Search Results (364)

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article

Background: Exposure to intimate partner violence (IPV) represents a major risk factor for both maternal psychological well-being and child development. Maternal psychopathology—particularly depression and post-traumatic stress disorder (PTSD)—has been shown to impair parenting functioning and increase children’s vulnerability to emotional and behavioral difficulties. Objectives: This study examined the associations between maternal depression and PTSD symptoms and children’s internalizing and externalizing problems, and explored whether parenting stress and maternal mentalization capacities mediate these relationships. Methods: The sample included 42 mothers (mean age = 43.38, SD = 10.56) and their preschool- and school-aged children (n = 42; mean age = 8.30, SD = 2.53) exposed to IPV. Mothers completed self-report measures assessing depressive and PTSD symptoms, parenting stress, and mentalization (uncertainty and certainty about mental states). Children’s internalizing and externalizing problems were assessed through maternal report. Mediation analyses with bootstrapping procedures were conducted to examine indirect effects. Results: Maternal depressive symptoms emerged as the strongest predictor of children’s internalizing problems. Parenting stress was associated with stronger relationships between maternal symptoms and children’s internalizing problems, while polarized mentalization—particularly uncertainty and, to a lesser extent, excessive certainty about mental states—partially mediated the relationship. Maternal PTSD symptoms predicted both internalizing and externalizing problems. Parenting stress fully mediated the association between PTSD symptoms and children’s externalizing behaviors, whereas excessive certainty and uncertainty about mental states showed partial mediation effects. Conclusions: These findings suggest that maternal psychopathology may influence child adjustment both directly and indirectly through increased parenting stress and dysregulated mentalization. The results highlight the importance of trauma-informed, dyadic interventions targeting maternal mental health, parenting stress, and reflective functioning to prevent the intergenerational transmission of trauma and support resilience in families exposed to IPV. Full article

This paper presents an engineering framework for smart grid virtual power plant (VPP) day-ahead scheduling using fractional calculus-enhanced particle swarm optimization, targeting practical deployment in energy management systems. A fractional calculus-enhanced particle swarm optimization algorithm was developed and validated for day-ahead scheduling in virtual power plants using authentic market data and rigorous statistical analysis. The algorithm incorporates Grünwald–Letnikov fractional derivatives with adaptive memory into particle velocity updates, enabling trajectory-aware search that leverages historical exploration patterns. A factorial experiment across 500 independent test cases (50 dates × 10 trials) with controlled random seeds demonstrated that fractional particle swarm optimization increased mean daily profit by $205, representing a 4.1% improvement over standard particle swarm optimization. Wilcoxon signed-rank tests confirmed statistical significance (p < 0.0001, Cohen’s d = 1.08), with superior performance observed in 89.4% of cases. The factorial design identified fractional calculus as the primary performance driver, while advanced scenario generation provided no significant additional benefit. Sensitivity analysis indicated that wind generation variability was the primary predictor of performance variance, with profit difference standard deviations ranging from $34 to $325 depending on meteorological conditions, supporting the use of adaptive computational strategies. Computation required approximately two minutes per optimization on standard hardware. These findings establish fractional calculus as a credible enhancement for operational energy systems and demonstrate that the quality of optimization algorithms outweighs the complexity of forecast uncertainty modeling. The results extend fractional calculus applications from benchmark functions to practical infrastructure scheduling, with projected annual value exceeding $74,000 for a 50-megawatt system. The three-stage optimization architecture is designed for integration with standard energy management systems and SCADA platforms, offering a deployable pathway for smart grid operators. Full article

