room air conditioning units, changing the cooling approach from passive to preemptive prevention of overheating. Compared with conventional proportional–integral–differential control, the developed system significantly reduces overall energy consumption and maintains all equipment within safe operating temperatures. Specifically, the framework has reduced the energy consumption of the cooling system by 37.5%, lowered the overall power usage effectiveness of the data center by 12% (1.48 to 1.30), and suppressed the cumulative hotspot duration (temperature 27 °C) by nearly 96% (from 48 to 2 h). 2026-01-12 ASI, Vol. 9, Pages 21: Proactive Cooling Control Algorithm for Data Centers Based on LSTM-Driven Predictive Thermal Analysis

Applied System Innovation doi: 10.3390/asi9010021

Authors: Jieying Liu Rui Fan Zonglin Li Napat Harnpornchai Jianlei Qian

The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that integrates distributed sensor arrays for predictive analysis. By deploying high-density temperature and humidity sensors both inside and outside server racks, a real-time, high-fidelity three-dimensional digital twin of the data center’s thermal environment is constructed. Time-series analysis combined with Long Short-Term Memory algorithms is employed to forecast temperature and humidity based on the extensive environmental data collected, achieving high predictive accuracy with a root mean square error of 0.25 and an R2 value of 0.985. Building on these predictions, a proactive cooling control strategy is formulated to dynamically adjust fan speeds and the opening degree of chilled-water valves in computer room air conditioning units, changing the cooling approach from passive to preemptive prevention of overheating. Compared with conventional proportional–integral–differential control, the developed system significantly reduces overall energy consumption and maintains all equipment within safe operating temperatures. Specifically, the framework has reduced the energy consumption of the cooling system by 37.5%, lowered the overall power usage effectiveness of the data center by 12% (1.48 to 1.30), and suppressed the cumulative hotspot duration (temperature 27 °C) by nearly 96% (from 48 to 2 h).

]]> Proactive Cooling Control Algorithm for Data Centers Based on LSTM-Driven Predictive Thermal Analysis Jieying Liu Rui Fan Zonglin Li Napat Harnpornchai Jianlei Qian doi: 10.3390/asi9010021 Applied System Innovation 2026-01-12 Applied System Innovation 2026-01-12 9 1 Article 21 10.3390/asi9010021 https://www.mdpi.com/2571-5577/9/1/21 ASI, Vol. 9, Pages 20: Spatial Risk Assessment: A Case of Multivariate Linear Regression https://www.mdpi.com/2571-5577/9/1/20 The acceptance or rejection of a measurement is determined based on its associated measurement uncertainty. In this procedure, there is a risk of making incorrect decisions, including the potential rejection of compliant measurements or the acceptance of non-conforming ones. This study introduces a mathematical model for the spatial evaluation of the global producer’s and global consumer’s risk, predicated on Bayes’ theorem and a decision rule that includes a guard band. The proposed model is appropriate for risk assessment within the framework of multivariate linear regression. Its applicability is demonstrated through an example involving the flatness of the workbench table surface of a coordinate measuring machine. The least-risk direction on the workbench was identified, and risks were quantified under varying selections of reference planes and differing measurement uncertainties anticipated in future measurement processes. Model evaluation was performed using confusion matrix-based metrics. The spaces of the commonly used metrics, constrained by the dimensions of the coordinate measuring machine workbench, were constructed. Using the evaluated metrics, the optimal guard band width was specified to ensure the minimum values of both the global producer’s and the global consumer’s risk. 2026-01-09 ASI, Vol. 9, Pages 20: Spatial Risk Assessment: A Case of Multivariate Linear Regression

Applied System Innovation doi: 10.3390/asi9010020

Authors: Dubravka Božić Biserka Runje Branko Štrbac Miloš Ranisavljev Andrej Razumić

The acceptance or rejection of a measurement is determined based on its associated measurement uncertainty. In this procedure, there is a risk of making incorrect decisions, including the potential rejection of compliant measurements or the acceptance of non-conforming ones. This study introduces a mathematical model for the spatial evaluation of the global producer’s and global consumer’s risk, predicated on Bayes’ theorem and a decision rule that includes a guard band. The proposed model is appropriate for risk assessment within the framework of multivariate linear regression. Its applicability is demonstrated through an example involving the flatness of the workbench table surface of a coordinate measuring machine. The least-risk direction on the workbench was identified, and risks were quantified under varying selections of reference planes and differing measurement uncertainties anticipated in future measurement processes. Model evaluation was performed using confusion matrix-based metrics. The spaces of the commonly used metrics, constrained by the dimensions of the coordinate measuring machine workbench, were constructed. Using the evaluated metrics, the optimal guard band width was specified to ensure the minimum values of both the global producer’s and the global consumer’s risk.

]]> Spatial Risk Assessment: A Case of Multivariate Linear Regression Dubravka Božić Biserka Runje Branko Štrbac Miloš Ranisavljev Andrej Razumić doi: 10.3390/asi9010020 Applied System Innovation 2026-01-09 Applied System Innovation 2026-01-09 9 1 Article 20 10.3390/asi9010020 https://www.mdpi.com/2571-5577/9/1/20 ASI, Vol. 9, Pages 19: Beyond Histotrust: A Blockchain-Based Alert in Case of Tampering with an Embedded Neural Network in a Multi-Agent Context https://www.mdpi.com/2571-5577/9/1/19 An intrusion into the operational network (OT) of a production site can cause serious damage by affecting productivity, reliability, and quality. The presence of embedded neural networks (NNs), such as classifiers, in physical devices opens the door to new attack vectors. Due to the stochastic behavior of the classifier and the difficulty of reproducing results, the Artificial Intelligence (AI) Act requires the NN’s behavior to be explainable. For this purpose, the platform HistoTrust enables tracing NN behavior, thanks to secure hardware components issuing attestations registered in a blockchain ledger. This solution helps to build trust between independent actors whose devices perform tasks in cooperation. This paper proposes going further by integrating a mechanism for detecting tampering of embedded NN, and using smart contracts executed on the blockchain to propagate the alert to the peer devices in a distributed manner. The use case of a bit-flip attack, targeting the weights of the NN model, is considered. This attack can be carried out by repeatedly injecting very small messages that can be missed by the Intrusion Detection System (IDS). Experiments are being conducted on the HistoTrust platform to demonstrate the feasibility of our distributed approach and to qualify the time required to detect intrusion and propagate the alert, in relation to the time it takes for the attack to impact decisions made by the AI. As a result, the blockchain may be a relevant technology to complement traditional IDS in order to face distributed attacks. 2026-01-08 ASI, Vol. 9, Pages 19: Beyond Histotrust: A Blockchain-Based Alert in Case of Tampering with an Embedded Neural Network in a Multi-Agent Context

Applied System Innovation doi: 10.3390/asi9010019

Authors: Antonio Pereira Dylan Paulin Christine Hennebert

An intrusion into the operational network (OT) of a production site can cause serious damage by affecting productivity, reliability, and quality. The presence of embedded neural networks (NNs), such as classifiers, in physical devices opens the door to new attack vectors. Due to the stochastic behavior of the classifier and the difficulty of reproducing results, the Artificial Intelligence (AI) Act requires the NN’s behavior to be explainable. For this purpose, the platform HistoTrust enables tracing NN behavior, thanks to secure hardware components issuing attestations registered in a blockchain ledger. This solution helps to build trust between independent actors whose devices perform tasks in cooperation. This paper proposes going further by integrating a mechanism for detecting tampering of embedded NN, and using smart contracts executed on the blockchain to propagate the alert to the peer devices in a distributed manner. The use case of a bit-flip attack, targeting the weights of the NN model, is considered. This attack can be carried out by repeatedly injecting very small messages that can be missed by the Intrusion Detection System (IDS). Experiments are being conducted on the HistoTrust platform to demonstrate the feasibility of our distributed approach and to qualify the time required to detect intrusion and propagate the alert, in relation to the time it takes for the attack to impact decisions made by the AI. As a result, the blockchain may be a relevant technology to complement traditional IDS in order to face distributed attacks.

