Search Results (12,093)

Industrial-grade printed circuit boards (PCBs) exhibit high structural order and inherent geometric symmetry, where minute surface defects essentially constitute symmetry-breaking anomalies that disrupt topological integrity. Detecting these anomalies is quite challenging due to issues like scale variation and low contrast. Therefore, this paper proposes a symmetry-aware object detection framework, DAS-YOLO, based on an improved YOLOv11. The U-shaped adaptive feature extraction module (Def-UAD) reconstructs the C3K2 unit, overcoming the geometric limitations of standard convolutions through a deformation adaptation mechanism. This significantly enhances feature extraction capabilities for irregular defect topologies. A semantic-aware module (SADRM) is introduced at the backbone and neck regions. The lightweight and efficient ESSAttn improves the distinguishability of small or weak targets. At the same time, to address information asymmetry between deep and shallow features, an iterative attention feature fusion module (IAFF) is designed. By dynamically weighting and calibrating feature biases, it achieves structured coordination and balanced multi-scale representation. To evaluate the validity of the proposed method, we carried out comprehensive experiments using publicly accessible datasets focused on PCB defects. The results show that the Recall, mAP@50, and mAP@50-95 of DAS-YOLO reached 82.60%, 89.50%, and 46.60%, respectively, which are 3.7%, 1.8%, and 2.9% higher than those of the baseline model, YOLOv11n. Comparisons with mainstream detectors such as GD-YOLO and SRN further demonstrate a significant advantage in detection accuracy. These results confirm that the proposed framework offers a solution that strikes a balance between accuracy and practicality in addressing the key challenges in PCB surface defect detection. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

By addressing the challenges of management difficulties, insufficient integration of driver analysis, and single-dimensional analysis in the governance of illegal land use and illegal construction (collectively referred to as the “Two Illegalities”) under rapid urbanization, this study designs and implements a GIS-based governance system using Xiamen City as the study area. First, we propose a standardized data-processing workflow and construct a comprehensive management platform integrating multi-source data fusion, spatiotemporal visualization, intelligent analysis, and customized report generation, effectively lowering the barrier for non-professional users. Second, utilizing methods integrated into the platform, such as Moran’s I and centroid trajectory analysis, we deeply analyze the spatiotemporal evolution and driving mechanisms of “Two Illegalities” activities in Xiamen from 2018 to 2023. The results indicate that the distribution of “Two Illegalities” exhibits significant spatial clustering, with hotspots concentrated in urban–rural transition zones. The spatial morphology evolved from multi-core diffusion to the contraction of agglomeration belts. This evolution is essentially the result of the dynamic adaptation between regional economic development gradients, urbanization processes, and policy-enforcement synergy mechanisms. Through a modular, open technical architecture and a “Data-Technology-Enforcement” collaborative mechanism, the system significantly improves information management efficiency and the scientific basis of decision-making. It provides a replicable and scalable technical framework and practical paradigm for similar cities to transform “Two Illegalities” governance from passive disposal to active prevention and control. Full article

Smallholder agricultural systems in tropical and subtropical regions are threatened by climate change. This systematic review of 218 peer-reviewed studies (2000–2024) synthesizes evidence on agroforestry’s role as a climate-smart economic strategy across Africa, Asia, and Latin America. Using a PRISMA-guided approach, we evaluated carbon sequestration pathways, biophysical adaptation benefits, and socioeconomic outcomes. Findings indicate that agroforestry systems can sequester 0.5–5 Mg C ha⁻¹ yr⁻¹ in biomass and soils. The results show that agroforestry has the potential to improve above- and below-ground carbon stocks, moderate microclimates, decrease erosion and improve functional biodiversity. The results, however, differ greatly depending on the type of system, ecology and practice. The socioeconomic advantages can be diversification of income and stability of the yield, and adoption is limited due to barriers related to the economy, lack of security in tenure, information asymmetry, and insufficient policy encouragement. We find that agroforestry is a multifunctional and climate resistant land-use approach, but the potential that agroforestry has cannot be fulfilled without context-specific policies, better extension services and inclusive carbon financing schemes. Full article

