Search Results (26,490)

With the acceleration of urbanization and the intensification of climate change, wind conditions have become a critical factor in architectural design. They not only affect a building’s wind resistance but also influence ventilation, pollutant dispersion, pedestrian comfort, and energy consumption. Traditional computational fluid dynamics (CFD) simulations are costly. Although the application of machine learning for CFD prediction has become a relatively mature technology, machine learning models still face challenges in actual architectural design workflows. Building upon recent advancements in the field, it proposes two core technologies: a method for predicting outdoor wind environments in buildings based on the Site-Specific Training for Design Tasks (SST-DT) strategy, and an automated machine learning workflow. These innovations improve upon existing wind environment analysis methods and systems, establishing a fully automated working framework that is easy for architects to learn and use. Within this framework, dataset acquisition and model training are performed automatically. Finally, this framework was validated across various prediction tasks with different objectives. It significantly lowers the barrier to entry for architects adopting machine learning, advances the performance-driven design paradigm, and facilitates the deep integration of machine learning technologies into architectural wind engineering. Full article

Ammonia is a key chemical for fertilizers, industrial processes, and emerging energy applications, yet its conventional production via the Haber–Bosch process is associated with high energy demand and significant greenhouse gas emissions. In this context, electrochemical routes for ammonia synthesis have attracted increasing attention as a potential sustainable alternative, enabling nitrogen conversion under milder conditions and using renewable electricity. This review examines recent advances in electrochemical ammonia production, focusing on nitrogen reduction mechanisms, catalyst development, and electrochemical system design. The main reaction pathways for nitrogen activation are analyzed, together with the role of electrocatalysts in determining activity and selectivity. Progress in catalyst engineering, electrolyte optimization, and reactor configuration is discussed, with particular emphasis on strategies to mitigate competing reactions such as hydrogen evolution. In addition, alternative approaches based on nitrate reduction are considered due to their promising performance and potential integration with wastewater treatment. Unlike many recent reviews primarily focused on catalyst development or individual reaction pathways, this review provides an integrated perspective encompassing nitrogen reduction, nitrate reduction, electrolyte engineering, reactor architectures, and techno-economic considerations, thereby highlighting the interdependence between materials design, reaction environment, and system-level integration for scalable electrochemical ammonia synthesis. Full article

Driven by demanding global emission regulations and the urgent requirements for sustainable mobility, Electric Vehicles (EVs) have emerged as the primary alternative to Internal Combustion Engine (ICE) vehicles. Central to this transition is the electric propulsion system (EPS), a multidisciplinary integration of power electronics, advanced motor drives, and electrochemical energy storage. This paper provides a comprehensive overview of the current landscape of power electronic drives, focusing on the evolution of high-efficiency traction motors and next-generation energy storage systems (ESSs), and advancements in ultra-fast chargers. The analysis explores the vital impact of power converters, evaluating recent breakthroughs in wide-bandgap (WBG) semiconductors and advanced control topologies that enhance energy density and thermal management. Furthermore, the study identifies critical challenges in the design, modulation, and operational reliability of converters under dynamic automotive environments. By synthesizing current research trends and technical bottlenecks, this paper offers insights into the future trajectory of power electronics in achieving high-performance, cost-effective, and carbon-neutral transportation. Full article

The increasing adoption of energy storage systems has driven the development of inductive power transfer (IPT) chargers operating under static and dynamic current references, while maintaining robust performance in the presence of disturbances such as misalignment. This article presents an experimental and analytical comparison of three classical current controllers—PI, PI with feed-forward loop (PI+FF), and integral (I)—applied to a low-power inductive power transfer charger (BC-IPT). In addition, a simple and practical criterion for controller gain selection is proposed and evaluated under identical operating conditions, using a 164 W experimental platform with unidirectional power transfer. The controllers (PI, PI+FF, and I) are compared in terms of settling time, overshoot, phase margin, gain margin, and disturbance rejection capability. The experimental results show that adjustable settling times between 1 and 12 ms can be achieved for static and dynamic current references. An overshoot below 8% was obtained, along with stable performance under the evaluated variations in input voltage and coupling factor. The settling time can be directly adjusted using the proposed gain-selection criterion. Overall, the results demonstrate that, under the studied operating conditions (including a 164 W platform, unidirectional power flow, and the selected topology), classical controllers provide an appropriate balance among dynamic performance, robustness, and tuning simplicity for current-regulated IPT battery charging applications. Full article

