Search Results (13,972)

Shifting cultivation (SC) is a predominant land use across the tropics, feeding hundreds of millions of marginalized people, causing significant deforestation in tropical regions. A key question is how to realize rapid and large-scale identification of the spatial distribution, cycle numbers, and fallow periods of SC. Building the LandCycler algorithm that fully considers the inter-annual cycle of SC based on Landsat imagery from 1988 to 2020, we identify the distribution and fallow period of SC in Southeast Asia, including Assam in India and Yunnan Province in China. The results show that the LandCycler for the identification of SC is satisfactory (producer’s accuracy 82.12% and user’s accuracy 81.37%), and the accuracy in detecting the average cycle number, and calculating the average fallow period is 83.71%, and 96%, respectively. We found that the total area of SC is as high as 16.79 × 10⁴ km² in Southeast Asia, which uses almost 10% of the total forests. Meanwhile, the average cycle number and the average fallow period of SC are two times and 10 years, respectively. More than 98% of SC has repeated deforestation four times or less. The shorter the distance from settlements and the distance from roads, the larger the cycle number of SC. Although there was no significant correlation between elevation and slope and the cycle number of SC, SCs were mainly distributed at slopes of 18 ± 5° and elevations of 800 ± 300 m. These findings provide effective tools for sustainable agroforestry management as well as for global SC mapping. Full article

Reliable pedestrian perception in autonomous driving depends not only on detecting the target, but also on how completely and consistently its three-dimensional geometry is captured from different sensor viewpoints. This study presents a LiDAR-based observability framework for evaluating pedestrian representation on the ANTA compact autonomous vehicle platform using a roof-mounted Top LiDAR (TL), a Front-Right LiDAR (FRL), and their fused configuration. The pedestrian was analyzed in a canonical local frame using geometric extent ratios, projected surface occupancy, voxel-based volumetric occupancy, and statistical descriptors of the local point distribution, integrated into a global observability score, $S_{3D}$. A Distance-Robustness Index (DRI), an overlap-based complementarity analysis, and a lightweight temporal centroid-sensitivity check over 20 consecutive frames were used to characterize performance across distance. Using ROS 2 bag data processed offline in MATLAB R2025b the fused configuration achieved the highest mean global score (0.563), compared with 0.504 for FRL and 0.432 for TL, and the highest robustness ($\mathrm{DRI}=0.5628$, $CV=10.7\%$). The results show that 1 m maximizes local density, 2–3 m maximize projected and volumetric completeness, and 7 m provides the best balanced observability. Within the evaluated platform and under the controlled benchmark conditions, complementary multi-LiDAR fusion provided the strongest overall geometry-aware pedestrian representation. Full article

Biomethane is emerging as a key renewable gas in both mature and developing energy systems worldwide. Driven by climate-neutrality objectives, energy-security concerns, and rising waste-to-energy ambitions, global biomethane production is expected to expand rapidly in the coming decade. In Europe, this growth is accelerated by the REPowerEU target of 35 billion m³ by 2030. However, as biomethane production increases and natural gas demand declines over time, distribution networks face growing operational challenges, including pressure build-up and biomethane curtailment caused by supply and demand mismatches. This study evaluates whether surplus biomethane can be converted into electricity as a multi-commodity strategy to alleviate these constraints. Using hourly operational data from two Dutch Distribution System Operators (DSOs), a simulation model was developed to assess the impact of generator-based biomethane-to-power conversion on both gas and electricity distribution networks. The results show that, for RENDO, the approach increases effective biomethane injection by 49.0%, reduces natural gas deliveries from the transmission system by 20.0%, and lowers electricity imports by 9.2%. For Coteq, the corresponding impacts are 106.8%, 30.6%, and 16.2%, respectively. These findings indicate that multi-commodity coupling through biomethane-to-power conversion provides a promising strategy for increasing biomethane injection and renewable electricity generation. Full article