Orbea is a morphologically diverse lineage within the subtribe Stapeliinae, yet plastome evolution in Arabian taxa remains insufficiently characterized. This study reports the first complete chloroplast genomes of Orbea sprengeri subsp. commutata and O. wissmannii var. eremastrum and investigates plastome structure, sequence variability, and phylogenetic relationships across tribe Ceropegieae. Chloroplast genomes were assembled, annotated, and compared with 13 published plastomes representing major Ceropegieae lineages. Both Arabian plastomes displayed the typical quadripartite structure and identical gene content of 114 unique genes, including 80 protein-coding genes, 30 transfer RNA genes, and four ribosomal RNA genes. However, O. wissmannii var. eremastrum exhibited pronounced structural divergence, possessing the largest plastome recorded for the tribe (170,054 bp), an 8.9 kb expansion of the inverted repeat regions, and an 8.4 kb inversion spanning the ndhG–ndhF region. Comparative analyses revealed conserved gene order across Ceropegieae but identified six highly variable loci (accD, clpP, ndhF, ycf1, psbM–trnD, and rpl32–trnL) as potential DNA barcodes. Selection pressure analyses indicated strong purifying selection across most genes, with localized adaptive signals in accD, ndhE, ycf1, and ycf2. Phylogenomic reconstruction consistently resolved the two Arabian Orbea taxa as a distinct clade separate from the African O. variegata. This study fills a gap in Ceropegieae plastid genomics and underscores the importance of sequencing additional Orbea species to capture the full extent of genomic variation within this diverse genus. Full article

We propose a novel clothing application technique designed for story visualization framework where various characters appear wearing a wide range of outfits. To achieve our goal, we extend a Virtual Try-On framework for synthetic garment fitting. Conventional Virtual Try-On methods are limited to generating images of a single person wearing a restricted set of clothes within a fixed style domain. To overcome these limitations, we apply an improved Virtual Try-On model trained with appropriately processed datasets, enabling the generation of upper and lower garments separately across diverse characters and producing images in four distinct styles: photorealistic, webtoon, animation, and watercolor. Our system collects character images and clothing images and performs accurate masking of garment regions. Our system takes a style-specific text prompt as input. Based on these inputs, garment-specific conditioning is applied to synthesize the clothing, followed by a cross-style diffusion process that generates Virtual Try-On images reflecting multiple visual styles. Our approach significantly enhances the adaptability and stylistic diversity of Virtual Try-On technology for story visualization applications. Full article

This study evaluated the fruit quality of 12 apple cultivars grafted onto the cold-resistant dwarfing interstock SH40 in the arid region of Lingwu, Ningxia, to identify well-adapted varieties for local production. A total of 21 indicators were measured, encompassing three major aspects: external quality (e.g., fruit size, shape index, peel color), internal flavor (e.g., soluble solids, soluble sugars, titratable acids, vitamin C content), and textural attributes (e.g., hardness, crispness, chewiness), and data were analyzed using principal component analysis and membership function methodology. The cultivars exhibited distinct quality profiles under identical management: ‘Red General’ performed well in fruit size, weight, and sugar–acid balance; ‘Yanfu 6’ showed the highest firmness and crispness; ‘Shengli’ had the greatest soluble solids content; and ‘Granny Smith’ was richest in vitamin C. Four principal components were extracted, explaining 80.06% of the total variance and simplifying the quality evaluation system. Based on the comprehensive membership function scores, ‘Red General’, ‘White Winter Pearmain’, and ‘Huashuo’ ranked highest in overall fruit quality. In conclusion, these three cultivars perform excellently on SH40 and are recommended for promotion, whereas ‘Red Delicious’ is not recommended due to poor performance. These findings offer a practical reference for selecting apple cultivars paired with SH40 in similar arid regions. Full article

Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this study introduces a novel concept of a difference map, which relies exclusively on DCE-MRI for the localization of peripheral zone prostate cancer using functional data analysis-based (FDA) signal processing. The proposed workflow uses discrete voxel-level DCE time–signal curves that are transformed into a continuous functional form. First-order derivatives are then used to determine patient-specific time points of greatest enhancement change that adapt to the intrinsic characteristics of each patient, producing diffmaps that highlight regions with pronounced enhancement dynamics, indicative of malignancy. A subsequent normalization step accounts for inter-patient variability, enabling consistent interpretation across subjects and probabilistic PCa localization. The approach is validated on a curated dataset of 20 patients. Evaluation of eight workflow variants is performed using weighted log loss, the best variant achieving a mean log loss of 0.578. This study demonstrates the feasibility and effectiveness of a single-modality, automated, and interpretable approach for peripheral prostate cancer localization based solely on DCE-MRI. Full article