]]> Beyond Histotrust: A Blockchain-Based Alert in Case of Tampering with an Embedded Neural Network in a Multi-Agent Context Antonio Pereira Dylan Paulin Christine Hennebert doi: 10.3390/asi9010019 Applied System Innovation 2026-01-08 Applied System Innovation 2026-01-08 9 1 Article 19 10.3390/asi9010019 https://www.mdpi.com/2571-5577/9/1/19 ASI, Vol. 9, Pages 18: Heterogeneous Graph Neural Network with Local and Global Message Passing for AC-Optimal Power Flow Solutions https://www.mdpi.com/2571-5577/9/1/18 The AC Optimal Power Flow (AC-OPF) problem remains a major computational bottleneck for real-time power system operation. Conventional solvers are accurate but time-consuming, while Graph Neural Networks (GNNs) offer faster approximations yet struggle to capture long-range dependencies and handle topological variations. To address these limitations, we propose a Heterogeneous Graph Transformer with bus-centric Local–Global Message Passing (LG-HGNN). The model performs type-specific local message passing over heterogeneous power graphs and applies a global Transformer only on bus nodes to capture system-wide correlations efficiently. Effective-resistance positional encodings and resistance-biased attention enhance electrical awareness, whereas bounded decoders and physics-informed regularization preserve operational feasibility. Experiments on IEEE 14-, 30-, and 118-bus systems show that LG-HGNN achieves near-optimal results within a few percent of the AC-OPF optimum and generalizes to thousands of unseen N-1 contingency topologies without retraining. Compared with interior-point solvers, it attains up to 190× speedup before power-flow correction and over 10× afterward on GOC 2000-bus systems, providing a scalable and physically consistent surrogate for real-time AC-OPF. 2026-01-05 ASI, Vol. 9, Pages 18: Heterogeneous Graph Neural Network with Local and Global Message Passing for AC-Optimal Power Flow Solutions

Applied System Innovation doi: 10.3390/asi9010018

Authors: Aihui Wen Bao Wen Jining Li Jin Xu

The AC Optimal Power Flow (AC-OPF) problem remains a major computational bottleneck for real-time power system operation. Conventional solvers are accurate but time-consuming, while Graph Neural Networks (GNNs) offer faster approximations yet struggle to capture long-range dependencies and handle topological variations. To address these limitations, we propose a Heterogeneous Graph Transformer with bus-centric Local–Global Message Passing (LG-HGNN). The model performs type-specific local message passing over heterogeneous power graphs and applies a global Transformer only on bus nodes to capture system-wide correlations efficiently. Effective-resistance positional encodings and resistance-biased attention enhance electrical awareness, whereas bounded decoders and physics-informed regularization preserve operational feasibility. Experiments on IEEE 14-, 30-, and 118-bus systems show that LG-HGNN achieves near-optimal results within a few percent of the AC-OPF optimum and generalizes to thousands of unseen N-1 contingency topologies without retraining. Compared with interior-point solvers, it attains up to 190× speedup before power-flow correction and over 10× afterward on GOC 2000-bus systems, providing a scalable and physically consistent surrogate for real-time AC-OPF.

]]> Heterogeneous Graph Neural Network with Local and Global Message Passing for AC-Optimal Power Flow Solutions Aihui Wen Bao Wen Jining Li Jin Xu doi: 10.3390/asi9010018 Applied System Innovation 2026-01-05 Applied System Innovation 2026-01-05 9 1 Article 18 10.3390/asi9010018 https://www.mdpi.com/2571-5577/9/1/18 ASI, Vol. 9, Pages 17: Beyond Answers: Pedagogical Design Rationale for Multi-Persona AI Tutors https://www.mdpi.com/2571-5577/9/1/17 This paper reports a design-rationale account of building and deploying a small ecosystem of AI-driven educational conversational agents with distinct pedagogical personas. Two strands target school contexts: (i) Talk to Bill, a historically grounded Shakespeare interlocutor intended to support close reading, contextual understanding, and interpretive dialogue; and (ii) Here to Help, a set of UK GCSE subject- and exam-board-specific tutors designed for formative practice in recognised question formats with feedback and iterative improvement. The third strand comprises six complementary assistants for an undergraduate Human–Computer Interaction (HCI) module, each bounded to a workflow-aligned role (e.g., empathise-stage coaching, study planning, course operations), with guardrails to privilege process quality over answer generation. We describe how persona differentiation is mapped to established learning, engagement, and motivation theories; how retrieval-augmented generation and provenance cues are used to reduce hallucination risk; and what early deployment observations suggest about orchestration, integration, and incentives. The contribution is a transferable, auditable rationale linking theory to concrete dialogue and UI moves for multi-persona tutoring ecosystems, rather than a claim of causal learning gains. 2025-12-31 ASI, Vol. 9, Pages 17: Beyond Answers: Pedagogical Design Rationale for Multi-Persona AI Tutors

Applied System Innovation doi: 10.3390/asi9010017

Authors: Russell Beale

This paper reports a design-rationale account of building and deploying a small ecosystem of AI-driven educational conversational agents with distinct pedagogical personas. Two strands target school contexts: (i) Talk to Bill, a historically grounded Shakespeare interlocutor intended to support close reading, contextual understanding, and interpretive dialogue; and (ii) Here to Help, a set of UK GCSE subject- and exam-board-specific tutors designed for formative practice in recognised question formats with feedback and iterative improvement. The third strand comprises six complementary assistants for an undergraduate Human–Computer Interaction (HCI) module, each bounded to a workflow-aligned role (e.g., empathise-stage coaching, study planning, course operations), with guardrails to privilege process quality over answer generation. We describe how persona differentiation is mapped to established learning, engagement, and motivation theories; how retrieval-augmented generation and provenance cues are used to reduce hallucination risk; and what early deployment observations suggest about orchestration, integration, and incentives. The contribution is a transferable, auditable rationale linking theory to concrete dialogue and UI moves for multi-persona tutoring ecosystems, rather than a claim of causal learning gains.

]]> Beyond Answers: Pedagogical Design Rationale for Multi-Persona AI Tutors Russell Beale doi: 10.3390/asi9010017 Applied System Innovation 2025-12-31 Applied System Innovation 2025-12-31 9 1 Article 17 10.3390/asi9010017 https://www.mdpi.com/2571-5577/9/1/17 ASI, Vol. 9, Pages 16: An Enterprise Architecture-Driven Service Integration Model for Enhancing Fiscal Oversight in Supreme Audit Institutions https://www.mdpi.com/2571-5577/9/1/16 The integration of IT services is a critical challenge for public organizations that seek to modernize their operational ecosystems and strengthen mission-oriented processes. In the field of fiscal oversight, supreme audit institutions (SAIs) increasingly require systematized and interoperable service architectures to ensure transparency, accountability, and effective public resource control. However, existing literature reveals persistent gaps concerning how service integration models can be deployed and validated within complex government environments. This study describes an enterprise architecture-driven service integration model designed and evaluated within the Office of the General Comptroller of the Republic of Colombia (Contraloría General de la República, CGR). The study tests the hypothesis that an Enterprise Architecture-driven integration model provides the necessary structural coupling to align technical IT performance with the legal requirements of fiscal oversight, which is an alignment that typically does not appear in generic governance frameworks. The methodological approach followed in this study combines an IT service management maturity assessment, process analysis, architecture repository review, and iterative validation sessions with institutional stakeholders. The model integrates ITILv4 (Information Technology Infrastructure Library), TOGAF (The Open Group Architecture Framework), COBIT (Control Objectives for Information and Related Technologies), and ISO20000 into a coherent framework tailored to the operational and regulatory requirements of an SAI. Results show that the proposed model reduces service fragmentation, improves process standardization, strengthens information governance, and enables a unified service catalog aligned with fiscal oversight functions. The empirical validation demonstrates measurable improvements in service delivery, transparency, and organizational responsiveness. The study contributes to the field of applied system innovation by: (i) providing an integration model, which is scientifically grounded and evidence-based, (ii) demonstrating how hybrid governance and architecture frameworks can be adapted to complex public-sector environments, and (iii) offering a replicable approach for SAIs that seek to modernize their technological service ecosystems through enterprise architecture principles. Future research directions are also discussed to provide guidelines to advance integrated governance and digital transformation in oversight institutions. 2025-12-31 ASI, Vol. 9, Pages 16: An Enterprise Architecture-Driven Service Integration Model for Enhancing Fiscal Oversight in Supreme Audit Institutions

Applied System Innovation doi: 10.3390/asi9010016

Authors: Rosse Mary Villamil Jaime A. Restrepo-Carmona Alejandro Escobar Alexánder Aponte-Moreno Juliana Arévalo Herrera Sergio Armando Gutiérrez-Betancur Luis Fletscher

The integration of IT services is a critical challenge for public organizations that seek to modernize their operational ecosystems and strengthen mission-oriented processes. In the field of fiscal oversight, supreme audit institutions (SAIs) increasingly require systematized and interoperable service architectures to ensure transparency, accountability, and effective public resource control. However, existing literature reveals persistent gaps concerning how service integration models can be deployed and validated within complex government environments. This study describes an enterprise architecture-driven service integration model designed and evaluated within the Office of the General Comptroller of the Republic of Colombia (Contraloría General de la República, CGR). The study tests the hypothesis that an Enterprise Architecture-driven integration model provides the necessary structural coupling to align technical IT performance with the legal requirements of fiscal oversight, which is an alignment that typically does not appear in generic governance frameworks. The methodological approach followed in this study combines an IT service management maturity assessment, process analysis, architecture repository review, and iterative validation sessions with institutional stakeholders. The model integrates ITILv4 (Information Technology Infrastructure Library), TOGAF (The Open Group Architecture Framework), COBIT (Control Objectives for Information and Related Technologies), and ISO20000 into a coherent framework tailored to the operational and regulatory requirements of an SAI. Results show that the proposed model reduces service fragmentation, improves process standardization, strengthens information governance, and enables a unified service catalog aligned with fiscal oversight functions. The empirical validation demonstrates measurable improvements in service delivery, transparency, and organizational responsiveness. The study contributes to the field of applied system innovation by: (i) providing an integration model, which is scientifically grounded and evidence-based, (ii) demonstrating how hybrid governance and architecture frameworks can be adapted to complex public-sector environments, and (iii) offering a replicable approach for SAIs that seek to modernize their technological service ecosystems through enterprise architecture principles. Future research directions are also discussed to provide guidelines to advance integrated governance and digital transformation in oversight institutions.

