Appl. Syst. Innov., Volume 8, Issue 5 (October 2025) – 30 articles

The growth of greenhouse gas emissions, driven by the use of internal combustion engines (ICE), highlights the urgent need for sustainable solutions, particularly in the shipping sector. Non-invasive predictive maintenance using acoustic signal analysis has emerged as a promising strategy for fault diagnosis in ICEs. In this context, the present study proposes a hybrid Deep Learning (DL) model and provides a novel publicly available dataset containing real operational sound samples of ICEs, labeled across 12 distinct fault subclasses. The methodology encompassed dataset construction, signal preprocessing using log-mel spectrograms, and the evaluation of several Machine Learning (ML) and DL models. Among the evaluated architectures, the proposed hybrid model, BiGRUT (Bidirectional GRU + Transformer), achieved the best performance, with an accuracy of 97.3%. This architecture leverages the multi-attention capability of Transformers and the sequential memory strength of GRUs, enhancing robustness in complex fault scenarios such as combined and mechanical anomalies. The results demonstrate the superiority of DL models over traditional ML approaches in acoustic-based ICE fault detection. Furthermore, the dataset and hybrid model introduced in this study contribute toward the development of scalable real-time diagnostic systems for sustainable and intelligent maintenance in transportation systems. Full article

Knowledge Distillation (KD) is a machine learning technique in which a compact student model learns to replicate the performance of a larger teacher model by mimicking its output predictions. Multi-Teacher Knowledge Distillation extends this paradigm by aggregating knowledge from multiple teacher models to improve generalization and robustness. However, effectively integrating outputs from diverse teachers, especially in the presence of noise or conflicting predictions, remains a key challenge. In this work, we propose a Multi-Round Parallel Multi-Teacher Distillation (MPMTD) that systematically explores and combines multiple aggregation techniques. Specifically, we investigate aggregation at different levels, including loss-based and probability-distribution-based fusion. Our framework applies different strategies across distillation rounds, enabling adaptive and synergistic knowledge transfer. Through extensive experimentation, we analyze the strengths and weaknesses of individual aggregation methods and demonstrate that strategic sequencing across rounds significantly outperforms static approaches. Notably, we introduce the Byzantine-Resilient Probability Distribution aggregation method applied for the first time in a KD context, which achieves state-of-the-art performance, with an accuracy of 99.29% and an F1-score of 99.27%. We further identify optimal configurations in terms of the number of distillation rounds and the ordering of aggregation strategies, balancing accuracy with computational efficiency. Our contributions include (i) the introduction of advanced aggregation strategies into the KD setting, (ii) a systematic evaluation of their performance, and (iii) practical recommendations for real-world deployment. These findings have significant implications for distributed learning, edge computing, and IoT environments, where efficient and resilient model compression is essential. Full article

Sensor-rich Automated Guided Vehicles (AGVs) are increasingly deployed in logistics, yet large fleets relying on fixed tracks face high maintenance costs and frequent route conflicts. This study targets rail-based material handling and proposes an end-to-end multi-AGV navigation pipeline under realistic operational constraints. A conflict-aware global planner, extended from the A* algorithm, generates feasible routes, while a multi-sensor perception stack integrates LiDAR and camera data to distinguish moving AGVs, static obstacles, and task targets. Based on this perception, a Deep Q-Network (DQN) policy with a tailored reward function enables real-time dynamic obstacle avoidance in complex traffic. Simulation results demonstrate that, compared with the Artificial Potential Field (APF) baseline, the proposed GG-DRL approach reduces collisions by ~70%, lowers planning time by 25–30%, shortens paths by 10–15%, and improves smoothness by 20–25%. On the Maze Benchmark Map, GG-DRL surpasses classical planners (e.g., RRT) and deep RL baselines (e.g., DDPG) in path quality, computation, and avoidance behavior, achieving an average path length of 81.12, computation time of 11.94 s, 5.2 avoidance maneuvers, and smoothness of 0.86. Robustness is maintained as a dynamic obstacles scale up to 30. These findings confirm that combining multi-sensor fusion with deep reinforcement learning enhances AGV safety, efficiency, and reliability, with broad potential for intelligent railway logistics. Full article

Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, their contribution to the decision-making process of solar energy systems lies in the possibility of illustrating risk factors and introducing the concepts of linguistic variables of data from solar energy applications. In solar energy systems, the primary beneficiaries and audience of the fuzzy logic techniques are solar energy policy makers, as it concerns decision-making models, ranking of criteria or weights, and assessment of the potential location of the installation of solar energy plants, depending on the case. In a real-world scenario, fuzzy logic allows easy and efficient controller configuration in a non-linear control system, such as a solar panel. This study attempts to review the role and contribution of fuzzy logic in solar energy based on its applications. The findings from the review revealed that the fuzzy logic application identifies and detects faults in solar energy systems as well as in the optimization of energy output and the location of solar energy plants. In addition, fuzzy model (predicting), hybrid model (simulating performance), and multi-criteria decision-making (MCDM) are components of fuzzy logic techniques. As the review indicated, these are useful as a solution to the challenges of solar energy systems. Importantly, the integration and incorporation of fuzzy logic and neural networks should be recommended for the efficient and effective performance of solar energy systems. Full article

Wavefront aberrations caused by thermal flows or arising from the quality of optical components can significantly impair wireless communication links. Such aberrations may result in an increased error rate in the received signal, leading to data loss in laser communication applications. In this study, we explored a newly developed combined stochastic gradient descent optimization algorithm aimed at compensating for optical distortions. The algorithm we developed exhibits linear time and space complexity and demonstrates low sensitivity to variations in input parameters. Furthermore, its implementation is relatively straightforward and does not necessitate an in-depth understanding of the underlying system, in contrast to the Stochastic Parallel Gradient Descent (SPGD) method. In addition, a developed switch-mode approach allows us to use a stochastic component of the algorithm as a rapid, rough-tuning mechanism, while the gradient descent component is used as a slower, more precise fine-tuning method. This dual-mode operation proves particularly advantageous in scenarios where there are no rapid dynamic wavefront distortions. The results demonstrated that the proposed algorithm significantly enhanced the total collected power of the beam passing through the 10 μm diaphragm that simulated a 10 μm fiber core, increasing it from 0.33 mW to 2.3 mW. Furthermore, the residual root mean square (RMS) aberration was reduced from 0.63 μm to 0.12 μm, which suggests a potential improvement in coupling efficiency from 0.1 to 0.6. Full article

Problem: Amid digital disruption and the cross-fertilization of RBV, DCV, and KBV, strategic management knowledge has grown fragmented with blurred boundaries. Conventional mapping (citation/co-word, LDA) lacks semantic and temporal resolution, obscuring overlaps, divergences, and turning points and hindering links to actionable indicators (e.g., the Balanced Scorecard). Hence, an integrated, semantically faithful, time-stamped map is needed to bridge research and operational metrics. Gap: Prior maps rely on citation/co-word signals, miss textual meaning, and treat RBV/DCV/KBV in isolation—lacking a theory-aligned, time-stamped, manager-oriented synthesis. Objectives: This study aims to (1) reveal how RBV, DCV, and KBV evolve and interrelate over time; (2) produce an integrated, semantically grounded map; and (3) translate selected themes into actionable managerial indicators. Method: We analyzed 25,907 WoS articles (1980–2025) with BERTopic (Sentence-BERT + UMAP + HDBSCAN + c-TF-IDF). We used an RBV/DCV/KBV lexicon to guide retrieval/interpretation (not to constrain modeling). We discovered 230 topics, retained 33 via coherence (C_V), and benchmarked them against LDA. Key findings: A concise set of 33 high-quality themes with a higher C_V than LDA on this corpus was established. A Fish-Scale view (overlapping subfields across economics, management, sociology) that clarifies RBV–DCV–KBV intersections was achieved. Era-sliced prevalence shows how themes emerge and recombine over 1980–2025. Selected themes mapped to Balanced Scorecard (BSC) indicators linking capabilities → processes → customer outcomes → financial results. Contribution: A clear, time-aware synthesis of RBV–DCV–KBV and a scalable, reproducible pipeline for structuring fragmented theory landscapes are presented in this study—bridging scholarly integration with managerial application via BSC mapping. Full article

