Abstract: Flexible photovoltaic modules offer an innovative approach for Building Integrated Photovoltaics (BIPV) on non-planar envelopes. However, the dynamic outdoor environment aggravates the photoelectric mismatch mechanism caused by complex curved geometries. This study experimentally investigates the outdoor experimental investigation into the dynamic electrical and thermal performance of large-scale curved CIGS modules equipped with bypass diodes. Six representative configurations—flat, length-convex(lgvx), length-concave(lgcv), width-convex(wdvx), width-concave(wdcv), and wavy—were continuously monitored under real weather conditions in Hefei, China. The results indicate that while flat modules maintain the highest daily energy yield (453.32 Wh) , the wdvx in longitudinal direction exhibits exceptional adaptability, achieving an average Performance Ratio (PR) of 91.46% and outperforming the flat type during low solar altitude periods in the day. Infrared thermal imaging reveals significant temperature gradients driven by the mismatch effect, with the lgcv module reaching a peak temperature of 65.88°C. Furthermore, the I-V characteristic curves demonstrate that non-uniform self-shading triggers bypass diode activation, resulting in severe step-like current drops and multiple power peaks in concave and wavy shapes. These findings offer crucial practical guidelines for optimizing cell layout and thermal management in curved BIPV envelops.

Abstract: Software aging and the corresponding need for system rejuvenation are well‑established concepts in computer science. As virtualization technologies are increasingly adopted within electric power utility infrastructures, early investigation into Software Aging and Rejuvenation (SAR) models, aging indicators, and empirical data collection becomes essential. Given the critical role of the electric power grid and the high dependability requirements of the protection and control systems that support its operation, proactive research in this area is timely and necessary. Motivated by this need, this work proposes a hierarchical framework that integrates an SAR model into the Reliability Block Diagram (RBD) representation of a Digital Substation Automation System (DSAS). The analysis shows that, for the selected parameter set, incorporating SAR into the VPAC reliability model results in higher estimated failure rates and increased annual downtime relative to hardware‑only models. When combined with substation primary system indices, however, the overall reliability indices remain largely unchanged, aside from reduced outage duration attributed to improved switching performance enabled by the DSAS architecture. Further examination reveals that the limited influence of SAR is primarily due to the lack of historical failure‑mode data for the secondary system. Availability of such empirical data is expected to significantly affect combined reliability indices and improve the accuracy of reliability evaluations. This highlights the importance of systematic data collection and aging‑indicator analysis as utility infrastructures transition toward virtualized and software‑dependent architectures.

Abstract: This paper presents a certification-oriented, system-level argument that Linux is unsuitable for safety-critical avionics. Because Linux is a feature-rich, high-performance general-purpose OS, it exhibits open and dynamic execution semantics that cannot be finitely bounded or frozen at integration time. Two consequences follow. First, airworthiness infeasibility: an oversized TCB, prohibitive DO-330 toolchain qualification burden, and continuous patch churn that prevents stable, certifiable baselines. Second, semantic complexity: temporal non-isolation and spatial non-isolation, materializing as mutable logical-to-physical mappings, driver-induced contamination of global kernel state, and lack of fault containment. We consolidate these observations into an avionics-oriented OS evaluation framework that makes certification implications explicit—closed-world timing analysis at the partition level, provable spatial and fault isolation, TCB minimization, and lifecycle-stable evidence under DO-178C/DO-330. The framework turns architectural properties into concrete certification risks and provides actionable guidance for OS selection and governance in integrated modular avionics [3,6].

Abstract: Artificial Intelligence (AI) and its subset, Machine Learning (ML), play transformative roles in the manufacturing sector, forming the foundation of the “Industry 4.0 and 5.0” frameworks. This research contributes to that evolution by developing AI-based advisory systems that utilize advanced data models to optimize casting processes. These systems exemplify the principles of smart manufacturing, where machines and processes are interconnected, adaptive, and driven by data. They support key objectives such as automation, seamless connectivity, real-time data exchange, human-centric innovation, operational resilience, and sustainability. The models developed in this work enable manufacturers to fine-tune product quality, minimize waste, and accelerate time-to-market through predictive analytics and dynamic process control. By integrating AI-based advisory systems, hybrid modeling, and reduced-order modeling techniques, the systems facilitate real-time decision-making and continuous improvement—essential for achieving flexible, efficient, and customized production environments. A real-world case study further demonstrates the effectiveness of these AI-based advisory systems in casting applications, detailing the steps involved in database construction, data training, and predictive modeling.

