Search Results (1,737)

As the core component of pumped storage stations (PSS), pumped storage units (PSU) require a scientific and comprehensive evaluation method to guide the selection of optimal units and support the development of the new-type power system (NPS). This paper aims to address the symmetry issues in PSU evaluation methods by proposing an innovative approach based on evolutionary combination weighting and cloud model theory, thereby adapting to the long-term asymmetric evolution of the power system. First, the subjective and objective weights of indicators at all levels for PSU are obtained using the analytic hierarchy process (AHP) and the entropy weight method (EWM). Then, the optimal combination coefficients for subjective and objective weights are determined through game theory, achieving symmetry and balance between the subjective and objective weights. Subsequently, dynamic correction of the indicator weights is realized using a designed evolutionary response function, enabling the weights to evolve dynamically in response to the asymmetric development of the power system. Finally, the cloud model is employed to characterize the randomness and fuzziness of evaluation boundaries, which enhances the adaptability of the evaluation process and the interpretability of results. The simulation results show that, when considering the long-term asymmetric evolution of the power system, the expected score deviations of secondary indicators are approximately 4.7%, 1.3%, 3.5%, and 7.7%, respectively, with an overall score deviation of about 6.4%. The proposed method not only achieves symmetry and balance between subjective and objective factors in traditional evaluation but also accommodates the asymmetric evolution requirements of the power system. Full article

The crisis of research integrity triggered by academic misconduct, such as scientific fraud and paper retractions, has emerged as a critical issue demanding urgent resolution within the academic community. Blockchain (BC), with its core features of distributed ledger, peer-to-peer transmission, consensus mechanisms, timestamps, and smart contracts, offers novel technical solutions for research institutions seeking efficient models of research credit supervision. By incorporating the psychological factors of risk perception among decision-makers and the dynamic evolution of behavioral decision-making, and drawing on prospect theory, this study has constructed an evolutionary game model involving researchers, scientific research institutions, and governmental entities to examine BC-enabled research credit supervision. This model analyzes the key determinants influencing scientific research institutions’ adoption of blockchain regulation (BC regulation), elucidates the behavioral characteristics and boundary conditions of research integrity among researchers under this new regulatory paradigm, and reveals the dynamic evolutionary trajectory of collaborative supervision between governments and scientific research institutions. The findings indicate the following: (1) Compared to traditional regulation, the BC regulation demonstrates superior regulatory effectiveness at equivalent levels of researcher integrity and misconduct costs, as well as under identical settings for reputational loss and penalties. (2) In addition to cost considerations and government subsidies, factors such as loss aversion coefficient, risk preference coefficient, and privacy breach losses are critical in influencing research institutions’ decisions to implement BC regulation. (3) The evolution of blockchain-empowered regulatory models encompasses three distinct evolutionary patterns. This study provides a theoretical foundation and a simulation case to optimize regulatory strategy formulation and resource allocation, thereby enhancing the effectiveness of research credit supervision. Full article

Black smoke emitted from diesel vehicles contains substantial amounts of hazardous substances. With the increasing annual levels of such emissions, there is growing concern over their detrimental effects on both the environment and human health. Therefore, it is imperative to strengthen the supervision and control of black smoke emissions. An effective approach is to analyze the smoke emission status of vehicles. Conventional object detection models often exhibit limitations in detecting black smoke, including challenges related to multi-scale target sizes, complex backgrounds, and insufficient localization accuracy. To address these issues, this study proposes a multi-dimensional detection algorithm. First, a multi-scale feature extraction method was introduced by replacing the conventional C2F module with a mechanism that employs parallel convolutional kernels of varying sizes. This design enables the extraction of features at different receptive fields, significantly improving the capability to capture black smoke patterns. To further enhance the network’s performance, a four-layer adaptive feature fusion detection head was proposed. This component dynamically adjusts the fusion weights assigned to each feature layer, thereby leveraging the unique advantages of different hierarchical representations. Additionally, to improve localization accuracy affected by the highly irregular shapes of black smoke edges, the Inner-IoU loss function was incorporated. This loss effectively alleviates the oversensitivity of CIoU to bounding box regression near image boundaries. Experiments conducted on a custom dataset, named Smoke-X, demonstrated that the proposed algorithm achieves a 4.8% increase in precision, a 5.9% improvement in recall, and a 5.6% gain in mAP50, compared to baseline methods. These improvements indicate that the model exhibits stronger adaptability to complex environments, suggesting considerable practical value for real-world applications. Full article

