Search Results (110,587)

To address issues such as significant scale differences, complex pose variations, strong background interference, and similar category characteristics of pests in the images obtained from field traps, this study proposes a pest recognition method based on a two-stage “segmentation–detection” approach to improve the accuracy of field pest situation monitoring. In the first stage, an improved segmentation model, BAE-UNet (Background-Aware and Edge-Enhanced U-Net), is adopted. Based on the classic U-Net framework, a Background-Aware Contextual Module (BACM), a Spatial-Channel Refinement and Attention Module (SCRA), and a Multi-Scale Edge-Aware Spatial Attention Module (MESA) are introduced. These modules respectively optimize multi-scale feature extraction, background suppression, and boundary refinement, effectively removing complex background information and accurately extracting pest body regions. In the second stage, the segmented pest body images are input into the YOLOv8 model to achieve precise pest detection and classification. Experimental results show that BAE-UNet performs excellently in the segmentation task, achieving an mIoU of 0.930, a Dice coefficient of 0.951, and a Boundary F1 of 0.943, significantly outperforming both the baseline U-Net and mainstream models such as DeepLabV3+. After segmentation preprocessing, the detection performance of YOLOv8 is also significantly improved. The precision, recall, mAP50, and mAP50–95 increase from 0.748, 0.796, 0.818, and 0.525 to 0.958, 0.971, 0.977, and 0.882, respectively. The results verify that the proposed two-stage recognition method can effectively suppress background interference, enhance the stability and generalization ability of the model in complex natural scenes, and provide an efficient and feasible technical approach for intelligent pest trap image recognition and pest situation monitoring. Full article

Background: Rainfall-induced landslides are a widespread and destructive geological hazard that resist precise prediction. They pose serious threats to human lives and property, ecological stability, and socioeconomic development. Methods: To address the challenges in mitigating rainfall-induced landslides in high-altitude mountainous regions, this study proposes a digital twin framework that couples multiple physical fields and is based on the spherical discrete element method. Results: Two-dimensional simulations identify a trapezoidal stress distribution with inward-increasing stress. The stress increases uniformly from 0 kPa at the surface to 210 kPa in the interior. The crest stress remains constant at 1.8 kPa under gravity, whereas the toe stress rises from 6.5 to 14.8 kPa with the slope gradient. While the stress pattern persists post-failure, specific magnitudes alter significantly. This study pioneers a three-dimensional close-packed spherical discrete element method, achieving enhanced computational efficiency and stability through streamlined contact mechanics. Conclusions: The proposed framework utilizes point-contact mechanics to simplify friction modeling, enhancing computational efficiency and numerical stability. By integrating stress, rainfall, and seepage fields, we establish a coupled hydro-mechanical model that enables real-time digital twin mapping of landslide evolution through dynamic parameter adjustments. Full article

Water scarcity continues to challenge arid regions such as Egypt, where growing population demands, climate change impacts, and increasing agricultural pressures intensify the need for sustainable water management. Treated wastewater has emerged as a viable alternative resource, provided that the effluent meets stringent quality standards for safe reuse. The purpose of this study was to develop a comprehensive model of the Khorshed Wastewater Treatment Plant (KWWTP) to depict the processes used for biological nutrient removal. Operational data was gathered and examined over a period of 18 months to describe the quality of wastewater discharged by the Advanced Sequencing Batch Reactor (ASBR) of the plant, using specific physicochemical parameters like TSS, COD, BOD₅, and N-NO₃⁻. A process flow diagram integrating the Activated Sludge Model No. 1 (ASM1) for biological nutrient removal was created using the GPS-X. The study determined the parameters influencing the nutrient removal efficiency by analyzing the responsiveness of kinetic and stoichiometric parameters. Variables related to denitrification, autotrophic growth, and yield for heterotrophic biomass were the main focus of the calibration modifications. The results showed that the Root Mean Square Error (RMSE) for the dynamic-state was COD (0.02), BOD₅ (0.07), N-NO₃⁻ (0.75), and TSS (0.82), and for the steady state was COD (0.04), BOD₅ (0.11), N-NO₃⁻ (0.67), and TSS (0.10). Since the model’s accuracy was deemed acceptable, it provides a validated foundation for future scenario analysis and operational decision support that produces a trustworthy model for predicting effluent data for the concentrations of TSS, COD, BOD₅, and N-NO₃⁻ in steady state conditions. Dynamic validation further confirmed model reliability, despite modest discrepancies in TSS and nitrate predictions; addressing this issue necessitates further research. Full article

