Search Results (853)

With the large-scale development of smart grids and the integration of renewable energy, the operational complexity and load volatility of power systems have increased significantly, placing higher demands on the accuracy and timeliness of electricity load forecasting. However, existing methods struggle to capture the nonlinear and volatile characteristics of load sequences, often exhibiting insufficient fitting and poor generalization in peak and abrupt change scenarios. To address these challenges, this paper proposes a deep learning model named CGA-LoadNet, which integrates a one-dimensional convolutional neural network (1D-CNN), gated recurrent units (GRUs), and a self-attention mechanism. The model is capable of simultaneously extracting local temporal features and long-term dependencies. To validate its effectiveness, we conducted experiments on a publicly available electricity load dataset. The experimental results demonstrate that CGA-LoadNet significantly outperforms baseline models, achieving the best performance on key metrics with an $R^2$ of 0.993, RMSE of 18.44, MAE of 13.94, and MAPE of 1.72, thereby confirming the effectiveness and practical potential of its architectural design. Overall, CGA-LoadNet more accurately fits actual load curves, particularly in complex regions, such as load peaks and abrupt changes, providing an efficient and robust solution for short-term load forecasting in smart grid scenarios. Full article

Lighting systems in sports venues have a significant impact on both the user experience and quality of events. However, owing to the large number of luminaires, high individual lamp power, and strict lighting standards, the lighting energy consumption of sports venues is high, accounting for approximately 30% of the total energy use. Therefore, introducing natural light through appropriate means during non-event periods and ensuring adequate lighting via collaborative control between natural light and artificial-lighting systems are crucial for reducing the lighting energy consumption of sports venues. Light pipe systems are a novel form of natural lighting and can effectively supplement artificial lighting. However, no clear methodology for selecting light pipes or designing light pipe systems in high spaces such as sports venues currently exists. Furthermore, developing a method for collaborative control between artificial-lighting systems and light pipe systems under various natural light conditions is an urgent issue in the optimization of the design of sports venue lighting. Therefore, we considered a conventional sports venue as a case study. By conducting HOLIGILM simulation experiments, we first investigated the factors affecting the transmission efficiency of light pipe systems and proposed optimization parameters for system design in terms of the pipe diameter, length, and configuration. Subsequently, using the Chinese Standard for Daylighting Design of Buildings (GB50033-2013) and the construction cost as optimization objectives, we optimized the pipe diameter, length, and placement of the light pipe system by applying non-dominated sorting genetic algorithm II. The simulation results showed that the optimized design of the light pipe system in the sports venue achieved a daylight factor of 1%, which met the standard requirements while reducing the construction cost by approximately 27%. Finally, to meet the indoor Class I (non-tournament) lighting standards stipulated in the Standard for Lighting Design and Test of Sports Venues (JGJ153-2016) and taking energy conservation as the optimization goal, we proposed a strategy for achieving collaborative control between the light pipe system and artificial-lighting system based on a greedy algorithm. The results indicated that under various weather conditions, the collaborative control strategy enabled the lighting of the field of play to meet Class I illuminance standards while reducing the annual lighting energy consumption by 35%. Thus, this study provides a methodological reference for optimizing the design of light pipe systems and achieving collaborative control with artificial-lighting systems in large-scale venues. Although these results were obtained based on meteorological data from Xi’an, China, the research method presented in this study can also be applied to other regions. The study provides a methodological reference for the design and optimization of light pipe systems and associated control systems to operate light pipes alongside artificial lighting systems in sports venues and other large multistory buildings. Full article

This study explores the role of ethical leadership in fostering employee green behaviors (EGBs) within higher education institutions (HEIs) in the UAE. While environmental initiatives are increasingly being integrated into university operations, there has been limited empirical research examining how leadership styles influence pro-environmental behaviors among academic staff. Using a mixed-methods sequential explanatory design, our study surveyed 105 HEI employees and conducted in-depth interviews with 6 of the participants. The quantitative findings reveal a moderate but significant positive correlation between ethical leadership (EL) and EGB (ρ = 0.314, p < 0.001). The reliability scores for both EL (α = 0.888) and EGB (α = 0.754) confirmed the internal consistency of the measurement items used. The qualitative insights support the theoretical foundation drawn from Social Learning, Value–Belief–Norm, and Environmental Stewardship Theories. Employees reported modeling their green behaviors on observable leadership actions aligning with their shared moral values. A key distinction emerged between authentic and performative green behaviors, with employees responding more positively to leaders who modeled consistency and sincerity. This study concludes that ethical leadership significantly influences the environmental culture in HEIs by embedding sustainability into daily practices and institutional values. This research addresses a regional and theoretical gap, contextualizing ethical leadership in the Middle Eastern academic setting and offering practical implications for leadership development, policy alignment, and sustainable cultural transformation. Full article