Machine Learning (ML) models developed for the Edge have seen a massive uptake in recent years, with many types of predictive analytics, condition monitoring and pre-emptive fault detection developed and in-use on Internet of Things (IoT) systems serving industrial power generators, environmental monitoring systems and more. At scale, these systems can be difficult to manage and keep upgraded, especially those devices that are deployed in far-Edge networks with unreliable networking. This paper presents a simple and novel platform architecture for deployment and management of ML at the Edge for increasing model and device reliability by reducing downtime and access to new model versions via the ability to manage models from both Cloud and Edge. This platform provides an Edge ML Operations “Mirror” that replicates and minimises cloud MLOps systems to provide reliable delivery and retraining of models at the network Edge, solving many problems associated with both Cloud-first and Edge networks. The paper explores and explains the architecture and components of the system, offering a prototype system that was evaluated by measuring time to deploy models with regard to differing network instabilities in a simulated environment to highlight the necessity for local management and federated training of models as a secondary function to Cloud model management. This architecture could be utilised by researchers to improve the deployment, recording and management of ML experiments on the Edge. Full article

The spatial layout of farmland involves coordinated planning across diverse functional zones. Irregular land boundaries and functional demands pose challenges to traditional CAD-based workflows and general optimization algorithms. To address these limitations, we propose an interactive farmland layout system based on the Graphic-Guided Evolutionary Layout (GGEL) algorithm. GGEL not only introduces a graph-based spatial pruning and encoding strategy to improve search efficiency, but also performs real-time spatial overlap detection based on functional region boundaries to ensure layout feasibility. Additionally, an interactive module enables real-time user customization to accommodate specific planning needs. Experimental results demonstrate that the system can efficiently generate complete multi-region layouts, significantly enhancing design productivity. A user study with 20 agricultural park experts confirms the system’s usability and effectiveness. This study highlights the potential of integrating evolutionary algorithms with topological graph representations to address the complex spatial planning requirements of digital agricultural parks. Full article

The complexity of smart home technologies and the need for personalized energy efficiency solutions highlight the importance of user-oriented decision-support tools. This study presents a Smart Home Management System (SHMS) selection solution that combines a web-based dashboard, a mobile application, and a relational database. A 54-question structured questionnaire was designed to capture user requirements, and four alternatives—KNX, JUNG Home, LB Management, and eNet Smart Home—were compared using the Simple Additive Weighting (SAW) method. Evaluation criteria included installation complexity, communication technology, integration and control capabilities, and user experience. The system was implemented with Next.js, React Native, and Post-greSQL, ensuring flexibility, scalability, and secure data management. Preliminary evaluation with specialists (system integrators, architects, designers) and students confirmed the coherence of the questionnaire, the adequacy of criteria, and the clarity of recommendations. Results showed that the tool improves user engagement, reduces decision-making uncertainty, and supports the adoption of energy-efficient residential solutions. The study’s main limitation is the small test sample, which will be expanded in future large-scale validation. Planned improvements include interactive product comparisons, cost estimation, adaptive questionnaire logic, and 3D visualizations. Overall, the system bridges the gap between technical SHMS solutions and user-oriented decision-making, offering practical and academic value. Full article

The novel design and modular implementation of a distributed control system for a fuel cell generator, aimed at monitoring and actuation, are presented. Two ESP32 NodeMCU microcontrollers and MCP2515 modules are used for the controller area network (CAN) bus communication protocol. To compare this setup with a traditional battery management system (BMS), small rated-power fuel cell generators were connected individually via the CAN bus to form a larger stacked output. An RFID interface was introduced into the CAN bus system to enhance its applicability in stacked fuel cells, without interfering with original message frames, arbitration mechanisms, or CRC efficiency across various sectors. Additionally, to provide a clearer understanding of the system’s features and functions, a PC-based logic analyzer was employed as an analytical tool to monitor and analyze data transmitted over the CAN bus. Comprehensive insights into the system’s performance are supported by logic analysis of its complex applications in series-connected fuel cells. The advantages of the RFID-based CAN bus are further enhanced by modern communication protocols, offering greater scalability and flexibility, with potential applications in industrial automation, autonomous vehicles, and smart green power grids. Full article

This study examines industrial digitalization, with a particular focus on the transformation from Industry 4.0 to Industry 5.0. The research is based on a database of 1441 Scopus-indexed articles, which forms the basis of a systematic literature review and bibliometric network analysis. The articles were ranked using Global Citation Score (GCS), followed by Co-Coupling Network (CCN) within VosViewer, the method to create arrays. The arrays were analyzed based on the connection strengths of the citations in them. Next, we performed Burst Detection using the CiteSpace app. Finally, the most relevant keywords, determined in the Burst Detection, were used for Co-Occurrence Network (CONK), with which we could create new arrays and analyze them. By connecting the various, fragmented scientific findings, our results highlight that digital twins, artificial intelligence, supply chain resilience and the Internet of Things are the focus of Industry 4.0, i.e., the technological side is dominant. In contrast, Industry 5.0 places employees at the center. It also emphasizes the analysis of human–machine interaction and the importance of green digital sustainability. The results provide a comprehensive picture of how decision-makers, researchers, and professionals can interpret a changing mindset and apply it as practical advice. Full article

