Search Results (6,968)

Time-series InSAR is an important means for early identification and monitoring of landslides. However, in complex mountainous areas, it still faces challenges such as significant geometric distortions and complicated deformation mechanisms. To address these issues, this paper proposes a landslide identification and prediction framework that integrates ascending and descending orbits InSAR observations with physics-guided deep learning. Taking Yangbi County, Yunnan Province, as a case study, we combined ascending and descending Sentinel-1A data and employed the SBAS-InSAR method to identify potential landslides, detecting a total of 41 hazardous sites. The cumulative displacement time series of typical landslides were further extracted along the slope aspect to more realistically reflect landslide movement characteristics. On this basis, wavelet decomposition was introduced to separate the displacement series into trend and periodic components. Gray relational analysis was then used to select influencing factors such as precipitation and temperature, and a stepwise prediction model based on LSTM (WT-LSTM) was constructed. The results indicate that the model achieves significantly higher prediction accuracy at characteristic points of the representative landslide (RMSE = 1.16–2.19 mm) compared to standalone LSTM and SVR models. These findings demonstrate its effectiveness and potential applicability in landslide deformation monitoring and prediction in complex mountainous areas, while also providing a useful reference for landslide risk early warning. Full article

Mountain orchards in southern China are characterized by fragmented and complex terrain with a wide slope variation range (5~30°), which easily leads to uneven pesticide distribution and pesticide accumulation on gentle slopes. These issues give rise to core technical bottlenecks such as low pesticide utilization rate, poor operational efficiency, and unclear atomization mechanism, hindering the optimization of pesticide application parameters, causing pesticide waste and environmental pollution, and restricting the sustainable development of the mountain fruit industry. To address this problem, this study designed a slope-classified pipeline layout and developed a high-efficiency fixed pipeline system for phytosanitary application in mountain orchards, featuring stable operation, low labor intensity, and easy intelligent transformation. Following the technical route of “theoretical design-atomization mechanism analysis-parameter optimization-laboratory verification-field application”, ruby nozzles with high wear resistance, uniform droplet distribution, and long service life were selected and optimized to meet the demand for long-term fixed pesticide application in mountain orchards. High-speed imaging technology was used to real-time capture the dynamic atomization process of nozzles, providing support for clarifying the atomization mechanism. Advanced methods such as fluorescence tracing were adopted to quantitatively evaluate key indicators including droplet deposition in canopies, and the system performance was verified through laboratory and field tests, laying a scientific foundation for its popularization and application. Field test results showed that the optimal spray pressure should not be less than 8 MPa. The XR9002 nozzle can generate fine droplets to achieve pesticide reduction while forming a stable hollow cone atomization flow. Fluorescence tracing analysis indicated that the droplet deposition on the adaxial leaf surface decreases with increasing altitude (presumably affected by wind speed), while the initial deposition on the abaxial leaf surface is low and shows no significant variation with altitude. Deposition on the adaxial leaf surface decreased with canopy height, while abaxial deposition was much lower (8.9–14.9%). This technology enables high-precision quantitative analysis of droplet deposition. The core innovations of this study are: clarifying the atomization mechanism of ruby high-pressure nozzles under pesticide application conditions in mountain orchards, constructing a slope-classified terrain-adaptive pipeline layout model, and establishing a closed-loop technical system of “atomization mechanism-pipeline layout-parameter optimization-deposition detection”. This study provides theoretical and technical support for green and precision pesticide application in mountain orchards, and has important academic value and broad application prospects for promoting the intelligent upgrading of the fruit industry in southern China. Full article