Abstract: The reaction of aluminum with water is a promising method for producing hydrogen on-demand for autonomous energy systems. However, its practical implementation faces the challenge of process control due to high exothermicity, leading to particle sintering and thermal instability, especially when using highly reactive nanopowders. The goal of this study is to implement an integrated approach to controlling this reaction, aimed at minimizing these risks. The approach is based on the principle of spatial and temporal distribution of reactants to ensure uniform heat release. Two process management methods were investigated: electrostatic application of aluminum powder to the reactor walls with its gradual release and pre-treatment of a nanopowder-ice mixture. Using a macrokinetic mathematical model, calculations of the conversion kinetics and heat release were performed and compared with experimental data. The results showed that both methods prevent slurry self-heating and achieve uniform hydrogen generation at a constant rate. In particular, the use of a pre-frozen mixture ensured stable hydrogen production over a long period of time without additional heating or stirring. The proposed approaches can be used in the design of safe and efficient hydrogen generators for autonomous power plants.

Abstract: To address the difficulty of simultaneously achieving effective heat dissipation and adequate humidification in open cathode air cooled proton exchange membrane fuel cells (PEMFCs) under medium and high power operation, this study proposes a hydrothermal management strategy based on coordinated ultrasonic atomization humidification and fan speed regulation. A three dimensional single cell multiphysics model is developed and validated using a 300 W experimental platform. The effects of atomization frequency and water temperature on stack performance and internal hydrothermal distribution are systematically investigated. Results show that ultrasonic atomization provides inlet precooling, latent heat absorption, and active region humidification, thereby improving hydrothermal uniformity within the stack. Under the optimal condition of 100 kHz and 55 °C, the peak stack power increases by 21.0% to 319.00 W, while voltage consistency and surface temperature uniformity are also improved. Analysis based on the Stokes number and Dalton’s law of partial pressures indicates that the optimum results from a balance between suppressing droplet agglomeration and inertial deposition, and limiting oxygen dilution caused by excessive water vapor. The proposed strategy provides a compact and practical approach for improving the stability, uniformity, and efficiency of air cooled PEMFCs.

Abstract: Optimising production layouts in manufacturing plants is a time-consuming and often manual process that typically only considers individual performance indicators. This paper presents an end-to-end pipeline that uses variational autoencoders to generate and optimise layouts. The method simultaneously considers multiple KPIs such as throughput time, energy consumption, space utilisation, machine density and material flow complexity. Different scenarios like standard, bottleneck, energy focus are supported. Results show that the proposed method generates valid layouts that outperform existing layouts in terms of efficiency, energy consumption and material flow. The pipeline enables fast, reproducible layout generation and can be directly integrated into production control systems to achieve measurable technical improvements.

Abstract: This paper studies a quantized implementation of a Gramian-based distributed observer for discrete-time systems observed by a network of sensor nodes. The starting point is a distributed observer based on local information matrices obtained from a distributed constructibility Gramian recursion. In order to account for limited communication, the information matrices and information vectors exchanged among neighboring nodes are quantized by means of a uniform quantizer with saturation. The resulting algorithm preserves the original distributed structure and requires only one communication round per sampling instant. A simple perturbation argument shows that the estimation error induced by quantization depends on the perturbation of the local information matrix and information vector, and therefore on the quantization resolution. Numerical results illustrate a sharp practical threshold on the number of communication bits: in the considered example the estimator remains stable for 25 bits, while 23 bits already lead to divergence.

Abstract: Maintaining the concentration of magnesium in potable water above minimum levels has been suggested to have public health benefits. A twelve-month trial of attempting this goal by partial replacement of limestone with dolomite in eight out of twenty-six post-treatment contactors at the Ras al Khair seawater desalination plant, the largest such plant in Saudi Arabia with a daily production of over 1,000,000 m³ of desalinated water. Over the course of the trial increases in Mg concentration in the range 1 to 2 ppm were achieved without necessitating increases in carbon dioxide utilization or any reduction in production volume. Alkalinity, calcium, and total dissolved solids remained within acceptable parameters. Calculated supersaturation values suggest strongly that it will not be possible to increase concentrations significantly further at the pH and temperature conditions of the study. Thus, while use of dolomite to this extent is a very low-cost strategy for magnesium supplementation, its scope of application without additional carbon dioxide consumption and capital investment is limited. The ratio of magnesium to chloride in SWRO product water was estimated in the course of the study and was found to be approximately half of the ratio in Standard Seawater, suggesting that under operational conditions (giving 1500 mg/L from first pass reverse osmosis) rejection of magnesium was significantly greater than rejection of sodium.