Mosquitoes are recognized as the arthropod group with the greatest vectorial capacity, and the viruses they transmit constitute a significant concern in the context of global One Health. In addition, these insects act as hosts for a wide diversity of insect-specific viruses (ISVs), which exclusively infect arthropods. Expanding knowledge of ISVs is particularly relevant, given their potential influence on arbovirus replication and their role in elucidating the evolutionary processes that shape virus–vector interactions. In this study, we report the isolation and molecular analysis of three negeviruses associated with different mosquito species of the genera Culex, Coquillettidia, Mansonia, and Ochlerotatus, collected in Belém, Pará State, in the Brazilian Amazon: Loreto virus, Wallerfield virus, and a putative new species, designated Terra firme virus. Eleven pools exhibited cellular alterations consistent with cytopathic effects in invertebrate C6/36 cells but showed no evidence of replication in vertebrate Vero cells. Notably, simultaneous infections by two or three negeviruses were detected in some mosquito pools, indicating the occurrence of multiple viral infections within individual samples. Genomic analyses revealed that the isolated strains share conserved domains with previously described isolates from other countries. Phylogenetic inferences demonstrated that the investigated strains are classified within the clades Nelorpivirus and Sandewavirus. Taken together, these findings expand the currently known diversity of the negevirus group and contribute to a more comprehensive understanding of its host range and geographic distribution. Full article

Magnetic hysteresis strongly influences energy dissipation and efficiency in power magnetic components under time-varying excitation. This work proposes a compact dynamic hysteresis model using a Hammerstein structure, consisting of a closed-form arctangent static operator followed by a first-order relaxation dynamic stage. The formulation enables direct datasheet-based parameterization and avoids iterative differential solvers or distributed hysteron representations, resulting in low calibration effort and computational cost. The static hysteresis behavior is characterized using four static parameters directly identified from manufacturer B-H datasheets, while dynamic effects are captured using two global calibration parameters derived from datasheet loss curves. This formulation enables accurate reconstruction of major and minor hysteresis loops, while introducing frequency-dependent phase lag and dynamic loop opening. Model performance is evaluated under diverse excitations, including sinusoidal, amplitude-modulated, FORC and chirp signals, showing waveform deviations below 7.2% peak-to-peak NRMSE relative to classical hysteresis models. Energy-loss predictions are validated against manufacturer datasheet curves for ferrite material 3C90 across multiple frequencies, yielding a root-mean-square relative error of 8.3% with 89% of operating points within ±20% deviation. The proposed model provides a datasheet-driven framework for hysteresis and energy-loss prediction in power magnetic components. Full article

Cultivated land, as a vital resource for human sustenance, requires region-specific protection strategies worldwide. Semantic segmentation technology for agricultural land remote sensing imagery offers a scientific foundation and decision-making support for cultivated land protection through accurate identification and dynamic monitoring. In China, the fragmented distribution, small parcel sizes, complex terrain, and indistinct boundaries of cultivated land pose challenges to the intelligent interpretation of high-resolution remote sensing (HRRS) imagery. Conventional semantic segmentation methods often struggle to address these complexities. To address this issue, we propose a hybrid network called STAR-Net (Swin Transformer Auxiliary Residual Structure) for semantic segmentation of agricultural land in HRRS imagery whose encoder integrates a Global-Local Feature Fusion Module to effectively merge complementary information from both branches. A Multi-Scale Aggregation Module within the decoder facilitates the fusion of shallow spatial details and deep semantic cues, enhancing the model’s ability to discriminate objects at varying scales. Using the LoveDA dataset, we show that STAR-Net generates the highest Intersection over Union (IoU) on the “Barren” and “Forest”, achieving the improvement of 9.88% and 7.05% respectively, while delivering comparable IoU performance on other categories. Overall performance improved by 0.46% in mIoU compared to state-of-the-art models. Across all target categories, the method also achieves the greatest count of leading segmentation metrics. Full article

Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article

Accurate identification of tree species is important for forest management, biodiversity studies, and precision forestry. Near-range LiDAR point clouds provide detailed three-dimensional information about individual trees. However, the complex structure of the point clouds and the unbalanced distribution of species make automatic classification difficult. To address these issues, this study presents a Transformer model with geometric enhancement. The model combines local geometric features and global attention to improve species recognition in forest environments. It uses geometric information with biological meaning, including point cloud normals, local density, vertical structure, and growth direction. A focal loss with class balance is also introduced to reduce the impact of species distributions with long tails. Experiments on the ForSpecial20K dataset show that the proposed method performs better than representative models based on convolution, graph methods, and Transformer architectures. It achieves higher overall accuracy (78.20%), higher mean class accuracy (73.48%), and a higher Macro-F1 score (73.21%). Results from confusion matrices and visual analysis of similar species further verify the effectiveness of the geometric features and the loss design. These results suggest that modeling structural information of forests helps improve robustness and generalization. The proposed method offers a practical solution for tree-level species mapping, fusion of LiDAR data from multiple sources, and fine-scale forest inventory. It also shows the value of combining high-resolution LiDAR data with deep learning for forestry applications. Full article