Time-series machine learning models represented by long short-term memory (LSTM) networks provide an effective way to obtain high-precision sound speed profiles (SSPs) quickly and at low cost, which can meet the practical application requirements of underwater sonar systems. However, in sea areas with frequent strong internal solitary waves, the large-amplitude sound speed anomalies caused by them will seriously interfere with model learning in the form of strong outlier features, resulting in a sharp drop in SSP prediction accuracy and significant degradation of the generalization stability and robustness of the model. To address this problem, this paper proposes a time-series SSP prediction method based on a bidirectional long short-term memory (Bi-LSTM) network. First, Empirical Orthogonal Function (EOF) decomposition is used to realize the low-dimensional feature representation of SSPs, and then the bidirectional time-series feature capture capability of Bi-LSTM is used to predict the SSP sequence with large disturbances caused by strong internal solitary waves. Multiple groups of comparative experiments based on the measured temperature chain data in the continental slope area of the South China Sea show that the Bi-LSTM model has a significant improvement in prediction accuracy and robustness compared with the classical LSTM model. Among them, the Bi-LSTM model with EOF decomposition achieves a correlation coefficient of 0.995 and an average Root Mean Square Error (RMSE) as low as 0.387 m/s. Under the condition of internal solitary wave disturbance, the classical LSTM is difficult to effectively capture the large abrupt change in sound speed, while the proposed Bi-LSTM model can still achieve accurate prediction of the SSP in the disturbance section, and has both the feature recognition and evolution prediction capabilities for the strongly nonlinear internal solitary wave process. This method provides effective technical support for the rapid and large-scale reconstruction of the sound speed field under the disturbance of strong internal solitary waves. Full article

Photovoltaic systems deployed on large floating platforms in nearshore waters are strongly influenced by hydroelastic effects, nonlinear shallow-water waves, and variable bathymetry. This study develops a time-domain hydroelastic framework that couples the fully nonlinear non-hydrostatic wave model NHWAVE with a Rankine-source potential-flow solver and a discrete-module Cummins formulation. The wave model provides incident pressures and kinematics over uneven seabeds, while the potential-flow solver evaluates radiation and diffraction effects and transfers the resulting hydrodynamic coefficients into the time domain. Numerical simulations are carried out for a 600 m modular floating structure under regular waves over flat and sloped bathymetries with $tan\alpha =0.0133$, wave periods of 4–6 s, and wave heights of 0.3–1.0 m. The results show that bathymetric variation intensifies shoaling-induced excitation, modifies added-mass and damping distributions, increases the spatial non-uniformity of hydroelastic motions, and amplifies bending-moment RMS responses relative to the flat-bottom case. Additional comparisons between rigid-body and hydroelastic models show clear period-dependent redistribution of motions and bending demand. These results demonstrate that both local bathymetry and structural elasticity must be considered for the reliable analysis and design of nearshore floating photovoltaic systems and other large floating structures. Full article

The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article

The Qipanshan area in Northeastern China has diverse stand types and abundant forest resources, but extremely low resistance to external disturbances such as forest fires. Thus, improving the accuracy of understory fuel moisture content prediction is crucial for local forest fire prevention. This study focused on surface branch fuels in four typical stands (Larix gmelinii (Rupr.) Kuzen forest, Betula platyphylla Sukaczev forest, Pinus sylvestris var. Mongholica Litv. forest and cutover land) to evaluate the prediction and extrapolation performance of three hourly scale models (Nelson, Simard, and meteorological element regression models), and analyze their variations with slope positions and stand types, filling the gap in local hourly fuel moisture prediction model application. Results indicated that obvious spatial heterogeneity in fuel moisture content, closely affected by slope, fuel decay degree and microclimate, and thick, badly decayed branches had higher moisture content, with the highest in the Betula platyphylla forest and the lowest in cutover land. In terms of prediction accuracy, the Nelson model performed best, followed by the Simard model, while the meteorological element regression model was the poorest; predictions were more accurate in Pinus sylvestris var. mongholica forest and cutover land, and better on upper slopes than middle and lower slopes. For extrapolation capacity, the Simard model was optimal, followed by the Nelson model, while the meteorological element regression model was unfit for extrapolation due to excessive errors; extrapolation accuracy was best in cutover land and upper slopes. This study clarifies the applicability of the three models, providing methodological support for accurate real-time forest fire danger forecasting in the region. Full article