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system identification technique, with deep Long Short-Term Memory (LSTM) neural networks to improve IMU signal accuracy in GNSS-denied navigation. The FOS algorithm efficiently models deterministic error patterns (such as bias drift and scale factor errors) using a small training dataset, while the LSTM learns the IMU’s complex time-dependent error dynamics from much longer training data. In the proposed method, FOS is first used to predict the output of a high-end IMU based on that of a low-end IMU, and the trained FOS model is then used to extend the training data for an LSTM-based predictor. We demonstrate the efficacy of this FOS–LSTM hybrid on real vehicular IMU data by training with a limited segment of high-precision reference measurements and testing on extended operation periods. The hybrid model achieves high predictive accuracy for predicting the high-end signal based on the low-end signal, with a mean squared error below 0.1% and yields more stable velocity estimates than models using FOS or LSTM alone. Although long-term position drift is not fully eliminated, the proposed method significantly reduces short-term uncertainty in the inertial solution. These results highlight a promising synergy between model-based system identification and data-driven learning for sensor error calibration in navigation systems. Key contributions include FOS-based pseudo-label bootstrapping for data-efficient LSTM training and a navigation-level evaluation illustrating how signal correction impacts dead reckoning drift. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

Biological systems regulate motion and suppress unwanted vibrations through learning, adaptation, and predictive control under uncertainty. Inspired by these principles, Bayesian system identification has emerged as a powerful framework for modeling and estimation, particularly in the presence of uncertainty in structural systems. Flexible structures in aerospace and robotics require advanced control to mitigate vibrations under model uncertainty. This paper proposes a data-driven strategy leveraging a Gaussian Process (GP) integrated within a Nonlinear Model Predictive Control (NMPC) framework. The core innovation lies in using a Gaussian Process Nonlinear AutoRegressive model with eXogenous input (GP-NARX) as a probabilistic predictor to capture structural dynamics while quantifying uncertainty. The operational mechanism involves a tight coupling where the GP provides multi-step-ahead forecasts that the NMPC optimizer uses to minimize a cost function subject to constraints. Validated through simulations on Duffing oscillators, linear oscillators, and cantilever beams, the GP-NMPC achieved an 88.2% reduction in displacement amplitude compared to uncontrolled systems. Quantitative analysis shows high predictive accuracy, with a Root Mean Square Error (RMSE) of 0.0031 and a Standardized Mean-Squared Error (SMSE) below 0.05. Furthermore, Mean Standardized Log Loss (MSLL) evaluations confirm the reliability of the predictive uncertainty within the control loop. These results demonstrate strong performance in both regulation and tracking tasks, justifying this Bayesian-predictive coupling as a powerful approach for high-performance structural vibration control and a potential foundation for bio-inspired mechanical design. Full article

The modern information retrieval field increasingly relies on hybrid search systems combining sparse retrieval with dense neural models. However, most existing hybrid frameworks employ static mixing coefficients and independent component training, failing to account for the specific needs of individual queries and corpus heterogeneity. In this paper, we introduce an adaptive hybrid retrieval framework featuring query-driven alpha prediction that dynamically calibrates the mixing weights based on query latent representations instantiated in a lightweight low-latency configuration and a full-capacity encoder-scale predictor, enabling flexible trade-offs between computational efficiency and retrieval accuracy without relying on resource-inefficient LLM-based online evaluation. Furthermore, we propose antagonist negative sampling, a novel training paradigm that optimizes the dense encoder to resolve the systematic failures of the lexical retriever, prioritizing hard negatives where BM25 exhibits high uncertainty. Empirical evaluations on large-scale multilingual benchmarks (MLDR and MIRACL) indicate that our approach demonstrates superior average performance compared to state-of-the-art models such as BGE-M3 and mGTE, achieving an nDCG@10 of 74.3 on long-document retrieval. Notably, our framework recovers up to 92.5% of the theoretical oracle performance and yields significant improvements in nDCG@10 across 16 languages, particularly in challenging long-context scenarios. Full article

