Search Results (80,065)

Green facades, commonly referred to as vertical plant systems, offer sustainable solutions. They improve the energy efficiency of buildings, reduce energy consumption, and positively impact the microclimate both at the microscale and at the urban level. Their ability to regulate temperature and improve thermal comfort, including mitigating the heat island effect, makes them a valuable element of sustainable architectural design. They also contribute to reduced energy consumption, reduced noise, mitigation of air pollution, and aesthetic and wind protection. The main goal of the study was to analyse the cooling effectiveness of green walls in a transitional temperate climate zone. The study was conducted on two experimental models located on the campus of the Wrocław University of Environmental and Life Sciences and at the Research and Educational Station in a suburban area. Both locations had different characteristics: the former contained urban development, while the latter contained open and sparsely developed areas. On warm and sunny days, the cooling effects of the systems were observed independently for both locations and their exposures. For data acquisition at a distance of 5 cm from the plants, a higher data concentration and a lower variability in the mean temperature drop were observed. In the same group, on sunny days, the cooling effect averaged 4–7 °C and depended on the location. On cloudy days, the mean maximum cooling in this group did not exceed 4 °C. Full article

To further enhance economic efficiency and optimize energy conservation and emission reduction performance, an optimized energy management strategy (EMS) tailored for the hybrid power system of rubber-tyred gantry cranes is proposed. Wavelet packet decomposition (WPD) was employed as the signal processing approach, and this method was further integrated with EMS for hybrid power systems. Through a three-layer progressive architecture comprising WPD frequency–domain decoupling, fuzzy logic real-time adjustment, and PSO offline global optimization, a cooperative optimization mechanism has been established in this study between the frequency-domain characteristics of signals, the physical properties of energy storage components, and the real-time and long-term states of the system. Firstly, the modeling and simulation of the power system were conducted. Subsequently, an EMS based on WPD and limit protection was developed: the load power curve was decomposed into different frequency bands, and power allocation was implemented via the WPD algorithm. Meanwhile, the operating states of lithium batteries and supercapacitors were adjusted in combination with state of charge limits. Simulation results show that this strategy can achieved reasonable allocation of load power, effectively suppressed power fluctuations of the auxiliary power unit system, and enhanced the stability and economy of the hybrid power system. Afterward, a fuzzy controller was designed to re-allocate the power of the hybrid energy storage system (HESS), with energy efficiency and battery durability set as optimization indicators. Furthermore, particle swarm optimization algorithms were adopted to optimize the EMS. The simulation results indicate that the optimized EMS enabled more reasonable power allocation of the HESS, accompanied by better economic performance and control effects. The proposed EMS demonstrated unique system-level advantages in enhancing energy efficiency, extending battery lifespan, and reducing the whole-life cycle cost. Full article

Lower limb exoskeleton systems require accurate recognition of the wearer’s movement intentions prior to action execution in order to achieve natural and smooth human–machine interaction. Surface electromyography (sEMG) signals can reflect neural activation of muscles before movement onset, making them a key physiological source for movement intention recognition. To improve sEMG-based recognition performance, this study proposes an innovative deep learning framework, IFIANet. First, a CNN–TCN-based spatiotemporal feature learning network is constructed, which efficiently models and represents multi-scale temporal–frequency features while effectively reducing model parameter complexity. Second, an IFIA (Frequency-Informed Integration Attention) module is designed to incorporate global frequency information, compensating for frequency components potentially lost during time–frequency transformations, thereby enhancing the discriminability and robustness of temporal–frequency features. Extensive ablation and comparative experiments on the publicly available MyPredict1 dataset demonstrate that the proposed framework maintains stable performance across different prediction times and achieves over 82% average recognition accuracy in within-experiments involving nine participants. The results indicate that IFIANet effectively fuses local temporal–frequency features with global frequency priors, providing an efficient and reliable approach for sEMG-based movement intention recognition and intelligent control of exoskeleton systems. Full article