Background/Objectives: Age-related macular degeneration, particularly geographic atrophy, is a major cause of irreversible vision loss and shares pathological features with myopic chorioretinal atrophy (CRA). This study was designed as an exploratory methodological analysis to evaluate the feasibility of using the fellow eye as an internal control in early-phase clinical trials for myopic CRA. Methods: This exploratory and methodological retrospective study included eight patients (16 eyes) with myopic CRA who visited the Department of Ophthalmology at Nagoya City University Hospital between January 2010 and August 2023. Atrophic areas in both eyes were measured, and the longitudinal changes were analyzed. Three mixed-effects models were compared to assess the impact of inter-individual and inter-ocular variability on atrophic area progression. Subsequently, fixed-effects and mixed-effects models were compared using the Akaike Information Criterion (AIC). Finally, the square root of the variance ratio was calculated to quantify the contribution of inter-ocular variability to atrophic area progression. Results: In all eyes, the square root of the atrophic area increased over time. The model including random intercepts and slopes for each eye nested within patients had the lowest AIC of 69.4, suggesting that accounting for both inter-individual and inter-ocular variability improved model accuracy. The mixed-effects model had a lower AIC than the fixed-effects model, indicating a better fit. The square root of the variance ratio was 0.34 in the mixed-effects model, indicating that the inter-ocular variability was lower than the inter-individual variability, though it remained appreciable. Conclusions: This study quantitatively supports the feasibility and methodological validity of inter-ocular comparison designs for early-phase clinical trials in myopic CRA. Full article

Background: Hippotherapy, a sensorimotor-rich intervention proposed for children with Autism Spectrum Disorder (ASD), is suggested to influence executive function (EF). However, the underlying electrophysiological mechanisms, particularly changes observed in resting-state Electroencephalography (EEG), remain underexplored. Methods: A total of forty-eight children with ASD, aged 9–12 years, participated in this quasi-experimental, non-randomized pre-test–post-test study. Participants were assigned to either a standardized 12-session hippotherapy program (n = 24) or a waitlist Control group (n = 24). EF was evaluated pre- and post-intervention using validated measures: the Wisconsin Card Sorting Test, Stroop Color–Word Test, Corsi Block-Tapping Task, and Tower of London. Resting-state EEG data (19 channels, 250 Hz) were recorded before and after the intervention and analyzed for spectral power, pairwise Pearson correlation, phase-based functional connectivity using the Phase Lag Index (PLI), and directed effective connectivity using Phase Transfer Entropy (PTE). EEG effects were tested with linear mixed models in MATLAB (fitlme), with the measured values in each ROI as the dependent variable, group and time as fixed effects, and SubjectID included as a random intercept; EF outcomes were analyzed with ANCOVA/MANCOVA, adjusting post-test scores for baseline. The assumptions of homogeneity of slopes, Levene’s test, and the Shapiro–Wilk test were examined, and the Holm–Bonferroni correction together with partial η² effect sizes were reported. Results: Following baseline adjustment, the hippotherapy group showed substantial and statistically significant improvements across all EF measures compared with controls partial η² range = 0.473–0.855; all adjusted p < 0.001; e.g., Stroop Incongruent Reaction Time (F(1,45) = 265.80, p < 0.001, ηp² = 0.855). EEG analyses revealed localized Group × Time interaction effects involving frontal delta power as well as selected alpha-, theta-, and beta-band connectivity measures within frontally anchored networks. In addition to these focal interaction effects, the hippotherapy group exhibited a narrower distribution of pre–post EEG changes across spectral power and connectivity metrics compared with controls, indicating greater temporal consistency in resting-state electrophysiological dynamics across sessions. Because group allocation was non-random (based on scheduling feasibility and parental preference), results should be interpreted as associations rather than causal effects. While the hippotherapy group exhibited significant EF improvements and relative stabilization in EEG spectral and connectivity metrics, particularly in frontal delta/theta/alpha/beta bands, a direct mapping between individual EEG changes and behavioral gains was not observed. Conclusions: A standardized 12-session hippotherapy program was associated with substantial improvements in EF and with relative stabilization of resting-state electrophysiological dynamics in children with ASD. However, the direct mechanistic link between these EEG and behavioral changes warrants further investigation. Larger randomized trials employing active control conditions, task-evoked electrophysiological measures, and extended longitudinal follow-up are needed to confirm efficacy, clarify mechanisms, and establish the durability of effects. Full article

