Search Results (4,169)

Heavy metal pollution in forest and agroforestry soils represents a persistent environmental challenge with direct implications for ecosystem functioning, food security, and human health. In tropical and subtropical regions, intense weathering, rapid organic matter turnover, and dynamic redox conditions strongly modulate metal mobility, bioavailability, and long-term soil vulnerability. This review synthesizes current knowledge on the sources, biogeochemical mechanisms, ecological impacts, monitoring approaches, and restoration strategies associated with heavy metal contamination in forest and agroforestry systems, with particular emphasis on tropical landscapes. We examine natural and anthropogenic metal inputs, highlighting how atmospheric deposition, legacy contamination, land-use practices, and soil management interact with mineralogy, organic matter, and hydrology to control metal fate. Key processes governing metal behavior include sorption and complexation, Fe–Mn redox cycling, pH-dependent solubility, microbial mediation, and rhizosphere dynamics. The ecological consequences of contamination are discussed in terms of soil health degradation, plant physiological stress, disruption of ecosystem services, and risks of metal transfer to food chains in managed systems. The review also evaluates integrated monitoring frameworks that combine field-based soil analyses, biomonitoring, and geospatial technologies, while acknowledging methodological limitations and scale-dependent uncertainties. Finally, restoration and remediation strategies—ranging from phytotechnologies and soil amendments to engineered Technosols—are assessed in relation to their effectiveness, scalability, and relevance for long-term functional recovery. By linking mechanistic understanding with management and policy considerations, this review provides a process-oriented framework to support sustainable management and restoration of contaminated forest and agroforestry soils in tropical and subtropical regions. Full article

Impulsive overtopping generated by dam-break surges is a critical hazard for dikes and flood-protection embankments, especially in reservoirs and mountainous catchments. Unlike classical coastal wave overtopping, which is governed by long, irregular wave trains and usually characterized by mean overtopping discharge over many waves, these dam-break-type events are dominated by one or a few strongly nonlinear bores with highly transient overtopping heights. Accurately predicting the resulting overtopping levels under such impulsive flows is therefore important for flood-risk assessment and emergency planning. Conventional cluster-then-predict approaches, which have been proposed in recent years, often first partition data into subgroups and then train separate models for each cluster. However, these methods often suffer from rigid boundaries and ignore the uncertainty information contained in clustering results. To overcome these limitations, we propose a GMM+MoE framework that integrates Gaussian Mixture Model (GMM) soft clustering with a Mixture-of-Experts (MoE) predictor. GMM provides posterior probabilities of regime membership, which are used by the MoE gating mechanism to adaptively assign expert models. Using SPH-simulated overtopping data with physically interpretable dimensionless parameters, the framework is benchmarked against XGBoost, GMM+XGBoost, MoE, and Random Forest. Results show that GMM+MoE achieves the highest accuracy ($R^2=0.9638$ on the testing dataset) and the most centralized residual distribution, confirming its robustness. Furthermore, SHAP-based feature attribution reveals that relative propagation distance and wave height are the dominant drivers of overtopping, providing physically consistent explanations. This demonstrates that combining soft clustering with adaptive expert allocation not only improves accuracy but also enhances interpretability, offering a practical tool for dike safety assessment and flood-risk management in reservoirs and mountain river valleys. Full article

This study proposes a hybrid forecast-enabled adaptive crowbar coordination strategy to enhance low-voltage ride-through (LVRT) performance of doubly fed induction generator (DFIG) wind turbines. A unified electro-mechanical model in the αβ/dq frames with dual closed-loop control for rotor- and grid-side converters is built in MATLAB/Simulink (R2018b), and LVRT constraints on current safety and DC-link energy are explicitly formulated, yielding an engineering crowbar-resistance range of 0.4–0.8 p.u. On the forecasting side, a CEEMDAN-based decomposition–modeling–reconstruction pipeline is adopted: high- and mid-frequency components are predicted by a dual-stream Informer–LSTM, while low-frequency components are modeled by XGBoost. Using six months of wind-farm data, the hybrid forecaster achieves best or tied-best MSE, RMSE, MAE, and R² compared with five representative baselines. Forecasted power, ramp rate, and residual-based uncertainty are mapped to overcurrent and DC-link overvoltage risk indices, which adapt crowbar triggering, holding, and release in coordination with converter control. In a 9 MW three-phase deep-sag scenario, the strategy confines DC-link voltage within ±3% of nominal, shortens re-synchronization from ≈0.35 s to ≈0.15 s, reduces rotor-current peaks by ≈5.1%, and raises the reactive-support peak to 1.7 Mvar, thereby improving LVRT safety margins and grid-friendliness without hardware modification. Full article