This submission is focused on the implementation of a system that acquires data from various types of sensors and securely stores them after encryption on a chip with a reconfigurable architecture. The system has the unique capability of encrypting the input data with a single secret cryptographic key, which is stored only inside the hardware of the system itself, so the key remains unrecognizable upon completion of the system synthesis for any unauthorized user. Being stored as a part of the whole system architecture, the cryptographic key cannot be attained. It is not stored separately on the system RAM or any other supported memory, making the collected data fully protected. The reported work shows a data acquisition system which measures temperature with a high level of precision, transforms it to degrees Celsius, stores the collected data, and transfers them via serial interface when requested. Before storage, the data are encrypted with a 256-bit key, applying the AES algorithm. The data which are stored in the system memory and sent as UART packets towards the main computer do not include the cryptographic key in the data stream, so it is impossible for it to be retrieved from them. We show the flexibility of such kinds of data acquisition systems for sensing different types of signals, emphasizing secure storage and transferring, including data from meteorological sensors or highly confidential or biometrical data. Full article

We analyze a model of the packet buffer in which a new packet can be discarded with a probability connected to the buffer occupancy through an arbitrary dropping function. Crucially, it is assumed that packet lengths can be correlated in any way and that the interarrival time has a general distribution. From an engineering perspective, such a model constitutes a generalization of many active buffer management algorithms proposed for Internet routers. From a theoretical perspective, it generalizes a class of finite-buffer models with the tail-drop discarding policy. The contributions include formulae for the distribution of buffer occupancy and the average buffer occupancy, at arbitrary times and also in steady state. The formulae are illustrated with numerical calculations performed for various dropping functions. The formulae are also validated via discrete-event simulations. Full article

Mass customisation in the automotive industry has exploded the number of wiring harness variants that must be assembled, tested and repaired on the shop floor. Existing CAD or schematic formats are too heavy and too coarse-grained to drive in-line, per-VIN validation, while supplier documentation is heterogeneous and often incomplete. This paper presents a pin-centric, two-tier graph model that converts raw harness tables into a machine-readable, wiring-aware digital twin suitable for real-time use in manufacturing plants. All physical and logical artefacts—pins, wires, connections, paths and circuits—are represented as nodes, and a dual-store persistence strategy separates attribute-rich JSON documents from a lightweight NetworkX property graph. The architecture supports dozens of vehicle models and engineering releases without duplicating data, and a decentralised validation pipeline enforces both object-level and contextual rules, reducing initial domain violations from eight to zero and eliminating fifty-two circuit errors in three iterations. The resulting platform graph is generated in 0.7 s and delivers 100% path-finding accuracy. Deployed at Ford’s Almussafes plant, the model already underpins launch-phase workload mitigation, interactive visualisation and early design error detection. Although currently implemented in Python 3.11 and lacking quantified production KPIs, the approach establishes a vendor-agnostic data standard and lays the groundwork for self-aware manufacturing: future work will embed real-time validators on the line, stream defect events back into the graph and couple the wiring layer with IoT frameworks for autonomous repair and optimisation. Full article

The health and safety of agricultural workers are critical concerns due to their exposure to extreme environmental conditions, physically demanding tasks, and limited access to immediate medical assistance. This study presents the design and development of a novel smartphone application that integrates multiple wearable physiological sensors—a fingertip pulse oximeter, a skin patch thermometer, and an inertial measurement unit (IMU)—via Bluetooth Low Energy (BLE) technology for real-time health monitoring and alert notifications. Unlike many existing platforms, the proposed system offers direct access to raw sensor data, modular multi-sensor integration, and a scalable software framework based on the Model–View–ViewModel (MVVM) architecture with Jetpack Compose for a responsive user interface. Experimental results demonstrated stable BLE connections, accurate extraction of oxygen saturation, heart rate, body temperature, and trunk inclination data, as well as reliable real-time alerts when the system detects anomalies based on predetermined thresholds. The system also incorporates automatic reconnection mechanisms to maintain continuous monitoring. Beyond agriculture, the proposed framework can be adapted to broader occupational safety domains, with future improvements focusing on additional sensors, redundant sensing, cloud-based data storage, and large-scale field validation. Full article