Island building change detection is a critical technology for environmental monitoring, disaster early warning, and urban planning, playing a key role in dynamic resource management and sustainable development of islands. However, the imbalanced distribution of class pixels (changed vs. unchanged) undermines the detection capability of existing methods and severe boundary misdetection. To address issue, we propose the MSDT-Net model, which makes breakthroughs in architecture, modules, and loss functions; a dual-branch twin ConvNeXt architecture is adopted as the feature extraction backbone, and the designed Edge-Aware Smoothing Module (MSA) effectively enhances the continuity of the change region boundaries through a multi-scale feature fusion mechanism. The proposed Difference Feature Enhancement Module (DTEM) enables deep interaction and fusion between original semantic and change features, significantly improving the discriminative power of the features. Additionally, a Focal–Dice–IoU Boundary Joint Loss Function (FDUB-Loss) is constructed to suppress massive background interference using Focal Loss, enhance pixel-level segmentation accuracy with Dice Loss, and optimize object localization with IoU Loss. Experiments show that on a self-constructed island dataset, the model achieves an F1-score of 0.9248 and an IoU value of 0.8614. Compared to mainstream methods, MSDT-Net demonstrates significant improvements in key metrics across various aspects. Especially in scenarios with few changed pixels, the recall rate is 0.9178 and the precision is 0.9328, showing excellent detection performance and boundary integrity. The introduction of MSDT-Net provides a highly reliable technical pathway for island development monitoring. Full article

Unmanned Aerial Vehicle (UAV) detection often faces challenges such as small target size, loss of textural details, and interference from complex backgrounds. To address these issues, this paper proposes a novel object detection algorithm named Spatial-Frequency Aware DETR (SFA-DETR), which integrates both spatial- and frequency-domain perception. For spatial-domain modeling, a backbone network named IncepMix is designed to dynamically fuse multi-scale receptive field information, enhancing the model’s ability to capture contextual information while reducing computational cost. For frequency-domain modeling, a Frequency-Guided Attention Block (FGA Block) is introduced to improve perception of target boundaries through frequency-aware guidance, thereby increasing localization accuracy. Furthermore, an adaptive sparse attention mechanism is incorporated into AIFI to emphasize semantically critical information and suppress redundant features. Experiments conducted on the DUT Anti-UAV dataset demonstrate that SFA-DETR improves mAP50 and mAP50:95 by 1.2% and 1.7%, respectively, while reducing parameter count and computational cost by 14.44% and 3.34%. The results indicate that the proposed method achieves a balance between detection accuracy and computational efficiency, validating its effectiveness in UAV detection tasks. Full article

Vector geographic data require strict preservation of coordinate precision and topological integrity. However, their open transmission poses simultaneous challenges for copyright protection and data security. To address these issues, this study proposes a reversible watermarking framework that integrates difference expansion (DE) for lossless coordinate recovery, the Arnold transform for watermark encryption, and a metadata-assisted dual restoration mechanism to ensure geometric and topological consistency after embedding. Experimental evaluations on multiple vector datasets—including administrative boundaries, hydrographic networks, and road layers—demonstrate that the proposed method achieves near-zero distortion (RMSE ≈ 10⁻¹⁶), complete reversibility, and strong robustness against geometric and noise attacks, outperforming conventional DFT- and QIM-based schemes in terms of imperceptibility and restoration accuracy. The approach provides an efficient and verifiable solution for secure sharing and copyright protection of vector geographic data, contributing to reliable data provenance and trustworthy spatial information management. Full article

An adaptive fault-tolerant containment control approach is developed for nonlinear multi-agent systems to address issues related to both communication link and actuator faults. This approach achieves fault-tolerant containment control through the introduction of a convex hull signal estimator and a fault compensation mechanism. First, a leader–follower network model with communication link faults is constructed, and distributed containment errors are established. The proposed framework involves three key components: the design of an adaptive backstepping control law, the introduction of a nonlinear filter for boundary error elimination, and the application of a radial basis function neural network (RBFNN) for the approximation of unknown nonlinear terms. Meanwhile, an adaptive convex hull estimator is designed to estimate the signals formed by the leaders, and an actuator fault estimator is constructed to compensate for fault signals online. Additionally, Lyapunov stability analysis demonstrates that all containment errors remain uniformly bounded. To support simulation teaching and validation, numerical simulations and autonomous underwater vehicle (AUV) simulations are used to not only to confirm the efficacy of the presented control technique but also to provide illustrative cases for educational purposes. Full article

Ship instance segmentation in remote sensing images is essential for maritime applications such as intelligent surveillance and port management. However, this task remains challenging due to dense target distributions, large variations in ship scales and shapes, and limited high-quality datasets. The existing YOLOv8 framework mainly relies on convolutional neural networks and CIoU loss, which are less effective in modeling global–local interactions and producing accurate mask boundaries. To address these issues, we propose CDSANet, a novel one-stage ship instance segmentation network. CDSANet integrates convolutional operations, Vision Transformers, and attention mechanisms within a unified architecture. The backbone adopts a Convolutional Vision Transformer Attention (CVTA) module to enhance both local feature extraction and global context perception. The neck employs dynamic-weighted DOWConv to adaptively handle multi-scale ship instances, while SIoU loss improves localization accuracy and orientation robustness. Additionally, CBAM enhances the network’s focus on salient regions, and a MixUp-based augmentation strategy is used to improve model generalization. Extensive experiments on the proposed VLRSSD dataset demonstrate that CDSANet achieves state-of-the-art performance with a mask AP (50–95) of 75.9%, surpassing the YOLOv8 baseline by 1.8%. Full article