Abstract: The construction industry is a key energy consumer and greenhouse gas emitter, and its green low-carbon transformation is critical to achieving China's "dual carbon" strategy. This study focuses on carbon emissions from the construction industry in Hunan Province, central China, using data from 2005 to 2022. An improved STIRPAT extended model combined with ridge regression is applied to identify key driving factors, and a CNN-LSTM-Attention hybrid model is constructed for multi-scenario carbon peak prediction from 2023 to 2040. The results show that industrial scale, urbanization rate, and energy intensity are the top three influencing factors, with energy intensity being the only significant inhibitory factor. Carbon emissions will continue to rise without peak under the high-carbon scenario, peak in 2035 under the baseline scenario, and peak in 2030 under the low-carbon scenario. The low-carbon scenario is the optimal path to meet Hunan's 2030 carbon peak target for the construction industry. Targeted policy suggestions are proposed for regional low-carbon development.

Abstract: With the rapid development of software-defined radio (SDR) technology, a digital, software-reconfigurable, and flexible solution is provided for microwave radiometers, particularly suitable for atmospheric water vapor and oxygen detection with wideband, multi-channel requirements, significantly improving system efficiency. Meanwhile, digitization helps improve channel consistency and address nonlinearity issues, while the digital zero-balancing mechanism implemented through adaptive integration is more suitable for digital platforms. This paper proposes a digital Dicke-type radiometer system based on an SDR platform, using Xilinx RFSoC XCZU47DR as the core hardware to achieve single-chip integration of RF signal sampling, digital local oscillator generation, and signal processing. The system implements a 46-channel channelized receiver (23 channels each for K-band and V-band) on FPGA using a polyphase filter bank. The prototype filters achieve 70 dB stopband attenuation and 0.5 dB passband ripple, with each polyphase branch requiring only 25 coefficients, significantly reducing hardware resource consumption. An adaptive integration method is proposed, where an adaptive switch controller dynamically adjusts the hot source injection time ratio by calculating the power difference between adjacent integration periods, enabling the Dicke zero-balancing mechanism to operate entirely in the digital domain. Furthermore, a complete hardware transfer model is established for three signal branches (antenna, hot source, and matched load), and full-chain calibration of all 46 channels is performed using a liquid nitrogen cold source, with calibration reliability verified through blackbody measurements. Experimental results demonstrate that the system achieves better than 0.7 K brightness temperature consistency across channels, with sensitivity less than 0.15 K at 1-s integration time, confirming its excellent channel consistency and measurement stability.

Abstract: Myocardial stiffness is a critical determinant of cardiac function and disease, influencing ventricular filling, contractility, mechanotransduction, and the progression of conditions such as hypertrophic cardiomyopathy, myocardial infarction, and heart failure with preserved ejection fraction. Over the past two decades, research in cardiac biomechanics has advanced from conventional ex vivo tissue characterization to multiscale experimental investigation, sophisticated constitutive modelling, and patient-specific computational inference based on imaging modalities such as magnetic resonance imaging and echocardiography.Despite these advances, the field remains fragmented across experimental biomechanics, computational modelling, and clinical imaging. Experimental studies commonly focus on isolated tissue characterization using biaxial testing, indentation, and rheological methods, whereas computational studies increasingly employ inverse finite element frameworks to estimate myocardial stiffness in vivo. At the same time, growing evidence indicates that myocardial viscoelasticity and other time-dependent mechanical behaviours play an important role in cardiac function, although these features are still insufficiently incorporated into many constitutive models.This review synthesises current knowledge on passive and viscoelastic myocardial stiffness across scales by integrating experimental methods, constitutive modelling strategies, and image-informed computational approaches. It examines the influence of myocardial microstructure, fibre architecture, extracellular matrix remodelling, and fibrosis on tissue stiffness, and reviews emerging techniques for non-invasive estimation of myocardial mechanical properties. The review also considers the potential of patient-specific cardiac digital twins for clinical decision support. Finally, it identifies key methodological challenges, unresolved questions, and future opportunities for advancing standardised mechanical characterisation and the clinical translation of cardiac biomechanics.

Abstract: Automated quality inspection is a central component of modern industrial production processes. Over the past few decades, machine vision has evolved from rule-based, traditional image processing methods to data-driven machine learning and deep learning approaches. In particular, with the advent of powerful neural networks, significant progress has been made in the detection, classification, and localization of defects. At the same time, industrial applications place high demands on robustness, real-time capability, explainability, and the handling of rare or unknown defect patterns. This brief survey provides an overview of machine vision methods for industrial quality inspection. It systematizes classical image processing approaches, supervised, unsupervised, and semi-supervised learning methods, and discusses their strengths and limitations in real-world production environments. Furthermore, it examines multisensory and three-dimensional inspection approaches, aspects of industrial implementation, and current developments in the field of explainable artificial intelligence. Finally, this brief overview identifies outstanding challenges and research gaps and outlines future trends in automated quality inspection.