Cooperative multi-UAV pursuit–evasion under occlusions and sensor noise is challenged by intermittent observability of the evader, varying observation-window lengths, and non-stationary evader tactics, all of which destabilize prediction and undermine safety-constrained cooperation. To address these challenges, we propose a safe decision-making framework that uses behavior mode and subgoal inference as intermediate representations for interpretable, uncertainty-aware cooperation. Specifically, an observation-driven generative intent–subgoal model infers the evader’s behavior mode and subgoal from short observation windows. Building on this model, a length-agnostic trajectory predictor is trained via multi-window knowledge distillation and consistency regularization to produce future trajectory predictions with calibrated uncertainty for arbitrary observation-window lengths, thereby reducing cross-window inference inconsistency and lowering online computational cost. Based on these predictions, we derive belief and risk features and develop a belief–risk-gated hierarchical multi-agent policy based on soft actor-critic with a safety projection layer, enabling adaptive strategy switching and a controllable trade-off between efficiency and safety. Experiments in obstacle-rich pursuit–evasion environments with randomized layouts and diverse obstacle configurations demonstrate more stable cooperative capture, safer maneuvering, and lower decision variance than representative baselines, indicating strong robustness and real-time feasibility. Specifically, across different observation-window settings, the proposed method improves the normalized expected return by approximately 5–7% over the strongest baseline and reduces pursuer losses by roughly 22–25%. Moreover, its end-to-end decision latency consistently remains within the 50 ms control cycle. Full article

Educational leadership increasingly operates under conditions of uncertainty, ambiguity, and competing demands. The rapid emergence of artificial intelligence (AI) in education intensifies these challenges, requiring school leaders to navigate tensions between innovation and ethics, autonomy and regulation, and professional judgment and accountability. This study examines AI integration primarily through the lens of educational leadership, proposing that leadership not only shapes teachers’ perceptions of AI but also strengthens the translation of those perceptions into practice. Drawing on transformational leadership theory and technology acceptance models (TAM; UTAUT2), the study tests an integrative model in which teachers’ perceptions of AI function as proximal predictors of use, while transformational leadership serves as a contextual moderator. Data were collected from 141 teachers and analyzed using correlational and regression-based moderation analyses. Findings indicate that transformational leadership significantly predicts teachers’ perceptions of AI and strengthens the relationship between perceptions and AI use. While leadership does not directly predict AI use once perceptions are accounted for, it plays a critical role in enabling the enactment of professional beliefs in instructional practice. These findings position school leadership as a central factor in understanding AI integration, highlighting leadership’s role as a contextual enabler of educational innovation. Full article

Background and Objectives: Endometrial polyps are common in women presenting with subfertility, yet uncertainty persists regarding which lesions warrant removal and how best to integrate hysteroscopic management with contemporary fertility treatment pathways. This narrative review synthesizes current evidence on pathophysiological mechanisms, diagnostic approaches, fertility outcomes, and practical clinical management for women under 40 years of age. Materials and Methods: PubMed/MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Library were searched for English-language human studies published between January 2005 and December 2025. From 2352 records identified, 83 studies were included after screening of 1517 unique records (7 randomized controlled trials, 12 systematic reviews/meta-analyses, 14 prospective cohort studies, 31 retrospective cohort studies, 5 case–control and other study designs, 11 narrative reviews and supporting evidence studies, 1 clinical guideline, and 2 targeted 2025 additions). This structured narrative review employed a systematic search strategy to ensure comprehensive coverage, with evidence synthesized thematically in accordance with the SANRA guidelines. No formal risk-of-bias assessment or pre-registered protocol was used. Results: Across treatment modalities, hysteroscopic polypectomy was consistently associated with improved fertility outcomes. The landmark Pérez-Medina randomized trial reported a relative risk of 2.1 (95% CI 1.5–2.9) for pregnancy after polypectomy before intrauterine insemination. For IVF/ICSI, reported clinical pregnancy rates after polypectomy range from 53–72% and live birth rates from 43–66%. Proposed mechanisms include mechanical interference, chronic inflammation with cytokine dysregulation, altered endometrial receptivity (including dysregulation of HOXA10/HOXA11), and impaired decidualization. Conclusions: Current evidence supports hysteroscopic polypectomy as an effective intervention to improve fertility outcomes in subfertile women with endometrial polyps, particularly prior to intrauterine insemination. For IVF/ICSI, polypectomy of documented polyps appears beneficial, though evidence quality is moderate and heterogeneity exists across studies. It is critical to distinguish routine screening hysteroscopy before IVF from targeted polypectomy when a polyp has been documented. Contemporary guidance (including the 2024 SOGC guideline) favors polypectomy for symptomatic polyps and those that meet specific clinical criteria; for small asymptomatic polyps (<10 mm), individualized decision-making is appropriate, given limited direct evidence and the potential for spontaneous regression. Future research should clarify molecular predictors of polyp-associated infertility, optimal timing relative to fertility treatment, and long-term reproductive outcomes. Full article

