Search Results (6,456)

Despite the popularity of electroencephalograms (EEGs) as tools for assessing brain health, they can sometimes be abstract and prone to noise, making them difficult to interpret. The following work aims to implement a Quaternion Convolutional Neural Network (QCNN) to detect abnormal EEGs obtained from a database that includes both people with excellent mental health and individuals with different types of mental illnesses. Unlike other approaches in which the QCNN is used exclusively for image processing, in the present work, a unique architecture with mainly quaternionic layers is proposed, specifically designed for the classification of time-varying signals. Using the database “The TUH EEG Abnormal Corpus”, the signals are preprocessed using the Wavelet Transform, a mathematical tool capable of performing simultaneous time and frequency analysis, configured with a level 4 decomposition value. Subsequently, the results are subjected to a partial spectrogram-type treatment to integrate the energy parameter into the analysis. They are then conditioned in each of the elements of the quaternion and processed by the QCNN, leveraging quaternion algebra to maintain the relationships between its elements, both in the input and in the convolutional product. In this way, it is possible to obtain significant percentages in the precision, recall, and accuracy metrics with values higher than 77%. Its performance, which uses 4 times less computational memory, allows the QCNN to be considered an alternative for classifying EEG signals. Finally, a comparison of the proposed model was made with other architectures commonly used in the literature, as well as with developments in other research and with a hybrid model whose performance places it at the highest classification standard, not to mention the ability of the QCNN to preserve multi-channel dependencies in EEG signals in a more natural way, achieving parameter efficiencies by leveraging quaternion algebra, reducing the computational cost compared to real-valued CNNs. Full article

The consequent-pole interior permanent-magnet (CPM) motor is a promising alternative for minimizing rare-earth magnet usage while supporting high-speed operation. However, rotor flux asymmetry often leads to distorted back-electromotive force waveforms and increased torque ripple. This study investigated a flared-type CPM motor that employs ferrite magnets arranged in a flared configuration to enhance flux concentration within a compact rotor. To address waveform distortion, structural modifications such as bridge removal and an asymmetric air-gap design were implemented. Three rotor parameters—polar angle, asymmetric air-gap length, and rotor opening length—were optimized using Latin hypercube sampling combined with an evolutionary algorithm. Finite element method analyses conducted under no-load and rated-load conditions showed that the optimized model achieved a 77.8% reduction in torque ripple, a 43.4% decrease in cogging torque, and a 0.5% improvement in efficiency compared with the basic model. Stress analyses were performed to examine the structural bonding strength and rotor deformation of the optimized model under high-speed operation. The results revealed a 5.5× safety margin at four times the rated speed. The proposed approach offers a cost-effective and sustainable alternative to rare-earth magnet machines for high-efficiency household appliances, where vibration reduction, cost stability, and energy efficiency are critical. Full article

The hybridization of photovoltaic (PV) and concentrated solar power (CSP) technologies offers a viable solution to enhance dispatchability and reduce energy costs in solar power systems. This study analyzes two CSP-PV hybrid configurations—parabolic trough and solar tower—in diverse Brazilian climatic conditions. Particular focus is given to Bom Jesus da Lapa, identified as the most favorable location in terms of solar resource and system performance. The CSP subsystem includes a two-tank direct thermal energy storage system with molten nitrate salts and a 50 MWe gross Rankine cycle. System performance and techno-economic metrics are assessed using the System Advisor Model (SAM). A parametric analysis investigates the impact of solar irradiation, solar multiple (SM), and thermal storage duration on annual energy output and levelized cost of energy (LCOE). Results indicate that the hybrid system consistently surpasses standalone PV and CSP in both performance and cost-effectiveness. In the solar tower configuration, capacity factors reach up to 90% with an SM of 3.5 and 12 h of storage. This work provides the first techno-economic assessment of PV/CSP hybrid plants tailored to Brazilian conditions, combining multi-city simulations with solar multiple and storage parametric analysis. Among all evaluated sites, Bom Jesus da Lapa presents the highest energy yield and lowest LCOE, supporting its potential suitability for hybrid CSP-PV deployment. Full article

