Search Results (1,270)

The present study examines the effect of a modified oval–round rolling scheme incorporating inclined oval calibers on the mechanical behavior and microstructural evolution of Mn–Si low-alloy steel (25G2S). Cylindrical billets were hot rolled through both classical and modified sequences under identical thermal and kinematic conditions. Tensile testing demonstrated that, relative to the unrolled condition (σ_0.2 ≈ 269 MPa; σᵤ ≈ 494 MPa), the classical route increased yield and ultimate strengths to ~444 MPa and ~584 MPa, respectively, whereas the modified scheme yielded comparable values (~433 MPa and ~572 MPa) while providing superior ductility (δ ≈ 26.8%, ψ ≈ 68.6%). Vickers microhardness decreased systematically from 244 HV (unrolled) to 213 HV (classical) and 184 HV (modified), with the modified scheme exhibiting the lowest scatter (±4.8 HV), confirming enhanced structural uniformity. Scanning electron microscopy revealed ferrite–pearlite refinement under both rolling sequences, with the modified scheme producing finer equiaxed ferrite grains (~3–5 µm) and attenuated longitudinal banding. These features are indicative of shear-assisted dynamic recrystallization, activated by the inclined oval calibers. The findings highlight that the modified rolling strategy achieves a favorable strength–ductility balance and improved homogeneity, suggesting its applicability for advanced thermomechanical processing of low-alloy steels. Full article

To address the issues of small-target missed detection, false alarms from cloud/fog interference, and low computational efficiency in traditional wildfire detection for transmission line corridors, this paper proposes a YOLOv11_MDS detection model by integrating Multi-Scale Convolutional Attention (MSCA) and Distribution-Shifted Convolution (DSConv). The MSCA module is embedded in the backbone and neck to enhance multi-scale dynamic feature extraction of flame and smoke through collaborative depth strip convolution and channel attention. The DSConv with a quantized dynamic shift mechanism is introduced to significantly reduce computational complexity while maintaining detection accuracy. The improved model, as shown in experiments, achieves an mAP@0.5 of 88.21%, which is 2.93 percentage points higher than the original YOLOv11. It also demonstrates a 3.33% increase in recall and a frame rate of 242 FPS, with notable improvements in detecting small targets (pixel occupancy < 1%). Generalization tests demonstrate mAP improvements of 0.4% and 0.7% on benchmark datasets, effectively resolving false/missed detection in complex backgrounds. This study provides an engineering solution for real-time wildfire monitoring in transmission lines with balanced accuracy and efficiency. Full article

Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10⁻⁴ yuan to 51 × 10⁻⁴ yuan. Network losses also dropped from 2.7 × 10⁻⁴ yuan to 2.5 × 10⁻⁴ yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management. Full article

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space. Full article

This study proposes an Improved Whale Migration Algorithm (IWMA) to overcome the shortcomings of the original Whale Migration Algorithm, which suffers from premature convergence and insufficient local exploitation in high-dimensional multimodal optimization. IWMA introduces three enhancements: circle chaotic initialization to improve population diversity, a three-layer cooperative search framework to achieve a stronger balance between exploration and exploitation, and a dynamic adaptive mechanism with t-distribution re-exploration to reinforce both global escaping and local refinement. On the CEC2017 benchmark suite, IWMA demonstrates clear superiority over seven representative algorithms, delivering the best results on 27 out of 29 functions by best, 25 by mean, and 23 by standard deviation in 30 dimensions, and on 25, 18, and 18 functions, respectively, in 50 dimensions. Compared with other migration-based optimizers, its average rank improves by more than 30 percent, while runtime analysis shows only a small additional overhead of 7 to 12 percent. These outcomes, supported by convergence curves, boxplots, radar charts, and Wilcoxon tests, confirm the effectiveness of the proposed improvements. In six multi-UAV path planning scenarios, IWMA reduces the average cost by 14.5 percent compared with WMA and achieves up to 32.1 percent reduction in the most complex case. Overall, its average cost decreases by 27.4 percent across seven competitors, with a 23.6 percent improvement in the best solutions. These results demonstrate that the proposed modifications are effective, enabling IWMA to transfer its performance gains from benchmark tests to practical multi-UAV cooperative mission planning, where it consistently produces safer and smoother trajectories under complex constraints. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