]]> An Enterprise Architecture-Driven Service Integration Model for Enhancing Fiscal Oversight in Supreme Audit Institutions Rosse Mary Villamil Jaime A. Restrepo-Carmona Alejandro Escobar Alexánder Aponte-Moreno Juliana Arévalo Herrera Sergio Armando Gutiérrez-Betancur Luis Fletscher doi: 10.3390/asi9010016 Applied System Innovation 2025-12-31 Applied System Innovation 2025-12-31 9 1 Article 16 10.3390/asi9010016 https://www.mdpi.com/2571-5577/9/1/16 ASI, Vol. 9, Pages 15: A Conceptual Logistic–Production Framework for Wastewater Recovery and Risk Management https://www.mdpi.com/2571-5577/9/1/15 Wastewater management plays a critical role in advancing the circular economy, as wastewater is increasingly considered a recoverable resource rather than a waste product. This paper reviews physical, chemical, biological, and combined treatment methodologies, highlighting a lack of a holistic framework in current research which includes both the operational phases of wastewater treatment and proper risk analysis tools. To address this gap, an innovative methodological framework for wastewater recovery and risk management within an integrated logistic–production process is proposed. The framework is structured in five steps: description of the logistic–production process, hazard identification, risk assessment through the Failure Modes, Effects, and Criticality Analysis (FMECA), prioritization of interventions using the Action Priority (AP) method, and definition of corrective actions. The application of the proposed methodology can optimize the usage of available resources across various sectors while minimizing waste products, thus supporting environmental sustainability. Furthermore, political, economic and social implications of adopting the proposed approach in the field of energy transition are discussed. 2025-12-29 ASI, Vol. 9, Pages 15: A Conceptual Logistic–Production Framework for Wastewater Recovery and Risk Management

Applied System Innovation doi: 10.3390/asi9010015

Authors: Massimo de Falco Roberto Monaco Teresa Murino

Wastewater management plays a critical role in advancing the circular economy, as wastewater is increasingly considered a recoverable resource rather than a waste product. This paper reviews physical, chemical, biological, and combined treatment methodologies, highlighting a lack of a holistic framework in current research which includes both the operational phases of wastewater treatment and proper risk analysis tools. To address this gap, an innovative methodological framework for wastewater recovery and risk management within an integrated logistic–production process is proposed. The framework is structured in five steps: description of the logistic–production process, hazard identification, risk assessment through the Failure Modes, Effects, and Criticality Analysis (FMECA), prioritization of interventions using the Action Priority (AP) method, and definition of corrective actions. The application of the proposed methodology can optimize the usage of available resources across various sectors while minimizing waste products, thus supporting environmental sustainability. Furthermore, political, economic and social implications of adopting the proposed approach in the field of energy transition are discussed.

]]> A Conceptual Logistic–Production Framework for Wastewater Recovery and Risk Management Massimo de Falco Roberto Monaco Teresa Murino doi: 10.3390/asi9010015 Applied System Innovation 2025-12-29 Applied System Innovation 2025-12-29 9 1 Article 15 10.3390/asi9010015 https://www.mdpi.com/2571-5577/9/1/15 ASI, Vol. 9, Pages 14: MixedPalletBoxes Dataset: A Synthetic Benchmark Dataset for Warehouse Applications https://www.mdpi.com/2571-5577/9/1/14 Mixed palletizing remains a core challenge in distribution centers and modern warehouse operations, particularly within robotic handling and automation systems. Progress in this domain has been hindered by the lack of realistic, freely available datasets for rigorous algorithmic benchmarking. This work addresses this gap by introducing MixedPalletBoxes, a family of seven synthetic datasets designed to evaluate algorithm scalability, adaptability and performance variability across a broad spectrum of workload sizes (500–100,000 records) generated via an open source Python script. These datasets enable the assessment of algorithmic behavior under varying operational complexities and scales. Each box instance is richly annotated with geometric dimensions, material properties, load capacities, environmental tolerances and handling flags. To support dynamic experimentation, the dataset is accompanied by a FastAPI-based tool that enables the on-demand creation of randomized daily picking lists simulating realistic inbound orders. Performance is analyzed through metrics such as pallet count, volume utilization, item distribution per pallet and runtime. Across all dataset sizes, the distributions of the physical attributes remain consistent, confirming stable generation behavior. The proposed framework combines standardization, feature richness and scalability, offering a transparent and extensible platform for benchmarking and advancing robotic mixed palletizing solutions. All datasets, generation code and evaluation scripts are publicly released to foster open collaboration and accelerate innovation in data-driven warehouse automation research. 2025-12-29 ASI, Vol. 9, Pages 14: MixedPalletBoxes Dataset: A Synthetic Benchmark Dataset for Warehouse Applications

Applied System Innovation doi: 10.3390/asi9010014

Authors: Adamos Daios Ioannis Kostavelis

Mixed palletizing remains a core challenge in distribution centers and modern warehouse operations, particularly within robotic handling and automation systems. Progress in this domain has been hindered by the lack of realistic, freely available datasets for rigorous algorithmic benchmarking. This work addresses this gap by introducing MixedPalletBoxes, a family of seven synthetic datasets designed to evaluate algorithm scalability, adaptability and performance variability across a broad spectrum of workload sizes (500–100,000 records) generated via an open source Python script. These datasets enable the assessment of algorithmic behavior under varying operational complexities and scales. Each box instance is richly annotated with geometric dimensions, material properties, load capacities, environmental tolerances and handling flags. To support dynamic experimentation, the dataset is accompanied by a FastAPI-based tool that enables the on-demand creation of randomized daily picking lists simulating realistic inbound orders. Performance is analyzed through metrics such as pallet count, volume utilization, item distribution per pallet and runtime. Across all dataset sizes, the distributions of the physical attributes remain consistent, confirming stable generation behavior. The proposed framework combines standardization, feature richness and scalability, offering a transparent and extensible platform for benchmarking and advancing robotic mixed palletizing solutions. All datasets, generation code and evaluation scripts are publicly released to foster open collaboration and accelerate innovation in data-driven warehouse automation research.

]]> MixedPalletBoxes Dataset: A Synthetic Benchmark Dataset for Warehouse Applications Adamos Daios Ioannis Kostavelis doi: 10.3390/asi9010014 Applied System Innovation 2025-12-29 Applied System Innovation 2025-12-29 9 1 Article 14 10.3390/asi9010014 https://www.mdpi.com/2571-5577/9/1/14 ASI, Vol. 9, Pages 13: Lower Limb Activity Classification with Electromyography and Inertial Measurement Unit Sensors Using a Temporal Convolutional Neural Network on an Experimental Dataset https://www.mdpi.com/2571-5577/9/1/13 Accurate recognition of lower limb activities is essential for wearable rehabilitation systems and assistive robotics like exoskeletons and prosthetics. This study introduces SDALLE, a custom hardware data acquisition system that integrates surface electromyography sensors (EMGs) and inertial measurement sensors (IMUs) into a wireless, portable platform for locomotor monitoring. Using this system, data were collected from nine healthy subjects performing four fundamental locomotor activities: walking, jogging, stair ascent, and stair descent. The recorded signals underwent an offline structured preprocessing pipeline consisting of time-series augmentation (jittering and scaling) to increase data diversity, followed by wavelet-based denoising to suppress high-frequency noise and enhance signal quality. A temporal one-dimensional convolutional neural network (1D-TCNN) with three convolutional blocks and fully connected layers was trained on the prepared dataset to classify the four activities. Classification using IMU sensors achieved the highest performance, with accuracies ranging from 0.81 to 0.95. The gyroscope X-axis of the left Rectus Femoris achieved the best performance (0.95), while accelerometer signals also performed strongly, reaching 0.93 for the Vastus Medialis in the Y direction. In contrast, electromyography channels showed lower discriminative capability. These results demonstrate that the combination of SDALLE hardware, appropriate data preprocessing, and a temporal CNN provides an effective offline sensing and activity classification pipeline for lower limb activity recognition and offers an open-source dataset that supports further research in human activity recognition, rehabilitation, and assistive robotics. 2025-12-28 ASI, Vol. 9, Pages 13: Lower Limb Activity Classification with Electromyography and Inertial Measurement Unit Sensors Using a Temporal Convolutional Neural Network on an Experimental Dataset

Applied System Innovation doi: 10.3390/asi9010013

Authors: Mohamed A. El-Khoreby A. Moawad Hanady H. Issa Shereen I. Fawaz Mohammed I. Awad A. Abdellatif