Abstract: Unconventional oil production plants are complex industrial systems characterized by harsh operating conditions, modular facility configurations, and tightly coupled electrical, instrumentation, and control subsystems. Conventional centralized control architectures, such as Distributed Control Systems (DCS) and Programmable Logic Controller (PLC)-based systems, often exhibit structural limitations in scalability, maintainability, and subsystem integration when applied to such distributed plant environments. This study proposes a Direct Digital Control (DDC)-based integrated electrical and instrumentation control system architecture for unconventional oil production plants from a systems engineering perspective. The proposed architecture adopts distributed field-level DDC controllers as autonomous system nodes, enabling direct processing of instrumentation signals and coordinated integration with electrical subsystems through a network-based structure. This transforms the control platform into a system-of-systems architecture in which process, electrical, instrumentation, safety, and supervisory layers operate as interoperable subsystems. The proposed system was implemented in a pilot-scale unconventional oil production plant to evaluate its practical applicability. The results indicate that the proposed architecture improves system scalability, modular adaptability, maintenance efficiency, and operational robustness compared with conventional centralized architectures. In particular, system expansion and module integration were achieved without structural redesign of the overall control platform. This study provides a practical architectural framework for integrated control of complex industrial plants and offers a foundation for future extensions toward smart plant operation, digital twin integration, and intelligent industrial system-of-systems engineering.

Abstract: Flexible photovoltaic(PV) technology not only has high power efficiency but also is thin and lightweight, enabling seamless adaption to the surface of curved buildings. However, the distinctive spatial geometry of curved surfaces leads to inhomogeneous irradiance, causing electrical mismatch losses. This paper presents a systematic indoor experimental study on the electrical performance of Copper Indium Gallium Selenide (CIGS) cells under various bending configurations, including length-convex (lgvx), length-concave (lgcv), width-convex (wdvx), and width-concave (wdcv). Tests were conducted under standard testing conditions (1000 W/m², 25°C) with central angles ranging from 0° to 180° and placed in longitudinal and horizontal orientations， respectively. Results indicate that width-bending configurations generally outperform length-bending ones due to lower mismatch losses. For width-bending, concave forms exhibit higher power output than convex forms due to a mutual reflection mechanism. Conversely, length-concave forms manifest the highest power mismatch loss (up to 319.70 mW at 180°) due to significant self-shading. These findings provide critical design guidelines for optimizing cell layouts in curved BIPV systems.

Abstract: Mental stress is a common issue in demanding occupational setups, such as smart industrial settings, particularly from working with robots, being one of the primary reasons for decreased performance and productivity. Quantifying and evaluating stress are critical for worker safety, performance, and overall well-being. A novel integrated framework is proposed in this research for digitising and assessing cognitive stress that combines neuroimaging (EEG and fNIRS), gaze tracking and machine learning. A factory workers’ stress assessment experiment is designed and implemented, which employs physiological, behavioural and subjective measures to assess mental stress from different perspectives. Physiological features extracted from multimodal data are used for training supervised classification and regression models. To further optimise the pipeline, multiple feature selection algorithms are tested, followed by ensemble learning approaches, and the best one is chosen for stress prediction. After implementing the novel stress quantification framework for the factory workers' stress assessment experiment, the ensemble learning approach produced the best results for both regression (RMSE: 10.86) and classification (accuracy: 84.1%) techniques using the STAI score as the target. The behavioural and subjective measures demonstrate the effect of varying process variables (light, noise, task speed, and complexity) during the experiment. Multimodal data, machine learning, and other computational approaches are integrated in this study to objectively quantify cognitive stress, utilising the novel stress quantification framework presented in this research, thereby bridging the gap between research and practical application. This study proposes a multi-domain framework for measuring stress, providing a promising solution for worker well-being in occupational setups.