Images of wind turbine blades captured by drones often feature complex backgrounds, and small targets such as minor defects or images have low resolution, leading to reduced recognition rates. To address environments with complex feature backgrounds, this paper proposes the PPS-MSDeim model. Based on the lightweight end-to-end detection framework DEIM-N, it introduces three core innovations to tackle the challenge of detecting small, irregular defects on wind turbine blades against complex backgrounds. First, we design an inverted multi-scale deep separable convolutional module (MDSC). After compressing channels via a bottleneck layer, it concurrently processes 3 × 3, 5 × 5, and 7 × 7 inverted deep separable convolutions. By first fusing channel information and then extracting multi-receiver-field spatial features, this approach enhances the ability to characterize morphologically variable defects while reducing computational overhead. The MDSC is then embedded into the backbone network HGNetv2. Second, we construct a Multi-Scale Feature Aggregation and Diffusion Pyramid Network (MFADPN). Through a Multi-Scale Feature Aggregation Module (MSFAM), it directly fuses features from layers P2 to P5, achieving deep integration of high-level semantics and low-level details. Combining dilated convolutions with expansion ratios of 1, 3, and 5 captures multi-level context, and a Sobel edge branch is introduced to enhance defect contours; subsequently, a feature diffusion operation is performed to distribute the enhanced features back to each level, shortening information paths and preventing signal decay; simultaneously, a high-resolution detection head is added to P2 and the P5 head is removed to improve sensitivity for small object detection. Finally, we propose the PPSformer module to replace the original Transformer encoding layer. It uses patch embedding to convert images into sequences and introduces a multi-head probabilistic sparse self-attention mechanism that focuses only on key-value pairs during attention computation. This design efficiently captures irregularly varying feature information and globally detects data anomalies induced by external defects. This study uses real engineering data sets, and the results show that PPS-MSDeim, based on DEIM, increased mAP@0.5 by 6.7%, reaching 95.1%. mAP@0.5–0.95 increased by 12.0%, reaching 70.1%. This indicates that the proposed method has a significant advantage in detecting defects in wind turbine blades. Full article

Conventional fixed phase-shift strategies for parallel PV converters fail to minimize output ripple under heterogeneous input conditions, while communication-based synchronous methods incur high costs and reliability risks. Furthermore, standard global optimization algorithms like conventional Particle Swarm Optimization (PSO) suffer from slow convergence, hindering real-time application. To address these limitations, this paper proposes a communication-free distributed ripple suppression method based on an enhanced PSO with randomized phase shifting. Unlike traditional approaches, our method enables autonomous convergence without inter-unit communication. Crucially, a randomized pre-scanning mechanism narrows the search space, accelerating convergence significantly. Simulation results demonstrate that the proposed method reaches a steady state in merely 5 ms, which is 50% faster than conventional PSO (~10 ms) and eliminates communication latency. Under severe heterogeneous conditions, the technique reduces output voltage ripple to 0.66 V (a 53% reduction) compared to the unoptimized 1.21 V, vastly outperforming fixed interleaving strategies that show negligible improvement. The approach also ensures robust stability during load steps and plug-and-play operations, offering a superior low-cost and high-speed solution for distributed PV systems. Full article

System logs have been critical for analyzing the operational status and abnormal behavior of highly distributed and heterogeneous edge computing nodes. In edge environments, logs exhibit cross-event and cross-field structural interactions, making it difficult to uncover potential anomaly patterns from isolated events. Moreover, sparse annotations and varying log formats limit the effectiveness of existing methods. To address these challenges, we propose a graph neural network (GNN) anomaly detection framework with prompt learning. It leverages few-shot prompt learning to automatically extract key fields and constructs a weighted directed graph that jointly models semantic embeddings and temporal dependencies, fully representing the structural interactions and semantic associations across events and fields. Furthermore, the framework performs graph-level anomaly detection by jointly optimizing graph representation learning and classification objective within an enhanced one-class directed graph convolutional network, enabling effective identification of global structural anomaly patterns in log graphs. Experimental results demonstrate that the proposed method achieves an average F1-score of 93.3%, surpassing the current state-of-the-art (SOTA) methods by 6.93%. Full article