Elevator Group Control Systems (EGCSs) play a key role in managing the passenger flow and consumption of energy in modern buildings. However, existing EGCS algorithms are typically only applied to real-time passenger calls, which does not take the long-term statistics of passenger requirement into account. To address this gap, we propose a standby strategy that proactively repositioning idle elevators even if there is no passenger call. It calculates a combined score that balances the expected waiting time and the energy consumption to determine the optimal standby floors for idle elevators. We implement this strategy on a simple baseline dispatcher using the closest car algorithm and introduce tunable parameters to adjust the standby behavior. Experiments on mid-rise and high-rise building scenarios show that the standby strategy significantly reduces the average waiting time for passengers by more than 24% in both cases. Moreover, because this strategy operates independently of the core dispatcher, it can be combined with existing EGCS algorithms to further improve waiting time without compromising core energy optimizations. These findings demonstrate that proactive standby repositioning is an effective complementary approach for next-generation elevator control systems and offers a practical way to reduce waiting times under realistic office building traffic conditions. Full article

The environmental impact of industrial processes, especially regarding CO₂ emissions, requires innovative tools to monitor and optimize resource consumption. This study presents a data-driven approach for a carded nonwoven production line, aiming to support integration into the sustainability framework FOREST (Framework for Resource, Energy, Sustainability Treatment). Real process data from a pilot line were pre-processed, analysed, and used to train machine learning models to predict energy consumption across multiple production stages. Using techniques such as recursive feature elimination and SHAP value interpretation, the most influential parameters for each process step were identified. Extra Trees Regression proved to be the most accurate and explainable model across all scenarios. The results allow real-time estimation of the Product Carbon Footprint (PCF) based on process parameters and provide a foundation for energy optimization in nonwoven manufacturing. Full article

In recent years, home energy management systems (HEMSs) have emerged as critical components within the concept of smart cities and grids. Within HEMSs, load flow analysis represents one of the fundamental tools for smart grid studies, forming the basis for a wide range of advanced applications, including state estimation, fault diagnosis, and optimal power flow computation. To achieve high levels of load flow accuracy and reliability, HEMSs must incorporate detailed models of all electrical elements found in modern residential units, including appliances, wiring, and energy resources. This paper proposes a load flow solution for smart home networks, featuring detailed models of wiring, appliances, and on-site generation systems. Moreover, a detailed appliance model derived from smart meter data, capable of representing both static-load devices and complex appliances with time-varying consumption profiles, is introduced. Additionally, a measurement-based validation of residential electrical wiring model is presented. The proposed models and calculation procedures have been verified by comparing the simulated results with the literature, yielding a deviation of approximately 0.7%. Analyses of a large-scale network have shown that this approach is up to six times faster compared to state-of-the-art procedures. The developed solution demonstrates practical applicability for use in industry-grade smart power management software. Full article

Straight-walled diffusers can boost the power density of horizontal-axis hydrokinetic turbines (HKTs), but are prone to boundary layer separation when the divergence angle is too large. We perform a systematic factorial study of three diffuser configurations, slotless, mid-length single-slot, and outlet-slot with dual divergence angles, using a two-dimensional, transient SST k–$\omega$ Reynolds-averaged Navier–Stokes model validated against wind tunnel data (maximum error 6.4%). Eight geometries per configuration are generated through a $2^3$ Design of Experiments with variation in the divergence angle, flange or slot position, and inlet section. The optimal outlet-slot design re-energises the boundary layer, shortens the recirculation zone by more than 50%, and raises the mean axial velocity along the diffuser centreline by 12.6% compared with an equally compact slotless diffuser, and by 42.6% relative to an open flow without a diffuser. Parametric analysis shows that the slot position in the radial (Y) direction and the first divergence angle have the strongest influence on velocity augmentation. In contrast, the flange angle and axial slot location (X) are second-order effects. The results provide fabrication-friendly guidelines, restricted to straight walls and a single slot, that are capable of improving HKT performance in shallow or remote waterways where complex curved diffusers are impractical. The study also identifies key geometric and turbulence model sensitivities that should be addressed in future three-dimensional and multi-slot investigations. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) often leads to acute exacerbations requiring hospitalization. While artificial intelligence (AI) has been increasingly used to improve COPD management, predicting whether the length of hospital stay (LOHS) will exceed clinically relevant thresholds remains insufficiently explored. Methods: This study presents a novel clinical decision support system to predict whether LOHS following an acute exacerbation will surpass specific cutoffs (6 or 10 days). The approach involves two stages: (1) clinical, demographic, and social variables are encoded into structured signals and transformed into spectrogram-like images via a pipeline based on sinusoidal encoding and Mel-frequency cepstral coefficients (MFCCs); and (2) these images are fed into a Convolutional Neural Network (CNN) to estimate the probability of extended hospitalization. Feature selection with XGBoost reduced dimensionality to 16 variables. The model was trained and tested on a dataset of over 500 patients. Results: On the test set, the model achieved an AUC of 0.77 for predicting stays longer than 6 days and 0.75 for stays over 10 days. Sensitivity and specificity were 0.79/0.72 and 0.74/0.80, respectively. Conclusions: This system leverages image-based data formalization to predict LOHS in COPD patients, facilitating early risk stratification and more informed clinical planning. Results are promising, but external validation with larger and more diverse datasets remains essential. Full article