This study collected 155 sets of test data for granite residual soils from the Fujian–Guangdong region and applied the chi-square test to analyze the distributions of eight common physical and mechanical parameters. Drained triaxial tests were then simulated using the Unified Hardening (UH) model, and a Sobol global sensitivity analysis of model parameters was conducted based on the distributions of soil properties. The results show that natural density and cohesion approximately follow Weibull distributions; void ratio, liquid limit and plastic limit follow lognormal distributions; water content and internal friction angle follow normal distributions; and plasticity index follows a Gumbel distribution. The Sobol analysis indicates that the critical state deviatoric stress mainly depends on the critical state stress ratio (M), the critical state volumetric strain is jointly controlled by M and the slope of the normal compression line (λ). The overall evolution of deviatoric stress mainly depends on M, and the overall evolution of volumetric strain mainly depends on λ, whereas Poisson’s ratio (ν) has little influence on the soil stress–strain response. These findings provide references for parameter selection and numerical simulation of granite residual soils in the Fujian–Guangdong region. Full article

Earthquake-induced landslides are a major hazard in mountainous regions, where complex topography and near-surface conditions jointly control ground-motion amplification and slope instability. In this context, ground-motion models used as triggering inputs for landslide analyses must accurately represent site effects in complex terrain. This study develops a geomorphometry-informed ground-motion model based on predictors derived from global remote sensing Digital Elevation Models (DEMs), conceived as a triggering component for earthquake-induced landslide applications. The model is based on the eXtreme Gradient Boosting (XGBoost) regression algorithm and predicts peak ground acceleration, peak ground velocity, and spectral accelerations by integrating seismic source parameters, finite-fault source-to-site metrics, and geomorphometric site proxies derived from global DEMs. The model is trained on an extended Italian strong-motion dataset comprising about 8300 recordings from 90 earthquakes with finite-fault rupture models and is evaluated using a strict leave-one-event-out validation scheme. Results show that finite-fault parameterization reduces prediction errors by about 11% compared to point-source formulations, while DEM-derived site proxies improve predictive performance by approximately 5% relative to V_S30 and 12% relative to the fundamental frequency f₀. Residual analysis yields inter-event variability of 0.19–0.22 and intra-event variability of 0.23–0.26. The proposed framework demonstrates how global remote sensing products provide value-added predictors for ground-motion triggering in complex terrain, suitable for integration with earthquake-induced landslide susceptibility models. Full article

The rapid advancement of high-technology industries critically depends on the supply of rare earth elements (REEs), which constitute strategic raw materials for knowledge-intensive sectors. This study proposes an integrated methodological framework for the development of the Kundybay REE deposit, combining geomechanical substantiation of open-pit slope stability with beneficiation optimization of weathering-crust ores. The novelty of the research lies in the justification of rational pit-wall parameters based on a coupled 3D geological–geotechnical model implemented in the Micromine environment. Structurally disturbed zones were identified and subjected to detailed stability analysis using numerical modeling techniques. Particular emphasis was placed on the delineation of geotechnical domains and factor-of-safety evaluation in Slide2, which enabled refinement of design solutions and optimization of slope geometry. Simultaneously, the study addresses processing challenges of refractory weathering-crust ores through the application of ultrasonic aerohydraulic desliming combined with a vibro-centrifugal separation device. Optimization of slope parameters resulted in a 30 Mt reduction in stripping volume, corresponding to a 16.7% economic gain. The proposed flowsheet achieved a concentrate grade of 948.89 g/t REEs with an overall recovery of 45.66%. The results confirm that integrating geomechanical design with process engineering provides a technically robust and economically efficient basis for REE deposit development. Full article