Accurate recognition of lower limb activities is essential for wearable rehabilitation systems and assistive robotics like exoskeletons and prosthetics. This study introduces SDALLE, a custom hardware data acquisition system that integrates surface electromyography sensors (EMGs) and inertial measurement sensors (IMUs) into a wireless, portable platform for locomotor monitoring. Using this system, data were collected from nine healthy subjects performing four fundamental locomotor activities: walking, jogging, stair ascent, and stair descent. The recorded signals underwent an offline structured preprocessing pipeline consisting of time-series augmentation (jittering and scaling) to increase data diversity, followed by wavelet-based denoising to suppress high-frequency noise and enhance signal quality. A temporal one-dimensional convolutional neural network (1D-TCNN) with three convolutional blocks and fully connected layers was trained on the prepared dataset to classify the four activities. Classification using IMU sensors achieved the highest performance, with accuracies ranging from 0.81 to 0.95. The gyroscope X-axis of the left Rectus Femoris achieved the best performance (0.95), while accelerometer signals also performed strongly, reaching 0.93 for the Vastus Medialis in the Y direction. In contrast, electromyography channels showed lower discriminative capability. These results demonstrate that the combination of SDALLE hardware, appropriate data preprocessing, and a temporal CNN provides an effective offline sensing and activity classification pipeline for lower limb activity recognition and offers an open-source dataset that supports further research in human activity recognition, rehabilitation, and assistive robotics.

]]> Lower Limb Activity Classification with Electromyography and Inertial Measurement Unit Sensors Using a Temporal Convolutional Neural Network on an Experimental Dataset Mohamed A. El-Khoreby A. Moawad Hanady H. Issa Shereen I. Fawaz Mohammed I. Awad A. Abdellatif doi: 10.3390/asi9010013 Applied System Innovation 2025-12-28 Applied System Innovation 2025-12-28 9 1 Article 13 10.3390/asi9010013 https://www.mdpi.com/2571-5577/9/1/13 ASI, Vol. 9, Pages 12: Towards Intelligent Water Safety: Robobuoy, a Deep Learning-Based Drowning Detection and Autonomous Surface Vehicle Rescue System https://www.mdpi.com/2571-5577/9/1/12 Drowning remains the third leading cause of accidental injury-related deaths worldwide, disproportionately affecting low- and middle-income countries where lifeguard coverage is limited or absent. To address this critical gap, we present Robobuoy, an intelligent real-time rescue system that integrates deep learning-based object detection with an unmanned surface vehicle (USV) for autonomous intervention. The system employs a monitoring station equipped with two specialized object detection models: YOLO12m for recognizing drowning individuals and YOLOv5m for tracking the USV. These models were selected for their balance of accuracy, efficiency, and compatibility with resource-constrained edge devices. A geometric navigation algorithm calculates heading directions from visual detections and guides the USV toward the victim. Experimental evaluations on a combined open-source and custom dataset demonstrated strong performance, with YOLO12m achieving an mAP@0.5 of 0.9284 for drowning detection and YOLOv5m achieving an mAP@0.5 of 0.9848 for USV detection. Hardware validation in a controlled water pool confirmed successful target-reaching behavior in all nine trials, achieving a positioning error within 1 m, with traversal times ranging from 11 to 23 s. By combining state-of-the-art computer vision and low-cost autonomous robotics, Robobuoy offers an affordable and low-latency prototype to enhance water safety in unsupervised aquatic environments, particularly in regions where conventional lifeguard surveillance is impractical. 2025-12-28 ASI, Vol. 9, Pages 12: Towards Intelligent Water Safety: Robobuoy, a Deep Learning-Based Drowning Detection and Autonomous Surface Vehicle Rescue System

Applied System Innovation doi: 10.3390/asi9010012

Authors: Krittakom Srijiranon Nanmanat Varisthanist Thanapat Tardtong Chatchadaporn Pumthurean Tanatorn Tanantong

Drowning remains the third leading cause of accidental injury-related deaths worldwide, disproportionately affecting low- and middle-income countries where lifeguard coverage is limited or absent. To address this critical gap, we present Robobuoy, an intelligent real-time rescue system that integrates deep learning-based object detection with an unmanned surface vehicle (USV) for autonomous intervention. The system employs a monitoring station equipped with two specialized object detection models: YOLO12m for recognizing drowning individuals and YOLOv5m for tracking the USV. These models were selected for their balance of accuracy, efficiency, and compatibility with resource-constrained edge devices. A geometric navigation algorithm calculates heading directions from visual detections and guides the USV toward the victim. Experimental evaluations on a combined open-source and custom dataset demonstrated strong performance, with YOLO12m achieving an mAP@0.5 of 0.9284 for drowning detection and YOLOv5m achieving an mAP@0.5 of 0.9848 for USV detection. Hardware validation in a controlled water pool confirmed successful target-reaching behavior in all nine trials, achieving a positioning error within 1 m, with traversal times ranging from 11 to 23 s. By combining state-of-the-art computer vision and low-cost autonomous robotics, Robobuoy offers an affordable and low-latency prototype to enhance water safety in unsupervised aquatic environments, particularly in regions where conventional lifeguard surveillance is impractical.

]]> Towards Intelligent Water Safety: Robobuoy, a Deep Learning-Based Drowning Detection and Autonomous Surface Vehicle Rescue System Krittakom Srijiranon Nanmanat Varisthanist Thanapat Tardtong Chatchadaporn Pumthurean Tanatorn Tanantong doi: 10.3390/asi9010012 Applied System Innovation 2025-12-28 Applied System Innovation 2025-12-28 9 1 Article 12 10.3390/asi9010012 https://www.mdpi.com/2571-5577/9/1/12 ASI, Vol. 9, Pages 11: A Hybrid Human-Centric Framework for Discriminating Engine-like from Human-like Chess Play: A Proof-of-Concept Study https://www.mdpi.com/2571-5577/9/1/11 The rapid growth of online chess has intensified the challenge of distinguishing engine-assisted from authentic human play, exposing the limitations of existing approaches that rely solely on deterministic evaluation metrics. This study introduces a proof-of-concept hybrid framework for discriminating between engine-like and human-like chess play patterns, integrating Stockfish’s deterministic evaluations with stylometric behavioral features derived from the Maia engine. Key metrics include Centipawn Loss (CPL), Mismatch Move Match Probability (MMMP), and a novel Curvature-Based Stability (ΔS) indicator. These features were incorporated into a convolutional neural network (CNN) classifier and evaluated on a controlled benchmark dataset of 1000 games, where ‘suspicious’ gameplay was algorithmically generated to simulate engine-optimal patterns, while ‘clean’ play was modeled using Maia’s human-like predictions. Results demonstrate the framework’s ability to discriminate between these behavioral archetypes, with the hybrid model achieving a macro F1-score of 0.93, significantly outperforming the Stockfish-only baseline (F1 = 0.87), as validated by McNemar’s test (p = 0.0153). Feature ablation confirmed that Maia-derived features reduced false negatives and improved recall, while ΔS enhanced robustness. This work establishes a methodological foundation for behavioral pattern discrimination in chess, demonstrating the value of combining deterministic and human-centric modeling. Beyond chess, the approach offers a template for behavioral anomaly analysis in cybersecurity, education, and other decision-based domains, with real-world validation on adjudicated misconduct cases identified as the essential next step. 2025-12-26 ASI, Vol. 9, Pages 11: A Hybrid Human-Centric Framework for Discriminating Engine-like from Human-like Chess Play: A Proof-of-Concept Study

Applied System Innovation doi: 10.3390/asi9010011

Authors: Zura Kevanishvili Maksim Iavich

The rapid growth of online chess has intensified the challenge of distinguishing engine-assisted from authentic human play, exposing the limitations of existing approaches that rely solely on deterministic evaluation metrics. This study introduces a proof-of-concept hybrid framework for discriminating between engine-like and human-like chess play patterns, integrating Stockfish’s deterministic evaluations with stylometric behavioral features derived from the Maia engine. Key metrics include Centipawn Loss (CPL), Mismatch Move Match Probability (MMMP), and a novel Curvature-Based Stability (ΔS) indicator. These features were incorporated into a convolutional neural network (CNN) classifier and evaluated on a controlled benchmark dataset of 1000 games, where ‘suspicious’ gameplay was algorithmically generated to simulate engine-optimal patterns, while ‘clean’ play was modeled using Maia’s human-like predictions. Results demonstrate the framework’s ability to discriminate between these behavioral archetypes, with the hybrid model achieving a macro F1-score of 0.93, significantly outperforming the Stockfish-only baseline (F1 = 0.87), as validated by McNemar’s test (p = 0.0153). Feature ablation confirmed that Maia-derived features reduced false negatives and improved recall, while ΔS enhanced robustness. This work establishes a methodological foundation for behavioral pattern discrimination in chess, demonstrating the value of combining deterministic and human-centric modeling. Beyond chess, the approach offers a template for behavioral anomaly analysis in cybersecurity, education, and other decision-based domains, with real-world validation on adjudicated misconduct cases identified as the essential next step.

