Search Results (134,652)

To address the problems of closed-loop task organization, strong corridor constraints, and path failure after local disturbances in unmanned aerial vehicle (UAV) inspection of gas-insulated switchgear (GIS) rooms, this paper proposes a topology-and-corridor-guided bias-suppressed D* (TCG-BS-D*) method for closed-loop three-dimensional (3D) path planning and local replanning. The proposed method constructs a structured guidance model based on the inspection-corridor topology, generates local 3D path segments according to a predetermined inspection sequence, and forms a nominal closed-loop inspection path through bias suppression and path regularization. Meanwhile, for local maintenance blockage and dynamic disturbance scenarios, an alternative local replanning strategy is applied to the affected path segments. Simulation results show that, under the static closed-loop inspection condition, the proposed method achieves a total path length of 700.22 m, a total inspection time of 269.32 s, an average safety clearance of 8.18 m, 37 large-angle turns, a corridor adherence rate of 80.73%, and a task completion rate of 100%, showing superior performance in inspection efficiency, safety margin, trajectory regularity, and corridor consistency. Under the local blockage condition, the replanned path introduces path-length and time increments of 71.29 m and 25.88 s, respectively, while maintaining the minimum safety clearance at 1.52 m and increasing the corridor adherence rate to 83.91%. Under dynamic disturbance conditions, the minimum dynamic safety clearance is improved from −2.71 m to 17.84 m, effectively eliminating the local dynamic collision risk. The results demonstrate that the proposed method can balance closed-loop path-generation efficiency, corridor-structure consistency, safety margin, and adaptability to local disturbances, providing an effective solution for UAV inspection path planning in GIS rooms. Full article

Evolutionary Strategies (ESs) are optimization metaheuristics largely adopted in Evolutionary Computation (EC). Since their introduction in the early 70s, researchers in the field have attempted to improve the efficacy of these algorithms. The most advanced ESs, such as the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) and Exponential Natural Evolution Strategies (xNESs), make use of covariance matrices storing relationships between parameters to be optimized, which enable the algorithms to fasten the search in the solution spaces. However, the computational cost of calculating covariance matrices linearly scales with the number of parameters. Recently, the OpenAI Evolutionary Strategy (OpenAI-ES) emerged as an effective ES in different domains, thanks to the parameter information stored in two momentum vectors. Furthermore, OpenAI-ES gains an advantage from the usage of symmetric sampling and weight decay techniques. In this work, I delve into the application of symmetric sampling and weight decay on CMA-ES, xNES and Separable Natural Evolution Strategies (sNESs), with the aim to improve their performance in domains in which they get stuck in local minima outcomes. Specifically, I propose three novel variants for each ES and verify their efficacy with respect to the PyBullet halfcheetah and hopper robot locomotion problems, and two collective tasks (i.e., swarm aggregation and swarm foraging). The findings reveal that symmetric sampling produces performance enhancements in all the domains, whereas the effect of weight decay varies across the considered problems. Furthermore, symmetric sampling allows ESs to keep parameter size limited, which is paramount in these scenarios. This research identifies techniques enhancing the success of modern ESs, proposes several ES variants, and discusses the relationship between algorithmic performance and task properties. Full article

AI-generated images (AIGIs) often contain perceptual defects that differ from the distortions commonly studied in conventional no-reference image quality assessment (NR-IQA). This work investigates image-only AIGC image quality assessment, where no prompt text is used and the quality score must be inferred from visual evidence such as artifacts, structure, and semantic plausibility. We propose a dual-pathway wavelet-attention framework built on a Swin Transformer V2-Base backbone. The artifact pathway employs a Noise Perceptive Attention Module (NPAM) with fixed Haar wavelet decomposition to describe generation-related sub-band degradation cues, whereas the image-perception pathway models semantic, structural, and contextual quality evidence using multi-scale attention, global–local spatial-channel attention, and pyramid pooling. The two pathways are integrated through adaptive fusion and a spatially weighted regression head with an auxiliary global prediction. Experiments on AGIQA-1K, AGIQA-3K, and AIGCIQA2023 demonstrate competitive in-domain performance, including SRCC values of 0.8418 on AGIQA-3K and 0.8445 on the quality dimension of AIGCIQA2023. The evaluation further covers individual module ablations, score-fusion variants, seed stability, qualitative error analysis, and cross-database transfer, revealing both the contribution of the proposed components and the remaining difficulty of source-disjoint generalization. Full article