Military settlements are an integral part of the military defense system of the Ming Great Wall, and the spatial layout of their constituent elements embodies the wisdom of ancient military geography. However, existing studies have predominantly focused on the macro-scale distribution of military settlements, with insufficient exploration of the spatial differentiation mechanisms of their micro-level constituent elements. Therefore, this study examines 61 military settlements in Miyun District, Beijing. Based on documentary research and field surveys, the types of constituent elements were systematically identified. This study employs kernel density analysis and the Optimal Parameters-based Geographical Detector (OPGD) model to explore their spatial patterns and driving mechanisms. The results show that (1) the constituent elements of military settlements collectively exhibit a spatial pattern of “one belt and three cores”, with pronounced spatial heterogeneity; (2) Fortress level, Military strength, and Distance to the Lu Fort are the core factors influencing the spatial differentiation of elements; and (3) when multiple factors interact, the interaction between Military strength and Distance to the Lu Fort demonstrates a significant nonlinear enhancement effect. This study reveals the spatial organizational logic of the Ming Great Wall military settlements at the micro-element level, providing a scientific basis for the graded protection and adaptive reuse of military settlements in Miyun District. Furthermore, the proposed analytical framework can also offer methodological insights for studies in other regions along the Great Wall. Full article

This article discusses the policy of Bussed Education in Türkiye in the context of the fourth Sustainable Development Goal, “quality education”. The contributions made by the policy of Bussed Education, which aims to facilitate the access of students living in disadvantaged areas to education under equal conditions, to quality education were examined. The sample of the study, which was carried out using a qualitative research method, consisted of 38 teachers and 39 school principals (77 participants in total) selected via purposeful sampling. The participants were working at schools in different regions of Türkiye where education by busing was being implemented. Data were collected in interviews carried out using a semi-structured interview form. The results revealed four themes in the context of which the policy of Bussed Education supported Sustainable Development Goal 4. It was concluded that the policy of Busing in Education was a policy that facilitated the access of students living in disadvantaged areas to schools and supported access to quality education in terms of equal opportunities, sustainability, inclusivity, and employment. Recommendations made for policymakers included the acknowledgment of the shortcomings of busing in education and the resolution of arising challenges by the consideration of contextual conditions. Full article

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R² values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L⁻¹, and RMSE of 0.14 and 0.91 mg L⁻¹ for NO₃⁻-N and NH₄⁺-N during calibration, and R² values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L⁻¹, and RMSE of 0.10 and 0.34 mg L⁻¹ during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO₃⁻-N) and ammonium-nitrogen (NH₄⁺-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO₃⁻-N and NH₄⁺-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO₃⁻-N and NH₄⁺-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions. Full article

Extensive coal mining activities can exert substantial negative impacts on surface ecosystems. Vegetation indices are widely recognized as effective indicators of land ecological conditions and provide valuable insights into long-term ecological changes in mining areas. In this study, the Zhaogu mining area of the Jiaozuo Coalfield was selected as the study site. Using the Google Earth Engine (GEE) platform, the Kernel Normalized Difference Vegetation Index (kNDVI) was constructed to generate a vegetation dataset covering the period from 2010 to 2024. The temporal dynamics and future trends of vegetation coverage were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, the Hurst index, and residual analysis. Furthermore, the relative contributions of climatic factors and human activities to vegetation changes were quantitatively assessed. The results indicate that: (1) vegetation coverage in the Zhaogu mining area exhibits an overall improving trend, affecting approximately 77.1% of the study area, while slight degradation is mainly concentrated in the southeastern region, accounting for about 15.2%; (2) vegetation dynamics are predominantly characterized by low and relatively low fluctuations, covering approximately 78.5% of the region, whereas areas with high fluctuations are limited and mainly distributed in zones with intensive mining activities; although the current vegetation trend is generally increasing, future projections suggest a potential decline in approximately 55.8% of the area; and (3) vegetation changes in the Zhaogu mining area are jointly influenced by climatic factors and human activities, with climatic factors promoting vegetation growth in approximately 70.6% of the study area, while human activities exert inhibitory effects in about 24.2%, particularly in regions affected by mining operations and urban expansion. Full article

