Search Results (11,898)

With the continuing advancement of global environmental change and rapid urbanization, rural human settlements are facing multiple pressures, including ecological degradation, spatial decline, population outflow, and functional weakening. Based on the vulnerability analysis framework, studies on rural vulnerability provide an important perspective for assessing villages’ risk exposure, disturbance response, and functional degradation when coping with internal and external disturbances. However, existing studies often rely on single-dimensional or linearly weighted evaluations, making it difficult to comprehensively reveal the coupling relationships among multiple discriminative variables and the spatial differentiation patterns of vulnerability. Taking rural areas in Hancheng City, Shaanxi Province, as the research object, this study selects 12 indicators from three dimensions—natural ecological constraints, settlement spatial organization, and public service support—to provide proxy representations of conditions related to potential rural vulnerability. K-means clustering was used to identify potential vulnerability configuration types under multidimensional indicator combinations. A Python-based XGBoost model was then employed as an interpretable surrogate model to assist in characterizing the clustering boundaries, while SHAP analysis was used to explain the key discriminative variables associated with type membership. The results show that the potential rural vulnerability configurations in Hancheng City present a significant west–central–east spatial differentiation pattern. Elevation, village core density, topographic wetness index, distance to town centers, accessibility of daily service facilities, distance to major roads, and normalized difference vegetation index are the main discriminative variables distinguishing different potential vulnerability configuration types. Among them, village core density shows a particularly strong explanatory role. Different key discriminative variables also exhibit evident nonlinear response characteristics across different potential types. Under the indicator system and the $K$ = 4 clustering scheme adopted in this study, the potential rural vulnerability configurations in Hancheng City can be summarized into four types: service-concentrated settlement type, complex terrain-constrained type, human–land coupling transitional type, and natural ecological isolation type. The findings reveal the spatial differentiation characteristics, variable combination relationships, and typological discriminative features of potential rural vulnerability configurations in Hancheng City. They can provide a case-based reference for identifying potential vulnerability, conducting spatial zoning diagnosis, and supporting classified governance in similar county-level rural areas within the loess hilly and gully region. In practical terms, this framework can serve as a diagnostic tool for local governments and planners in classified rural governance. It can be used to identify priority areas for public service and infrastructure investment, review key risk-control areas in complex terrain zones, delineate low-intensity use and protection boundaries in ecologically isolated areas, and guide differentiated resource allocation for different types of villages. Full article

This study presents (i) a hybrid experimental strategy combining a large-scale wave flume and harbor basin for broad-period buoy hydrodynamic characterization, with internal consistency assessment across the facility transition, (ii) a comprehensive, uncertainty-quantified dataset for a shallow-draft disk-shaped buoy (D/T ≈ 10) including RAOs with repeatability statistics, extreme sea-state responses, and environmental load coefficients with uncertainty bounds, and (iii) new physical insights into the roll damping mechanism of such geometries without appendages. A hybrid experimental strategy was employed, integrating a large-scale wave flume (for long-period waves and currents) with a harbor basin (for short-period waves and wind), aiming to mitigate the scale effects inherent in Froude-scaled models, particularly with regard to drag force measurements. The test matrix included free decay in calm water, RAOs under regular waves, motion and mooring line tension under irregular waves, and measurements of wind and current drag coefficients. Key results indicate a natural roll period of approximately 3.0 s (prototype) with a notably high dimensionless damping ratio (ζ ≈ 0.14–0.15), which is conducive to rapid motion attenuation. A pronounced resonance peak in the roll RAO (26.6°/m) was observed near the 3.0 s. Under an extreme sea state (prototype: H_s = 13.8 m, T_p = 16.1 s), the maximum roll angle and dynamic mooring line tension reached 21.30° and 61.56 kN, respectively, the latter being about 3.0 times the static pretension. The mean wind drag coefficient and current drag coefficient were determined as 0.76 and 0.44. This research provides a comprehensive dataset with quantified uncertainty and critical insights for the design, mooring system optimization, and operational safety assessment of such disk-shaped buoys. The hybrid testing approach demonstrated qualitative consistency across the two facilities, pending quantitative cross-validation through dedicated overlapping tests, and the measured roll damping (ζ = 0.14–0.15, expanded uncertainty ±0.01–0.011) is favorable for motion stability within the tested Reynolds-number range. Full-scale validation is recommended to confirm these findings under prototype conditions. Wind, wave, and current effects were tested separately and then comprehensively assessed. Full article