As a core component of aero-engines, double-row ball bearings’ lubrication performance directly impacts the operational stability of the aircraft engine. However, existing under-race lubrication designs primarily rely on empirical knowledge, with insufficient understanding of the complex oil–air two-phase flow mechanisms, leading to bottlenecks in optimizing lubrication efficiency. Therefore, based on the computational fluid dynamics (CFD) method, a two-phase flow model for double-row ball bearings was established to systematically analyze the influence patterns of key parameters—including rotational speed, oil supply rate, number of under-race holes, diameter of under-race holes, and oil properties (viscosity, density)—on the distribution of the oil–air two-phase flow. The findings reveal that (1) the oil in the circumferential direction of the bearing cavity exhibits periodic distribution characteristics correlated with the number of under-race holes; (2) the self-rotation effect of balls hinders the migration of oil toward the outer raceway region, resulting in a significant reduction in the oil volume fraction within the bearing cavity; (3) compared with the single-sided oil supply configuration, the double-sided oil supply structure demonstrates superior lubrication performance. These research results provide theoretical support and reference data for the optimal design of under-race lubrication systems for double-row ball bearings. Full article

The evaluation and enhancement of image aesthetics play a pivotal role in the development of visual media, impacting fields including photography, design, and computer vision. Composition, a key factor shaping visual aesthetics, significantly influences an image’s vividness and expressiveness. However, existing image optimization methods face practical challenges: compression-induced distortion, imprecise object extraction, and cropping-caused unnatural proportions or content loss. To tackle these issues, this paper proposes an image aesthetic evaluation with composition and similarity (IACS) method that harmonizes composition aesthetics and image similarity through a unified function. When evaluating composition aesthetics, the method calculates the distance between the main semantic line (or salient object) and the nearest rule-of-thirds line or central line. For images featuring prominent semantic lines, a modified Hough transform is utilized to detect the main semantic line, while for images containing salient objects, a salient object detection method based on luminance channel salience features (LCSF) is applied to determine the salient object region. In evaluating similarity, edge similarity measured by the Canny operator is combined with the structural similarity index (SSIM). Furthermore, we introduce a Framework for Image Aesthetic Evaluation with Composition and Similarity-Based Optimization (FIACSO), which uses semantic segmentation and generative adversarial networks (GANs) to optimize composition while preserving the original content. Compared with prior approaches, the proposed method improves both the aesthetic appeal and fidelity of optimized images. Subjective evaluation involving 30 participants further confirms that FIACSO outperforms existing methods in overall aesthetics, compositional harmony, and content integrity. Beyond methodological contributions, this study also offers practical value: it supports photographers in refining image composition without losing context, assists designers in creating balanced layouts with minimal distortion, and provides computational tools to enhance the efficiency and quality of visual media production. Full article

Pumped storage power stations are effective stabilizers and regulators of the power grids. However, during the transient process, especially the operating point entering the S-shaped region, the internal flow patterns and pressure pulsations in the pump–turbine unit change violently, seriously affecting the safety of the power stations, which requires enough optimizations in the design stage of the pump–turbine. In this paper, to explore the key factors which influence the evolutions of flow patterns and pressure pulsations during the runaway process, three pump–turbine runners with different inlet blade lean, including positive angle, no angle and negative angle, were selected to simulate by using the three-dimensional method. The results show that the changes in the inlet blade lean angles have significant effects on the variation periods and maximum values of the macro parameters during the runaway process, and especially the runner with no lean angle results in the smallest oscillation periods and pressure pulsations but enlarges the runner radial forces. In addition, backflows generate from the hub side under the cases with positive or no blade lean angle, while those occur from the shroud side due to the negative angle. The results provide a basic reference for the design of the pump–turbine. Full article