The emergence of citizen energy communities under the European Clean Energy Package is creating new opportunities for neighboring households to collectively reduce electricity costs through local energy sharing. This paper presents a distributed multi-agent energy management system for a two-household residential energy community in which each household is equipped with photovoltaic generation and a battery energy storage system operating under realistic hourly-varying electricity prices. Each household is managed by an independent Deep Q-Learning agent that learns a cost-optimal charging and discharging policy using only local observations. In parallel, a coordination agent, implemented on the SPADE platform with XMPP-based messaging, oversees real-time peer-to-peer energy transfers between households, enabling energy exchange whenever one household has surplus generation and another faces a deficit. The two households are deliberately configured with complementary profiles: one has higher PV generation capacity while the other has higher energy consumption. This setup creates natural opportunities for local energy sharing between them. Performance is assessed through a three-level evaluation framework: (i) individual household economics (cost reduction, battery management, grid exchanges), (ii) coordination efficiency (transfer frequency, direction, and volume), and (iii) aggregate community performance, which isolates the added value of peer-to-peer sharing beyond what each household achieves through individual BESS optimization. Numerical experiments using GEFCom2014 solar generation data, synthetic residential load profiles calibrated following documented consumption patterns, and day-ahead price signals representative of the Spanish electricity market demonstrate that both Deep Q-Learning agents independently learn effective charge/discharge strategies aligned with price signals and PV availability. They also show that the coordination layer further reduces community grid dependence by routing surplus energy locally rather than exchanging it with the main grid at less favorable rates. The results confirm that a well-engineered integration of decentralized reinforcement learning with a lightweight coordination protocol can deliver measurable economic benefits in realistic residential energy communities without requiring centralized training, shared data, or complex multi-agent reinforcement learning architectures. Full article

Against the backdrop of the global energy transition and China’s dual-carbon strategy, the photovoltaic (PV) industry is entering a new stage of large-scale, intensive development, where efficiency improvement and cost control in module encapsulation have become the core of industrial competition. To address the drawbacks of traditional silicone plate laminators—frequent consumable replacement, high maintenance costs, and poor adaptability to dual-glass module encapsulation—this paper proposes a flat-plate laminator technical scheme. By replacing flexible silicone plates with rigid pressure plates and optimizing pressure transmission paths and sealing structures, we achieved efficient, low-cost lamination. We first compared the working principles of flat-plate and silicone plate laminators, completed the structural design of five core modules with an optimized rigid platen and annular silicone sealing system, developed a modular retrofitting scheme for existing equipment, and verified performance via engineering tests. Tests show that the retrofitted equipment achieves a module thickness deviation ≤ ±0.06 mm, a product yield of 99.88%, annual cost savings of USD 342,000 per unit, and a 0.61-year investment payback period. This work provides theoretical support and an engineering reference for technical innovation in PV module encapsulation equipment, with significant promotion and application value. Full article

This study develops a Fuzzy C-Means-based mathematical framework for the storage-oriented evaluation and classification of hybrid energy system alternatives. The analysis considers fifteen hybrid configurations generated through pairwise combinations of solar, wind, biomass, geothermal, hydropower, and fossil-based energy sources. These alternatives are evaluated with respect to fourteen storage-related criteria, namely energy efficiency, exergy efficiency, entropy, lifetime, cost, CO₂ emissions, recyclability, decarbonization potential, discharge duration, charge duration, power capacity, energy capacity, sustainability, and environmental impact. After constructing and normalizing the decision matrix, the Fuzzy C-Means algorithm is employed to identify latent similarity structures and to determine the degree of membership of each hybrid alternative to multiple clusters. To support the selection of an analytically meaningful partition, alternative cluster structures are compared in terms of partition quality and interpretability. The results indicate that the considered hybrid configurations can be grouped into distinct yet partially overlapping storage-oriented profiles, reflecting differences in technical performance, environmental burden, and sustainability characteristics. In particular, hydropower-supported systems are associated with more stable and infrastructure-compatible profiles, while biomass- and geothermal-related combinations occupy more balanced transitional positions. By extending fuzzy clustering to the storage-oriented analysis of hybrid energy systems, the study provides a mathematically transparent basis for comparative assessment, exploratory classification, and preliminary decision support. Full article