Vehicle indoor air quality (VIAQ) remains poorly standardized despite its growing health relevance. This study developed and applied a real-road test protocol to quantify in-cabin exposure to particulate and gaseous pollutants under different heating, ventilation, and air-conditioning (HVAC) modes: outside air (OA), recirculation (RC), and automatic (Auto). Concentrations of PM_2.5, particle number (PN), NO, and NO₂ were simultaneously measured inside and outside passenger vehicles using validated instruments. In-cabin PM_2.5 levels were lowest in RC, intermediate in Auto, and highest in OA, showing strong HVAC dependence. Particle number distributions were dominated by submicron particles (<1.0 μm). Under RC, NO gradually increased while NO₂ decreased, likely due to NO–NO₂ interconversion and activated-carbon filtration. Short-duration, reproducible on-road tests were conducted under standardized vehicle, occupant, and HVAC settings to minimize variability. Although external conditions could not be fully controlled, consistent routes and configurations ensured comparability. The findings highlight HVAC operation as the dominant factor governing short-term VIAQ and provide practical insight toward harmonized test procedures and design improvements for cabin air management. Full article

This paper reviews and analyzes three types of vertical-axis wind rotors: the classic Savonius, spiral Savonius, and Darrieus designs. Using numerical modeling methods, including computational fluid dynamics (CFD), their aerodynamic characteristics, power output, and efficiency under different operating conditions are examined. Key parameters such as lift, drag, torque, and power coefficient are compared to identify the strengths and weaknesses of each rotor. Results highlight that the Darrieus rotor demonstrates the highest efficiency at higher wind speeds due to lift-based operation, while the spiral Savonius offers improved stability, smoother torque characteristics, and adaptability in turbulent or low-wind environments. The classic Savonius, though less efficient, remains simple, cost-effective, and suitable for small-scale urban applications where reliability is prioritized over high performance. In addition, the study outlines the importance of blade geometry, tip speed ratio, and advanced materials in enhancing rotor durability and efficiency. The integration of modern optimization approaches, such as CFD-based design improvements and machine learning techniques, is emphasized as a promising pathway for developing more reliable and sustainable vertical-axis wind turbines. Although the primary analysis relies on numerical simulations, the observed performance trends are consistent with findings reported in experimental studies, indicating that the results are practically meaningful for design screening, technology selection, and siting decisions. Unlike prior studies that analyze Savonius and Darrieus rotors in isolation or under heterogeneous setups, this work (i) establishes a harmonized, fully specified CFD configuration (common domain, BCs, turbulence/near-wall treatment, time-stepping) enabling like-for-like comparison; (ii) couples the transient aerodynamic loads p(θ,t) into a dynamic FEA + fatigue pipeline (rainflow + Miner with mean-stress correction), going beyond static loading proxies; (iii) quantifies a prototype-stage materials choice rationale (aluminum) with a validated migration path to orthotropic composites; and (iv) reports reproducible wake/torque metrics that are cross-checked against mature models (DMST/actuator-cylinder), providing design-ready envelopes for small/medium VAWTs. Overall, the work provides recommendations for selecting rotor types under different wind conditions and operational scenarios to maximize energy conversion performance and long-term reliability. Full article