Radar-based hand motion recognition (HMR) presents several challenges, including sensor interference, clutter, and the limitations of small datasets, which collectively hinder the performance and real-time deployment of deep learning (DL) models. To address these issues, this paper introduces a novel real-time HMR framework that integrates advanced signal pre-processing, a hybrid convolutional neural network–support vector machine (CNN–SVM) architecture, and efficient hardware deployment. The pre-processing pipeline applies filtration, squared absolute value computation, and normalization to enhance radar data quality. To improve the robustness of DL models against noise and clutter, time-series radar signals are transformed into binarized images, providing a compact and discriminative representation for learning. A hybrid CNN-SVM model is then utilized for hand motion classification. The proposed model achieves a high classification accuracy of 98.91%, validating the quality of the extracted features and the efficiency of the proposed design. Additionally, it reduces the number of model parameters by approximately 66% relative to the most accurate recurrent baseline (CNN–GRU–SVM) and by up to 86% relative to CNN–BiLSTM–SVM, while achieving the highest SVM test accuracy of 92.79% across all CNN–RNN variants that use the same binarized radar images. For deployment, the model is quantized and implemented on two System-on-Chip (SoC) FPGA platforms—the Xilinx Zynq ZCU102 Evaluation Kit and the Xilinx Kria KR260 Robotics Starter Kit—using the Vitis AI toolchain. The system achieves end-to-end accuracies of 96.13% (ZCU102) and 95.42% (KR260). On the ZCU102, the system achieved a 70% reduction in execution time and a 74% improvement in throughput compared to the PC-based implementation. On the KR260, it achieved a 52% reduction in execution time and a 10% improvement in throughput relative to the same PC baseline. Both implementations exhibited minimal accuracy degradation relative to a PC-based setup—approximately 1% on ZCU102 and 2% on KR260. These results confirm the framework’s suitability for real-time, accurate, and resource-efficient radar-based hand motion recognition across diverse embedded environments. Full article

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model. Full article

Detecting small drones in Infrared (IR) sequences poses significant challenges due to their low visibility, low resolution, and complex cluttered backgrounds. These factors often lead to high false alarm and missed detection rates. This paper frames drone detection as a spatio-temporal anomaly detection problem and proposes a remarkably lightweight pipeline solution (well-suited for edge applications), by employing a statistical temporal anomaly detector (known as the temporal Reed Xiaoli (TRX) algorithm), in parallel with a light-weight convolutional neural network known as the TCRNet. While the TRX detector is unsupervised, the TCRNet is trained to discriminate between drones and clutter using spatio-temporal patches (or chips). The confidence maps from both modules are additively fused to localize drones in video imagery. We compare our method, dubbed TRX-TCRnet, to other state-of-the-art drone detection techniques using the Detection of Aircraft Under Background (DAUB) dataset. Our approach achieves exceptional computational efficiency with only 0.17 GFLOPs with 0.83 M parameters, outperforming methods that require 145–795 times more computational resources. At the same time, the TRX–TCRNet achieves one of the highest detection accuracies (mAP₅₀ of 97.40) while requiring orders of magnitude fewer computational resources than competing methods, demonstrating unprecedented efficiency–performance trade-offs for real-time applications. Experimental results, including ROC and PR curves, confirm the framework’s exceptional suitability for resource-constrained environments and embedded systems. Full article