Invasive candidiasis is a severe opportunistic infection whose incidence may be influenced by major disruptive events. The COVID-19 pandemic substantially altered hospital dynamics in Colombia. This study aimed to evaluate temporal trends, seasonality, and potential changes in the incidence of invasive candidiasis between 2019 and 2024. We conducted an observational time-series study using confirmed cases of invasive candidiasis from medium- and high-complexity hospitals in three major Colombian cities. Cases were aggregated quarterly. An interrupted time-series (ITS) analysis was performed. A total of 1294 cases were analyzed. An increasing trend was observed until mid-2022, followed by a decline during 2023. Seasonal decomposition revealed persistent seasonality with recurrent peaks in the second and fourth quarters. The ITS analysis did not demonstrate statistically significant changes in level or slope after the interruption (p > 0.05), although clinically relevant fluctuations were observed. No significant differences in temporal trends were identified across Candida species. Invasive candidiasis in Colombia exhibited a complex temporal evolution during and after the COVID-19 pandemic characterized by sustained seasonality and an increase followed by a decline. Although the ITS analysis did not identify statistically significant post-pandemic changes, the findings support the use of time-series models as valuable tools for epidemiological surveillance and trend monitoring. Full article

Background/Objectives: Integrating heterogeneous One Health time series into transparent and usable surveillance workflows remains difficult because data preparation, modeling, and interpretation are often separated across tools. In this paper, we introduce OH-MEMA (One Health Mixed-Effects Modeling and Analytics), an interactive visual analytics framework that integrates heterogeneous One Health data streams, including human clinical outcomes, environmental factors, and wastewater surveillance data, to support syndromic surveillance and pandemic preparedness. Methods: The system enables users to upload and analyze multi-source datasets through an interactive web-based interface. The modeling component supports fixed effects for multi-source predictors, random effects for spatial, temporal, and demographic grouping variables, optional random slopes, and rolling time-series validation. Model results are visualized as time series comparing observed and predicted outcomes, with evaluation metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation. To support iterative exploration, the system incorporates analytic provenance through a visual model tree that records prior configurations. Results: OH-MEMA was validated through both quantitative and qualitative evaluations. Quantitatively, mixed-effects models were assessed across multiple counties and outcomes using RMSE, MAE, and correlation, demonstrating robust predictive performance. Qualitatively, expert users, including epidemiologists and disease surveillance analysts, evaluated the system using the NASA Task Load Index and open-ended interviews, indicating improved interpretability, manageable cognitive workload, and effective workflow integration. Conclusions: OH-MEMA provides an interpretable, human-in-the-loop platform for exploratory forecasting and comparative model analysis in syndromic surveillance. The framework effectively bridges data integration, modeling, and interpretation, supporting user-centered analytical reasoning and decision-making in One Health applications. Full article

Teacher well-being and performance represent critical challenges for educational systems worldwide. While organizational compassion has been identified as a protective resource, it remains unclear for whom compassion is most beneficial. Drawing on Job Demands–Resources (JD-R) theory and Conservation of Resources (COR) theory, we examined whether teachers’ self-efficacy moderates the relationship between workplace compassion and performance perceptions, testing differential patterns for individual versus organizational performance evaluations. Italian public-school teachers (N = 218; 82% female; M teaching experience = 11.6 years) completed an online survey measuring compassion at work, self-efficacy, and perceptions of individual and organizational performance. We employed a two-stage approach, first validating the measurement model through Confirmatory Factor Analysis (CFA), then testing moderation hypotheses using path analysis with mean-centered variables. Bootstrap confidence intervals (5000 iterations) verified the reliability of interaction effects. Self-efficacy significantly moderated the effect of compassion on individual performance perceptions (β = −0.006, p = 0.006; bootstrap 95% CI: [−0.010, −0.002]), revealing a compensatory pattern. Teachers with lower self-efficacy benefited substantially from workplace compassion (simple slope β = 0.31, p < 0.001), whereas teachers with high self-efficacy showed no significant benefit (β = 0.06, ns). The hypothesized synergistic effect on organizational performance perceptions was not supported (β = 0.006, p = 0.027; bootstrap CI included zero). Organizational compassion functions as a compensatory resource, most powerfully supporting teachers who lack personal resources. This challenges assumptions that organizational interventions uniformly benefit all employees and suggests that compassion-based interventions should be strategically targeted toward teachers experiencing lower self-efficacy. The study advances theoretical understanding of resource substitution mechanisms and provides actionable guidance for optimizing limited organizational resources in educational settings. Full article

