Applied System Innovation

30 pages, 7541 KB

Open AccessArticle

Spatiotemporal Ergonomic Fatigue Analysis in Seated Postures Using a Multimodal Smart-Skin System: A Comparative Study Between Mannequin and Human Measurements

by Giva Andriana Mutiara, Muhammad Rizqy Alfarisi, Paramita Mayadewi, Lisda Meisaroh and Periyadi

Appl. Syst. Innov. 2026, 9(4), 67; https://doi.org/10.3390/asi9040067 - 24 Mar 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Human-Computer Interaction)

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16 pages, 1577 KB

Open AccessArticle

Signal Processing Techniques for Enhancing an Areal Density in Two-Reader/Three-Track Detection of Staggered Bit-Patterned Magnetic Recording Systems

by Natthakan Rueangnetr, Satra Tor. Wattanaphol, Kittipon Kankhunthod, Simon J. Greaves and Chanon Warisarn

Appl. Syst. Innov. 2026, 9(3), 66; https://doi.org/10.3390/asi9030066 - 23 Mar 2026

Abstract

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 [...] Read more.

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. Full article

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13 pages, 3099 KB

Open AccessArticle

Modular Linear Fresnel Solar Concentrator for Integrated Photovoltaic Thermal Energy Systems: A Comprehensive Design and Numerical Analysis

by Juan Carlos Castro-Dominguez, Oscar Alejandro López-Núñez, Jorge O. Aguilar, Karla G. Cedano-Villavicencio and Oscar A. Jaramillo

Appl. Syst. Innov. 2026, 9(3), 65; https://doi.org/10.3390/asi9030065 - 23 Mar 2026

Abstract

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 [...] Read more.

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%. Full article

(This article belongs to the Section Industrial and Manufacturing Engineering)

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18 pages, 1956 KB

Open AccessArticle

Integration of AI Content Generation-Enabled Virtual Museums into University History Education

by Shirong Tan, Yuchun Liu and Lei Wang

Appl. Syst. Innov. 2026, 9(3), 64; https://doi.org/10.3390/asi9030064 - 18 Mar 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Topic Social Sciences and Intelligence Management, 2nd Volume)

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48 pages, 6279 KB

Open AccessArticle

Data-Driven Fleet Optimization Using ML Algorithms and a Decision-Making Grid Framework

by Ashraf Labib, Coralia Tǎnǎsuicǎ (Zotic), Turuna S. Seecharan and Mihai-Daniel Roman

Appl. Syst. Innov. 2026, 9(3), 63; https://doi.org/10.3390/asi9030063 - 17 Mar 2026

Abstract

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, [...] Read more.

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. Full article

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21 pages, 916 KB

Open AccessArticle

EKA—Enterprise Knowledge Assistant: Collaborative Multi-Agent AI for Large Claims Handling

by Alberto Loffredo, Yunting Liu, Zhengdao Chen, Yifei Fu, Joerg Ahrens, Yifeng Lu and Dong Chen

Appl. Syst. Innov. 2026, 9(3), 62; https://doi.org/10.3390/asi9030062 - 17 Mar 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Collaborative and Intelligent Multi-Agent Systems for Real-World Applications)

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23 pages, 376 KB

Open AccessArticle

INTELLECTUM: A Hybrid AR-VR Metaverse Framework for Smart Cities

by Andrey Nechesov and Janne Ruponen

Appl. Syst. Innov. 2026, 9(3), 61; https://doi.org/10.3390/asi9030061 - 17 Mar 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Information Industry and Intelligence Innovation)

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30 pages, 6483 KB

Open AccessArticle

Design of the Electric Power Control System for a Hydrogen-Fed AEMFC Polymeric Fuel Cell Generator to Power a 0.75 KW DC Motor

by Mario Alejandro Benavides Álvarez, Fredy E. Hoyos and John E. Candelo-Becerra

Appl. Syst. Innov. 2026, 9(3), 60; https://doi.org/10.3390/asi9030060 - 16 Mar 2026

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Topic Collection Series on Applied System Innovation)

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25 pages, 4085 KB

Open AccessArticle

Load Frequency Control in Multi-Area Power Systems Using Incremental Proportional–Integral–Derivative and Model-Free Adaptive Control

by Md Asif Shaharear, Chengyu Zhou, Shahin Shaikh and Md Mehedy Hasan Faruk

Appl. Syst. Innov. 2026, 9(3), 59; https://doi.org/10.3390/asi9030059 - 16 Mar 2026

Abstract

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 [...] Read more.