Radiation detection technology is critical in medical diagnosis, high-energy physics experiments, nuclear environmental monitoring, and radiation safety protection. Its technological iteration stems from innovations in high-performance radiation detection materials. Traditional materials often have narrow dose–response intervals, insufficient high-precision measurement capability, low spatial resolution, and poor stability, failing to meet high-precision detection requirements. Ag-doped phosphate glass (Ag-PG), based on radio-photoluminescence (RPL), effectively addresses these limitations with its comprehensive advantages: high radiation sensitivity, a wide linear dose–response range, submicron spatial resolution for radiation imaging, write-erase-rewrite capability, and visualized dose monitoring potential, and it also boasts significant fundamental research value and engineering application prospects. Specifically, while existing RPL reviews mainly provide a comprehensive analysis from the perspective of RPL and present typical RPL material systems, this paper systematically analyzes the structural characteristics of the Ag-PG matrix and the coordination configuration and site occupation of Ag ions. It clarifies RPL luminescence properties, dose–response mechanisms, and the evolution of luminescence centers, while reviewing advancements in applications such as radiation dose detection and high-resolution X-ray imaging. By summarizing the current research status, technical advantages and existing challenges of Ag-PG, this study provides theoretical references and conceptual insights to promote breakthroughs in its fundamental research and practical applications in high-precision radiation dose detection, advanced medical imaging, micro-nano-scale radiation detection, and nuclear industry non-destructive testing. Full article

The rapid expansion of artificial intelligence has intensified the demand for hardware systems capable of emulating brain-like information processing with high accuracy, energy efficiency, and reliability. Neuromorphic computing based on memristive artificial synapses has emerged as a promising approach to overcome the limitations of conventional von Neumann architectures. Although inorganic and oxide-based synaptic memristors have been widely explored for neuromorphic systems, they often suffer from poor linearity, asymmetric potentiation/depression behavior, limited conductance states, and device variability, which restrict learning accuracy and scalability. In contrast, polymer-based memristors have gained significant attention owing to their intrinsic advantages, including mechanical flexibility, molecular tunability, controllable electronic/ionic transport, low-temperature processability, and compatibility with large-area fabrication. This review critically examines recent advances in polymer—based memristive materials and devices for achieving linear and symmetric artificial synaptic behavior. Polymer synapses are classified into pure polymer, polymer composite, and polymer-hybrid systems through a mechanism to function framework. Rather than providing a general compilation of organic memristor studies, this review analyzes how polymer chemistry, ion-migration control, trap state distribution, redox activity, electrode selection, active layer thickness, and interface engineering govern conductance update linearity, symmetry, and uniformity. Fundamental switching mechanisms, material classifications, device architectures, key synaptic characteristics, and system-level neuromorphic performance, including pattern-recognition applications, are critically discussed. By explicitly linking material and device design to conductance update fidelity, learning accuracy, training convergence, and pattern-recognition reliability, this review provides practical design guidelines and future perspectives for next-generation polymer-based neuromorphic hardware with improved linearity, symmetry, reliability, and scalability. Full article

This research proposes a conceptual framework that employs generative artificial intelligence (AI) to automatically generate dynamic biomimetic façade designs for reducing building carbon emissions. Biomimetic façades show strong carbon-reduction potential; however, their application remains limited by interdisciplinary requirements and time-intensive optimization processes. Existing studies primarily rely on traditional multi-objective optimization for energy performance, while machine learning integration and carbon-oriented evaluation remain limited in biomimetic façade research. To address this gap, this study proposes an AI system for biomimetic façade generation in tropical climates by combining reinforcement learning–based multi-objective optimization with deep learning–based parameter prediction models. A carbon payback assessment method integrating operational and embodied carbon is further proposed to evaluate carbon reduction performance. Preliminary validation through pilot experiments and K-fold cross-validation achieved an average RMSE of 8.7% and an average R² value of 0.547, while façade parameter prediction for new building conditions could be completed within approximately 10 s. Simulated cases also indicated that the generated façade strategies generally remained within predefined carbon payback thresholds under different material configurations. The framework supports carbon-oriented biomimetic façade design and early-stage low-carbon design decision-making. Full article

Poor indoor air quality (IAQ) in naturally ventilated school buildings remains a widespread problem, particularly during the heating season, when limited ventilation leads to elevated CO₂ concentrations. At the same time, increasing ventilation rates may significantly increase energy demand, creating a conflict between IAQ and energy efficiency. This study aims to evaluate whether CO₂-based demand-controlled mechanical ventilation, particularly with heat recovery (HRV), can improve IAQ while maintaining acceptable energy performance in existing school buildings. A previously validated CONTAM model of a Polish primary school classroom was used to simulate natural ventilation, mechanical exhaust ventilation, and balanced ventilation with heat recovery. In mechanical systems, CO₂-based demand-controlled ventilation (DCV) was applied. The resulting airflow rates were then used in EnergyPlus simulations to assess seasonal heating and primary energy demand under Kraków climatic conditions. Increasing the outdoor air supply rate significantly reduced indoor CO₂ concentration but led to higher heating demand in exhaust ventilation systems. In contrast, HRV reduced heating energy demand by more than 80% compared with exhaust ventilation while maintaining comparable indoor air quality. Although HRV required additional electricity for fan operation, the total primary energy consumption remained low. The results demonstrate that CO₂-based DCV systems with heat recovery provide an effective balance between indoor air quality and energy performance. These findings support the application of HRV as a practical retrofit solution for improving ventilation in existing school buildings. Full article