]]> A Hybrid Human-Centric Framework for Discriminating Engine-like from Human-like Chess Play: A Proof-of-Concept Study Zura Kevanishvili Maksim Iavich doi: 10.3390/asi9010011 Applied System Innovation 2025-12-26 Applied System Innovation 2025-12-26 9 1 Article 11 10.3390/asi9010011 https://www.mdpi.com/2571-5577/9/1/11 ASI, Vol. 9, Pages 10: An Artificial Intelligence Enhanced Transfer Graph Framework for Time-Dependent Intermodal Transport Optimization https://www.mdpi.com/2571-5577/9/1/10 In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing. 2025-12-26 ASI, Vol. 9, Pages 10: An Artificial Intelligence Enhanced Transfer Graph Framework for Time-Dependent Intermodal Transport Optimization

Applied System Innovation doi: 10.3390/asi9010010

Authors: Khalid Anbri Mohamed El Moufid Yassine Zahidi Wafaa Dachry Hassan Gziri Hicham Medromi

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing.

]]> An Artificial Intelligence Enhanced Transfer Graph Framework for Time-Dependent Intermodal Transport Optimization Khalid Anbri Mohamed El Moufid Yassine Zahidi Wafaa Dachry Hassan Gziri Hicham Medromi doi: 10.3390/asi9010010 Applied System Innovation 2025-12-26 Applied System Innovation 2025-12-26 9 1 Article 10 10.3390/asi9010010 https://www.mdpi.com/2571-5577/9/1/10 ASI, Vol. 9, Pages 9: A Statistical Method and Deep Learning Models for Detecting Denial of Service Attacks in the Internet of Things (IoT) Environment https://www.mdpi.com/2571-5577/9/1/9 The flourishing of the Internet of Things (IoT) has not only improved our lives in smart homes and healthcare but also made us more susceptible to cyberattacks. Legacy intrusion detection systems are simply overwhelmed by the scale and diversity of IoT traffic, which is why there is a need for more intelligent forensic solutions. In this paper, we present a statistical technique, the Averaging Detection Method (ADM), for detecting attack traffic. Furthermore, the five deep learning models SimpleRNN, LSTM, GRU, BLSTM, and BGRU are compared for malicious traffic detection in IoT network forensics. A smart home dataset with a simulated DoS attack was used for performance analysis of accuracy, precision, recall, F1-score, and training time. The results indicate that all models achieve high accuracy, above 97%. BiGRU achieves the best performance, 99% accuracy, precision, recall, and F1-score, at the cost of high training time. GRU achieves perfect precision and recall (100%) with faster training, which can be considered for resource-constrained scenarios. SimpleRNN trains faster with comparable accuracy, while LSTMs and their bidirectional counterparts are better at capturing long-term dependencies but are computationally more expensive. In summary, deep learning, especially BiGRU and GRU, holds great promise for boosting IoT forensic investigation by enabling real-time DoS detection and reliable evidence collection. Meanwhile, the proposed ADM is simpler and more efficient at classifying DoS traffic than deep learning models. 2025-12-26 ASI, Vol. 9, Pages 9: A Statistical Method and Deep Learning Models for Detecting Denial of Service Attacks in the Internet of Things (IoT) Environment

Applied System Innovation doi: 10.3390/asi9010009

Authors: Ruuhwan Rendy Munadi Hilal Hudan Nuha Erwin Budi Setiawan Niken Dwi Wahyu Cahyani

The flourishing of the Internet of Things (IoT) has not only improved our lives in smart homes and healthcare but also made us more susceptible to cyberattacks. Legacy intrusion detection systems are simply overwhelmed by the scale and diversity of IoT traffic, which is why there is a need for more intelligent forensic solutions. In this paper, we present a statistical technique, the Averaging Detection Method (ADM), for detecting attack traffic. Furthermore, the five deep learning models SimpleRNN, LSTM, GRU, BLSTM, and BGRU are compared for malicious traffic detection in IoT network forensics. A smart home dataset with a simulated DoS attack was used for performance analysis of accuracy, precision, recall, F1-score, and training time. The results indicate that all models achieve high accuracy, above 97%. BiGRU achieves the best performance, 99% accuracy, precision, recall, and F1-score, at the cost of high training time. GRU achieves perfect precision and recall (100%) with faster training, which can be considered for resource-constrained scenarios. SimpleRNN trains faster with comparable accuracy, while LSTMs and their bidirectional counterparts are better at capturing long-term dependencies but are computationally more expensive. In summary, deep learning, especially BiGRU and GRU, holds great promise for boosting IoT forensic investigation by enabling real-time DoS detection and reliable evidence collection. Meanwhile, the proposed ADM is simpler and more efficient at classifying DoS traffic than deep learning models.

]]> A Statistical Method and Deep Learning Models for Detecting Denial of Service Attacks in the Internet of Things (IoT) Environment Ruuhwan Rendy Munadi Hilal Hudan Nuha Erwin Budi Setiawan Niken Dwi Wahyu Cahyani doi: 10.3390/asi9010009 Applied System Innovation 2025-12-26 Applied System Innovation 2025-12-26 9 1 Article 9 10.3390/asi9010009 https://www.mdpi.com/2571-5577/9/1/9 ASI, Vol. 9, Pages 8: Fuzzy Decision Support System for Single-Chamber Ship Lock for Two Vessels https://www.mdpi.com/2571-5577/9/1/8 Ship lock zones represent bottlenecks and a particular challenge for authorities managing vessel traffic. Traditionally, the control strategy of such systems has relied heavily on the subjective judgment, experience, and tacit knowledge of ship lock operators. To address the inherent uncertainty and imprecision associated with these subjective assessments, fuzzy logic and fuzzy set theory have been adopted as appropriate mathematical frameworks. In this work, the control strategy and the Fuzzy Decision Support System (FDSS) of a single-chamber ship lock designed for two vessels on a two-way waterway are analyzed and modeled. The input data is generated based on a synthesized dataset reflecting the annual schedule of vessel arrivals. The software is based on proposals and suggestions of experienced ship lock operators, and it is further validated through vessel traffic simulations. Moreover, the development of an appropriate Supervisory Control and Data Acquisition (SCADA) system integrated with a Programmable Logic Controller (PLC) is detailed, providing the necessary infrastructure for real-time deployment of the fuzzy control algorithm. The proposed control system represents an original contribution and offers practical applications both as a decision-support tool for real-time lock management and as a training platform for novice or less experienced operators. 2025-12-26 ASI, Vol. 9, Pages 8: Fuzzy Decision Support System for Single-Chamber Ship Lock for Two Vessels

Applied System Innovation doi: 10.3390/asi9010008

Authors: Vladimir Bugarski Todor Bačkalić Željko Kanović

Ship lock zones represent bottlenecks and a particular challenge for authorities managing vessel traffic. Traditionally, the control strategy of such systems has relied heavily on the subjective judgment, experience, and tacit knowledge of ship lock operators. To address the inherent uncertainty and imprecision associated with these subjective assessments, fuzzy logic and fuzzy set theory have been adopted as appropriate mathematical frameworks. In this work, the control strategy and the Fuzzy Decision Support System (FDSS) of a single-chamber ship lock designed for two vessels on a two-way waterway are analyzed and modeled. The input data is generated based on a synthesized dataset reflecting the annual schedule of vessel arrivals. The software is based on proposals and suggestions of experienced ship lock operators, and it is further validated through vessel traffic simulations. Moreover, the development of an appropriate Supervisory Control and Data Acquisition (SCADA) system integrated with a Programmable Logic Controller (PLC) is detailed, providing the necessary infrastructure for real-time deployment of the fuzzy control algorithm. The proposed control system represents an original contribution and offers practical applications both as a decision-support tool for real-time lock management and as a training platform for novice or less experienced operators.