Tension between the Mahāyāna and Theravāda Buddhist schools has persisted since early Buddhist times and remains a complex issue. However, recent decades have seen growing joint religious activities and cultural exchanges between followers of these traditions. This paper examines the presence and experiences of Chinese Mahāyāna Buddhist monastics in Myanmar, where Theravāda Buddhism predominates. Given the limited research on Chinese Buddhism’s expansion beyond East Asia, this study addresses an important gap by focusing on Myanmar’s unique sociocultural context. The paper is divided into two main parts. The first provides a historical overview of Chinese Mahāyāna Buddhism’s evolution in Burma during the late 19th and early 20th centuries. The second, more extensive, section utilizes fieldwork data to analyze the contemporary experiences of Chinese monastics living as religious minorities in a predominantly Theravāda and ethnically Burmese environment. Relations between the two Buddhist communities have improved since the mid-20th century, despite ongoing institutional marginalization. Key factors include second-generation bilingual monastics, international Buddhist exchanges, and joint charitable activities. The Chinese Buddhist Sangha Association’s response to the March 2025 earthquake near Naypyidaw, including substantial aid to Theravāda monasteries, illustrates how humanitarian crises can generate cooperation across sectarian boundaries. Through examining these interactions, challenges, and identity negotiations, this study offers a detailed account of how Chinese Mahāyāna Buddhist monastics navigate Myanmar’s religious landscape as a minority tradition. Full article

Adjoint-based mesh adaptation method serves as an effective approach to improve the predictive accuracy of aerodynamic characteristics. However, viscous boundary layer grids often encounter issues such as hanging nodes, negative volumes, and directional constraints during adaptation, significantly limiting their practical application. To address these challenges, this study proposes an innovative polyhedral conversion strategy. Cells containing hanging nodes resulting from refinement are converted into polyhedra, effectively eliminating topological constraints between adjacent mesh elements. This approach is combined with surface-conforming projection and distance function-based mesh deformation techniques to ensure precise geometric representation and high mesh quality after adaptation. Numerical experiments demonstrate that the proposed viscous boundary layer mesh adaptation strategy successfully handles both refinement and coarsening of boundary layer grids. In a typical high-angle-of-attack case for the NACA0012 airfoil, the adjoint-based mesh adaptation method reduced lift coefficient error from 4.21% to 0.30% after four adaptation cycles. For the CHN-F1 low-aspect-ratio flying wing configuration, the method reduced the lift discrepancy from 10.05% to 6.65% at 40° angle of attack. The polyhedral conversion approach effectively resolves common challenges in viscous boundary layer mesh adaptation, providing a robust solution for high-fidelity prediction of aerodynamic characteristics with significantly improved accuracy. Full article

Water scarcity and increased human pressures are crucial issues facing Jordan. Chemical pollutants significantly influence groundwater quality in Jordan due to increased pollution risks, ease of contamination, and various human activities that release harmful compounds into the groundwater. The Yarmouk River Groundwater Basin (YRB) is one of the main basins in northern Jordan. It is exploited for domestic, drinking, agricultural, and industrial uses. This study assessed the groundwater vulnerability for the YRB through the implementation of a dual-method approach, employing the SINTACS intrinsic groundwater vulnerability model and the Random Forest (RF) machine learning method. The results revealed similarities and differences between the two models. The delineation of low-vulnerability zones was similar, suggesting that the intrinsic hydrogeological characteristics of these areas provide robust natural protection against contamination. In addition, both models suggest that the eastern, northern, and southern parts are areas of ‘high’ and ‘very high’ vulnerability. Subtle differences can be observed, particularly in the precise delineation of boundaries and the fragmentation of vulnerability zones. Generally, the results show that over (47%) and (43%) of the basin area falls into the high- and very high-vulnerability classes, while the very low and low classes make up about (14%) and (15%), based on the SINTACS and RF models, respectively. Using the SINTACS and RF groundwater vulnerability assessments in the YRB provides valuable insights into groundwater susceptibility in this critical area of Jordan. The identified high- and very high-vulnerability areas within YRB highlight the urgent need for protective measures to safeguard this vital groundwater resource for both present and future generations. The SINTACS model proves to be a reliable tool for intrinsic vulnerability assessment in the study area, consistent with its application in other parts of Jordan and similar dry regions. Full article