Free-form grid structures offer both aesthetic appeal and structural efficiency in long-span roof design and application, yet the potential of morphological design to optimize thermal performance has been long overlooked. This study proposes a multi-objective synergistic optimization framework which can improve the thermal environment and mechanical performance simultaneously for the roof. Focusing on public buildings in hot–humid climates, the research investigates the impact of roof geometry on indoor temperature under extreme thermal loading conditions and long-term thermal loading conditions. Furthermore, the evolution of thermal performance during mechanical performance-driven surface optimization is systematically analyzed. Subsequently, a dynamic proportional adjustment factor is introduced to explore the performance of the optimized results under different performance weights, with thermal and mechanical performance serving as the optimization objectives. Results demonstrate that thermal performance-driven optimization generates saddle-shaped free-form surfaces with alternating peak–valley configurations to achieve self-shadowing effects, reducing indoor temperature by approximately 2 °C but significantly compromising structural stiffness. Conversely, strain energy minimization yields moderate indoor temperature reductions, revealing a positive correlation between strain energy decrease and thermal performance improvement. In the multi-objective optimization considering thermal and mechanical properties, when the strain energy ratio is 0.5–0.7 (optimization balance zone), the indoor temperature decreases, while the structural stiffness and stability bearing capacity increase. This study provides a morphological–structural–environmental synergistic design reference for low-carbon long-span building roofs. Full article

Entrepreneurship is widely recognized as a critical engine of economic growth. This is especially true in rural areas, where resources, policy support, and talent pools are often constrained. Stimulating entrepreneurial vitality in these regions has thus become an urgent policy and research priority. This study adopts an inclusive growth perspective, selecting six key elements—economic level, industrial structure, financial development, educational condition, medical condition, and social security—to construct a theoretical model exploring the configuration pathways that drive rural entrepreneurial activity. Using fuzzy set qualitative comparative analysis (fsQCA), the study examines 982 rural regions in China and draws the following conclusions: (1) None of the six key elements is a necessary condition for rural entrepreneurial activity. (2) The “finance and healthcare-driven” type (C1), “industrial and educational balance” type (C2), “financial and educational synergy” type (C3), and “industrial and healthcare support” type (C4) are the configuration paths to achieve high rural entrepreneurial activity. The findings provide both theoretical and practical insights for stimulating entrepreneurship in rural China. Specifically, they highlight how different developmental configurations can activate local entrepreneurial ecosystems, expand employment and entrepreneurship opportunities for vulnerable groups, and contribute to sustainable poverty alleviation. Full article

The mesh size significantly affects the accuracy and computational efficiency of finite-element analysis (FEA) simulations. This study investigates mesh regularization to mitigate mesh dependency, align numerical results with experimental data, and optimize the computational time for carbon/epoxy composites. Mesh regularization was implemented using the ^∗MAT_ADD_GENERALIZED_DAMAGE (MAGD) model in LS-DYNA, which incorporates a scaling factor based on the ply orientation and stress triaxiality to adjust the material failure criterion. To address the limitations of trial-and-error methods for determining scaling factors, four analytical models were developed to predict these factors as functions of element size. These predictions were validated against experimentally derived scaling factors for unidirectional carbon/epoxy composites across three ply orientations (0°, 45°, and 90°) and three stress triaxiality conditions (tension, compression, and shear) using mesh sizes ranging from 0.5 mm to 1.5 mm. The scaling factor effectively reduced the mesh dependency in the tested configurations. A clear relationship between ply orientation and mesh regularization was established; however, no definitive correlation was observed with stress triaxiality. Among the theoretical approaches, the stress degradation model yielded the most consistent predictions, although discrepancies with the experimental results indicate the need for further refinement. This study proposes integrating scaling factors into a material model as a practical approach to mesh regularization for orthotropic materials and evaluates existing theoretical models for predicting these factors. Full article

Air liquefaction systems are essential in cryogenic engineering and energy storage, yet their performance is often constrained by significant exergy destruction. This study develops an exergy-based assessment of the Linde–Hampson air liquefaction cycle to identify dominant sources of inefficiency and explore strategies for improvement. The analysis shows that throttling (≈41%) and compression (≈40%) represent the major contributions to exergy losses, followed by finite-temperature heat transfer (≈15%) in the recuperative heat exchanger. To mitigate these losses, fractional throttling and optimized inlet conditions are proposed, leading to reduced compressor work and improved overall efficiency. A comparative study of a two-stage throttling configuration demonstrates a decrease in throttling-related exergy destruction to approximately 30%. Reverse Pinch analysis is employed to verify the thermal coupling of hot and cold streams and to determine the minimum feasible temperature difference. The design optimization of the recuperative heat exchanger identifies an optimal velocity ratio that minimizes pressure losses and quantifies how compression pressure affects the required heat transfer surface area. The results provide a systematic framework for improving the thermodynamic performance of air liquefaction cycles, highlighting exergy analysis as a powerful tool for guiding structural modifications and functional optimization. Full article