In recent years, artificial intelligence (AI) technology has advanced rapidly and gradually permeated fields such as healthcare, the Internet of Things, and industrial production, and the dance field is no exception. Currently, various aspects of dance, including choreography, teaching, and performance, have initiated exploration into integration with AI technology. This paper focuses on the research and application of AI technology in the dance field, expounds on the core technical system and application scenarios of AI, analyzes existing issues restricting the prosperity and development of the dance field, summarizes and introduces specific research and application cases of AI technology in this domain, and presents the practical achievements of technology–art integration. Finally, it proposes the problems to be addressed in the future application of AI technology in the dance field. Full article

Motor impairments significantly affect individuals’ ability to perform activities of daily living, reducing autonomy and quality of life. In response to this, robot-assisted rehabilitation has emerged as an effective and practical solution, enabling controlled limb movements and supporting functional recovery. This study presents the development of an upper-limb exoskeleton designed to assist rehabilitation by integrating neurophysiological signal processing and real-time control strategies. The system incorporates a proportional–derivative (PD) controller to execute cyclic flexion and extension movements based on a sinusoidal reference signal, providing repeatability and precision in motion. The exoskeleton integrates a brain–computer interface (BCI) that utilizes electroencephalographic signals for therapy selection and engagement enabling user-driven interaction. The EEG data extraction was possible by using the UltraCortex Mark IV headset, with electrodes positioned according to the international 10–20 system, targeting alpha-band activity in channels O1, O2, P3, P4, Fp1, and Fp2. These channels correspond to occipital (O1, O2), parietal (P3, P4), and frontal pole (Fp1, Fp2) regions, associated with visual processing, sensorimotor integration, and attention-related activity, respectively. This approach enables a more adaptive and personalized rehabilitation experience by allowing the user to influence therapy mode selection through real-time feedback. Experimental evaluation across five subjects showed an overall mean accuracy of 86.25% in alpha wave detection for EEG-based therapy selection. The PD control strategy achieved smooth trajectory tracking with a mean angular error of approximately 1.70°, confirming both the reliability of intention detection and the mechanical precision of the exoskeleton. Also, our core contributions in this research are compared with similar studies inspired by the rehabilitation needs of stroke patients. In this research, the proposed system demonstrates the potential of integrating robotic systems, control theory, and EEG data processing to improve rehabilitation outcomes for individuals with upper-limb motor deficits, particularly post-stroke patients. By focusing the exoskeleton on a single degree of freedom and employing low-cost manufacturing through 3D printing, the system remains affordable across a wide range of economic contexts. This design choice enables deployment in diverse clinical settings, both public and private. Full article

This study examined how digital technology facilitated early childhood music learning in multi-sensory, engaging experiences. In a 16-week quasi-experimental, mixed-method study that used the Holistic Music Educational Approach for Young Children (HMEAYC) with 103 children and 36 pre-service teachers in Taiwan, sensor-based audio devices and responsive technologies were used instead of screens. Observations and video analysis showed that after an initial phase of adaptation, children exhibited growth in spontaneous and imitative musical behaviors, sensory integration, motor coordination, and creativity. Full article