With the rapid development of distribution–microgrid (DN–MG) systems, they have become increasingly important in the construction of modern power systems. However, existing scheduling approaches often overlook the frequency security risks faced by microgrids when transitioning into unintentional islanding during contingencies. To address this issue, this paper proposes a projection-based coordinated scheduling method for DN–MG systems under microgrid frequency security constraints. First, an approximate frequency response curve is derived to characterize the maximum frequency deviation of microgrids after unintentional islanding, which is explicitly embedded into the microgrid optimization model to ensure frequency security. Second, to achieve efficient coordination, a power–energy boundary-based feasible region approximation is proposed for microgrids, which accurately characterizes the projection feasible region under inter-temporal coupling while reducing conservativeness. This enables a non-iterative coordination framework. Finally, case studies on a modified IEEE 33-bus system containing three microgrids demonstrate that the proposed method effectively limits the maximum frequency deviation to within 0.5 Hz, while the projection-based feasible region achieves 87.62% coverage, which is twice that of conventional box approximations. Overall, the proposed method ensures microgrid frequency security while balancing computational efficiency and privacy protection, highlighting its strong potential for practical engineering applications. Full article

Guided bone regeneration membranes are essential for bone formation. While non-resorbable membranes require removal surgery, resorbable membranes such as poly (lactic-co-glycolic acid) PLGA are widely used; however, issues with animal-derived components and degradation control have been identified. A novel bilayer membrane composed of synthetic poly (L-lactic acid-co-ε-caprolactone) (PBM) was developed, offering prolonged degradability and elasticity. This study compared the wound-healing effects of PBM and PLGA membranes in vivo and in vitro experiments. In vivo, maxillary molars were extracted from rats, and membranes were placed over the sockets. Healing was evaluated histologically at 1, 2, 3, 4 and 8 weeks. In vitro, oral epithelial cells and fibroblasts were seeded on both sides of PBM. Adhesion and permeability of the membranes were assessed. In vivo, both groups displayed similar mucosal healing. However, PBM preserved a clear bone-soft tissue boundary. In vitro, the surface of PBM supported significantly greater oral epithelial cell adhesion than the reverse side, with no differences for fibroblasts. Both sides of PBM exhibited better protein permeability compared to PLGA. PBM maintained distinct bone-soft tissue separation in rat extraction sockets, suggesting its potential as an effective space maintainer in guided bone regeneration. Further studies are warranted to investigate the mechanisms underlying these favorable properties. Full article

Increase in temperatures significantly reduce photovoltaic (PV) panel efficiency by increasing thermalization losses and carrier recombination. To mitigate this issue, phase change material (PCM-RT35) is integrated with the PV system. This study investigates the thermal performance of a PV–PCM hybrid system under the summer climatic conditions of Islamabad, Pakistan. A transient computational model was developed in ANSYS Fluent 2021 R1 and validated against published experimental data, showing an accuracy within 5% for panel temperature. Monthly average hourly heat flux and ambient temperature profiles were used as boundary conditions in three separate simulations. The results show that, although all three months reach a 100% melt fraction, only April achieves complete solidification within a 24-h cycle. In May solidification merely begins before the end of the period, and in June no solidification occurs at all. Importantly, integrating the PCM lowers the PV module’s peak temperature by up to 15 °C and boosts its power-output efficiency by about 6%. By keeping the module cooler, its electrical efficiency is therefore significantly improved. Full article

Semantic segmentation of high-resolution remote sensing images is of great application value in fields like natural disaster monitoring. Current multimodal semantic segmentation methods have improved the model’s ability to recognize different ground objects and complex scenes by integrating multi-source remote sensing data. However, these methods still face challenges such as blurred boundary segmentation and insufficient perception of multi-scale ground objects when achieving high-precision classification. To address these issues, this paper proposes MMA-Net, a semantic segmentation network enhanced by two key modules: cross-layer multimodal fusion module and multi-scale multi-attention module. These modules effectively improve the model’s ability to capture detailed features and model multi-scale ground objects, thereby enhancing boundary segmentation accuracy, detail feature preservation, and consistency in multi-scale object segmentation. Specifically, the cross-layer multimodal fusion module adopts a staged fusion strategy to integrate detailed information and multimodal features, realizing detail preservation and modal synergy enhancement. The multi-scale multi-attention module combines cross-attention and self-attention to leverage long-range dependencies and inter-modal complementary relationships, strengthening the model’s feature representation for multi-scale ground objects. Experimental results show that MMA-Net outperforms state-of-the-art methods on the Potsdam and Vaihingen datasets. Its mIoU reaches 88.74% and 84.92% on the two datasets, respectively. Ablation experiments further verify that each proposed module contributes to the final performance. Full article