Accurate wildlife detection in Unmanned Aerial Vehicle (UAV)-captured imagery is crucial for biodiversity conservation, yet it remains challenging due to the visual similarity of species, environmental disturbances, and the small size of target animals. To address these challenges, this paper introduces YOLO-WL, a wildlife detection algorithm specifically designed for UAV-based monitoring. First, a Multi-Scale Dilated Depthwise Separable Convolution (MSDDSC) module, integrated with the C2f-MSDDSC structure, expands the receptive field and enriches semantic representation, enabling reliable discrimination of species with similar appearances. Next, a Multi-Scale Large Kernel Spatial Attention (MLKSA) mechanism adaptively highlights salient animal regions across different spatial scales while suppressing interference from vegetation, terrain, and lighting variations. Finally, a Shallow-Spatial Alignment Path Aggregation Network (SSA-PAN), combined with a Spatial Guidance Fusion (SGF) module, ensures precise alignment and effective fusion of multi-scale shallow features, thereby improving detection accuracy for small and low-resolution targets. Experimental results on the WAID dataset demonstrate that YOLO-WL outperforms existing state-of-the-art (SOTA) methods, achieving 94.2% mAP@0.5 and 58.0% mAP@0.5:0.95. Furthermore, evaluations on the Aerial Sheep and AI-TOD datasets confirm YOLO-WL’s robustness and generalization ability across diverse ecological environments. These findings highlight YOLO-WL as an effective tool for enhancing UAV-based wildlife monitoring and supporting ecological conservation practices. Full article

Streptococcus agalactiae is a major cause of bovine mastitis, which affects the quality and yield of milk. The main strategy for controlling this pathogen on dairy farms is the use of antibiotics. This study investigated the clonality, serotype distribution, antimicrobial susceptibility, and presence of resistance and virulence genes in 46 S. agalactiae isolates obtained from raw bovine milk in northeastern Brazil. Capsular types were determined using multiplex PCR and antibiotic susceptibility profiles were determined using disc diffusion or the gradient strip method. Clonal diversity was evaluated via pulsed-field gel electrophoresis. Eight isolates were sequenced using short- and long-read methods. There was high overall genetic diversity, whereas the resistance and virulence profiles were largely homogeneous within herds. Tetracycline and macrolide resistance was frequent and mediated by tetO and ermB and less frequently by tetM. Genome analysis demonstrated that resistance genes are present in mobile genetic elements that are also present in human isolates, and phylogenomic analyses identified ST-103 as the predominant and multi-host-adapted lineage, whereas ST-91 clustered with the bovine-adapted lineage. These findings expand the molecular epidemiology of S. agalactiae in dairy farms of a region in northeast Brazil and highlight the importance of surveillance strategies for guiding mastitis control and mitigating the spread of antimicrobial resistance. Full article

Agricultural production remains highly susceptible to water-related risks, such as drought and flooding. Although hydrologic forecasting systems, such as the National Water Model (NWM), have advanced considerably, their outputs are rarely used for real-time agricultural decision-making. This study developed the Agricultural Water Risk Indicator (AWRI), a framework that translates NWM streamflow forecasts into crop-specific risk assessment indicators. The AWRI framework has three key components: (1) the hydrological threat and exposure characterization based on NWM streamflow forecasts (B1); (2) crop sensitivity by growth stage and water needs (B2); and (3) adaptive capacity reflecting the presence of irrigation or drainage infrastructure (B3). The AWRI was evaluated across three NWM reach IDs covering five farm sites in the Black Belt region of Alabama, USA. The results show that the AWRI captured variations in hydrologic conditions, risk, and crop tolerance across the research sites within the one- to four-week forecast range. Crops in the reproductive stage were especially sensitive. Without resilience measures, up to 55% of the crops simulated at some sites had high-risk AWRI categories. Including irrigation or drainage decreased risk scores by one to two levels. The AWRI tool provides farmers and stakeholders with critical information to support proactive agricultural water management. Full article

Residential buildings on the Qinghai–Tibet Plateau face persistent thermal discomfort due to high-altitude climatic extremes. This study investigates how building morphology—including aspect ratio (AR), orientation, and area scaling—affects indoor thermal comfort. Field surveys in Xinghai County informed representative dwelling reconstructions, which were simulated using Ladybug 1.8.0 and Honeybee 1.8.0. Thermal performance was evaluated using PMV, SET, Winter solstice apparent form factor (WSAFF), and surface-to-volume ratio (S/V). Results indicate that compact, near-square forms enhance seasonal thermal stability, with higher WSAFF improving winter solar gains but raising summer overheating risk. South-facing orientations (0° to −30°) optimize summer comfort, while geometric scaling (0.4–2.0) stabilizes indoor temperatures and improves summer PMV and SET, though winter benefits are limited. Comparison of prototype layouts shows that elongated footprints increase vertical variation in comfort, highlighting upper-floor sensitivity to geometry. The study provides a climate-specific framework linking building form with indoor thermal performance. These insights offer practical guidance for sustainable settlement planning and adaptive building design in cold, high-altitude regions. Full article