Wireless sensor networks (WSNs) are deployed in terrestrial environments. However, on the sea surface, sensor nodes can drift due to ocean currents and wind; thus, network topologies continuously evolve, and the communication between nodes is frequently disrupted. These unstable connections significantly degrade data transmission stability and overall network performance. These problems are particularly significant in maritime regions where the sea state changes rapidly, thus imposing stringent technical requirements on the design of long-range, reliable, low-latency, and persistent sensing systems. This study proposes a wireless sensor network architecture for sea surface drifting nodes, which is termed Drift-Aware Routing and Clustering with Recovery (DARCR). The proposed system consists of three major components: (1) an enhanced dynamic drift model that more accurately predicts node movement for realistic ocean conditions; (2) a cluster-based framework that prevents disconnection and minimizes delay, which improves cluster stability and adaptability to dynamic environments through refined clustering and route setup mechanisms; and (3) a self-recovery routing strategy for re-establishing communication after disconnection. The proposed method is evaluated using ocean current data from the Copernicus Ocean Data Center simulating a 60-h drifting scenario around the central Taiwan Strait. The experimental results show that the average hourly disconnection rate is maintained at 6.2%, with a variance of 0.31%, and the transmission of newly sensed data is completed within 3 to 5 s, with a maximum delay of approximately 10 s. These findings demonstrate the feasibility of maintaining communication stability and low-latency data transmission for sea surface WSNs that operate in highly dynamic marine conditions. Full article

Compacted and degraded soils pose increasing challenges to agricultural practices, necessitating innovative approaches to soil tillage. This paper evaluates the performance of a vibratory-enhanced moldboard plowing system, designed to improve energy efficiency and tillage quality under compacted and moisture-deficient conditions, typical of low-moisture soils. Field experiments were conducted across four distinct Romanian regions with varying soil types and climatic conditions, all characterized by significant compaction and limited soil moisture. The vibratory system, mounted directly on each plow body, employed sinusoidal oscillations generated by a DC moto-vibrator, to reduce soil adhesion and traction force requirements, thereby lowering fuel consumption. Key parameters including fuel consumption, working speed, soil fragmentation, weed incorporation, and traction force were measured and compared with the conventional plowing method. The results showed enhanced soil fragmentation and more effective residue incorporation, along with notable reductions in traction effort and fuel use at optimal oscillation settings. These findings highlight the potential of vibratory tillage to be used as a soil preparation method for compaction-prone areas, improving the soil structure while increasing operational energy efficiency. Full article

The cultivation of edible lilies is highly susceptible to weed infestation during its growth period, and the application of herbicides is often impractical, leading to the rampant growth of diverse weed species. Laser weeding, recognized as an efficient and precise method for field weed management, presents a novel solution to the weed challenges in lily fields. The accurate localization of weed regions and the optimal selection of laser targeting points are crucial technologies for successful laser weeding implementation. In this study, we propose a two-stage weed detection and localization method specifically designed for lily fields. In the first stage, we introduce an enhanced detection model named YOLO-Morse, aimed at identifying and removing lily plants. YOLO-Morse is built upon the YOLOv8 architecture and integrates the RCS-MAS backbone, the SPD-Conv spatial enhancement module, and an adaptive focal loss function (ATFL) to enhance detection accuracy in conditions characterized by sample imbalance and complex backgrounds. Experimental results indicate that YOLO-morse achieves a mean Average Precision (mAP) of 86%, reflecting a 3.2% improvement over the original YOLOv8, and facilitates stable identification of lily regions. Subsequently, a ResNet-based segmentation network is employed to conduct semantic segmentation on the detected lily targets. The segmented results are utilized to mask the original lily areas in the image, thereby generating weed-only images for the subsequent stage. In the second stage, the original RGB field images are first converted into weed-only images by removing lily regions; these weed-only images are then analyzed in the HSV color space combined with morphological processing to precisely extract green weed regions. The centroid of the weed coordinate set is automatically determined as the laser targeting point.The proposed system exhibits superior performance in weed detection, achieving a Precision, Recall, and F1-score of 94.97%, 90.00%, and 92.42%, respectively. The proposed two-stage approach significantly enhances multi-weed detection performance in complex environments, improving detection accuracy while maintaining operational efficiency and cost-effectiveness. This method proposes a precise, efficient, and intelligent laser weeding solution for weed management in lily fields. Although certain limitations remain, such as environmental lighting variation, leaf occlusion, and computational resource constraints, the method still exhibits significant potential for broader application in other high-value crops. Full article