]]> Fuzzy Decision Support System for Single-Chamber Ship Lock for Two Vessels Vladimir Bugarski Todor Bačkalić Željko Kanović doi: 10.3390/asi9010008 Applied System Innovation 2025-12-26 Applied System Innovation 2025-12-26 9 1 Article 8 10.3390/asi9010008 https://www.mdpi.com/2571-5577/9/1/8 ASI, Vol. 9, Pages 7: ETA-Hysteresis-Based Reinforcement Learning for Continuous Multi-Target Hunting of Swarm USVs https://www.mdpi.com/2571-5577/9/1/7 Swarm unmanned surface vehicles (USVs) have been increasingly explored for maritime defense and security operations, particularly in scenarios requiring the rapid detection and interception of multiple attackers. The target detection reliability and defender–target assignment stability are significantly crucial to ensure quick responses and prevent mission failure. A key challenge in such missions lies in the assignment of targets among multiple defenders, where frequent reassignment can cause instability and inefficiency. This paper proposes a novel ETA-hysteresis-guided reinforcement learning (RL) framework for continuous multi-target hunting with swarm USVs. The approach integrates estimated time of arrival (ETA)-based task allocation with a dual-threshold hysteresis mechanism to balance responsiveness and stability in multi-target assignments. The ETA module provides an efficient criterion for selecting the most suitable defender–target pair, while hysteresis prevents oscillatory reassignments triggered by marginal changes in ETA values. The framework is trained and evaluated in a 3D-simulated water environment with multiple continuous targets under static and dynamic water environments. Experimental results demonstrate that the proposed method achieves substantial measurable improvements compared to basic MAPPO and MAPPO-LSTM, including faster convergence speed (+20–30%), higher interception rates (improvement of +9.5% to +20.9%), and reduced mean time-to-capture (by 9.4–19.0%), while maintaining competitive path smoothness and energy efficiency. The findings highlight the potential of integrating time-aware assignment strategies with reinforcement learning to enable robust, scalable, and stable swarm USV operations for maritime security applications. 2025-12-25 ASI, Vol. 9, Pages 7: ETA-Hysteresis-Based Reinforcement Learning for Continuous Multi-Target Hunting of Swarm USVs

Applied System Innovation doi: 10.3390/asi9010007

Authors: Nur Hamid Haitham Saleh

Swarm unmanned surface vehicles (USVs) have been increasingly explored for maritime defense and security operations, particularly in scenarios requiring the rapid detection and interception of multiple attackers. The target detection reliability and defender–target assignment stability are significantly crucial to ensure quick responses and prevent mission failure. A key challenge in such missions lies in the assignment of targets among multiple defenders, where frequent reassignment can cause instability and inefficiency. This paper proposes a novel ETA-hysteresis-guided reinforcement learning (RL) framework for continuous multi-target hunting with swarm USVs. The approach integrates estimated time of arrival (ETA)-based task allocation with a dual-threshold hysteresis mechanism to balance responsiveness and stability in multi-target assignments. The ETA module provides an efficient criterion for selecting the most suitable defender–target pair, while hysteresis prevents oscillatory reassignments triggered by marginal changes in ETA values. The framework is trained and evaluated in a 3D-simulated water environment with multiple continuous targets under static and dynamic water environments. Experimental results demonstrate that the proposed method achieves substantial measurable improvements compared to basic MAPPO and MAPPO-LSTM, including faster convergence speed (+20–30%), higher interception rates (improvement of +9.5% to +20.9%), and reduced mean time-to-capture (by 9.4–19.0%), while maintaining competitive path smoothness and energy efficiency. The findings highlight the potential of integrating time-aware assignment strategies with reinforcement learning to enable robust, scalable, and stable swarm USV operations for maritime security applications.

]]> ETA-Hysteresis-Based Reinforcement Learning for Continuous Multi-Target Hunting of Swarm USVs Nur Hamid Haitham Saleh doi: 10.3390/asi9010007 Applied System Innovation 2025-12-25 Applied System Innovation 2025-12-25 9 1 Article 7 10.3390/asi9010007 https://www.mdpi.com/2571-5577/9/1/7 ASI, Vol. 9, Pages 6: Comparative Evaluation of YOLO Models for Human Position Recognition with UAVs During a Flood https://www.mdpi.com/2571-5577/9/1/6 Reliable recognition of people on water from UAV imagery remains a challenging task due to strong glare, wave-induced distortions, partial submersion, and small visual scale of targets. This study proposes a hybrid method for human detection and position recognition in aquatic environments by integrating the YOLO12 object detector with optical-flow-based motion analysis, Kalman tracking, and BlazePose skeletal estimation. A combined training dataset was formed using four complementary sources, enabling the detector to generalize across heterogeneous maritime and flood-like scenes. YOLO12 demonstrated superior performance compared to earlier You Only Look Once (YOLO) generations, achieving the highest accuracy (mAP@0.5 = 0.95) and the lowest error rates on the test set. The hybrid configuration further improved recognition robustness by reducing false positives and partial detections in conditions of intense reflections and dynamic water motion. Real-time experiments on a Raspberry Pi 5 platform confirmed that the full system operates at 21 FPS, supporting onboard deployment for UAV-based search-and-rescue missions. The presented method improves localization reliability, enhances interpretation of human posture and motion, and facilitates prioritization of rescue actions. These findings highlight the practical applicability of YOLO12-based hybrid pipelines for real-time survivor detection in flood response and maritime safety workflows. 2025-12-25 ASI, Vol. 9, Pages 6: Comparative Evaluation of YOLO Models for Human Position Recognition with UAVs During a Flood

Applied System Innovation doi: 10.3390/asi9010006

Authors: Nataliya Bilous Vladyslav Malko Iryna Ahekian Igor Korobiichuk Volodymyr Ivanichev

Reliable recognition of people on water from UAV imagery remains a challenging task due to strong glare, wave-induced distortions, partial submersion, and small visual scale of targets. This study proposes a hybrid method for human detection and position recognition in aquatic environments by integrating the YOLO12 object detector with optical-flow-based motion analysis, Kalman tracking, and BlazePose skeletal estimation. A combined training dataset was formed using four complementary sources, enabling the detector to generalize across heterogeneous maritime and flood-like scenes. YOLO12 demonstrated superior performance compared to earlier You Only Look Once (YOLO) generations, achieving the highest accuracy (mAP@0.5 = 0.95) and the lowest error rates on the test set. The hybrid configuration further improved recognition robustness by reducing false positives and partial detections in conditions of intense reflections and dynamic water motion. Real-time experiments on a Raspberry Pi 5 platform confirmed that the full system operates at 21 FPS, supporting onboard deployment for UAV-based search-and-rescue missions. The presented method improves localization reliability, enhances interpretation of human posture and motion, and facilitates prioritization of rescue actions. These findings highlight the practical applicability of YOLO12-based hybrid pipelines for real-time survivor detection in flood response and maritime safety workflows.

]]> Comparative Evaluation of YOLO Models for Human Position Recognition with UAVs During a Flood Nataliya Bilous Vladyslav Malko Iryna Ahekian Igor Korobiichuk Volodymyr Ivanichev doi: 10.3390/asi9010006 Applied System Innovation 2025-12-25 Applied System Innovation 2025-12-25 9 1 Article 6 10.3390/asi9010006 https://www.mdpi.com/2571-5577/9/1/6 ASI, Vol. 9, Pages 5: An Intelligent Support Method for the Formation of Control Actions in Proactive Management of Complex Systems https://www.mdpi.com/2571-5577/9/1/5 This paper addresses the problem of ensuring the continuous operation of cyber–physical systems (CPS) under conditions of component degradation and wear. To achieve this goal, a transition to the concept of Proactive Prognostics and Health Management (PPHM) is proposed, focused on proactive control of the technical condition of equipment. A key stage of PPHM is the generation of control actions aimed at extending the remaining useful life by adapting the operational parameters of the system. This paper proposes an intelligent support method for generating control actions to optimize the operational conditions. The proposed method integrates an RUL prediction model with optimization procedures based on genetic algorithm. The method was experimentally validated using XJTU-SY Bearing test rig and a bearing-degradation dataset. The obtained results demonstrate its effectiveness and confirm its applicability for extending the service life of technical systems. The proposed method is general and can be adapted to any CPS where controllable parameters affect the degradation rate 2025-12-25 ASI, Vol. 9, Pages 5: An Intelligent Support Method for the Formation of Control Actions in Proactive Management of Complex Systems

Applied System Innovation doi: 10.3390/asi9010005

Authors: Vladimir Artyushin Kirill Dereguzov Maxim Shcherbakov Konstantin Zadiran Alla Kravets

This paper addresses the problem of ensuring the continuous operation of cyber–physical systems (CPS) under conditions of component degradation and wear. To achieve this goal, a transition to the concept of Proactive Prognostics and Health Management (PPHM) is proposed, focused on proactive control of the technical condition of equipment. A key stage of PPHM is the generation of control actions aimed at extending the remaining useful life by adapting the operational parameters of the system. This paper proposes an intelligent support method for generating control actions to optimize the operational conditions. The proposed method integrates an RUL prediction model with optimization procedures based on genetic algorithm. The method was experimentally validated using XJTU-SY Bearing test rig and a bearing-degradation dataset. The obtained results demonstrate its effectiveness and confirm its applicability for extending the service life of technical systems. The proposed method is general and can be adapted to any CPS where controllable parameters affect the degradation rate