Urban soundscapes are increasingly recognized as fundamental for both ecological integrity and human well-being, yet the complex interplay between the vegetation structure, seasonal dynamics, and microclimatic factors in shaping these soundscapes remains poorly understood. This study tests the hypothesis that vegetation structure and seasonally driven biological activity mediate the balance and the quality of the urban acoustic environment. We investigated seasonal and spatial variations in five acoustic indices (NDSI, ACI, AEI, ADI, and BI) within a historical urban garden in Castelfranco Veneto, Italy. Using linear mixed-effects models, we analyzed the effects of season, microclimatic variables, and vegetation characteristics on soundscape composition. Non-parametric tests were used to assess spatial differences in vegetation metrics. Results revealed strong seasonal patterns, with spring showing increased NDSI (+0.17), ADI (+0.22), and BI (+1.15) values relative to winter, likely reflecting bird breeding phenology and enhanced biological productivity. Among microclimatic predictors, temperature (p < 0.001), humidity (p = 0.014), and solar radiation (p = 0.002) showed significant relationships with acoustic indices, confirming their influence on both animal behaviour and sound propagation. Spatial analyses showed significant differences in acoustic patterns across points (Kruskal–Wallis p < 0.01), with vegetation metrics such as tree density and evergreen proportion correlating with elevated biophonic activity. Although the canopy height model did not emerge as a significant predictor in the models, the observed spatial heterogeneity supports the role of vegetation in shaping urban sound environments. By integrating ecoacoustic indices, LiDAR-derived vegetation data, and microclimatic parameters, this study offers novel insights into how vegetational components should be considered to manage urban green areas to support biodiversity and foster acoustically restorative environments, advancing the evidence base for sound-informed urban planning. Full article

Diabetic peripheral neuropathy (DPN) is a common complication of type 2 diabetes that impairs gait and balance, increasing fall risk. This study investigated gait characteristics and postural control in individuals with DPN, compared to age- and gender-matched healthy controls. Fifteen DPN patients and fifteen controls underwent assessments of gait, static balance, and mobility. Gait parameters were measured during overground walking using motion capture and force platforms. Static balance was evaluated via tandem stance tests (eyes open/closed), while mobility was assessed with the Timed-Up-and-Go (TUG) test. Dynamic stability was assessed by computing the center-of-pressure Time-to-Contact (TTC) with the mediolateral (ML) stability boundary. We hypothesized that patients with DPN would exhibit an altered gait and reduced ML postural stability during walking. The study results show no significant differences in ML center-of-pressure (COP) excursion or its velocity during walking between groups. Patients with DPN walked relatively slowly, with shorter steps, and showed markedly poorer static balance (earlier failure during tandem stance test), as well as slower TUG performance. Clinically, these findings support routine fall risk screening in DPN using both static balance tests (e.g., tandem stance) and mobility measures (e.g., TUG or gait speed). These findings further suggest that while dynamic postural control during walking may be preserved, DPN patients exhibit gait adaptations and significant static balance deficits, highlighting the need for comprehensive balance assessment in this population. Full article

The structural parameters of Stewart platforms play a critical role in enhancing dynamic performance, improving motion accuracy, and enabling effective control strategies. However, practical applications face several key limitations, including the metric balancing for optimization, the limited singularity-free workspace, and low computational efficiency. To overcome those shortcomings, this work proposes a multi-objective optimal design of the structural parameters for Stewart platform based on Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Kolmogorov–Arnold Network (KAN). Firstly, under the stroke constraints of the Stewart platform, this work focuses on optimizing the platform’s key structural parameters. This approach enables both the optimization of existing equipment and the design of new devices. Secondly, this work employs KAN to establish a model that characterizes the relationship between the structural parameters and diverse postures within the maximum singularity-free workspace. This approach not only enhances computational efficiency but also ensures high precision. Finally, this study proposes six performance metrics and utilizes NSGA-III to optimize the structural parameters, thereby achieving a trade-off among these diverse objectives. Simulation and experimental results demonstrate that KAN significantly outperforms the Multi-Layer Perceptron (MLP) in predicting workspace postures. Compared with MLP, KAN achieves higher prediction accuracy and lower error rates across both training and test datasets. When comparing NSGA-III with NSGA-II, the proposed approach demonstrates modest improvements in most performance metrics while preserving acceptable trade-offs between the optimization objectives. Full article