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

\pm 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 (

I A E_{t o t a l}

) 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. Full article

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29 pages, 9782 KB

Open AccessArticle

Automated Real-Time Detection and Correction of Children’s Kinesthetic Learning Using Expert-User Performance and Smartphones as Wearables

by Carla Gómez-Monroy, Alejandro C. Ramírez-Reivich, Vicente Borja, José Luis Jimenez-Corona and Victor Gonzalez

Appl. Syst. Innov. 2026, 9(3), 58; https://doi.org/10.3390/asi9030058 - 12 Mar 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Applied Systems on Educational Innovations and Emerging Technologies)

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22 pages, 2888 KB

Open AccessArticle

Bayesian Hyperparameter Optimization of GRU and LSTM Models for Short-Term Traffic Flow Prediction: A Case Study of Globe Roundabout in Saudi Arabia

by Sara Atef, Siraj Zahran and Ahmed Karam

Appl. Syst. Innov. 2026, 9(3), 57; https://doi.org/10.3390/asi9030057 - 10 Mar 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Mathematical Models and Computational Intelligence for Transportation System Planning and Management)

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29 pages, 2407 KB

Open AccessArticle

Evaluating Maintainable Asset Criticality in Production Systems via a Network-Level, Consequence-Based Profitability Framework Enabled by Complex Repairable Flow Network Simulation

by Nicholas Kaliszewski, Romeo Marian and Javaan Chahl

Appl. Syst. Innov. 2026, 9(3), 56; https://doi.org/10.3390/asi9030056 - 6 Mar 2026

Abstract

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 [...] Read more.

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. Full article

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42 pages, 2328 KB

Open AccessReview

Artificial Neural Network Applications in Supply Chain Management: A Literature Review and Classification

by Iman Ghalehkhondabi

Appl. Syst. Innov. 2026, 9(3), 55; https://doi.org/10.3390/asi9030055 - 28 Feb 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Industrial and Manufacturing Engineering)

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61 pages, 5584 KB

Open AccessArticle

Mechatronic Reference Model for Innovation: Connecting Complex Design to Business Issues Through the Concepts of Cycles and Revisions

by Sanderson Barbalho and Mariannys Rodríguez-Gasca

Appl. Syst. Innov. 2026, 9(3), 54; https://doi.org/10.3390/asi9030054 - 28 Feb 2026

Abstract

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 [...] Read more.

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. Full article

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15 pages, 743 KB

Open AccessSystematic Review

Systematized Literature Review: Model-Based Test Case Generation for Requirements Verification at the Subsystem Level

by Jana Wendt, Umut Volkan Kizgin, Dirk Clasen and Thomas Vietor

Appl. Syst. Innov. 2026, 9(3), 53; https://doi.org/10.3390/asi9030053 - 27 Feb 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Control and Systems Engineering)

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23 pages, 772 KB

Open AccessArticle

Leveraging Machine Learning to Evaluate the ESG Performance of Listed and OTC Firms in a Small Open Economy

by Hui-Juan Xiao, Tsung-Nan Chou, Jian-Fa Li and Kuei-Kuei Lai

Appl. Syst. Innov. 2026, 9(3), 52; https://doi.org/10.3390/asi9030052 - 27 Feb 2026

Abstract

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 [...] Read more.

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. Full article

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20 pages, 2989 KB

Open AccessArticle

ZernikeViewer: An Open-Source Framework for Fast Simulation and Real-Time Reconstruction of Phase, Fringe, and PSF Maps

by Ilya Galaktionov

Appl. Syst. Innov. 2026, 9(3), 51; https://doi.org/10.3390/asi9030051 - 26 Feb 2026

Abstract

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 [...] Read more.

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. Full article

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22 pages, 5548 KB

Open AccessArticle

Predictive Thermal Management for Dual PWM Fans in High-Power Audio Amplifiers

by Andrei Militaru, Emanuel-Valentin Buica and Horia Andrei

Appl. Syst. Innov. 2026, 9(3), 50; https://doi.org/10.3390/asi9030050 - 26 Feb 2026

Abstract

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 [...] Read more.

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. Full article

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20 pages, 4579 KB

Open AccessArticle

Explainable Hybrid CNN–XGBoost Framework for Multi-Class IoT Intrusion Detection with Leakage-Aware Feature Selection

by Deemah AlFuraih, Lotfi Mhamdi and Abdullah S. Karar

Appl. Syst. Innov. 2026, 9(3), 49; https://doi.org/10.3390/asi9030049 - 26 Feb 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Artificial Intelligence)

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Applied System Innovation

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