This study presents the design, development, and experimental validation of an automated flat blade grinding machine for the wood processing and plastic recycling industries in Ecuador. The machine was engineered following the VDI 2221/2222/2225 design methodology, integrating SolidWorks-based 3D modeling and ANSYS finite element analysis (FEA) to validate critical structural components. The selected configuration includes a Type 6 alumina grinding wheel (38A-60-K-VS), a mechanical clamping system, cutting fluid cooling, and a hardwired electromechanical control system that does not require a programmable logic controller (PLC). FEA results confirmed adequate safety factors (ηs > 16; ηf > 14) for the ACME 3/4–8 power screw under operational loads. Experimental testing on blade specimens (thickness: 3 mm; length: 70 mm; steel up to 60 HRC) demonstrated that four grinding passes at a 45° inclination angle reduced mean surface roughness (Ra) from 5.39 ± 1.83 µm (used blades) to 0.162 ± 0.092 µm, achieving values comparable to new blades (Ra = 0.601 ± 0.153 µm): a point-estimate reduction of 97% in mean Ra relative to the used-blade condition. The automated process reduced average grinding time by approximately 30% compared to manual methods, while maintaining noise levels within the 85 dB occupational exposure limit. Operator satisfaction surveys rated the system above 4.5/5.0 across all ergonomic and usability criteria. These results validate the proposed machine as a cost-effective, locally manufacturable solution to standardize blade maintenance in small and medium enterprises (SMEs) across Latin America. Full article

The transition to net-zero energy systems involves substantial uncertainty in exogenous conditions such as policy, fuel prices, and technology deployment. Conventional energy system optimization models, formulated as forward problems, excel at identifying a single least-cost solution but provide limited insight into the diverse configurations feasible within an acceptable cost range. This study proposes a hierarchical inverse-analysis framework integrating a genetic algorithm (GA) and linear programming (LP). The upper-level GA explores a broad space of exogenous conditions, including selected fuel-price assumptions, technology-cost conditions, equipment capacities, end-use electrification rates, CO₂-capture installation rates, and CO₂-storage limits, while the lower-level LP rigorously optimizes operations for each candidate. The framework applies explainable AI (SHAP) to identify dominant cost-determining factors and their interactions, and employs k-means clustering to compress the high-dimensional feasible solution space into illustrative archetypes. As an illustrative demonstration, the framework is applied to a hypothetical 2050 net-zero case for the Kanto region. The framework, under the assumed conditions, generates diverse feasible solutions, identifies influential cost-related conditions and their interactions, and organizes the generated solution set into five illustrative archetypes. The proposed framework extends energy system modeling beyond single-optimum solutions toward interpretable decision-support analytics for long-term net-zero planning under deep uncertainty. Full article

Deep rock blasting under high in situ stress is challenged by the suppression of stress wave propagation and the strong directional dependence of crack growth, which together make fragmentation control notoriously difficult. Existing studies remain largely qualitative and lack predictive mathematical relationships linking crack length to the in situ stress state. In this study, we combine theoretical analysis with LS-DYNA numerical simulations to investigate eight stress cases (σ = 10~40 MPa, lateral stress coefficient K = 0~2). For the first time, a dimensionless confinement parameter, χ = σ_m/σ_t^* + α_χΔσ/σ_c, combining mean and deviatoric stresses, is introduced. And a unified exponential scaling relationship is established: L_c/a = 122.54exp(−0.80χ) + 4.9(R² = 0.94). This quantitative relationship reveals a hoop stress phase transition: the tensile phase vanishes completely when the hydrostatic stress reaches approximately 30 MPa, and provides a practical theoretical basis for optimizing blasting parameters and predicting fragmentation extents in deep, high-stress mining and tunneling. Full article

Polar-orbiting microwave radiometers provide indispensable all-weather measurements of the atmospheric state, yet revisit intervals of many hours leave critical gaps during rapidly evolving weather events. To address this limitation, we developed MIDAS (Microwave Inference via Diffusion Across Satellites), a probabilistic framework that estimates microwave brightness temperature (BT) fields across the geostationary full-disk domain from infrared observations at 10 min intervals. This study focuses on the five Microwave Humidity Sounder-2 (MWHS-2) humidity-sounding channels near 183 GHz, which provide vertically resolved water vapor information. MIDAS achieves relative errors below 0.5% for the majority of cases, with a channel-averaged mean absolute error of 1.15 K, outperforming a deterministic U-Net baseline (1.43 K). Beyond per-sample evaluation, MIDAS reproduces large-scale climatological patterns across the full-disk domain over a three-month summer period, consistent with Radiative Transfer for TOVS–Scattering (RTTOV-SCATT) simulations. In deep convective scenes where reconstruction is most difficult, the ensemble spread naturally tracks reconstruction difficulty, providing a built-in indicator of prediction confidence. Notably, MIDAS incorporates real-time polar-orbiting observations as physical constraints via a merge-sampling mechanism, reducing ensemble RMSE by over 20% and improving probabilistic calibration by more than 30%. Proof-of-concept assimilation experiments for two high-impact weather cases show that MIDAS-generated fields yield forecast improvements comparable to those from real satellite observations, reducing tropical cyclone track errors from approximately 110 km to 40 km and improving heavy precipitation forecasts at extreme rainfall thresholds where direct infrared assimilation shows no benefit. Overall, our framework demonstrates the potential of generative models to supplement sparse observational coverage and provide physically plausible microwave humidity fields for downstream applications. Full article