Accurate skin lesion classification is crucial for the early detection of malignant lesions, including melanoma, as well as improved patient outcomes. While convolutional neural networks (CNNs) excel at capturing local morphological features, they struggle with global context modeling essential for comprehensive lesion assessment. Vision transformers address this limitation but suffer from quadratic computational complexity O(n²), hindering deployment in resource-constrained clinical environments. We propose DermaMamba, a novel dual-branch fusion architecture that integrates CNN-based local feature extraction with Vision Mamba (VMamba) for efficient global context modeling with linear complexity O(n). Our approach introduces a state space fusion mechanism with adaptive weighting that dynamically balances local and global features based on lesion characteristics. We incorporate medical domain knowledge through multi-directional scanning strategies and ABCDE (Asymmetry, Border irregularity, Color variation, Diameter, Evolution) rule feature integration. Extensive experiments on the ISIC dataset show that DermaMamba achieves 92.1% accuracy, 91.7% precision, 91.3% recall, and 91.5% mac-F1 score, which outperforms the best baseline by 2.0% accuracy with 2.3× inference speedup and 40% memory reduction. The improvements are statistically significant based on a significance test (p < 0.001, Cohen’s d > 0.8), with greater than 79% confidence also preserved on challenging boundary cases. These results establish DermaMamba as an effective solution bridging diagnostic accuracy and computational efficiency for clinical deployment. Full article

This work suggests a new use for the Non-Monopolize Optimization (NO) method to improve the dynamic stability and robustness of PID and PIDA controllers in Automatic Voltage Regulator (AVR) systems when there are load disruptions. The NO algorithm is a new search method that does not use metaphors and only looks for one answer. It utilizes adaptive dimension modifications to strike a balance between exploration and exploitation. Its addition to AVR control makes parameter tweaking more efficient, without relying on random metaphors or population-based heuristics. MATLAB/Simulink R2025a runs full simulations to check how well the system works in both the time domain (step response, root locus) and the frequency domain (Bode plot). We compare the results to those of well-known optimizers like WOA, TLBO, ARO, GOA, and GA. The suggested NO-based PID and PIDA controllers always show less overshoot, faster rise and settling periods, and higher phase and gain margins, which proves that they are more stable and responsive. A robustness test with a load change of ±50% shows that NO-tuned controllers are even more reliable. The results show that using NO to tune different controllers could be a good choice for real-time AVR controller tuning in modern power systems because it is lightweight and works well. Full article

Farmland shelterbelts are vital ecological infrastructure for sustaining agriculture in arid regions, where high winds, soil erosion, and water scarcity severely constrain productivity. While their protective functions—reducing wind speed, controlling erosion, moderating microclimates, and enhancing yields—are well documented, previous studies have largely examined individual structural elements in isolation, leaving their interactive effects and trade-offs poorly understood. This review synthesizes current research on the structural optimization of shelterbelts, emphasizing the critical relationship between their physical and biological attributes and their protective functions. Key structural parameters—such as optical porosity, height, width, orientation, and species composition—are examined for their individual and interactive impacts on shelterbelt performance. Empirical and modeling studies indicate that moderate porosity maximizes wind reduction efficiency and extends the leeward protection zone, while multi-row, multi-species configurations effectively suppress soil erosion and improve microclimate conditions. Sheltered areas experience reduced evapotranspiration, increased humidity, and moderated temperatures, collectively enhancing crop water use efficiency and yielding significant improvements in crop production. Advanced methodologies, including field monitoring, wind tunnel testing, computational fluid dynamics, and remote sensing, are employed to quantify benefits and refine designs. A multi-objective optimization framework is essential to balance competing goals: maximizing wind reduction, minimizing water consumption, enhancing biodiversity, and ensuring economic viability. Future challenges involve adapting designs to climate change, integrating water-efficient and native species, leveraging artificial intelligence for predictive modeling, and addressing socio-economic barriers to implementation. Building on this evidence, we propose a multi-objective optimization framework to balance competing goals: maximizing wind protection, minimizing water use, enhancing biodiversity, and ensuring economic viability. We identify key research gaps including unresolved porosity thresholds, the climate resilience of alternative species compositions, and the limited application of optimization algorithms and outline future priorities such as region-specific design guidelines, AI-driven predictive models, and policy incentives. This review offers a novel, trade-off–aware synthesis to guide next-generation shelterbelt design in arid agriculture. Full article