]]> An Intelligent Support Method for the Formation of Control Actions in Proactive Management of Complex Systems Vladimir Artyushin Kirill Dereguzov Maxim Shcherbakov Konstantin Zadiran Alla Kravets doi: 10.3390/asi9010005 Applied System Innovation 2025-12-25 Applied System Innovation 2025-12-25 9 1 Article 5 10.3390/asi9010005 https://www.mdpi.com/2571-5577/9/1/5 ASI, Vol. 9, Pages 4: Event-Triggered Fuzzy-Networked Control System for a 3-DOF Quadcopter with Limited-Bandwidth Communication https://www.mdpi.com/2571-5577/9/1/4 Quadcopters are attracting widespread attention due to their growing demand for use in various applications. Since wired communication would severely restrict a quadcopter’s range, maneuverability, and applications, quadcopters usually communicate via wireless networks. Although wireless communication allows the freedom of movement necessary for a wide array of quadcopter applications, it is subject to bandwidth constraints. When multiple quadcopters operate simultaneously, the bandwidth of a wireless network will not meet the requirements. To address this issue, we propose an event-triggered fuzzy-networked control system for 3-DOF quadcopters that reduces the bandwidth requirement. We utilized a fuzzy-networked controller to control a 3-DOF quadcopter. After that, we adopted an event-triggered control approach to reduce the bandwidth requirement. Using the proposed method, one only needs to translate the signals while the event-triggering condition is satisfied, thus reducing the amount of data transmitted over the network. Also, to analyze the stability of the overall system, the Lyapunov stability theorem was adopted. Finally, the proposed method was validated through a 3-DOF quadcopter simulation model. The computer simulations are presented to demonstrate that the proposed control strategy enables a 75.2% (without external disturbance) reduction in bandwidth, which is sufficient to achieve the control objective. This reflects the fact that the proposed control scheme can achieve good control performance with relatively little bandwidth resources and indicates its potential to allow scalable deployment of UAVs. 2025-12-22 ASI, Vol. 9, Pages 4: Event-Triggered Fuzzy-Networked Control System for a 3-DOF Quadcopter with Limited-Bandwidth Communication

Applied System Innovation doi: 10.3390/asi9010004

Authors: Ti-Hung Chen

Quadcopters are attracting widespread attention due to their growing demand for use in various applications. Since wired communication would severely restrict a quadcopter’s range, maneuverability, and applications, quadcopters usually communicate via wireless networks. Although wireless communication allows the freedom of movement necessary for a wide array of quadcopter applications, it is subject to bandwidth constraints. When multiple quadcopters operate simultaneously, the bandwidth of a wireless network will not meet the requirements. To address this issue, we propose an event-triggered fuzzy-networked control system for 3-DOF quadcopters that reduces the bandwidth requirement. We utilized a fuzzy-networked controller to control a 3-DOF quadcopter. After that, we adopted an event-triggered control approach to reduce the bandwidth requirement. Using the proposed method, one only needs to translate the signals while the event-triggering condition is satisfied, thus reducing the amount of data transmitted over the network. Also, to analyze the stability of the overall system, the Lyapunov stability theorem was adopted. Finally, the proposed method was validated through a 3-DOF quadcopter simulation model. The computer simulations are presented to demonstrate that the proposed control strategy enables a 75.2% (without external disturbance) reduction in bandwidth, which is sufficient to achieve the control objective. This reflects the fact that the proposed control scheme can achieve good control performance with relatively little bandwidth resources and indicates its potential to allow scalable deployment of UAVs.

]]> Event-Triggered Fuzzy-Networked Control System for a 3-DOF Quadcopter with Limited-Bandwidth Communication Ti-Hung Chen doi: 10.3390/asi9010004 Applied System Innovation 2025-12-22 Applied System Innovation 2025-12-22 9 1 Article 4 10.3390/asi9010004 https://www.mdpi.com/2571-5577/9/1/4 ASI, Vol. 9, Pages 3: A System-Level Approach to Pixel-Based Crop Segmentation from Ultra-High-Resolution UAV Imagery https://www.mdpi.com/2571-5577/9/1/3 This paper proposed a two-level hybrid stacking model for the classification of crops—wheat, soybean, and barley—based on multispectral orthomosaics obtained from uncrewed aerial vehicles. The proposed method unites gradient boosting algorithms (LightGBM, XGBoost, CatBoost) and tree ensembles (RandomForest, ExtraTrees, Attention-MLP deep neural network), whose predictions fuse at the meta-level using ExtraTreesClassifier. Spectral channels, along with a wide range of vegetation indices and their statistical characteristics, are used to construct the feature space. Experiments on an open dataset showed that the proposed model achieves high classification accuracy (Accuracy ≈ 95%, macro-F1 ≈ 0.95) and significantly outperforms individual algorithms across all key metrics. An analysis of the seasonal dynamics of vegetation indices confirmed the feasibility of monitoring phenological phases and early detection of stress factors. Furthermore, spatial segmentation of orthomosaics achieved approximately 99% accuracy in constructing crop maps, making the developed approach a promising tool for precision farming. The study’s results showed the high potential of hybrid ensembles for scaling to other crops and regions, as well as for integrating them into digital agricultural information systems. 2025-12-22 ASI, Vol. 9, Pages 3: A System-Level Approach to Pixel-Based Crop Segmentation from Ultra-High-Resolution UAV Imagery

Applied System Innovation doi: 10.3390/asi9010003

Authors: Aisulu Ismailova Moldir Yessenova Gulden Murzabekova Jamalbek Tussupov Gulzira Abdikerimova

This paper proposed a two-level hybrid stacking model for the classification of crops—wheat, soybean, and barley—based on multispectral orthomosaics obtained from uncrewed aerial vehicles. The proposed method unites gradient boosting algorithms (LightGBM, XGBoost, CatBoost) and tree ensembles (RandomForest, ExtraTrees, Attention-MLP deep neural network), whose predictions fuse at the meta-level using ExtraTreesClassifier. Spectral channels, along with a wide range of vegetation indices and their statistical characteristics, are used to construct the feature space. Experiments on an open dataset showed that the proposed model achieves high classification accuracy (Accuracy ≈ 95%, macro-F1 ≈ 0.95) and significantly outperforms individual algorithms across all key metrics. An analysis of the seasonal dynamics of vegetation indices confirmed the feasibility of monitoring phenological phases and early detection of stress factors. Furthermore, spatial segmentation of orthomosaics achieved approximately 99% accuracy in constructing crop maps, making the developed approach a promising tool for precision farming. The study’s results showed the high potential of hybrid ensembles for scaling to other crops and regions, as well as for integrating them into digital agricultural information systems.

]]> A System-Level Approach to Pixel-Based Crop Segmentation from Ultra-High-Resolution UAV Imagery Aisulu Ismailova Moldir Yessenova Gulden Murzabekova Jamalbek Tussupov Gulzira Abdikerimova doi: 10.3390/asi9010003 Applied System Innovation 2025-12-22 Applied System Innovation 2025-12-22 9 1 Article 3 10.3390/asi9010003 https://www.mdpi.com/2571-5577/9/1/3 ASI, Vol. 9, Pages 2: Object-Centric Process Mining Framework for Industrial Safety and Quality Validation Using Support Vector Machines https://www.mdpi.com/2571-5577/9/1/2 Ensuring reliable inspection and quality control in complex industrial settings remains a significant challenge, particularly when traditional manual methods are applied to dynamic, multi-object environments. This paper presents and validates a new hybrid framework that integrates Object-Centric Process Mining (OCPM) with Support Vector Machines (SVMs) to improve industrial safety and quality assurance. The aims are: (1) to uncover and model the complex, multi-object processes characteristic of modern manufacturing using OCPM; (2) to assess these models in terms of conformance, performance, and the detection of bottlenecks; and (3) to design and embed a predictive layer based on Support Vector Regression (SVR) to anticipate process outcomes and support proactive control.The proposed methodology comprises a comprehensive pipeline: data fusion and OCEL structuring, OCPM for process discovery and conformance analysis, feature engineering, SVR for predictive modeling, and a multi-objective optimization layer. By applying this framework to a timber sawmill dataset, the study successfully modeled complex lumber drying operations, identified key object interactions, achieving a process conformance fitness score of 0.6905, and testing the integration of a predictive SVR layer. The SVR model’s predictive accuracy for production yield was found to be limited (R2=0.0255) with the current feature set, highlighting the challenges of predictive modeling in this complex, multi-object domain. Despite this predictive limitation, the multi-objective optimization effectively balanced defect rates, energy consumption, and process delays, yielding a mean objective function value of 0.0768. These findings demonstrate the framework’s capability to provide deep, object-centric process insights and support data-driven decision-making for operational improvements in Industry 4.0. Future research will focus on improving predictive model performance through advanced feature engineering and exploring diverse machine learning techniques. 2025-12-22 ASI, Vol. 9, Pages 2: Object-Centric Process Mining Framework for Industrial Safety and Quality Validation Using Support Vector Machines

Applied System Innovation doi: 10.3390/asi9010002

Authors: Michael Maiko Matonya István Budai

Ensuring reliable inspection and quality control in complex industrial settings remains a significant challenge, particularly when traditional manual methods are applied to dynamic, multi-object environments. This paper presents and validates a new hybrid framework that integrates Object-Centric Process Mining (OCPM) with Support Vector Machines (SVMs) to improve industrial safety and quality assurance. The aims are: (1) to uncover and model the complex, multi-object processes characteristic of modern manufacturing using OCPM; (2) to assess these models in terms of conformance, performance, and the detection of bottlenecks; and (3) to design and embed a predictive layer based on Support Vector Regression (SVR) to anticipate process outcomes and support proactive control.The proposed methodology comprises a comprehensive pipeline: data fusion and OCEL structuring, OCPM for process discovery and conformance analysis, feature engineering, SVR for predictive modeling, and a multi-objective optimization layer. By applying this framework to a timber sawmill dataset, the study successfully modeled complex lumber drying operations, identified key object interactions, achieving a process conformance fitness score of 0.6905, and testing the integration of a predictive SVR layer. The SVR model’s predictive accuracy for production yield was found to be limited (R2=0.0255) with the current feature set, highlighting the challenges of predictive modeling in this complex, multi-object domain. Despite this predictive limitation, the multi-objective optimization effectively balanced defect rates, energy consumption, and process delays, yielding a mean objective function value of 0.0768. These findings demonstrate the framework’s capability to provide deep, object-centric process insights and support data-driven decision-making for operational improvements in Industry 4.0. Future research will focus on improving predictive model performance through advanced feature engineering and exploring diverse machine learning techniques.