To address the high deployment cost, insufficient local coverage, and limited timeliness of conventional wave-observation methods in onboard real-time applications, this study conducts a comparative investigation of centre-of-gravity-equivalent instantaneous wave-height estimation models based on three-degree-of-freedom ship motion responses under the framework of the wave buoy analogy (WBA). The heave, roll, and pitch responses of a 1:2 scaled Series 62 4667-1 planing craft model in regular head seas are used as inputs, while the synchronous instantaneous wave-height signal measured by a wave probe near the centre of gravity is used as the label. A unified protocol is established with consistent inputs, labels, window construction, data partitioning, and evaluation metrics. Six models, namely SVR, TCN, LSTM, CNN-LSTM, Transformer, and LSTM-MHA, are compared and validated using STAR-CCM+ numerical simulation data and towing-tank experimental data. The results indicate that, in the simulated case of H = 0.10 m and T = 1.5 s, LSTM-MHA achieves the highest estimation accuracy, with RMSE and R² values of 0.001231 and 0.997848, respectively, but it also has the largest model size and computational cost. In comparison, TCN achieves near-optimal accuracy with a smaller parameter count and lower inference latency, and shows stable performance across multiple conditions. The towing-tank experimental results further show that both LSTM-MHA and TCN clearly outperform the SVR baseline. Overall, accuracy in the simulation domain, robustness in the towing-tank experimental domain, and cross-domain generalisation capability are not fully consistent. Therefore, the selection of onboard instantaneous wave-height estimation models should jointly consider estimation error, model complexity, computational latency, window length, and practical deployment requirements. Full article

We propose a quantitative, spatially explicit framework for assessing local self-sufficiency and resilience within the Water–Energy–Food (WEF) Nexus. The methodology introduces normalized, per capita indicators that quantify the degree of dependence on local versus external resources, explicitly incorporating physical availability, renewability, energy requirements, infrastructure, and land-use constraints. In contrast to conventional composite indices, the proposed framework adopts a non-compensatory structure, whereby deficiencies in one sector cannot be offset by surpluses in another, reflecting the physical constraints of the nexus. Indicator values range from 0 (complete dependence on external resources) to 1 (full local self-sufficiency) and are formulated dynamically, enabling comparison across existing conditions and alternative infrastructural or policy scenarios. The framework is applied as a proof of concept to a small rural settlement in North Euboea, Greece. The results indicate substantial potential for food and renewable energy self-sufficiency under optimized infrastructure configurations, while also revealing critical vulnerabilities associated with groundwater-dependent water supply and seasonal energy imbalances. The analysis further demonstrates how spatial proximity, energy–water coupling, and land-use competition jointly constrain achievable self-sufficiency levels, highlighting trade-offs that are often overlooked in sectoral or purely volumetric assessments. By explicitly linking resource flows with spatial proximity and infrastructural choices, the proposed indicators provide a robust and transparent tool for resilience-oriented planning under conditions of climatic, environmental, and systemic uncertainty. Full article

Joint detection of strawberry growth stages and defective fruit is needed for harvest planning and quality screening, but field images make this task difficult because stage-related visual differences are subtle, flowers and early fruits are often small and densely distributed, and occlusion weakens localization reliability. This study develops Multi-Scale Refined Detection and Classification YOLO (MRDC-YOLO), a lightweight detector based on the YOLO11s framework, for this fine-grained detection scenario. The backbone, neck, and detection head are redesigned with three modules: a Multi-Scale Adaptive Edge Enhancement Module (MAEM), a Reparameterized Progressive Feature Aggregation (RPFA) module, and a Decoupled Cross-Scan Head (DCSH). MAEM strengthens boundary and texture responses for visually similar categories, RPFA reduces redundant multi-scale fusion while maintaining features for dense small targets, and DCSH introduces task-aware classification and regression branches with cross-scan-inspired spatial modeling for occlusion-sensitive localization. Experiments on a five-class strawberry dataset containing 5114 images show that MRDC-YOLO achieves 95.63% mAP@0.5 and 82.39% mAP@0.5:0.95. Over YOLO11s, the model yields a 2.06-percentage-point gain in precision and 1.34- and 1.53-percentage-point gains in mAP@0.5 and mAP@0.5:0.95, together with 10.7% fewer parameters and 8.9% lower GFLOPs. These results suggest that MRDC-YOLO improves fine-grained category discrimination and localization while retaining a smaller model size than the YOLO11s baseline. Full article