The Yan goose (YE, Anser cygnoides) is a valuable indigenous poultry genetic resource, renowned for its superior meat quality and environmental adaptability. Despite its economic importance, the genetic basis underlying these adaptive traits remains unclear. In this study, we employed whole-genome resequencing (WGS) to perform high-throughput sequencing on a conserved population of 15 samples. Bioinformatic analyses were conducted to systematically evaluate the population’s genetic structure, and a genome-wide scan for selection signals related to economically significant traits was performed using the integrated haplotype score (iHS) method. An average of 4.43 million high-quality SNPs were identified, which were predominantly located in intergenic and intronic regions. Population structure analysis revealed a close genetic relationship within the conserved population of YE, with no significant lineage stratification observed. Pairwise sequentially Markovian coalescent (PSMC) analysis indicated that the YE underwent a severe genetic bottleneck during the Last Glacial Maximum (LGM), followed by gradual population recovery in the early Neolithic period. Genome-wide selection signal scanning identified multiple genomic regions under strong selection, annotating key genes associated with growth and development (e.g., GHRL, AKT1, and MAPK3), lipid deposition (e.g., PLPP4, SAMD8, and LPIN1), and disease resistance and stress resilience (e.g., TP53, STAT3). Functional enrichment analysis revealed significant enrichment of these genes in pathways related to glycerophospholipid metabolism (p < 0.01), purine metabolism (p < 0.01), and immune response (p < 0.01). This study not only provides a theoretical foundation for the scientific conservation of the YE germplasm resources but also offers valuable genomic resources for identifying functional genes underlying important economic traits and advancing molecular breeding strategies. Full article

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds. Full article

This study analyzes a hybrid computational framework that combines peridynamics (PD) and the finite element (FE) method to model wave propagation in a one-dimensional bar, focusing on their integration for enhanced accuracy and efficiency. The analysis investigates PD’s ability to capture non-local interactions in regions near loading points, with computationally efficient coarse discretization in other areas through finite element methods. The dynamic response to symmetric and asymmetric axial loading, including loading and unloading phases, is analyzed through time-dependent external forces, solving displacement, velocity, and acceleration fields at each time step. The effects of PD-specific parameters, such as the horizon size, and the FE–PD node spacing size ratios on the performance of the hybrid model in wave propagation are investigated. Additionally, the study examines the von Neumann stability for PD to ensure stability and reliability, offering a robust framework for integrating PD and FE in dynamic analyses. Full article

Large-Format Additive Manufacturing (LFAM) offers the ability to 3D print composites at multi-meter scale and high throughput by utilizing a screw-based extrusion system that is compatible with pelletized feedstock. As such, LFAM systems like the Big Area Additive Manufacturing (BAAM) system provide a pathway for incorporating AM techniques into industry-scale production. Despite significant growth in LFAM techniques and usage in recent years, typical Multi-Material (MM) techniques induce weak points at discrete material boundaries and encounter a higher frequency of delamination failures. A novel dual-hopper configuration was developed for the BAAM platform to enable in situ switching between material feedstocks that creates a graded transition region in the printed part. This research studied the influence of extrusion screw speed, component design, transition direction, and material viscosity on the transition behavior. Material transitions were monitored using compositional analysis as a function of extruded volume and modeled using a standard Weibull cumulative distribution function (CDF). Screw speed had a negligible influence on transition behavior, but averaging the Weibull CDF parameters of transitions printed using the same configurations demonstrated that designs intended to improve mixing increased the size of the blended material region. Further investigation showed that the relative difference and change in complex viscosity influenced the size of the blended region. These results indicate that tunable properties and material transitions can be achieved through selection and modification of composite feedstocks and their complex viscosities. Full article

In the context of the era where the maritime power strategy converges with the wave of the digital economy, the digital economy provides a critical transformational opportunity for marine fisheries to break through the traditional extensive model and achieve high-quality development. Based on panel data from 41 coastal cities in China from 2003 to 2022, this study empirically examines the enabling effect of the digital economy on marine fisheries from the perspective of total factor productivity. The findings are as follows: First, the development of the digital economy promotes the improvement of total factor productivity in marine fisheries, but this is primarily achieved through “innovation-driven” expansion of the production frontier, while its potential in “efficiency catch-up” has not yet been fully realized. Second, the enabling effect exhibits distinct spatial heterogeneity, with its positive impact concentrated in cities in the South China Sea region, where industrial foundations and policy environments are more aligned. Third, the influence of the digital economy demonstrates nonlinear threshold characteristics; when technology promotion and industrial collaboration surpass specific thresholds, the enabling effect significantly strengthens, but as innovation capability improves, its marginal contribution shows a diminishing trend. Accordingly, it is recommended to deepen the application of digital technologies in core processes, transitioning from “isolated applications” to “systematic integration.” Simultaneously, tailored regional development strategies should be formulated to align with the resource endowments and development stages of each maritime region. On this basis, efforts should be made to improve technology promotion and industrial support systems, construct a collaborative and efficient digital fishery ecosystem, and facilitate the sustainable transition of marine fisheries from factor-driven to innovation-driven growth. Full article