This study investigates whether Battery Electric Vehicles (BEVs) or Fuel Cell Electric Vehicles (FCEVs) represent the superior alternative to conventional vehicles for last-mile delivery, with a particular focus on large enterprises that prioritize both economic feasibility and environmental performance. Life Cycle Assessment and Life Cycle Cost methodologies are applied to evaluate both technologies across the full cradle-to-grave life cycle within a unified framework. The functional unit is defined as one kilometer traveled by a BEV or FCEV in last-mile transportation, and the system boundary includes vehicle manufacturing, operation, maintenance, and end-of-life treatment. The environmental impacts are assessed using the ReCiPe 2016 Midpoint (H) method implemented in OpenLCA 2.0.4, and normalization follows the standards provided by the official ReCiPe 2016 framework. The East China Power Grid serves as the baseline electricity mix for the operational stage. Regarding GHG emissions, FCEVs demonstrate a 12.36% reduction in carbon dioxide (CO₂) emissions compared to BEVs. This reduction is particularly significant during the operational phase, where FCEVs can lower CO₂ emissions by 53.51% per vehicle relative to BEVs, largely due to hydrogen energy’s higher efficiency and durability. In terms of economic costs, BEVs hold a slight advantage over FCEVs, costing approximately 0.8 RMB/km/car less. However, during the manufacturing phase, FCEVs present greater environmental challenges. It is recommended that companies fully consider which environmental issues they wish to make a greater contribution to when selecting vehicle types. This study provides insight and implications for large companies with financial viability concerns about environmental impact regarding selecting the two types of vehicles for last-mile transportation. The conclusions offer guidance for companies assessing which vehicle technology better aligns with their long-term operational and sustainability priorities. It can also help relevant practitioners and researchers to develop solutions to last-mile transportation from the perspective of different enterprise sizes. Full article

The low thermal conductivity of phase-change materials (PCMs) remains a key limitation in latent heat thermal energy storage systems, leading to slow melting and incomplete energy recovery. To address this challenge, this study explores extended Y-Fin geometries as a novel heat transfer enhancement strategy within a concentric-tube latent heat thermal energy storage configuration. Six fin designs, derived from a baseline Y-shaped structure, were numerically compared to assess their influence on the melting and solidification behavior of stearic acid. A two-dimensional transient enthalpy–porosity model was developed and rigorously verified through grid, temporal, and residual convergence analyses. The results indicate that fin geometry plays a critical role in enhancing heat transfer within the PCM domain. The extended Y-Fin configuration achieved the fastest melting time, 28% shorter than the baseline Y-Fin case, due to improved thermal penetration and bottom-region accessibility. Additionally, the thermal performance was evaluated using nano-enhanced PCMs (10% Al₂O₃ and CuO in stearic acid) and paraffin wax. The addition of Al₂O₃ nanoparticles significantly improved thermal conductivity, while paraffin wax exhibited the shortest melting duration due to its lower melting point and latent heat. This study introduces an innovative fin architecture combining extended conduction paths and improved convective reach for efficient latent heat storage systems. Full article

The contamination of water bodies by polycyclic aromatic hydrocarbons (PAHs) poses a significant concern for the ecological systems, along with public health. Magnetic adsorption stands out as a green and practical solution for treating polluted water. To make the process more efficient and economical, it is important to create materials that not only absorb contaminants effectively but also allow for easy recovery and reuse. This study proposes a simple yet effective method for coating Fe₃O₄ nanoparticles with hydroxypropyl-β-cyclodextrin polymer (HP-β-CDCP). The physicochemical properties of the synthesized sorbent were characterized using a transmission electron microscope (TEM), Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and Vibrating Sample Magnetometer (VSM) analysis. The adsorption performance of HP-β-CDCP/Fe₃O₄ nanoparticles was well-described by the pseudo-second-order kinetic model, thermodynamic analysis, and the Freundlich isotherm model, indicating multiple interaction mechanisms with PAHs, such as π–π interactions, hydrogen bonding, and van der Waals forces. Using HP-β-CDCP/Fe₃O₄ nanoparticles as the adsorbent, the purification rates for the fifteen representative PAHs were achieved within the range of 33.9–93.1%, compared to 15.3–64.8% of the unmodified Fe₃O₄ nanoparticles. The adsorption of all studied PAHs onto HP-β-CDCP/Fe₃O₄ nanocomposites was governed by pH, time, and temperature. Equilibrium in the uptake mechanism was obtained within 15 min, with the largest adsorption capacities for PAHs in competitive adsorption mode being 6.46–19.0 mg·g⁻¹ at 20 °C, pH 7.0. This study points to the practical value of incorporating cyclodextrins into tailored polymer frameworks for improving the removal of PAHs from polluted water. Full article