Fine particulate matter (PM_2.5) can have considerable negative effects on human health. An increasing number of scholars are finding that green space can not only decrease PM_2.5 levels but also exacerbate PM_2.5 levels. Few scholars have provided comprehensive reviews on this subject. This study reviews research from 1995 to 2024, including 118 studies based on a search of three databases (Web of Science, Engineering Village, and ResearchGate). We found that at the macro (e.g., city-wide) and meso (e.g., high-density built-up areas) scales, most studies report that green space can play a positive role in mitigating PM_2.5 concentrations. However, at the micro-scale under specific temporal conditions, green spaces may increase PM_2.5 concentrations in some micro-environments. Whether vegetation reduces or elevates local PM_2.5 levels, these processes are influenced by various factors, including green space configuration, microclimatic conditions, built-environment characteristics, and emission source distributions. Mechanistically, vegetation can both decrease ambient PM_2.5 levels through deposition, adsorption, and absorption and block its dispersion. In the process of exploring and optimizing the effect of greening on PM_2.5, we should not only consider these factors in isolation but also account for the environmental factors that can significantly change the effect. Based on our review of a myriad of studies from different disciplinary backgrounds and scales, we propose an optimization strategy consisting of promoting ventilation through weakening sources and strengthening sinks. Full article

In recent years, the fusion of camera data with LiDAR measurements has emerged as a powerful approach to enhance spatial understanding. This study introduces a novel, hardware-agnostic methodology that generates colourised point clouds from mechanical LiDAR using multiple camera inputs, providing complete 360-degree coverage. The primary innovation lies in its robustness under low-light conditions, achieved through the integration of a low-light image enhancement module within the fusion pipeline. The system requires initial calibration to determine intrinsic camera parameters, followed by automatic computation of the geometric transformation between the LiDAR and cameras—removing the need for specialised calibration targets and streamlining the setup. The data processing framework uses colour correction to ensure uniformity across camera feeds before fusion. The algorithm was tested using a Velodyne Puck Hi-Res LiDAR and a four-camera configuration. The optimised software achieved real-time performance and reliable colourisation even under very low illumination, successfully recovering scene details that would otherwise remain undetectable. Full article

This study evaluates the feasibility of using a lightweight, off-the-shelf sensing system for short-term stress detection during exergaming. Most existing studies in stress detection compare rest and task conditions, providing limited insight into continuous stress dynamics, and there is no agreement on optimal sensor configurations. To address these limitations, we investigated dynamic stress responses induced by a cognitive–motor task designed to simulate rehabilitation-like scenarios. Twenty-three participants completed the experiment, providing electrodermal activity (EDA), blood volume pulse (BVP), self-report, and in-game data. Features extracted from physiological signals were analyzed statistically, and shallow machine learning classifiers were applied to discriminate among stress levels. EDA-based features reliably differentiated stress conditions, while BVP features showed less consistent behavior. The classification achieved an overall accuracy of 0.70 across four stress levels, with most errors between adjacent levels. Correlations between EDA dynamics and perceived stress scores suggested individual variability possibly linked to chronic stress. These results demonstrate the feasibility of low-cost, unobtrusive stress monitoring in interactive environments, supporting future applications of dynamic stress detection in rehabilitation and personalized health technologies. Full article