Against deglobalization and intensifying geopolitical conflicts, maritime bulk commodity supply chain vulnerability and resilience governance are strategic priorities for 75% of countries. To tackle rising global uncertainty, this study proposes the country-level risk identification, monitoring, and extrapolation (RIME) framework for such supply chains, which aligns with the theoretical demand for macro, end-to-end risk integration beyond the traditional firm-level focus. Based on the “supplier country–shipping route–importing country” spatiotemporal linkage, we construct the first standardized country-level vulnerability index. It overcomes the limitations of existing static and localized assessments by integrating spatiotemporal, multi-source risks across the full physical chain, thereby enabling dynamic, macro-level monitoring and supporting systematic diagnostics and trend tracking of national supply chain security. We also develop an emergent risk simulation technique to quantify the direction and intensity of compound disturbances as well as the system’s dynamic responses. Empirical validation with China’s iron ore imports shows that the index effectively captures risk evolution, while the simulations confirm that sudden disruptions amplify systemic risk. This framework fills national strategic security theoretical gaps and provides governments with dynamic monitoring, quantitative assessment, and policy forecasting tools. Full article

Background and Clinical Significance: Chronic non-bacterial osteomyelitis (CNO) is a rare autoinflammatory bone disorder that primarily affects children and adolescents, with females more frequently impacted. The condition remains poorly understood, though cytokine dysregulation and inflammasome activation are believed to contribute to its pathogenesis. Clinically, CNO is often difficult to distinguish from infectious osteomyelitis, as presenting symptoms such as bone pain, swelling, and functional limitation are nonspecific, while cultures are frequently negative. As a diagnosis of exclusion, delays in recognition can lead to prolonged or unnecessary antibiotic exposure and uncertainty in management. Case Presentation: A 14-year-old male with a history of left second toe osteomyelitis initially diagnosed in 2021. Despite negative cultures and limited histopathologic findings, he received multiple antibiotic courses with little improvement, and the digit remained chronically swollen. Three years later, a repeat evaluation revealed osseous resorption of the middle and distal phalanges, and a biopsy confirmed acute and mild chronic fibrosing osteomyelitis, consistent with CNO. Given the risk of progression and possible amputation, surgical reconstruction was pursued. The patient underwent autologous calcaneal bone grafting with digital fusion using a K-wire. At three months and one year postoperatively, radiographs demonstrated solid fusion of the digit with maintained activity and resolution of pain. Conclusions: This case emphasizes the diagnostic complexity of CNO and the importance of considering it in children with culture-negative or recurrent osteomyelitis. It further illustrates how timely surgical intervention can preserve function and quality of life while avoiding unnecessary amputation. Full article

One of the most significant impacts of climate change is sea-level rise, which is increasingly threatening to the coastal setting, infrastructure, and socioeconomic systems. Since a change at the sea level is spatially non-uniform and highly modulated by local oceanographic and climatic events, local or regional-scale measurements are necessary—especially in semi-enclosed basins. This paper examines the long-term variability of sea levels throughout the Persian Gulf and illustrates a strong spatial variance of the trends over the past and the future. Using three decades of satellite-derived observations, regional sea-level trends were estimated from monthly sea-level anomaly (SLA) data, which were also used to generate future projections to 2100. The analysis shows that the rate of sea-level rise along the UAE–Oman stretch is 3.88 mm year⁻¹ and that of the Strait of Hormuz is 5.23 mm year⁻¹, with a mean of 4.44 mm year⁻¹ in the basin. Statistical forecasts of sea-level change were projected by a statistical forecasting scheme with high predictive ability with the optimal configuration of an average of 0.0391 m, an RMSE of 0.0492 m, and an R² of 0.80 when independent validation was conducted. It is estimated that by 2100, the average rise of the sea level in the Persian Gulf is about 0.30–0.40 m, and the peak rise in sea level is at the Strait of Hormuz. Since these projections are based on statistical extrapolation rather than physics-based climate models, they are interpreted within the uncertainty envelope defined by IPCC AR6 scenarios. This study presents a unique, regionally resolved viewpoint on sea-level rise that is relevant to coastal risk management and adaptation planning in semi-enclosed marine basins by connecting robust statistical performance with physically interpretable regional patterns. Full article