Electric vehicles (EVs) are promoted to decarbonise road transport, yet uptake remains slow in many emerging markets. This study examines consumer resistance to EV adoption in Sri Lanka by modelling status quo bias (SQB) using a Prospect Theory lens. An online survey of urban vehicle owners and near-term buyers yielded 157 responses; after screening and removing influential outliers, 151 cases were analysed using partial least squares structural equation modelling (PLS-SEM). The model tests five Prospect Theory-aligned antecedents, namely, loss aversion, reference dependence, risk perception, framing effects, and uncertainty aversion, and evaluates environmental concern as a moderator. Results indicate that loss aversion has a significant positive effect on SQB (β = 0.216, p = 0.005) and uncertainty aversion is the strongest predictor (β = 0.453, p < 0.001), while reference dependence, risk perception, and framing effects show positive but statistically non-significant direct effects. Moderation tests show that environmental concern significantly moderates the effects of reference dependence (β = 0.181, p = 0.039) and framing effects (β = 0.179, p = 0.037) on SQB, but does not significantly moderate the loss aversion, risk perception, or uncertainty aversion paths. Overall, perceived losses and—especially—ambiguity surrounding EV ownership appear to sustain reliance on internal combustion vehicles in this developing-country context, underscoring the need for interventions that reduce uncertainty (credible infrastructure signals, stable policy, service capability) and mitigate perceived losses (warranties, resale assurances) alongside carefully framed communications. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

Urban road collapses are characterized by sudden occurrence and strong spatial heterogeneity, posing substantial challenges for proactive infrastructure management. Susceptibility mapping can provide spatially explicit evidence to support targeted inspection and early-warning strategies. Using Futian District, Shenzhen (China) as a case study, a total of 315 road collapse events recorded during 2019–2023 were compiled to develop an integrated framework for urban road collapse relative susceptibility mapping based on multi-source remote sensing and urban spatial data. First, an indicator-based susceptibility index (SI) was constructed using eight conditioning factors, including PS-InSAR-derived deformation, topographic–hydrological conditions, and distance-based infrastructure variables (distance to underground utilities, metro lines, and roads). Factor weights were determined by coupling the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM), producing a comprehensive SI for historical collapse locations. Subsequently, a set of 17 remote-sensing predictors, including Sentinel-2 spectral bands, Sentinel-2 GLCM texture features, and Sentinel-1 SAR backscatter variables, was used to train a Random Forest model to predict SI and generate continuous susceptibility maps at the urban road-network scale. The influence of neighborhood window size on predictive performance was systematically evaluated. Results show that the Random Forest model performed best at the 5 × 5 window scale (R² = 0.70, RMSE = 0.0172, MAE = 0.0122), outperforming both pixel-based inputs (1 × 1) and larger windows. Uncertainty analysis further indicated that the 5 × 5 RF configuration yielded the most stable and spatially coherent predictions, whereas overly small windows and less robust learners produced more fragmented or higher-uncertainty susceptibility patterns. Spatiotemporal analysis indicates that susceptibility patterns remained broadly stable from 2019 to 2023, with moderate susceptibility accounting for 50.82–57.89% and high susceptibility for 21.94–23.30%, while very high susceptibility consistently remained below 1%. Overall, this study demonstrates that integrating multi-source remote sensing with scale-optimized machine learning provides an effective approach for fine-scale susceptibility mapping of urban road collapses, offering practical guidance for differentiated monitoring and risk prevention along critical road corridors. Full article