Satellite-based land surface temperature (LST) products are frequently affected by cloud cover and atmospheric conditions, resulting in missing data that significantly limits the continuous monitoring of the thermal environment in complex terrains, such as the Tibetan Plateau. Existing spatiotemporal interpolation methods face clear accuracy limitations when addressing extensive data gaps, while physical models often struggle due to insufficient meteorological inputs in complex landscapes. Moreover, conventional data-driven approaches usually overlook local spatial variations, resulting in smoothed thermal patterns and systematic errors. To overcome these issues, we propose a Physically Constrained Spatial Residual Learning framework. In this framework, we use the Enhanced Annual Temperature Cycle (EATC) model to capture the temporal baseline of LST first. Then, we integrate multi-source auxiliary data into the Geographical-XGBoost (G-XGBoost) algorithm to model spatial nonlinear residuals. Using simulated cloud masks on the 2017 MODIS LST dataset from the Qinghai–Tibet Engineering Corridor, we show that the hybrid model outperforms both individual physical models and global machine learning models in accuracy and spatial detail recovery. Validation results yield an R² of 0.88, an RMSE of 1.92 K, and a mean bias of 0.07 K. Seasonal evaluations indicate best performance in winter (RMSE = 1.19 K) with robust performance in summer. Furthermore, the framework reduces boundary artifacts and accurately reproduces thermal spatial patterns in complex terrain through adaptive local bandwidth and weight adjustments. This approach provides a reliable method for high-precision LST reconstruction over heterogeneous alpine surfaces. Full article

Automatic classification of dog vocalizations can support bioacoustic monitoring and animal welfare, but many systems require spectral or cepstral preprocessing. This study evaluates a lightweight one-dimensional convolutional neural network (1D-CNN) for Bark and Howl classification directly from raw waveforms under controlled additive noise. The dataset comprised 46 Bark and 57 Howl recordings. Audio was converted to mono, resampled to 16 kHz, and standardized to 2.0 s. The network contains 7130 trainable parameters, occupies 27.85 KB with 32-bit weights, and requires 18.35 MFLOPs. The complete five-fold cross-validation procedure was repeated ten times with independently generated run-specific seeds and newly shuffled partitions. Under the no-added-noise condition, mean accuracy was 93.40 ± 2.62%, and macro F1-score was 93.20 ± 2.75%. Performance remained within run-to-run variability between 30 and 5 dB SNR for Gaussian and uniform additive noise, whereas mean accuracy decreased to 79.42% at 0 dB. In the seed-42 reference ablation, removing noise augmentation preserved no-added-noise accuracy but reduced 5 dB accuracy by approximately 20 percentage points. The findings provide preliminary recording-level evidence for efficient Bark and Howl classification under controlled conditions. Generalization to unseen dogs and field recordings remains unverified. Full article

Image segmentation in complex scenes remains challenging due to intensity inhomogeneity, intricate textures, and noise interference. Traditional active contour models (ACMs) offer topological adaptability while suffering from over-segmentation and boundary leakage under such conditions. In this paper, we propose MFJD-Seg, a novel ACM that integrates morphological fitting with an energy formulation derived from Jeffreys divergence for robust and efficient image segmentation. Morphological erosion and dilation are applied to construct foreground and background fitting images, which capture fine-grained structural features while suppressing background interference. Subsequently, a symmetric discrepancy consistent with Jeffreys divergence is leveraged to quantify the statistical difference between the original image and the fitting representations, enabling the compact construction of an unbiased energy function. An arctangent energy constraint and mean filtering are further incorporated to stabilize contour evolution and suppress redundant artifacts. Extensive experiments on BSDS, ADE20K, and COCO datasets show that MFJD-Seg achieves the best mIoU and mDSC in comparisons with five representative ACMs and five mainstream deep learning segmentation models, improving ACM baselines by up to 4.8% in both metrics while maintaining the highest FPS among ACMs and competitive speed against deep learning counterparts. These results verify the superior segmentation capabilities of MFJD-Seg in challenging imaging scenarios. Full article