The utilization of unconventional feed resources offers a sustainable strategy to mitigate feed shortages particularly in tropical and subtropical regions where access to conventional feeds is often limited. Among these, water hyacinth (Eichhornia crassipes) is one of the world’s most aggressive aquatic weeds, which has drawn attention due to its dual role as a problematic invasive species and a potential livestock feed. This plant reduces water quality, contributes to biodiversity loss and causes economic damage in farming systems. At the same time, its high capacity for nutrient absorption makes it a viable source of protein and energy for ruminants when properly harvested and processed into forms such as hay, dried leaves, and silage. However, its utilization requires caution, as the plant can accumulate toxins and heavy metals from polluted water, which may harm animal health if unprocessed. This review focuses on the potential of water hyacinth to improve ruminant growth performance, nutrient digestibility and rumen fermentation. Including water hyacinth in ruminant diet safely can possibly improve animal productivity, contribute to sustainable weed management and also provide a practical strategy to alleviate feed shortage in dry seasons, thereby encouraging resilience and sustainable ruminant production. Full article

Accurate prediction of hydrodynamic forces on confined oscillating structures is essential in applications related to nuclear engineering, energy systems, offshore devices, and mechanical components subjected to flow-induced vibrations. In this work, two computational fluid dynamics (CFD) methodologies implemented in ANSYS CFX are compared to determine the added-mass coefficients for a square cross-section cylinder confined within a square container: a deforming-mesh method (DMM) and an immersed-boundary method (IBM). Unlike previous studies restricted either to concentric square cylinders or to eccentric configurations treated with potential flow, the present study addresses eccentric confined configurations by solving the incompressible Navier–Stokes equations and focuses primarily on the prediction of added mass under strong confinement. Horizontal, vertical, and combined eccentric displacements are analyzed in detail. Mesh-independence, domain-size sensitivity, and temporal-convergence analyses are performed. Results show that both methods provide closely matching added-mass predictions over a wide range of eccentricities, with relative differences typically below 1 % for moderate eccentricities, although discrepancies increase under extreme confinement. Relative to the concentric configuration, the added-mass coefficient increases by about 44 % for the most eccentric vertical case and by about 87 % for the most eccentric corner-approach case. Force decomposition and pressure-field analysis show that this increase is governed primarily by pressure-induced inertial effects, whereas viscous shear plays a secondary role under the conditions considered. From a practical standpoint, the immersed-boundary method reduced the computational time by approximately 92% in the most demanding case. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article

Introduction: Vitamin B12 (VB12), particularly its active form methylcobalamin (MeB12), contributes to neuromuscular function and energy metabolism, which may be relevant for sports performance. However, evidence on the acute effects of MeB12 supplementation in athletes remains limited. Objective: To evaluate the effects of short-term (3-day) MeB12 supplementation on anaerobic and cognitive performance in amateur cyclists. Methods: A randomized, triple-blind, placebo-controlled crossover clinical trial was conducted in 18 amateur cyclists. Participants received formulations containing MeB12 (1 mg/day; MecobalActive^®, HTBA, Murcia, Spain) or placebo for three consecutive days. Anaerobic performance was assessed using a repeated Wingate protocol, and cognitive performance was evaluated using a light-based mental agility/reaction test system. Biochemical analyses included serum VB12 concentrations. Primary outcomes included peak power output (absolute and relative), fatigue index across repeated sprints, and cognitive response time. Results: Compared with placebo, MeB12 supplementation was associated with higher peak power output, with increases in absolute maximal power (PMAX: +4.1%, p = 0.016) and relative maximal power (PMAXREL: +4.4%, p = 0.013). MeB12 supplementation was associated with a smaller decline in performance across repeated sprints, with a smaller drop in fatigue index from the first to the fifth sprint (p = 0.012). Pre-exercise cognitive performance improved, with a shorter total reaction test time (−4.9%, p < 0.001) versus placebo. Serum VB12 concentrations increased by 16.8% following MeB12 supplementation. Conclusions: A brief, 3-day intervention with methylcobalamin (1 mg/day) was associated with positive changes, when compared with placebo, in selected markers of anaerobic performance (peak power and fatigue-related decline) and pre-exercise cognitive performance in recreationally trained amateur cyclists, suggesting a possible involvement of peripheral and central mechanisms. Full article