In increasingly complex Engineer-to-Order (EtO) supply chains, accurately predicting supplier delivery delays is essential for ensuring operational resilience. This study proposes an intelligent Internet of Things (IoT)-enhanced probabilistic framework for early warning and dynamic prediction of supplier lead times in smart manufacturing contexts. Within this framework, three novel Early Warning Systems (EWS) are introduced: the Baseline Probabilistic Alert System (BPAS) based on fixed thresholds, the Smart IoT-Calibrated Alert System (SIoT-CAS) leveraging IoT-driven calibration, and the Adaptive IoT-Driven Risk Alert System (AID-RAS) featuring real-time threshold adaptation. Supplier lead times are modeled using statistical distributions and dynamically adjusted with IoT data to capture evolving disruptions. A comprehensive Monte Carlo simulation was conducted across varying levels of lead time uncertainty (σ), alert sensitivity ($P_{threshold}$), and delivery constraints ($L_{max}$), generating over 1000 synthetic scenarios per configuration. The results highlight distinct trade-offs between predictive accuracy, sensitivity, and robustness: BPAS minimizes false alarms in stable environments, SIoT-CAS improves forecasting precision through IoT calibration, and AID-RAS maximizes detection capability and resilience under high-risk conditions. Overall, the findings advance theoretical understanding of adaptive, data-driven risk modeling in EtO supply chains and provide practical guidance for selecting appropriate EWS mechanisms based on operational priorities. Furthermore, they offer actionable insights for integrating predictive EWS into MES (Manufacturing Execution System) and digital control tower platforms, thereby contributing to both academic research and industrial best practices. Full article

Underwater biological object detection plays a critical role in intelligent ocean monitoring and underwater robotic perception systems. However, challenges such as image blurring, complex lighting conditions, and significant variations in object scale severely limit the performance of mainstream detection algorithms like the YOLO series and Transformer-based models. Although these methods offer real-time inference, they often suffer from unstable accuracy, slow convergence, and insufficient small object detection in underwater environments. To address these challenges, we propose FPH-DEIM, a lightweight underwater object detection algorithm based on an improved DEIM framework. It integrates three tailored modules for perception enhancement and efficiency optimization: a Fine-grained Channel Attention (FCA) mechanism that dynamically balances global and local channel responses to suppress background noise and enhance target features; a Partial Convolution (PConv) operator that reduces redundant computation while maintaining semantic fidelity; and a Haar Wavelet Downsampling (HWDown) module that preserves high-frequency spatial information critical for detecting small underwater organisms. Extensive experiments on the URPC 2021 dataset show that FPH-DEIM achieves a mAP@0.5 of 89.4%, outperforming DEIM (86.2%), YOLOv5-n (86.1%), YOLOv8-n (86.2%), and YOLOv10-n (84.6%) by 3.2–4.8 percentage points. Furthermore, FPH-DEIM significantly reduces the number of model parameters to 7.2 M and the computational complexity to 7.1 GFLOPs, offering reductions of over 13% in parameters and 5% in FLOPs compared to DEIM, and outperforming YOLO models by margins exceeding 2 M parameters and 14.5 GFLOPs in some cases. These results demonstrate that FPH-DEIM achieves an excellent balance between detection accuracy and lightweight deployment, making it well-suited for practical use in real-world underwater environments. Full article

Cutting operational costs is a critical component for transportation agencies. To reduce these costs, agencies must optimize their scheduling. Typically, the total operating costs of transport include vehicle expenses and driver wages. Solving such tasks is complex, and optimal planning is usually broken down into multiple stages. These stages can include vehicle scheduling, driver shift planning, and driver assignment. This paper focuses specifically on developing a near-optimal driver schedule for a specified set of vehicle schedules. It shows how to efficiently assign drivers to predetermined optimal vehicle routes while ensuring compliance with regulatory constraints on driving hours. We address this challenge using a mathematical model based on the set covering problem, building on a framework established perviously. The set covering problem is typically formulated as an integer programming problem, solvable through column generation techniques. Our algorithm combines this method with heuristics, taking into account the practical aspects of the problem. The article also presents a computational analysis of the method using benchmark and real data. Full article