Dengue fever poses an ongoing challenge to global and Colombian public health. Although surveillance microdata are widely available, there remains a gap in converting them into actionable epidemiological intelligence. This study presents a reproducible dataset and analytical resource for transforming routine records into a spatiotemporal framework for territorial risk stratification. To this end, 15 years (2010–2024) of anonymized records from the SIVIGILA in Colombia’s Caribbean region were consolidated, covering 303,801 cases across 197 municipalities. The microdata were aggregated into municipality–year analytical units using seven indicators of magnitude, severity, demographics, and surveillance performance. Subsequently, an unsupervised learning model (K-means) was validated using the Elbow and Silhouette methods. The algorithm consistently identified four heterogeneous epidemiological profiles: high-transmission urban settings, dispersed rural risk municipalities, territories with a pediatric predominance, and clusters of high clinical severity with elevated hospitalization and case fatality rates. In conclusion, this dataset and its methodological framework transform static historical information into an operational tool that facilitates strategic surveillance, the development of interactive dashboards, and the territorial prioritization of evidence-based public health interventions. Full article

While plug-in hybrid electric vehicles (PHEVs) can significantly reduce fuel consumption and emissions, their real-world benefits strongly depend on heterogeneous driver usage patterns. Understanding these usage patterns is therefore essential for optimizing energy management and electrification policies. This study presents a data-driven framework for identifying and characterizing PHEV driving behavior using two primary indicators: the Utility Factor (UF), which quantifies the proportion of electric-mode driving, and the annual vehicle kilometers traveled (VKT), which measures driving intensity. A Gaussian Mixture Model (GMM) is employed in a transformed feature space characterized by the logit of UF and the logarithm of VKT to capture nonlinear relationships and diverse usage patterns. The Bayesian Information Criterion (BIC) is used to find the optimal number of behavioral clusters. To assess the robustness of the clustering, we perform a bootstrap stability analysis and compare it with k-means and a density-based clustering method (DBSCAN). Based on an analysis of real-world PHEVs, three distinct usage patterns are identified. The dominant cluster (96.6%) exhibits moderate electric usage (UF = 0.38) with an annual mileage of 18,617 km and fuel consumption of 3.89 L/100 km, whilst two smaller clusters represent near-full electric operation (2.3%, UF about 1.0, negligible fuel) and low-mileage users (1.1%, approximately 2444 km/year). The clustering demonstrates high assignment confidence (posterior entropy $<{10}^{-4}$) and moderate stability, with a mean Adjusted Rand Index (ARI) of 0.448. The findings indicate that the proposed probabilistic clustering framework offers a comprehensible and statistically robust method for identifying diverse PHEV usage patterns. These insights can support adaptive energy management strategies, effective planning of charging infrastructure, and evidence-based policies to maximize the real-world electrification benefits of PHEVs. Full article

This study compared unsupervised and supervised machine learning, and deep learning (U-Net) classifiers on Unmanned Aerial Vehicle (UAV) multispectral imagery to identify nitrogen status in potato crops under nitrogen (N) fertilization treatments, irrigation (I), and their interaction (N × I). The U-Net model outperformed all other methods, achieving accuracies for crop nitrogen status of 65–99% in N, 84–100% in I, and 41–82% in N × I treatments, with variation due to different input data. Supervised machine learning also performed well, with Support Vector Machine achieving 53–87, 66–86, and 32–66% respectively, and Random Forest 61–96, 70–81, and 33–65%. Unsupervised K-means yielded the lowest accuracies (47–58, 9–65, and 8–34%), demonstrating necessity of substantial supervision to delineate crop nitrogen and water status. These findings were confirmed by repeated analyses of UAV imagery acquired later in the growing season with consistent results. Comparable classification performance was observed for crop water status and leaf area index at both time points. Despite being demonstrated in a single-field, single-crop framework, the results provide proof of concept for applying deep learning classifiers to detect subtle nitrogen and water stress under field conditions in precision agriculture. Future research could test diverse agroecosystems and growing seasons, alternative deep learning algorithms, and sensor data fusion to improve classification accuracies. Full article