This study examines the incorporation of biodiversity loss into the Life Cycle Assessment (LCA) of buildings, with a specific focus on the Danish construction sector. Motivated by the ecological crisis reflected in the Planetary Boundaries and the Kunming-Montreal Global Biodiversity Framework, it addresses regulatory gaps that prioritise climate indicators, such as Global Warming Potential (GWP), while largely ignoring biodiversity. The study analyses 73 Danish building cases for GWP and a custom method linking material quantities to ReCiPe 2016 endpoint data for biodiversity loss. The findings indicate key methodological issues include the quality of Environmental Product Declarations (EPDs), the regional relevance of assessment methods, and differences in European standards. While average GWP levels mostly meet upcoming Danish limits, variability, especially in Office and Other building categories, supports the need for differentiated regulations. Results show embodied impacts mainly drive GWP, while biodiversity loss is split between embodied and operational impacts. Detached and Terraced houses, which use more bio-based materials, have low embodied GWP but higher biodiversity loss, highlighting trade-offs in regenerative design. The shift in GWP impacts to end-of-life phases stresses the need to consider forest dynamics. Operational impacts rank similarly, despite differences in the data. The study concludes that progress toward regenerative design requires addressing climate and biodiversity together to avoid shifting environmental burdens. Full article

Rural residential structures account for a substantial share of carbon emissions within the construction industry. Enhancing building envelopes can diminish structural carbon emissions, thereby facilitating the attainment of “dual carbon” objectives. Current algorithm-driven research on the low-carbon retrofitting of residential building envelopes generally neglects temperate regions in low-latitude plateaus, often misses embodied carbon, and utilizes rather limited methodologies for issue identification. This study focuses on rural dwellings in Lijiang, utilizing a cross-validation method that incorporates sensitivity analysis, infrared thermal imaging, and energy efficiency criteria to systematically identify vulnerable regions in the building envelope. Consequently, critical issues are converted into optimization variables for the NSGA-II method, aiming to minimize both embodied carbon and operational energy usage. BAPV is concurrently implemented to partially mitigate renovation expenses. A weighted summation approach delineates stakeholder preferences, resulting in three optimum options. The findings reveal that all three methods correspond to their unique preferences, illustrating distinct trade-offs among energy efficiency, carbon reduction, and economic feasibility. The government-oriented approach attained an energy saving rate (ESR) of 45.11%, a life cycle carbon reduction (LCCR) of 1215.76 kgCO₂/m², and a dynamic payback period (DPP) of 3.65 years. The architect-oriented approach realized the highest energy savings and carbon reduction (45.41%, 1218.96 kgCO₂/m²), with a payback period of 3.99 years. The villager-oriented approach emphasized economic viability, achieving an energy savings rate of 41.55%, a carbon reduction of 1149.46 kgCO₂/m², and the shortest payback period of 2.87 years. This study provides an optimization process and reference parameters for building envelopes in a low-carbon design for residential buildings in temperate regions of low-latitude plateaus. Full article

Throughout history, humans have used the environment to build structures for defense. Fortifications are clear examples of buildings created to better protect important settlements and homes. Over time, these structures have gone beyond their original purpose of shielding residents inside and around the walls, now functioning as complex centers for political, economic, administrative, and cultural governance. Additionally, communication networks have been established between strongholds, forming a defensive system for a region or country. Therefore, Fortifications and military heritage exemplify typologies of heritage developed in an organic relationship with the unique environment shaped by human activities. Walled towns are safeguarded by maintaining their functions or being designated cultural heritage among these fortifications and military heritage. Through this study, we analyze the Haemi-eupseong Walled Town (in Korean ‘읍성,’ in Chinese ‘邑城’) as one of Korea’s typical walled towns concerning the attributes that reflect the authenticity according to ‘the Operational Guidelines for the Implementation of the World Heritage Convention’ and examine the efforts of the conservation management entity to sustain and utilize this authority by applying the theory and methodology outlined in the ICOMOS Guidelines on Fortifications and Military Heritage, officially adopted in 2021 by ICOMOS, the cultural heritage advisory body under UNESCO’s World Heritage Committee, to Haemi-eupseong. The goal is to explore theoretical approaches to heritage value, develop systematic methods for heritage utilization, and propose strategies for sustainably preserving the importance of heritage. Full article