Three-dimensional UAV operations require path planning methods that can jointly maintain route efficiency, threat avoidance, and trajectory smoothness under spatially distributed and time-varying constraints. To address this problem, this paper develops an integrated Dual-Layer Adaptive T-perturbation and Opposition-based Multi-Objective Particle Swarm Optimization framework, termed DATO-MOPSO, for 3D UAV path planning in complex threat environments. The method integrates a dual-layer adaptive inertia-weight and velocity-regulation mechanism with symmetric T-perturbation, an elite quasi-opposition-based learning strategy for diversity recovery and feasible local exploitation, and an archive-driven simulated annealing rule for stagnation-aware personal-best updating. A three-objective model minimizing path length, threat exposure, and path smoothness is established, and comparative experiments against MOPSO, ZAMOPSO, NSGA-II, and SPEA2 are conducted in both static and dynamic environments, together with statistical and ablation analyses. In the static scenario, DATO-MOPSO achieved the highest mean HV and stable repeated-run performance, but its IGD was comparable to ZAMOPSO with higher computational cost. In the dynamic scenario, DATO-MOPSO showed its main advantage, achieving the highest mean HV and the lowest mean IGD with statistically significant HV and IGD improvements over all baselines. Overall, DATO-MOPSO is most advantageous in time-varying complex threat environments, whereas its static-scenario advantages are accompanied by higher computational cost. Full article

Populations of European bison (Bison bonasus), Eurasian beaver (Castor fiber), and Eurasian moose (Alces alces) in Poland are currently experiencing significant growth. These species are subject to strict legal protection or specific regulatory frameworks. The purpose of the study is to analyze Polish legislation concerning the protection of selected species and to identify legislative actions that could ensure healthy, sustainable, and well-managed population levels in Poland. The study also explores carefully regulated forms of sustainable use, including the potential consumption of meat from these species. During this research, the methodology of analysis and scientific interpretation of legal acts was used. Case law and relevant socio-economic and environmental factors were also analyzed and highlighted. The results show that the law currently in force and its interpretation may pose challenges to achieving fully effective conservation outcomes. Wildlife protection requires effective, locally adapted population management. Proposals for legal changes that would support diversified and sustainable management approaches, while maintaining a high level of protection, ensuring environmental stability and sustainability, and ensuring the highest standards of public safety, are presented. De lege ferenda postulates indicate that it is essential to balance the legitimate interests of wildlife conservation, public health, and society. Full article

Accurate and rapid detection of Volvariella volvacea (straw mushroom) fruiting bodies at harvestable maturity is a critical prerequisite for automated industrial cultivation. However, existing detection methods often yield high false-negative and false-positive rates when processing a small-scale, densely distributed, and heavily occluded targets against complex straw substrate backgrounds. Furthermore, these methods frequently struggle to balance the competing requirements of architectural efficiency (such as parameter volume and computational complexity) and real-time performance for edge computing. To address these challenges, this study proposes a YOLO11n-CPDM, a lightweight detection model based on an improved YOLO11n architecture. The model incorporates synergistic optimizations across feature extraction, fusion, and reconstruction. First, a Dual Coordinate Attention Feature Extraction mechanism is integrated into the C3k2 bottleneck blocks of the backbone network. This enhances target perception in complex, occluded environments by concurrently modeling global context and local salient features. Second, within the neck network, the standard attention module is replaced with the PnPNystraAttention module, coupled with the DySample dynamic upsampling operator. This modification strengthens contextual relationships among multi-scale features and improves spatial consistency during reconstruction while preserving linear computational complexity. Finally, the detection head is optimized using MBConv blocks based on an inverted residual structure to minimize parameter volume. Experimental results on a custom V. volvacea dataset demonstrate that the proposed YOLO11n-CPDM model achieves significant performance gains, with Precision (P), Recall (R), and Mean Average Precision (mAP50) reaching 86.8%, 87.5%, and 88.4%, respectively. These figures represent improvements of 2.7, 3.0, and 3.2 percentage points over the baseline YOLO11n model. Additionally, the model size is reduced to 4.8 MB (a 12.7% decrease), while achieving inference speeds of 42.7 FPS on Jetson AGX Orin and 21.2 FPS on Jetson Nano, outperforming the baseline model on both embedded platforms. Consequently, the proposed model effectively enhances detection performance in complex environments while maintaining excellent lightweight characteristics and deployment flexibility, providing a solid technical foundation for intelligent perception and automated harvesting of V. volvacea. Full article