Abstract: This study develops and validates a climate-based, user-centred and data-informed framework to improve lighting performance in educational buildings through the integrated use of daylight and smart LED control systems. The research was conducted in a university facility in Madrid, Spain, using a mixed-methods approach combining on-site illuminance measurements, climate-based lighting simulations (CBMS) with Dialux Evo 12.1, and structured surveys on user perception. The objective was to quantify the dynamic interaction between daylight availability, artificial lighting demand, and perceived visual comfort, while assessing the energy-saving potential of daylight-responsive control strategies. Results show that existing LED systems meet current illuminance standards while maintaining low lighting power density (LPD). Daylight and electric lighting act complementarily, with daylight reducing artificial lighting demand by up to 50% in optimally oriented classrooms, particularly during spring and summer. Smart dimming and adaptive control systems provide additional energy savings ranging from 27% to 46%, with estimated payback periods of approximately four years. Overall, the findings demonstrate that integrating daylight and adaptive LED systems is an effective and scalable strategy for reducing energy use while maintaining visual comfort in educational buildings under Mediterranean climatic conditions.

Abstract: This study investigates the safety measures associated with blending hydrogen (H₂) with methane (CH₄) to reduce carbon emissions in the hard-to-abate industries, trans-portation sectors and domestic uses. The results highlighted significant safety risks due to hydrogen's lower ignition energy (IE) and broader flammability range, especially under high-pressure conditions. Using Aspen HYSYS chemical process simulation and the HSC Chemistry platform, the study quantified carbon emissions and combustion heat release of H₂-CH₄ mixtures at various H₂ contents, temperatures, and pressures. The results suggest that blending H₂ with CH₄ can be beneficial, provided H₂ content does not exceed safe thresholds and stays within a recommended Wobbe Index (WI) range of 45 - 55 MJ/m³. The WI increases with H₂ concentration exceeding 50 mole% due to density effects outweighing HHV reductions. Hydrogen's high buoyancy and diffusivity reduce localized accumulation in open areas but pose risks in confined spaces due to its wide flammability range. H₂-CH₄ blends with ≤ 20 mole% H₂ are safer than higher concentrations or pure H₂. For blends with > 20 mole% H₂, engineered safety features (ESF) like leak detection, alarms, ventilation, and spark-free environ-ments are essential. Managing concentrations to avoid the detonation range (pure H₂: 18 - 59 mole% & pure CH₄: 6.3 - 13.5 mole%) is critical. Adhering to H2 safety codes limiting H₂ to ≤ 20 mole% in pipelines is recommended. Conservatively, < 18 mole% H2 reduces detonation risk, and ≤ 10 mole% provides added safety margins. These find-ings can guide policymakers and industry stakeholders in developing safe, efficient hydrogen-enhanced energy systems, hence supporting carbon reduction goals.

Abstract: Automatic modulation classification (AMC) is a key component of spectrum awareness, cognitive radio, and signal intelligence, enabling receivers to identify modulation schemes from noisy in-phase and quadrature (IQ) observations. Traditional approaches rely on likelihood-based methods or handcrafted feature extraction, which often struggle under channel impairments and real-world variability. Recent advances in deep learning enable models to learn directly from multiple signal representations, including raw IQ samples, engineered features, and time–frequency or constellation-based encodings, improving adaptability across diverse signal conditions. This paper presents a structured review of deep learning approaches for AMC, including CNNs, RNN/LSTM models, and transformer-based architectures, with a focus on performance, robustness, and system-level trade-offs. We analyze how representation choices, dataset design, and evaluation protocols influence reported results, and highlight key challenges such as domain shift, low-SNR environments, and multi-signal interference. Finally, we outline future directions focused on improving generalization, integrating classical signal processing with learning-based methods, and enabling efficient deployment in real-world and resource-constrained systems.

Abstract: The static bending behaviour of unmanned aerial vehicle (UAV) wings fabricated from composite materials is a crucial determinant of structural performance, particularly under progressive deformation demands that span from nominal service loads to severe deflection conditions. This study develops a progressive, displacement-controlled framework to compare the static bending response of hybrid E-glass/epoxy and carbon-fibre-reinforced polymer (CFRP)/epoxy wings, both with Paulownia internal structure, and a full Paulownia baseline, under increasing tip displacements. Finite element simulations capture load–displacement response, stress redistribution, and energy absorption across displacement regimes from −5 to −50 mm. Results demonstrate that CFRP-skinned wings exhibit higher initial stiffness in the elastic regime, whereas E-glass skins provide improved energy absorption and more progressive stress distribution at large displacements. Conversely, Paulownia alone performs poorly under severe bending, confirming the essential role of composite skins for bending load resistance. The findings underscore the importance of displacement regime classification in static bending assessments and suggest that E-glass composites can offer effective, damage-tolerant alternatives to CFRP for UAV wing applications, particularly where large deformation tolerance is required.