Geothermal energy is a crucial component of climate adaptation and sustainability transitions, as it provides a dependable, low-carbon source of baseload power that can accelerate sustainable energy transitions and enhance climate resilience. Yet, in East Africa—one of the world’s most promising geothermal regions, with the East African Rift—a unique climate-energy opportunity zone—the harnessing of geothermal power remains slow and uneven. This study examines the contextual conditions that facilitate the successful and sustainable development of geothermal power in the region. Drawing on semi-structured interviews with 17 experienced professionals who have worked extensively on geothermal projects across East Africa, the analysis identifies how technical, institutional, managerial, and relational circumstances interact to shape outcomes. The findings indicate an interdependent configuration of success conditions, with structural, institutional, managerial, and meta-conditions jointly influencing project trajectories rather than operating in isolation. The most frequently emphasised enablers were resource confirmation and technical design, leadership and team competence, long-term stakeholder commitment, professional project management and control, and collaboration across institutions and communities. A co-occurrence analysis reinforces these insights by showing strong patterns of overlap between core domains—particularly between structural and managerial factors and between managerial and meta-conditions, highlighting the mediating role of managerial capability in translating contextual conditions into operational performance. Together, these interrelated circumstances form a system in which structural and institutional foundations create the enabling context, managerial capabilities operationalise this context under uncertainty, and meta-conditions sustain cooperation, learning, and adaptation over time. The study contributes to sustainability research by providing a context-sensitive interpretation of how project success conditions manifest in geothermal development under climate transition pressures, and it offers practical guidance for policymakers and partners working to advance SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action) in Africa. Full article

This paper examines the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for voltage regulation in a small-scale wind turbine (SWT) system intended for off-grid rural electrification in Uzbekistan. The proposed architecture consists of a wind turbine, a permanent-magnet DC generator, and a buck converter supplying a regulated 48 V DC load. While ANFIS-based control has been reported previously for wind energy systems, the novelty of this work lies in its focused application to a DC-generator-based SWT topology using real wind data from the Bukhara region, together with a rigorous quantitative comparison against a conventional PI controller under both constant- and reconstructed variable-wind conditions. Dynamic performance was evaluated through MATLAB/Simulink simulations incorporating IEC-compliant wind turbulence modeling. Quantitative results show that the ANFIS controller achieves faster settling, reduced voltage ripple, and improved disturbance rejection compared to PI control. The findings demonstrate the technical feasibility of ANFIS-based voltage regulation for decentralized DC wind energy systems, while recognizing that economic viability and environmental benefits require further system-level and experimental assessment. Full article

The effect of afforestation in infertile mountainous areas is closely related to the soil ecological environment. Soil enzyme activities and the structure and functions of microbial communities are core indicators reflecting soil quality. Clarifying the response patterns of the two to Cyclobalanopsis gilva afforestation in infertile mountainous areas can provide a key scientific basis for targeted improvement of the cultivation efficiency of C. gilva plantations under different site conditions in the eastern subtropical region of China. In this study, 7-year-old C. gilva young forests in infertile mountainous areas and control woodland areas were selected in Shouchang Forest Farm, Jiande, Zhejiang Province, located in the subtropical region of China. Soil enzyme activities and microbial biomass in different soil layers, as well as metagenomes of rhizosphere and bulk soils, were determined to explore the effects and internal correlations of site conditions on soil enzyme activities and microbial community characteristics of C. gilva forests. The results showed that the activities of urease and catalase, as well as the content of microbial biomass nitrogen in the surface soil of infertile mountainous areas, were significantly lower than those in control woodland areas. The shared dominant phyla in the two types of sites included Proteobacteria and Acidobacteria, and the shared dominant genera included Bradyrhizobium. In addition, the relative abundances of three unclassified populations of Proteobacteria and functional genes related to cofactor and vitamin metabolism in the rhizosphere soil of infertile mountainous areas were significantly higher than those in control woodland areas. Meanwhile, the dominant microbial phyla in the rhizosphere soil of infertile mountainous areas had a closer correlation with soil enzyme activities and microbial biomass. This study clarified the ecological strategy of C. gilva young forests adapting to infertile mountainous areas: by increasing the relative abundances of functional genes related to cofactor and vitamin metabolism in rhizosphere microorganisms, promoting the enrichment of microorganisms associated with soil nitrogen cycling, and enhancing the correlations between dominant microbial phyla and soil enzyme activities and microbial biomass, the nitrogen resource limitation on soil microbial activity in infertile mountainous areas is balanced. This finding provides direct guidance for optimizing the afforestation and management techniques of C. gilva in infertile mountainous areas and has important practical value for promoting forest ecological restoration. Full article