The rural dwelling in southern Ecuador’s Andean region is the product of a long-term process of cultural and technical hybridization in which colonial typologies are overlaid with local building know-how adapted to temperate and cold climates. This study examines how intermediate spaces—portals, hallways, patios, porches, and corridors—operate as fundamental strategies for social sustainability. These spaces facilitate interaction between domestic interiors and the surrounding environment, mediate social relations, and accommodate productive, ritual, and everyday practices. Methodologically, the research integrates morphological and typological analysis with ethnographic methods and detailed graphic representations, yielding a spatial ethnography of thirty-five dwellings distributed across distinct ecological zones of Loja Province. The findings reveal how intermediate spaces undergo transformation, appropriation, and reconfiguration over time, demonstrating notable functional adaptability while maintaining cultural continuity. Beyond environmental and climatic functions, these spaces act as vital hubs of community life, sustaining intergenerational knowledge transmission, syncretic rituals, and household microeconomies. Their logics of spatial mediation and multifunctionality position them as key architectural devices that foster the social and cultural resilience of Andean rural housing. Understanding their configuration and use offers actionable insights for contemporary design, enabling the critical reinterpretation of vernacular principles to address ongoing challenges of habitability, sustainability, and belonging in evolving rural contexts. Full article

In the research, a novel accelerometer concept leveraging the mode-localization phenomenon is put forward. The sensor measures external acceleration through monitoring changes in the relative amplitude ratio among coupled resonators. The sensing part of the presented accelerometer comprises a doubly clamped beam coupled with a cantilever beam. Its design ensures the initial bending mode of the clamped beam approximates the secondary bending mode of the cantilever. Drawing on Euler–Bernoulli beam theory, the governing formulas of the coupled resonators are deduced and analyzed via Galerkin discretization integrated with the multiple-scale method. During working in both linear as well as nonlinear operating regions, this sensor’s dynamic behavior can be tuned by adjusting the drive voltage. The obtained results demonstrate that the nonlinear dynamics increases the accelerometer sensitivity, which can be further enhanced by adjusting the coupling voltage without severe mode overlap. The presented model offers one viable method to enhance the overall performance in multi-mode MEMS accelerometers. Full article

Tropical cyclones (TCs) rank among the most destructive natural hazards globally, with core damaging potential originating from regions of intense wind shear and steep wind speed gradients within the eyewall and spiral rainbands. Accurately characterizing these fine-scale structural features is therefore critical for understanding TC intensity evolution, wind hazard distribution, and disaster mitigation. Recently, the deep learning-based downscaling methods have shown significant advantages in efficiently obtaining high-resolution wind field distributions. However, existing methods are mainly used to downscale general wind fields, and research on downscaling extreme wind field events remains limited. There are two main difficulties in downscaling TC wind fields. The first one is that high-quality datasets for TC wind fields are scarce; the other is that general deep learning frameworks lack the ability to capture the dynamic characteristics of TCs. Consequently, this study proposes a novel deep learning framework, CycloneWind, for downscaling TC surface wind fields: (1) a high-quality dataset is constructed by integrating Cyclobs satellite observations with ERA5 reanalysis data, incorporating auxiliary variables like low cloud cover, surface pressure, and top-of-atmosphere incident solar radiation; (2) we propose CycloneWind, a dynamically constrained Transformer-based architecture incorporating three wind field dynamical operators, along with a wind dynamics-constrained loss function formulated to enforce consistency in wind divergence and vorticity; (3) an Adaptive Dynamics-Guided Block (ADGB) is designed to explicitly encode TC rotational dynamics using wind shear detection and wind vortex diffusion operators; (4) Filtering Transformer Layers (FTLs) with high-frequency filtering operators are used for modeling wind field small-scale details. Experimental results demonstrate that CycloneWind successfully achieves an 8-fold spatial resolution reconstruction in TC regions. Compared to the best-performing baseline model, CycloneWind reduces the Root Mean Square Error (RMSE) for the U and V wind components by 9.6% and 4.9%, respectively. More significantly, it achieves substantial improvements of 23.0%, 22.6%, and 20.5% in key dynamical metrics such as divergence difference, vorticity difference, and direction cosine dissimilarity. Full article