Image segmentation is a critical step in computer vision systems for phytosanitary diagnosis in cacao production. However, the reliability of classical segmentation methods remains insufficiently assessed under real field conditions, where images captured under non-standardized conditions are often affected by variable illumination, complex backgrounds, partial occlusions, and chromatic similarity between cacao pods and surrounding vegetation. This study compares global thresholding, K-means clustering, and GrabCut using 343 cocoa pod images captured in uncontrolled agricultural environments with non-standardized mobile devices; low-resolution images were retained to preserve external validity. Robustness was assessed on the full dataset using unsupervised structural metrics, including the segmented area ratio (AS), the largest component ratio (LCR), and the catastrophic failure rate (FC), while accuracy was validated on 50 manually annotated images using Intersection over Union (IoU). Wilcoxon signed-rank tests indicated statistically significant differences among methods. GrabCut achieved the best performance (IoU = 0.814), high structural coherence (LCR = 0.985), and a low catastrophic failure rate (FC = 1.7%). In contrast, K-means showed severe fragmentation and instability, whereas global thresholding was highly sensitive to illumination variability and complex backgrounds. These results indicate that GrabCut provides a robust training-free baseline for cocoa pod segmentation under uncontrolled field conditions, particularly for offline phytosanitary analysis where annotated datasets, supervised training, or GPU-based deployment are limited. Full article

Wind turbines are critical components in renewable energy systems, where early fault detection is essential to ensure reliable operation and reduce maintenance costs. Vibration-based monitoring using multichannel signals provides rich information about the dynamic behavior of the system, although it also introduces challenges related to high dimensionality and feature redundancy. This paper proposes a machine learning-based methodology for fault classification that combines Discrete Wavelet Transform (DWT) for time–frequency feature extraction with Linear Discriminant Analysis (LDA) for dimensionality reduction within a structured processing pipeline. The approach incorporates a Group K-Fold cross-validation strategy to prevent data leakage and ensure a reliable evaluation when working with segmented signals. Experimental results show that the proposed framework achieves high classification performance, reaching a mean accuracy of $98.84\pm 1.16\%$ and a weighted F1-score of $0.9905\pm 0.0089$ using a Support Vector Machine (SVM) classifier over five Group K-Fold splits. The results also indicate that dimensionality reduction plays a critical role in improving class separability, having a greater impact than the specific choice of wavelet transform. Findings demonstrate that the proposed DWT–LDA-based approach provides an effective solution for rotor imbalance detection in the laboratory-scale wind turbine evaluated in this study. Full article

Background/Objectives: Visual impairment is an underrecognized but functionally disabling complication of stroke that adversely affects rehabilitation potential, autonomy, and quality of life. Clinical, anatomical, and ophthalmologic determinants of post-stroke visual recovery remain incompletely defined, particularly in Eastern European tertiary stroke units where structured visual follow-up is not standardized. This study aimed to identify clinical, imaging, and ophthalmologic predictors of favorable visual recovery and to evaluate whether integrating these domains improves early prognostic stratification beyond standard neurological assessment. Methods: We conducted a retrospective single-center cohort study of 71 consecutive adult patients admitted with acute stroke and a documented visual complication between January 2022 and September 2025 at Pius Brinzeu Emergency County Hospital and Victor Babes University of Medicine and Pharmacy Timisoara. Favorable recovery was defined as ≥50% improvement in visual field index (VFI) at 6 months. Group comparisons used Student’s t-test, Mann–Whitney U test, chi-square test, and Fisher’s exact test. Multivariable logistic regression, Cox proportional hazards modeling, and unsupervised k-means clustering were performed. Results: Twenty-nine patients (40.8%) achieved favorable recovery, while 42 (59.2%) had persistent impairment. Responders were younger (62.8 ± 10.7 vs. 70.4 ± 10.8 years, p = 0.005) and had lower admission National Institutes of Health Stroke Scale (NIHSS) (6.4 ± 2.9 vs. 10.3 ± 4.4, p < 0.001), smaller lesion volumes (18.7 ± 11.4 vs. 33.2 ± 18.7 mL, p < 0.001), thicker peripapillary retinal nerve fiber layer (89.3 ± 7.6 vs. 78.2 ± 9.4 μm, p < 0.001), and earlier rehabilitation initiation (11.4 ± 5.3 vs. 21.7 ± 9.8 days, p < 0.001). NIHSS, time to rehabilitation, and optical coherence tomography (OCT) pRNFL thickness remained independent predictors. The full integrated model achieved an area under the receiver operating characteristic curve (AUC) of 0.87. Cluster analysis identified three distinct phenotypes with favorable recovery rates of 79.2%, 34.8%, and 8.3%. Conclusions: Combined clinical, neuroimaging, and ophthalmologic profiling—particularly OCT pRNFL—meaningfully refines early prediction of post-stroke visual recovery and supports phenotype-driven rehabilitation planning. Full article