Recent advancements in structural engineering have driven the development of sophisticated damping mechanisms aimed at reducing the detrimental effects of structural vibrations. As a result, accurate numerical modeling and analytical evaluation have become essential for assessing structural stability and enhancing seismic resilience. This study introduces a model-updating framework to develop analytical constitutive models for structural damping systems. The proposed approach employs a genetic algorithm (GA) to calibrate model parameters by minimizing the discrepancy between analytical predictions and experimental responses. Experimental force–displacement hysteresis data and displacement time-history records are used at both the element and system levels for model calibration. The methodology is applied to a rubber isolator, a 10-story structure equipped with Pall friction dampers, and a 6-story structure with friction dampers to evaluate its performance under different dynamic characteristics and damping mechanisms. The results indicate that the proposed approach achieves very high accuracy, with prediction errors reduced to negligible levels for both force and displacement responses in all cases. Consistent performance is observed using both global and local displacement measures in friction-damped systems, indicating the robustness of the proposed method. Overall, the findings indicate that the GA-based model-updating framework provides an efficient and reliable tool for improving the predictive capability of analytical models of structures with nonlinear damping devices and is suitable for practical structural engineering applications. Full article

Cathaya argyrophylla Chun et Kuang is an endangered relict gymnosperm endemic to China. Its habitat has been severely fragmented due to Quaternary glaciations, a condition further exacerbated by modern, fragmented administrative management. We compiled 98 spatially filtered occurrence records across four provinces and developed a combined analysis framework integrating the Biomod2 ensemble model with the Marxan systematic planning algorithm. Our optimal model (TSS = 0.911, AUC = 0.986) identified mean diurnal range and ultraviolet-B seasonality radiation as the dominant ecophysiological drivers of the species’ distribution. Currently, suitable habitats cover 7.10% of the study area, with highly suitable habitats accounting for only 3.08% (21.76 × 10³ km²). Priority conservation areas account for 2.48% (17.55 × 10³ km²) of the total area. A gap analysis revealed that 76.98% (13.51 × 10³ km²) of the optimized priority conservation areas currently lack formal protection under China’s protected area system and the World Database on Protected Areas. Under four future climate scenarios (2030s–2090s), projections indicated overall habitat contraction, with limited spatial expansion observed only under specific scenarios (SSP1-2.6 in the 2030s and 2090s; SSP5-8.5 in the 2030s), and the population centroid was projected to shift southeastward by an average of 42.67 km in Huaihua City. Twenty-one core habitat patches were identified under current climate conditions. As these core habitat patches are concentrated along interprovincial boundaries, specifically the Dalou Mountains and the Yuecheng Ridge, our findings emphasize the need to bridge local administrative barriers. This spatial framework provides actionable guidelines for establishing transboundary protected areas, optimizing in situ conservation networks, and implementing model-based assisted migration. Full article