In the context of global climate change, the “dual carbon” goals, and land space planning, this study integrates the Patch-generating Land Use Simulation (PLUS) model, the Carnegie-Ames-Stanford Approach (CASA) model, and a soil respiration model (Heterotrophic Respiration, R_h) to simulate land use change and estimate Net Ecosystem Productivity (NEP) from 2002 to 2023. It projects the carbon sink pattern for 2030 using Hot Spot Analysis. The results show the following: (1) From 2020 to 2030, land use in the Beijing–Tianjin–Hebei region will be characterized by decreases in cropland and grassland and increases in impervious and forest, with cropland-to-impervious conversion dominating. (2) The spatial pattern of NEP exhibits a clear “high in mountainous areas and low in plains” distribution, where forest, grassland, and cropland function as carbon sinks, with forest having the strongest sequestration capacity. The carbon sink core areas cover approximately 59,479 km² and account for about 27.40% of the total area. (3) By 2030, the total carbon sink in the Beijing–Tianjin–Hebei region is projected to range from 31.81 to 32.39 Tg C under different scenarios, with forest contributing nearly 70%. The carbon sink core areas account for approximately 19.12–19.16 Tg C, representing about 60% of the total carbon sink. Full article

Human parainfluenza virus type 4 (hPIV4) remains poorly characterized compared with other hPIV serotypes and information on its genomic diversity is particularly limited for Russia and Eastern Europe. In this study, we report the first complete genome sequences of hPIV4 isolates from Russia and place them in the context of global hPIV4 genetic diversity. Eight hPIV4 viruses were isolated in cell culture from respiratory samples collected from hospitalized children in Saint Petersburg between 2017/2018 and 2023/2024. Complete viral genomes were recovered using a metagenomic whole-genome amplification approach based on SMART-9N technology. Phylogenetic analysis of 178 complete hPIV4 genomes showed clear separation into hPIV4a (n = 132) and hPIV4b (n = 46) subtypes. Based on genetic distance approach, hPIV4a formed two major clusters, with the dominant cluster B subdivided into four subclusters (B1–B4); and subcluster B4 further resolved into four genetic lineages. All Russian isolates belonged to the subcluster B4 and were distributed among multiple co-circulating lineages. In contrast, hPIV4b genomes segregated into three distinct clusters, reflecting structured genetic diversity within the subtype. Collectively, this study provides, to the best of our knowledge, the first p-distance-based framework for hPIV4 whole-genome classification and contributes new complete genome sequences for an underrepresented region. Full article

To mitigate local congestion and address the adaptability limitations of traditional static routing under dynamic traffic, this paper proposes an end-to-end routing method based on a Graph Attention Network (GAT), termed Congestion-Aware Graph Attention Routing (CA-GAR). To alleviate the issue of local optima in traditional heuristic iterative optimization, we design a dynamic link cost optimization algorithm with multi-start parallel exploration. This algorithm employs a ”penalty–reselection–reward” closed-loop feedback mechanism, performing global searches from multiple random initial states to generate a high-quality, empirically near-optimal cost matrix as supervised labels. Building on this, CA-GAR leverages a multi-head attention mechanism to adaptively aggregate high-order topological features of nodes and edges, and incorporates a staged hierarchical hyperparameter optimization strategy to map real-time network states to link costs. Simulation results demonstrate that CA-GAR outperforms traditional static routing under light, medium, and heavy loads. Under high-load burst conditions, the method exhibits effective congestion avoidance capability, reducing end-to-end delay by approximately 50% and lowering the packet loss rate to as low as 2%. Compared with QLRA, CA-GAR shows promising performance in multi-path traffic splitting and possesses robust fast rerouting capabilities during node failures, thereby achieving intelligent traffic distribution and global load balancing. Full article

The transition toward renewable energy sources has positioned wind energy as a critical technology for achieving global carbon neutrality targets. While large-scale wind farms dominate current installations, micro-scale horizontal-axis wind turbines present significant potential for distributed energy generation in remote and rural areas. This study presents a comprehensive methodology for designing micro-scale wind turbine blades through comparative analysis of three computational approaches: classical blade element momentum theory (BEMT), QBlade 2.0.9.6 software, and Computational Fluid Dynamics (CFD) simulations, with the design methodology selected based on a trade-off between accuracy and computational cost. A numerical campaign for airfoil assessment was conducted to identify optimal blade geometries, with performance evaluated based on power coefficient distribution, peak power output, and cut-in wind speed. The investigation reveals that steady CFD simulations predict peak power coefficients 23.34% higher than those predicted by BEMT and 22.46% higher than those predicted by QBlade due to three-dimensional effects, including rotational stall delay. Considering unsteady effects, the CFD simulations show a decrease of 4.08% with respect to steady simulations. The addition of endplates to the optimized blade design demonstrates significant performance improvements. This multi-fidelity approach provides a robust framework for micro-scale wind turbine design, balancing computational efficiency with accuracy requirements, and examines the impact of adding endplates. Full article