This study presents a novel deep learning framework for classifying visual images based on brain responses recorded through electroencephalogram (EEG) signals. The primary challenge in EEG-based visual pattern recognition lies in the inherent spatiotemporal variability of neural signals across different individuals and recording sessions, which severely limits the generalization capabilities of classification models. Our work specifically addresses the task of identifying which image category a person is viewing based solely on their recorded brain activity. The proposed methodology incorporates three primary components: first, a brain hemisphere asymmetry-based dimensional reduction approach to extract discriminative lateralization features while addressing high-dimensional data constraints; second, an advanced channel selection algorithm utilizing Fisher score methodology to identify electrodes with optimal spatial representativeness across participants; and third, a Dynamic Temporal Warping (DTW) alignment technique to synchronize temporal signal variations with respect to selected reference channels. Comprehensive experimental validation on a visual image classification task using a publicly available EEG-based visual classification dataset, ImageNet-EEG, demonstrates that the proposed disambiguation framework substantially improves classification accuracy while simultaneously enhancing model convergence characteristics. The integrated approach not only outperforms individual component implementations but also accelerates the learning process, thereby reducing training data requirements for EEG-based applications. These findings suggest that systematic spatiotemporal disambiguation represents a promising direction for developing robust and generalizable EEG classification systems across diverse neurological and brain–computer interface applications. Full article

The significant increase in renewable energy sources and HVDC transmission has resulted in a substantial reduction in power system stability, thereby giving rise to a growing concern regarding the safety and stability of the voltage and frequency of DC power systems. A survey of the extant literature pertaining to both DC outgoing systems and new energy power systems reveals a preponderance of studies that employ the short-circuit ratio or multi-site short-circuit ratio as a metric for strength evaluation. However, it is evident that there is an absence of a universally applicable and comprehensive strength definition index for new energy and DC-accessed sending/receiving systems. Thus, the present paper puts forward a novel voltage stiffness-based strength evaluation index for new energy and DC-accessed sending/receiving systems and provides a qualitative analysis from the perspective of static voltage stability support. The static stability limit and transient overvoltage limit correspond to impedance ratios of 1 and 2.56, respectively. The findings demonstrate the efficacy of the proposed index in accurately gauging the strength of the sending system. The index’s versatility is further highlighted by its wide applicability in the sending/receiving systems of new energy and HVDC access. Full article

This study applies a multicriteria decision analysis to explore how experts from different backgrounds assess traditional and emerging criteria for urban cycling. A hierarchical model with 7 main criteria and 31 subcriteria was evaluated by 30 specialists from academic, technical, and user-focused groups. Using pairwise comparisons and aggregated judgments, this study reveals points of agreement and divergence among expert priorities. Safety and infrastructure were rated as the most important factors. In contrast, contextual and technological aspects, such as Multimodality, Environmental Quality, Shared Systems, and Digital Solutions, received moderate to lower weights, with differences linked to expert profiles. These results highlight how different disciplinary perspectives influence the understanding of bikeability-related factors. Conceptually, the findings support a broader view of cycling conditions that incorporates both established and emerging criteria. Methodologically, this study demonstrates the value of the Analytic Hierarchy Process (AHP) as a participatory and transparent tool to integrate diverse stakeholder opinions into a structured evaluation model. This approach can support cycling mobility planning and policymaking. Future applications may include case studies in specific cities, combining expert-based priorities with local spatial data, as well as longitudinal research to track changes in cycling conditions over time. Full article

Toward the innovative design of tunable structures for energy generation, this paper presents an extended Floating Frame of Reference (FFR) formulation capable of modeling slope discontinuities in flexible multibody systems—overcoming a key limitation of conventional FFR methods that assume slope continuity. The model is validated using a spatial double-pendulum structure composed of circular beam elements, representative of out-of-plane energy harvesting systems. To investigate the influence of boundary constraints on dynamic behavior, three electromagnetic clamping configurations—Fixed–Free–Free (XFF), Fixed–Free–Fixed (XFX), and Free–Fixed–Free (FXF)—are implemented. Tri-axial accelerometer measurements are analyzed via Fast Fourier Transform (FFT), revealing natural frequencies spanning from 38.87 Hz (lower frequency range) to 149.01 Hz (higher frequency range). For the lower frequency range, the FFR results (38.76 Hz) show a close match with the experimental prediction (38.87 Hz) and ANSYS simulation (36.49 Hz), yielding 0.28% error between FFR and experiments and 5.85% between FFR and ANSYS. For the higher frequency range, the FFR model (148.17 Hz) achieves 0.56% error with experiments (149.01 Hz) and 0.85% with ANSYS (146.91 Hz). These high correlation percentages validate the robustness and accuracy of the proposed FFR formulation. The study further shows that altering boundary conditions enables effective frequency tuning in discontinuous structures—an essential feature for the optimization of application-specific systems such as wave energy converters. This validated framework offers a versatile and reliable tool for the design of vibration-sensitive devices with geometric discontinuities. Full article