Applied System Innovation

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

This research proposes a tailored Systems Engineering (SE) design process for the development of Attitude and Orbit Control Systems (AOCS) for small satellites operating in formation. These missions, known as Distributed Spacecraft Missions (DSMs), involve groups of satellites—commonly referred to as satellite constellations—whose primary objective is to maintain controlled relative positioning in three dimensions. In these configurations, each satellite may serve a specific role. For instance, one may act as a navigation reference, while another functions as a communication relay. These roles support synchronized control and ensure mission cohesion. To achieve precise relative positioning, the system must integrate specialized sensors and maintain continuous inter-satellite communication. This capability enables precise navigation across both the space and ground segments, while ensuring high control accuracy. As such, the development of AOCS must be approached as a complex systems challenge, involving the coordinated behavior of multiple autonomous elements working toward a shared mission objective. This study tailors the SE process using the ISO/IEC 15288 standard and incorporates a Model-Based Systems Engineering (MBSE) approach to enhance traceability, consistency, and architectural coherence throughout the system lifecycle. As a result, it proposes a customized SE process for AOCS development that begins in the mission’s conceptual phase and addresses the specific functional and operational demands of formation flying. A conceptual example illustrates the proposed process. It focuses on subsystem coordination, communication needs, and the architecture required to support an AOCS for autonomous satellite formations. Full article

This study presents a systematic classification of acoustic agglomeration systems, developed on the basis of an extensive review of experimental and numerical studies, specifically addressing fine particles. The classification framework encompasses wave type, geometric orientation, level of functional integration, chamber composition, and auxiliary enhancement mechanisms. By organizing the diverse configurations into consistent categories, this study enables a comparative analysis of system performance and suitability for practical applications. This review highlights typical design features, operational ranges, and implementation contexts, while identifying key advantages and limitations of each system type. Strengths such as scalability, compatibility with filtration units, and enhancement of particle capture are contrasted with challenges including acoustic intensity requirements, resonance sensitivity, and integration constraints. The proposed classification serves as a practical tool for guiding future design, optimization, and application of acoustic agglomeration technologies in air pollution control. Full article