Fuzzing deep learning (DL) libraries is essential for uncovering security vulnerabilities in AI systems. Existing approaches enhance large language models (LLMs) with external knowledge such as bug reports to improve the quality of generated seeds. However, most approaches still rely on static strategies or single knowledge sources, limiting their ability to produce syntactically valid inputs that also expose deeper bugs. To address this challenge, we propose an adaptive seed generation approach that models knowledge-guided prompt selection as a multi-armed bandit problem. Our method first constructs two knowledge bases from API documentation and bug reports, then dynamically selects and refines prompt strategies based on real-time feedback. These strategies are tailored to the knowledge types in the respective bases. We design a multi-dimensional reward function to evaluate each batch of generated seeds by measuring their error-triggering potential and behavioral diversity, enabling a balanced exploration of both syntactically valid and bug-triggering test cases. Our experiments on three DL libraries, PaddlePaddle, MindSpore, and OneFlow, identify 17 previously unknown crash bugs, demonstrating the effectiveness and generalizability of the proposed approach. Full article

Forest fire risk assessment methodologies vary considerably, presenting challenges for adaptation to specific local contexts. This study provides a systematic analysis of forest fire assessment approaches across the Mediterranean basin, American, African, and Asian regions through a comprehensive review of 112 peer-reviewed studies published from 2015 to 2025. Statistical significance testing (Chi-square tests, p < 0.05) confirmed significant regional variation in methodological preferences and indicator usage patterns. Key findings revealed that Multi-Criteria Decision Analysis dominates the field (44% of studies, n = 49), with Analytical Hierarchical Process being the most utilized method (39 studies). Machine learning approaches represent 25% (n = 28), with Random Forest leading significantly (22 applications). The analysis identified 67 indicators across seven major categories, with topographic factors (slope: 105 studies) and anthropogenic indicators (road networks: 92 studies) showing statistically significantly highest usage rates (p < 0.001), representing a statistically significant critical gap in vulnerability assessment (p < 0.01). Organizational factors remain severely underrepresented (a maximum of 14 studies for any factor), representing a statistically significant critical gap in risk assessments (p < 0.01). Statistical analysis revealed that while Mediterranean approaches excel in integrating historical and cultural factors, American methods emphasize advanced technology integration, while Asian approaches focus on socio-economic dynamics and land-use interactions. This study serves as a foundation for developing tailored assessment frameworks that combine remote sensing analysis, ground-based surveys, and community input while accounting for local constraints in data availability and technical capacity. The study concludes that effective forest fire risk assessment requires a balanced integration of global best practices with local environmental, social, and technical considerations, offering a roadmap for future forest fire risk assessment approaches in different regions worldwide. Full article

With the deepening implementation of the coordinated development strategy for energy exploitation and ecological conservation, green coal mining technology has become a critical pathway to achieve balanced resource development and environmental protection. This study investigates the stress field evolution and dynamic fracture propagation mechanisms in overlying strata during shallow coal seam mining in the Shenfu mining area. By employing a multidisciplinary approach combining triaxial compression tests (0–15 MPa confining pressure), scanning electron microscopy (SEM) microstructural characterization, elastoplastic theoretical modeling, and FLAC3D numerical simulations, the synergistic failure mechanisms of overlying strata were systematically revealed. Gradient-controlled triaxial tests demonstrated significant variations in stress-strain responses across lithological types. Notably, Class IV sandstone exhibited exceptional uniaxial compressive strength of 106.7 MPa under zero confining pressure, surpassing the average strength of Class I–III sandstones (86.2 MPa) by 23.6%, attributable to its highly compacted grain structure. A nonlinear regression-derived linear strengthening model quantified that each 1 MPa increase in confining pressure enhanced axial peak stress by 4.2%. SEM microstructural analysis established critical linkages between microcrack networks/grain-boundary slippage at the mesoscale and macroscopic brittle failure patterns. Numerical simulations demonstrated that strata failure manifests as tensile-shear composite fractures, with lateral crack propagation inducing bed separation spaces. The stress field exhibited spatiotemporal heterogeneity, with maximum principal stress concentrating near the initial mining cut during early excavation. Fractures propagated obliquely at angles of 55–65° to the horizontal plane in an ‘inverted V’ pattern from the goaf boundaries, extending vertically 12–18 m before transitioning to the bent zone, ultimately forming a characteristic three-zone structure. Experimental and simulated vertical stress distributions showed minimal deviation (≤2.8%), confirming constitutive model reliability. This research quantitatively characterizes the spatiotemporal synergy of strata failure mechanisms in ecologically vulnerable northwestern China, proposing a confining pressure-effect quantification model for support parameter optimization. The revealed fracture dynamics provide critical insights for determining ecological restoration timelines, while establishing a novel theoretical framework for optimizing green mining systems and mitigating ecological damage in the Shenfu mining area. Full article