Appl. Syst. Innov., Volume 9, Issue 5 (May 2026) – 22 articles

While the use of artificial intelligence (AI) in higher education is widespread, students’ experiences with AI-supported learning in their regular courses remain underexplored. Objective: This research examines the relationships among perceived usefulness, perceived ease of use, and academic engagement among undergraduate students enrolled in AI-supported courses at a Taiwan university. It adopts the Technology Acceptance Model, where learning desire indicates perceived usefulness, and technology self-efficacy indicates perceived ease of use. Methods: The study takes a questionnaire with six dimensions of technology self-efficacy, learning desire, learning methods, learning planning, learning habits, and learning process to evaluate students’ attitudes toward AI-supported learning and their academic engagement. Results: Students’ attitudes toward AI-supported learning were moderate to positive. Multiple regression analysis showed that perceived usefulness was significantly and positively associated with academic engagement, whereas perceived ease of use showed a positive but non-significant association. Implications: Students’ academic engagement is influenced more by how useful AI tools are perceived for learning, rather than by their confidence in using AI tools. This paper enriches the literature on student-centered AI in higher education and gives insights for designing AI-supported courses that integrate AI tools with meaningful learning tasks. Future research can examine larger and more diverse samples and use longitudinal or experimental designs to test how students’ perceptions of AI tools develop over time. Full article

With the rapid expansion of the smart clothing market, designers face increasing pressure to balance functional performance, material suitability, environmental impact, and development efficiency. Conventional design workflows and rule-based assistance methods often struggle to provide adaptive and data-driven support for multi-constraint decision-making. To address this issue, this study proposes an AI-driven decision support system for sustainable smart clothing design based on a multi-scale dynamic graph convolutional network (MDGCN). The proposed system integrates material properties, environmental indicators, and user-oriented design requirements into a unified decision-support framework and further enhances feature extraction through an attention mechanism. Two datasets, the Wearable Technology Material Properties Dataset (WTMPD) and the Environmental Impact Assessment Dataset (EIAD), were used to validate the model and system effectiveness. Experimental results showed that the MDGCN-based model achieved accuracies of 0.964 and 0.943, with recalls of 0.923 and 0.920 on the WTMPD and EIAD datasets, respectively. In system-level evaluation, the proposed decision support system reduced design time from 120 h to 60 h, improved material selection accuracy to 90.2%, and achieved superior operational performance in terms of resource utilization (77.45%), energy consumption (115.25 kWh), and response time (1.56 s). These results demonstrate that the proposed framework can effectively support complex design decision-making while improving efficiency, sustainability, and adaptability in smart clothing development. The study provides a practical AI-enabled system innovation approach for sustainable smart clothing design by linking flexible material selection, environmental impact prediction, and designer-oriented decision support. In addition, the prototype deployment demonstrates the feasibility of applying the proposed system as a design-stage wearable AI tool for mediating human, technological, and environmental considerations in smart clothing development. Full article

Sentiment analysis has become increasingly important across various domains, particularly in business intelligence, where it is crucial for improving the performance of companies by identifying the sentiments and emotions expressed in customer feedback on products and services. Despite its growing relevance, sentiment analysis still faces several challenges, including class imbalance in datasets, limitations in feature extraction techniques, and the selection of appropriate classification models. Effectively addressing these challenges requires the integration of robust representation methods, reliable data balancing strategies, and efficient classification frameworks. In this study, we propose a novel sentiment analysis approach that combines SBERT for contextual feature extraction, WGAN-based synthetic data generation for addressing class imbalance, and a soft voting ensemble classifier for improved prediction. The proposed approach is evaluated on five datasets, including two English datasets and three Arabic datasets, in order to assess its performance in a multilingual setting. We compare the effectiveness of the proposed model with several baseline machine learning classifiers, as well as with commonly used data balancing techniques such as the synthetic minority over-sampling technique (SMOTE) and adaptive synthetic (ADASYN). The evaluation is conducted using multiple performance metrics, including accuracy, precision, recall, F1-score, MCC, ROC–AUC and training and inference time, along with different validation strategies including fixed train–test splits and k-fold cross-validation. The experimental results demonstrate the effectiveness and stability of the proposed approach. In particular, they highlight the importance of capturing sentence-level contextual representations and generating realistic synthetic samples to address class imbalance. Full article

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision. Full article

SSH honeypots serve as critical infrastructure for cyber threat intelligence, but existing LLM-based systems suffer from context window limitations causing state loss and hallucination-driven inconsistencies, making them easily detectable through simple verification tests. To address these limitations, we propose LLM-SSHH, a framework combining explicit state management with LLM generation to achieve long-term interaction consistency. The system maintains a persistent state snapshot organized as a three-component tuple capturing file system state, runtime context, and system metadata. The framework serializes the current state into LLM prompts and validates generated responses against state constraints to reject hallucinations. Validated responses update the state snapshot, forming a closed loop that ensures consistent state evolution throughout extended interactions. Experimental results demonstrate that LLM-SSHH achieves a mean detection rate of 0.150, representing a 3 to 4 times improvement over existing methods, significantly extending honeypot survivability for threat intelligence collection. Full article

Hand gesture recognition (HGR) is a critical area in computer vision that supports intuitive human–computer interaction and sign language communication, yet existing systems remain sensitive to lighting variations, background clutter, and diverse hand postures. This study introduces two contributions to address these limitations: a Gradient-Based Augmentation Validation (GBAV) framework that establishes structurally safe augmentation ranges before training, and a multi-backbone Convolutional Neural Network (CNN) architecture combining ResNet50 and InceptionV3 with optional attention-based pooling. GBAV uses magnitude-weighted gradient orientation histograms with Pearson correlation and Kullback–Leibler divergence thresholds to verify label invariance under spatial transformations, providing a classifier-agnostic pre-training calibration mechanism. The proposed framework is evaluated on three static gesture datasets, Indonesian Sign Language (BISINDO), American Sign Language (ASL), and Hand Gesture 14 (HG14), yielding validation accuracies of 96.87%, 99.92%, and 95.25%, respectively, with 5-fold cross-validation on HG14 confirming result stability (93.51% ± 2.31%). Quantitative attention localization, cross-dataset transfer evaluation, and computational efficiency analysis (26.8 ms per image, ~37 FPS) further support the framework’s robustness and practical deployability. These findings establish GBAV-calibrated augmentation as the principal performance driver, which complements the multi-backbone architecture for robust hand gesture recognition across diverse visual contexts. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article