]]> Object-Centric Process Mining Framework for Industrial Safety and Quality Validation Using Support Vector Machines Michael Maiko Matonya István Budai doi: 10.3390/asi9010002 Applied System Innovation 2025-12-22 Applied System Innovation 2025-12-22 9 1 Article 2 10.3390/asi9010002 https://www.mdpi.com/2571-5577/9/1/2 ASI, Vol. 9, Pages 1: Reviews of the Static, Adoptive, and Dynamic Sampling in Wafer Manufacturing https://www.mdpi.com/2571-5577/9/1/1 Semiconductor wafer manufacturing is one of the most complex and data-intensive processes in the industry, encompassing the front-end (FEOL), middle-end (MOL), and back-end (BEOL) stages, involving thousands of interdependent processes. Each stage can introduce potential variability, thereby reducing yield, making metrology and inspection crucial for process control. However, due to capacity, cost, and destructive testing constraints, exhaustive metrology for every wafer or die is impractical. Therefore, this study aims to introduce sampling strategies that have evolved to balance the accuracy, risk, and efficiency of measurement allocation. This review presents a literature review of static, adaptive, and dynamic sampling and discusses recent intelligent sampling techniques. The results show that traditional static sampling provides fixed, rule-based inspection schemes that ensure comparability and compliance but lack responsiveness to process variations. Adaptive sampling introduces flexibility, allowing measurement density to be adjusted based on detected drift, anomalies, or statistical control limits. Building on this, dynamic sampling represents a paradigm shift towards predictive, real-time decision-making driven by machine learning, risk analysis, and digital twin integration. The dynamic framework continuously assesses process uncertainties and prioritizes metrology to maximize information gain, thereby significantly reducing metrology workload without impacting yield or quality. Static, adaptive, and dynamic sampling together constitute a continuous evolution from deterministic control to self-optimizing intelligence. As semiconductor nodes move towards sub-3 nm, this intelligent sampling technology is crucial for maintaining yield, cost competitiveness, and process flexibility in autonomous, data-centric wafer fabs. 2025-12-19 ASI, Vol. 9, Pages 1: Reviews of the Static, Adoptive, and Dynamic Sampling in Wafer Manufacturing

Applied System Innovation doi: 10.3390/asi9010001

Authors: Hsuan-Yu Chen Chiachung Chen

Semiconductor wafer manufacturing is one of the most complex and data-intensive processes in the industry, encompassing the front-end (FEOL), middle-end (MOL), and back-end (BEOL) stages, involving thousands of interdependent processes. Each stage can introduce potential variability, thereby reducing yield, making metrology and inspection crucial for process control. However, due to capacity, cost, and destructive testing constraints, exhaustive metrology for every wafer or die is impractical. Therefore, this study aims to introduce sampling strategies that have evolved to balance the accuracy, risk, and efficiency of measurement allocation. This review presents a literature review of static, adaptive, and dynamic sampling and discusses recent intelligent sampling techniques. The results show that traditional static sampling provides fixed, rule-based inspection schemes that ensure comparability and compliance but lack responsiveness to process variations. Adaptive sampling introduces flexibility, allowing measurement density to be adjusted based on detected drift, anomalies, or statistical control limits. Building on this, dynamic sampling represents a paradigm shift towards predictive, real-time decision-making driven by machine learning, risk analysis, and digital twin integration. The dynamic framework continuously assesses process uncertainties and prioritizes metrology to maximize information gain, thereby significantly reducing metrology workload without impacting yield or quality. Static, adaptive, and dynamic sampling together constitute a continuous evolution from deterministic control to self-optimizing intelligence. As semiconductor nodes move towards sub-3 nm, this intelligent sampling technology is crucial for maintaining yield, cost competitiveness, and process flexibility in autonomous, data-centric wafer fabs.

]]> Reviews of the Static, Adoptive, and Dynamic Sampling in Wafer Manufacturing Hsuan-Yu Chen Chiachung Chen doi: 10.3390/asi9010001 Applied System Innovation 2025-12-19 Applied System Innovation 2025-12-19 9 1 Review 1 10.3390/asi9010001 https://www.mdpi.com/2571-5577/9/1/1 ASI, Vol. 8, Pages 191: Optimization of Crowbar Resistance for Enhanced LVRT Capability in Wind Turbine Doubly Fed Induction Generator https://www.mdpi.com/2571-5577/8/6/191 Recently, the installed generation capacity of wind energy has expanded significantly, and the doubly fed induction generator (DFIG) has gained a prominent position amongst wind generators owing to its superior performance. It is extremely vital to enhance the low-voltage ride-through (LVRT) capability for the wind turbine DFIG system because the DFIG is very sensitive to faults in the electrical grid. The major concept of LVRT is to keep the DFIG connected to the electrical grid in the case of an occurrence of grid voltage sags. The currents of rotor and DC-bus voltage rise during voltage dips, resulting in damage to the power electronic converters and the windings of the rotor. There are many protection approaches that deal with LVRT capability for the wind turbine DFIG system. A popular approach for DFIG protection is the crowbar technique. The resistance of the crowbar must be precisely chosen owing to its impact on both the currents of the rotor and DC-bus voltage, while also ensuring that the rotor speed does not exceed its maximum limit. Therefore, this paper aims to obtain the optimal values of crowbar resistance to minimize the crowbar energy losses and ensure stable DFIG operation during grid voltage dips. A recent optimization technique, the Starfish Optimization (SFO) algorithm, was used for cropping the optimal crowbar resistance for improving LVRT capability. To validate the accuracy of the results, the SFO results were compared to the well-known optimization algorithm, particle swarm optimizer (PSO). The performance of the wind turbine DFIG system was investigated by using Matlab/Simulink at a rated wind speed of 13 m/s. The results demonstrated that the increases in DC-link voltage and rotor speed were reduced by 42.5% and 45.8%, respectively. 2025-12-16 ASI, Vol. 8, Pages 191: Optimization of Crowbar Resistance for Enhanced LVRT Capability in Wind Turbine Doubly Fed Induction Generator

Applied System Innovation doi: 10.3390/asi8060191

Authors: Mahmoud M. Elkholy M. Abdelateef Mostafa

Recently, the installed generation capacity of wind energy has expanded significantly, and the doubly fed induction generator (DFIG) has gained a prominent position amongst wind generators owing to its superior performance. It is extremely vital to enhance the low-voltage ride-through (LVRT) capability for the wind turbine DFIG system because the DFIG is very sensitive to faults in the electrical grid. The major concept of LVRT is to keep the DFIG connected to the electrical grid in the case of an occurrence of grid voltage sags. The currents of rotor and DC-bus voltage rise during voltage dips, resulting in damage to the power electronic converters and the windings of the rotor. There are many protection approaches that deal with LVRT capability for the wind turbine DFIG system. A popular approach for DFIG protection is the crowbar technique. The resistance of the crowbar must be precisely chosen owing to its impact on both the currents of the rotor and DC-bus voltage, while also ensuring that the rotor speed does not exceed its maximum limit. Therefore, this paper aims to obtain the optimal values of crowbar resistance to minimize the crowbar energy losses and ensure stable DFIG operation during grid voltage dips. A recent optimization technique, the Starfish Optimization (SFO) algorithm, was used for cropping the optimal crowbar resistance for improving LVRT capability. To validate the accuracy of the results, the SFO results were compared to the well-known optimization algorithm, particle swarm optimizer (PSO). The performance of the wind turbine DFIG system was investigated by using Matlab/Simulink at a rated wind speed of 13 m/s. The results demonstrated that the increases in DC-link voltage and rotor speed were reduced by 42.5% and 45.8%, respectively.

]]> Optimization of Crowbar Resistance for Enhanced LVRT Capability in Wind Turbine Doubly Fed Induction Generator Mahmoud M. Elkholy M. Abdelateef Mostafa doi: 10.3390/asi8060191 Applied System Innovation 2025-12-16 Applied System Innovation 2025-12-16 8 6 Article 191 10.3390/asi8060191 https://www.mdpi.com/2571-5577/8/6/191