For hydrological parameterization in high-Andean catchments, it is necessary to understand whether near-surface hydro-structural soil properties can provide a surrogate signal of particle-size composition when direct texture information is sparse. This study evaluated the extent to which sand, silt, and clay fractions can be approximated from organic matter ($\mathrm{OM}$), bulk density (${\rho }_b$), and saturated hydraulic conductivity ($K_{\mathrm{sat}}$) in the Zamora Huayco (ZH) and Irquis catchments, southern Ecuador. A harmonized dataset ($n=44$) was analyzed through exploratory statistics, compositional assessment, correlation analysis, PCA, fraction-wise regression, $\mathrm{ILR}$-based modeling, AIC/BIC term reduction, sensitivity analysis excluding $\mathrm{OM}$, nested $\mathrm{LOOCV}$, and bootstrap-based uncertainty intervals. Among LULC classes, samples classified as paramo occupied a distinct high-Andean hydro-edaphic domain, characterized by a differentiated relationship between soil physical properties and hydrological behavior. PCA showed that the dominant covariance structure involved $\mathrm{OM}$, ${\rho }_b$, $K_{\mathrm{sat}}$, and the redistribution between sand and silt. The BIC-reduced $\mathrm{ILR}$ model provided the most balanced formulation, with positive nested $\mathrm{LOOCV}$ performance for sand, silt, and clay ($R_{\mathrm{LOOCV}}^2=0.147$, $0.704$, and $0.124$, respectively) and exact $100\%$ compositional closure after inverse transformation. Silt was the most stable predicted fraction, whereas sand and clay retained larger residual uncertainty, stronger tail departures, and partial compression of the observed variability. The proposed equations provide local hydro-pedotransfer support, although their predictive signal remains dependent on further refinement, uncertainty assessment, and external validation before regional application. Full article

Local impedance variations in structural waveguides partially reflect and absorb incident
flexural waves, motivating wave-based strategies for passive vibration control. This study
develops and experimentally validates a wave-energy framework to quantify and optimize
flexural wave absorption by Kelvin–Voigt attachments on a finite Timoshenko beam.
A finite element model is validated against Scanning Laser Doppler Vibrometry measurements
from a clamped–clamped aluminum beam with a passive damper mounted near
one end, with dashpot parameters identified through two independent approaches and
the discrepancies attributed to parameter uncertainty. Wave decomposition of the simulated
and measured velocity fields yields the power reflection coefficient ρ(ω) and power
absorption coefficient α(ω) over the 0–15.3 kHz band. The spring stiffness and damping
coefficient exhibit frequency-dependent optima and act as complementary, jointly tuned design
variables. Expressing dashpot location in wavelength-normalized coordinates reveals
a recurring spatial pattern in which absorption minima cluster around half-wavelength
multiples, while multiple spanwise positions yield near-peak absorption at any given
frequency. This pattern is governed primarily by the flexural wavelength, decoupling
placement from parameter tuning, and persists across clamped–clamped, clamped–free,
and free–free boundary conditions. Two independently tuned dampers further broaden the
effective absorption band by suppressing local minima in α(ω). These results demonstrate
that measurement-driven wave decomposition provides compact, physically grounded
guidelines for passive damper placement in beam structures. Full article

This study investigated the impact of recreational use on trails in the Atlantic Forest (Ubatuba Municipality, São Paulo State, Brazil) using physical, chemical and geochemical indicators. Five trails with different morphological characteristics were selected, and paired samples were collected from the trail surface (TR) and trail-side slope (TA). The statistical approach combined local analyses for each trail with global clustering (n = 19) using Student’s t-test, along with multivariate modeling through Principal Component Analysis (PCA) and Pearson correlation. The analysis included physical attributes (bulk density, particle size and porosity), chemical attributes (pH, organic matter and macronutrients) and geochemical compositions (major oxides and trace elements determined by XRF). The overall results reveal systematic compaction in the trail surface (TR), with bulk density increasing from 1.32 g/cm³ (TA) to 1.37 g/cm³ (TR) (p = 0.038), and total porosity decreasing from 47.26% to 45.34% (p = 0.016). In contrast, the geochemical oxide composition (SiO₂, Al₂O₃, Fe₂O₃) remained stable (p > 0.05), indicating the resilience of the mineral matrix. However, significant local dynamics (p < 0.05) in K₂O and MgO were observed in more preserved trails, associated with surface compaction and fragmentation of the litter layer, and phosphorus showed strong dependence on organic matter (r = 0.85). Multivariate analysis indicates that degradation is predominantly physical and micromorphological at the local scale, with bulk density and porosity being the most sensitive indicators for environmental monitoring. Full article