Grinding semiconductor wafers with high hardness, such as SiC, remains a significant challenge due to the need to maximize material removal rates while minimizing subsurface damage. In the back-grinding process, two key parameters—the elastic modulus (Eb) of the grinding wheel bond and the fracture toughness (K_IC) of the wafer—play a critical role in governing the behavior of diamond and the extent of wafer damage. This study systematically investigated the effect of Eb and K_IC on diamond protrusion height (h_p), surface roughness (Ra), grinding forces, and the morphology of generated debris. The study encompassed four wafer types—Si, GaP, sapphire, and ground SiC—using five Back-Grinding Wheels (BGWs), with Eb ranging from 95.24 to 131.38 GPa. A log–linear empirical relationship linking ℎₚ to Eb and K_IC was derived and experimentally verified, demonstrating high predictive accuracy across all wafer–wheel combinations. Surface roughness (Ra) was measured in the range of 0.486 − 1.118𝜇m, debris size ranged from 1.41 to 14.74𝜇m, and the material removal rate, expressed as a thickness rate, varied from 555 to 1546𝜇m/h (equivalent to 75−209 mm³/min using an effective processed area of 81.07 cm²). For SiC, increasing the bond modulus from 95.24 to 131.38 GPa raised the average h_p from 9.0 to 1.2 um; the removal rate peaked at 122.07 GPa, where subsurface damage (SSD) was minimized, defining a practical grindability window. These findings offer practical guidance for selecting grinding wheel bond compositions and configuring process parameters. In particular, applying a higher Eb is recommended for harder wafers to ensure sufficient diamond protrusion, while an appropriate dressing must be employed to prevent adverse effects from excessive stiffness. By balancing removal rate, surface quality, and subsurface damage constraints, the results support industrial process development. Furthermore, the protrusion model proposed in this study serves as a valuable framework for optimizing bond design and grinding conditions for both current and next-generation semiconductor wafers. Full article

Buildings with electrified heat pump systems, onsite photovoltaic (PV) generation, and energy storage offer strong potential for demand flexibility. This study compares two storage configurations, thermal energy storage (TES) and battery energy storage (BESS), to evaluate their impact on cooling performance and cost savings. A Model Predictive Control (MPC) framework was developed to optimize system operations, aiming to minimize costs while maintaining occupant comfort. Results show that both configurations achieve substantial savings relative to a baseline. The TES system reduces daily operating costs by about 50%, while the BESS nearly eliminates them (over 90% reduction) and cuts grid electricity use by more than 65%. The BESS achieves superior performance because it can serve both the controllable heating, ventilation, and air conditioning (HVAC) system and the home’s broader electrical loads, thereby maximizing PV self-consumption. In contrast, the TES primarily influences the thermal load. These findings highlight that the choice between thermal and electrical storage greatly affects system outcomes. While the BESS provides a more comprehensive solution for whole-home energy management by addressing all electrical demands, further techno-economic evaluation is needed to assess the long-term feasibility and trade-offs of each configuration. Full article

In the context of carbon neutrality, ammonia-coal co-firing is considered an effective way to reduce emissions from coal-fired units. This paper takes a 125 MW tangential combustion boiler as the research object and combines CFD and CHEMKIN models to study the effects of ammonia injection position (L1–L3) and blending ratio (0–30%) on combustion characteristics and NO generation. The results indicate that L1 (same-layer premixed injection) can form a continuous and stable flame structure and maintain low NO emissions. L2 (fuel-staged configuration) shows the highest burnout rate and strong denitration potential under high mixing conditions, while L3 has an unstable flow field and the worst combustion structure. NO emissions show a typical “first rise and then fall” trend with the blending ratio. L1 performs optimally in the range of 15–20%, and L2 peaks at 20%. Mechanism analysis indicates that R430 is the main NO generation reaction, while R15 and R427 dominate the NO reduction process. The synergistic reaction between NH_x free radicals and coke can effectively inhibit the formation of NO and improve combustion efficiency. Full article