Arbitration efficiency is widely recognized as a factor influencing outcomes in construction disputes. To increase the chance of finding and designating the best-fit arbitrator, a large number of candidate profiles must be investigated, which is an overwhelming, time-consuming process. This study develops and evaluates a large language model (LLM)- enabled framework for arbitrator selection based on dispute details and predefined expert criteria. To reach this goal, 500 standardized, anonymized arbitrator resumes were evaluated using a unified scoring structure. These resumes were scored and classified using two GPT-5 models with different levels of detail in their prompts. The results of these models were then compared with expert evaluations to assess their ability to replicate human decision-making patterns in resume evaluation and classification. According to the results, the second model, with a high level of detail in its prompt structure, achieved an accuracy of 84%, while the first model, with a concise prompt that provides only a brief description of the experts’ expectations, achieved an overall accuracy of 53%. As can be concluded, the accuracy of the LLM-assisted resume analysis framework improves when guided by a detailed, expert-aligned prompt structure. From a research perspective, this study’s results highlight the importance of prompt engineering in an AI-assisted decision-support system for professional evaluation tasks. Since this framework is limited to resumes in English, future research should examine the effectiveness of LLMs in evaluating and classifying resumes in languages other than English. Moreover, future studies might consider replicating this study using other large language models to compare precision and accuracy across different LLMs. Full article

Wave energy is gaining momentum as a viable and environmentally sustainable source of renewable power, with the potential to contribute significantly to the global energy mix. Central to wave energy conversion is the power take-off system, where electromagnetic generators play a crucial role in determining overall system performance, reliability, and efficiency. This paper provides a review of wave energy conversion devices and classifies the main power take-off mechanisms. It evaluates and compares key generator types based on their performance under wave energy conditions. Among these, Permanent Magnet Synchronous Generators have demonstrated strong potential due to their high efficiency, power density, and suitability for low-speed direct drive configurations typical of wave environments. The review presents a detailed analysis of advanced permanent magnet generator topologies, focusing on structural designs, control methods, and wave-specific trade-offs. It also investigates hierarchical control strategies, where high-level decisions are based on wave conditions and low-level control ensures accurate generator operation. The paper aims to provide a broad perspective on the design and control of electromagnetic generators for wave energy systems. Full article

While driving rapid economic growth, China’s real estate industry has also caused severe environmental issues. The green development and transformation of this sector have become crucial for achieving the “dual carbon” goals. Accurately evaluating the green development efficiency of real estate enterprises and analyzing pathways for improvement are therefore essential. The green development efficiency of real estate enterprises was calculated in this study. Building upon this foundation, the allocative effects of environmental regulations, technological innovation, and transformation willingness on efficiency improvement were explored. The findings reveal: (1) The average green efficiency of the sample enterprises is 0.758, showing an overall increasing trend, but with significant inter-firm differences; (2) Three pathways for green transformation exist: co-driven by environmental investment and transition intentions, co-driven by R&D innovation and environmental penalties, and driven solely by environmental regulations. (3) The government can effectively enhance corporate green development efficiency by establishing appropriate environmental regulation intensity. Enterprises, in turn, need to increase innovation investment and transformation intentions while establishing environmental management systems. Full article