Urban rooftop photovoltaic systems represent a substantial yet still underutilized renewable energy resource, particularly in high-density residential environments. Accurate large-scale assessment of rooftop solar potential, however, remains challenging due to the complex geometry of urban morphology and the limited availability of high-resolution geospatial data. This study presents a large-scale methodological framework for estimating the theoretical photovoltaic potential of urban rooftop spaces using Unmanned Aerial System (UAS)-based digital photogrammetry and GIS-based spatial analysis. The approach integrates centimeter-resolution Digital Surface Models (DSMs) and orthophotos derived from fixed-wing UAS surveys with detailed rooftop vectorization and solar radiation modeling implemented in a GIS environment. The methodology accounts for rooftop geometry, surface orientation, slope, shading effects, and rooftop-mounted obstacles. The methodology consists of data collection of high-resolution RGB imagery suitable for detailed three-dimensional reconstruction. The images are captured with a UAS equipped with a S.O.D.A. 3D photogrammetric camera, creating a dense, georeferenced three-dimensional point cloud based on UAS imagery. Based on the point cloud, a high-resolution Digital Surface Model (DSM) was produced. Rooftop boundaries and rooftop-mounted structures were digitized on the basis of an orthophoto created from UAS imagery. The analysis workflow consists of solar modeling using ArcGIS Pro, including calculating the solar radiation. The next methodological step is to filter low radiation rooftops, steep slopes, and northern-oriented rooftops. Finally, we calculate the potential electricity production. The framework was applied to high-density residential districts in Sofia, Bulgaria, dominated by prefabricated panel buildings with predominantly flat rooftops. Drone applications in such studies are typically restricted to modeling individual roofs, which severely limits their scalability for district-wide evaluations. To overcome this, the study employs a specialized fixed-wing UAS uniquely certified for legal operations over densely populated urban environments. This platform rapidly maps large territories, ensuring consistent lighting and shading conditions that significantly enhance the accuracy of subsequent rooftop digitization. Furthermore, the resulting centimeter-level precision enables the exact vectorization of micro-rooftop obstacles. Capturing these intricate details is a critical innovation that effectively prevents the overestimation of solar energy potential commonly observed in conventional large-scale models. Solar radiation was modeled at the pixel level for a full annual cycle and filtered using photovoltaic suitability criteria, including minimum annual radiation thresholds, slope, and aspect constraints. Theoretical electricity production was subsequently estimated using zonal statistics and system performance parameters representative of contemporary photovoltaic installations. The results indicate a total theoretical annual electricity potential of approximately 76.7 GWh for the analyzed rooftop spaces, with an average production of about 34 MWh per rooftop and pronounced spatial variability driven by rooftop geometry and exposure conditions. The findings demonstrate the significant renewable energy potential embedded in existing urban rooftop infrastructure and highlight the applicability of UAS-based photogrammetry for high-resolution, large-area solar potential assessments. The proposed framework provides actionable information for urban energy planning, municipal solar cadaster development, and the strategic integration of photovoltaic systems into dense urban environments, particularly in regions lacking open-access high-resolution geospatial datasets. Full article