Phenotypic plasticity is a key mechanism by which crops adjust to fluctuating environmental conditions, yet its genetic basis under drought remains poorly characterized in barley (Hordeum vulgare). We hypothesized that phenotypic plasticity under drought is controlled by a distinct, trait-specific genetic architecture that can be detected using complementary plasticity metrics and genome-wide association studies (GWAS). Here, we examined data from 1277 spring barley genotypes grown under well-watered and water-limited conditions to quantify plastic responses across two developmental traits (i.e., heading time, and maturity) and seven productivity-related traits (i.e., total dry matter, plant grain yield, grain number, grain weight, harvest index, vegetative dry weight, and grain-filling period). The experimental design, based on contrasting water regimes across a large diversity panel, allowed robust assessment of genotype-by-environment interactions. We combined five complementary plasticity estimators with four independent GWAS approaches to resolve the genomic architecture underlying trait-specific plasticity. Environmental effects dominated variation in yield-related traits, whereas developmental traits remained more genetically determined. The different plasticity metrics captured distinct but partially overlapping response dimensions, and their integration greatly increased the robustness of association signals. A total of 239 high-confidence SNPs obtained for top traits, those associated across metrics and methods, were enriched in coding regions and mapped to genes involved in osmoregulation, carbohydrate metabolism, hormonal pathways, and ion transport. A total of 27 high-confidence SNPs were located in coding regions, showing genotype-specific differences in the magnitude and even direction of phenotypic plasticity. These loci exhibited opposite allelic effects across water regimes, consistent with context-dependent antagonistic pleiotropy. The fact that candidate alleles for the plastic response modulate environmental sensitivity differently highlights that drought resilience arises from environment-contingent genetic architectures. Overall, these results provide a comprehensive framework for dissecting plasticity and identify concrete genomic targets for indirect selection targeting crop resilience with improved performance under increasingly variable water availability. Full article

Bone defects in the maxillofacial region severely impair patient function and esthetics. Free autologous bone grafting remains the gold-standard treatment; however, surgical intervention at donor sites limits clinical applicability. Treatment using artificial materials also presents challenges, including insufficient bone regeneration and poor biocompatibility. Bio three-dimensional (3D) printing, which enables the fabrication of scaffold-free 3D constructs from cellular spheroids has emerged as a promising regenerative approach. This study investigated the osteogenic potential of scaffold-free constructs composed of human dental pulp stem cell (DPSC) spheroids in a rat mandibular defect model. DPSCs isolated from extracted human teeth were used to generate spheroids, which were assembled into 3D constructs using a Bio 3D printer. The spheroids exhibited higher mRNA expression of stem cells and early osteogenic markers than monolayer cultures. The constructs were transplanted into mandibular defects of immunodeficient rats, and bone regeneration was assessed eight weeks post-transplantation. Radiographic and micro-Computed Tomography analyses revealed significantly greater bone volume and mineral density in the 3D construct group. Histological and immunohistochemical examinations confirmed newly formed bone containing osteogenic cells derived from the transplanted DPSCs. These findings indicate that Bio 3D-printed, scaffold-free DPSC constructs promote mandibular bone regeneration and may provide a novel strategy for maxillofacial reconstruction. Full article

The study and understanding of spatial and temporal changes in the landscape is essential for assessing environmental trends and predicting future developments in the area. Changes in land cover and vegetation dynamics are key indicators of the ecological stability of an area. This study analyzes long-term changes in land cover and vegetation dynamics in Jelšava and neighboring municipalities. The selected area has long been classified as one of the areas with poor air quality in Slovakia. The analysis is based on data from the CORINE Land Cover program for the period 1990–2018 and Landsat data from 1990 to 2025. The condition and vitality of vegetation were assessed using the Normalized Difference Vegetation Index (NDVI), while temporal trends were assessed using non-parametric Mann–Kendall and Sen’s slope tests. The results show a decrease in the area of class 31—Forests between 2012 and 2018, accompanied by an increase in the area of class 324—Transitional woodland–shrub. Analysis of the NDVI confirmed a slightly positive trend in vegetation cover development, with statistically significant growth (p < 0.05) recorded on approximately 43% of the territory. The combination of remote sensing data and spatial analysis in a GIS environment has proven to be an effective approach to monitoring ecological dynamics and provides valuable insights for regional environmental management and sustainable land use planning. Full article