The construction of a digital government is a significant initiative to modernize the national governance system and enhance governance capabilities, with the creation of public value focused on optimizing the supply level of basic public services within improved governance efficiency. This research begins with public value theory and technology empowerment theory to explain the theoretical mechanisms by which digital government can promote the supply level of basic public services. Utilizing panel data from 31 provinces in China from 2017 to 2021, this study investigates the impact of digital government construction on the supply level of basic public services and further examines the moderating effects of government support, the level of digital technology development, and regional heterogeneity. The research findings indicate that digital government construction can significantly enhance the supply level of basic public services, with more pronounced effects in regions where government support is strong or the level of digital technology development is high. Analysis of regional heterogeneity shows that the improvement in the supply level of basic public services due to digital government construction is more significant in the eastern region compared to the central and western regions. This study, based on the practice of digital government construction, provides a theoretical basis and decision-making reference for optimizing the top-level design of digital government, improving the supply level of public services, and achieving the integration of “digital + public services.” Full article

Background/Objectives: Histopathological images are fundamental for the morphological diagnosis and subtyping of lung cancer. However, their high resolution, color diversity, and structural complexity make automated segmentation highly challenging. This study aims to address these challenges by developing a novel hybrid metaheuristic approach for multilevel image thresholding to enhance segmentation accuracy and computational efficiency. Methods: An adaptive hybrid metaheuristic algorithm, termed SCSOWOA, is proposed by integrating the Sand Cat Swarm Optimization (SCSO) algorithm with the Whale Optimization Algorithm (WOA). The algorithm combines the exploration capacity of SCSO with the exploitation strength of WOA in a sequential and adaptive manner. The model was evaluated on histopathological images of lung cancer from the LC25000 dataset with threshold levels ranging from 2 to 12, using PSNR, SSIM, and FSIM as performance metrics. Results: The proposed algorithm achieved stable and high-quality segmentation results, with average values of 27.9453 dB in PSNR, 0.8048 in SSIM, and 0.8361 in FSIM. At the threshold level of T = 12, SCSOWOA obtained the highest performance, with SSIM and FSIM scores of 0.9340 and 0.9542, respectively. Furthermore, it demonstrated the lowest average execution time of 1.3221 s, offering up to a 40% improvement in computational efficiency compared with other metaheuristic methods. Conclusions: The SCSOWOA algorithm effectively balances exploration and exploitation processes, providing high-accuracy, low-variance, and computationally efficient segmentation. These findings highlight its potential as a robust and practical solution for AI-assisted histopathological image analysis and lung cancer diagnosis systems. Full article

Country parks are an important instrument for implementing China’s strategies on ecological civilization and integrated urban–rural development. This study conducted field surveys in seven country parks of Shanghai. Meanwhile, stakeholder seminars were organized with local residents and park authorities. To assess visitor satisfaction, a questionnaire survey was administered both on-site and online. Through case analysis and a policy review, this study systematically identifies key challenges in leveraging country parks for rural revitalization. The findings indicate that visitors highly value the ecological qualities of the parks, and basic infrastructure like roads and resting facilities generally meets expectations. However, shuttle services and smart guiding systems remain notable shortcomings that hinder the overall visitor experience. Moreover, gaps in service quality, local cultural representation, and the depth of nature education constitute the primary weaknesses affecting visitor satisfaction. Regarding rural revitalization, this study identifies four main limitations in the contribution of country parks: (1) Inadequate functional positioning and weak integration with surrounding resources; (2) Low land use efficiency and an unbalanced provision of supporting facilities; (3) Homogenized industrial formats with limited innovation and integration capacity; and (4) Restricted participation of local farmers and underdeveloped multi-stakeholder governance mechanisms. To address these issues, this study proposes four strategic recommendations: (1) Develop distinctive local brands and strengthen synergies with surrounding resources; (2) Promote mixed land use and enhance supporting service facilities; (3) Foster diversified business formats and facilitate the value realization of ecological products; and (4) Expand income-generation channels for farmers and improve multi-stakeholder governance frameworks. The research demonstrates that optimizing the functions of country parks can improve ecological and recreational services and help establish an integrated “ecology–industry–community” framework through industrial chain extension and community participation, thereby supporting rural revitalization. Full article