Background: Annuloplasty ring sizing is critical for durable outcomes in surgical mitral valve repair (MVr). However, there is no clear consensus on optimal sizing strategies. Artificial intelligence (AI)-based imaging tools may help to reduce uncertainty in preoperative decision-making by providing objective, reproducible and reliable measurements. This study evaluated the predictive capability of a fully automated, computed tomography (CT)-based AI-driven tool for annuloplasty ring sizing in patients undergoing minimally invasive MVr (MI-MVr). Methods: A total of 71 consecutive patients undergoing MI-MVr for Carpentier type II mitral valve insufficiency during the study period were included. Preoperative CT scans were analyzed using a cloud-based, fully automated AI tool to quantify mitral valve geometric parameters. Correlations between AI-derived measurements and implanted ring sizes were assessed using the Pearson correlation test. Univariable and multivariable linear regression analyses were performed to identify independent predictors of ring size selection. Results: Several AI-derived parameters correlated significantly with implanted ring size, with the strongest correlations observed for commissural width (R = 0.693, p < 0.001) and mitral annular area (R = 0.693, p < 0.001). In multivariable regression analysis, these parameters were the strongest predictors of annuloplasty ring size (R² = 0.504, p < 0.001). Using this model, accurate annuloplasty ring sizing could be predicted in 78.8% of patients. There were no in-hospital mortality and residual mitral regurgitation at discharge. Conclusions: A fully automated, CT-based AI-driven tool demonstrated good accuracy for preoperative annuloplasty ring size prediction in MI-MVr and may have the potential to support surgical decision-making, reduce operator dependence, and improve reproducibility. Full article

Stephania tetrandra is a medicinal plant with ecological, germplasm, and economic value whose wild resources are increasingly constrained by overexploitation and climate change. To support conservation planning and sustainable cultivation, we quantified current and future potential habitat suitability across China using an ensemble species distribution modeling (SDM) framework and translated the outputs into climate-based priority areas for protection, germplasm safeguarding, monitoring, and phased cultivation trials. Occurrence records were compiled from multiple sources and preprocessed via cleaning and spatial thinning to reduce sampling bias. Current predictors were derived from WorldClim (1970–2000) and complemented with topographic and edaphic variables; future climates were represented by CMIP6 projections for the 2050s, 2070s, and 2090s under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Multiple algorithms were trained in a consistent cross-validation workflow and filtered using AUC (ROC) and TSS before generating a weighted ensemble (EMwmean). Current projections indicate a well-defined suitability core in the humid subtropical monsoon region south of the Yangtze River. Nationally, high-, moderate-, and low-suitability areas were estimated at 51.90 × 10⁴ km², 22.95 × 10⁴ km², and 31.05 × 10⁴ km², respectively. Future impacts are dominated by suitability-grade reallocation rather than a collapse of total suitable extent. Under SSP5-8.5 in the 2090s, high suitability declines to 13.32 × 10⁴ km² (≈74% reduction), accompanied by contraction of stable habitat (48.95 × 10⁴ km²) and expansion of loss areas (33.64 × 10⁴ km²), while gains remain limited (4.30 × 10⁴ km²). Extrapolation diagnostics (Multivariate Environmental Similarity Surface, MESS; Most Dissimilar Variable, MoD) highlight elevated uncertainty in northwestern arid/high-elevation and strongly seasonal transition zones. Environmental-space niche overlap decreases moderately (Schoener’s D = 0.51–0.67), indicating niche displacement and a narrowing suitability window. These results represent potential climatic habitat suitability rather than guaranteed future occupancy. They support prioritizing in situ protection and germplasm safeguarding in areas that are currently highly suitable and remain comparatively stable under future climates, while treating marginal gain zones as candidates for monitoring and carefully phased cultivation or introduction trials. Full article