Accurate early-warning forecasting of corporate financial distress remains a critical challenge due to nonlinear financial relationships, severe data imbalance, and the high operational costs of false alarms in risk-monitoring systems. This study proposes an explainable voting ensemble framework for early-warning forecasting of corporate financial distress using lagged accounting-based financial information. The proposed framework integrates heterogeneous base learners, including Decision Tree, Neural Network, and k-Nearest Neighbors models, and is evaluated using financial statement data from 752 publicly listed firms in Thailand, comprising sixteen financial ratios across six dimensions: liquidity, operating efficiency, debt management, profitability, earnings quality, and solvency. To ensure robustness under imbalanced and rare-event conditions, the study employs feature selection, data normalization, stratified cross-validation, resampling techniques, and repeated validation procedures. Empirical results demonstrate that the proposed Voting Ensemble delivers a precision-oriented and decision-relevant forecasting profile, outperforming classical classifiers and maintaining greater early-warning reliability when benchmarked against advanced tree-based ensemble models. Probability-based evaluation further confirms the robustness and calibration stability of the proposed framework under repeated cross-validation. By adopting a forward-looking, early-warning perspective and integrating ensemble learning with explainable machine learning principles, this study offers a transparent and scalable approach to financial distress forecasting. The findings offer practical implications for auditors, investors, and regulators seeking reliable early-warning tools for corporate risk assessment, particularly in emerging market environments characterized by data imbalance and heightened uncertainty. Full article

Accurate prediction of individual treatment effects in survival analysis is often complicated by the presence of competing risks and the inherent unobservability of counterfactual outcomes. While machine learning models offer improved discriminative power, they typically lack rigorous guarantees for uncertainty quantification, which are essential for safety-critical clinical decision-making. In this paper, we introduce CONFIDE (CONFormal Inference for Distribution-free Estimation), a novel framework that bridges causal inference and conformal prediction to construct valid prediction sets for cause-specific cumulative incidence functions. Unlike traditional confidence intervals for population-level parameters, CONFIDE provides individual-level prediction sets for time-to-event outcomes, which are more clinically actionable for personalized treatment decisions by directly quantifying uncertainty in future patient outcomes rather than uncertainty in population averages. By integrating semi-parametric hazard estimation with targeted bias correction strategies, CONFIDE generates calibrated prediction sets that cover the true potential outcome with a user-specified probability, irrespective of the underlying data distribution. We empirically validate our approach on four diverse medical datasets, demonstrating that CONFIDE achieves competitive discrimination (C-index up to 0.83) while providing robust finite-sample marginal coverage guarantees (e.g., 85.7% coverage on the Bone Marrow Transplant dataset). We note two key limitations: (1) coverage may degrade under heavy censoring (>40%) unless inverse probability of censoring weighted (IPCW) conformal quantiles are used, as demonstrated in our sensitivity analysis; (2) while the method guarantees marginal coverage averaged over the covariate distribution, conditional coverage for specific covariate values is theoretically impossible without structural assumptions, though practical approximations via locally-adaptive calibration can improve conditional performance. Our framework effectively enables trustworthy personalized risk assessment in complex survival settings. Full article

The integration of high-penetration renewable energy sources (RESs) and electric vehicles (EVs) increases the risk of voltage fluctuations in distribution networks. Traditional static partitioning strategies struggle to handle the intermittency of wind turbine (WT) and photovoltaic (PV) generation, as well as the spatiotemporal randomness of EV loads. Furthermore, existing scheduling methods typically optimize EV active power or reactive compensation independently, missing opportunities for synergistic regulation. The main novelty of this paper lies in proposing a spatiotemporally coupled voltage-stability optimization framework. This framework, based on an hourly updated electrical distance matrix that accounts for RES uncertainty and EV spatiotemporal transfer characteristics, enables hourly dynamic network partitioning. Simultaneously, coordinated active–reactive optimization control of EVs is achieved by regulating the power factor angle of three-phase six-pulse bidirectional chargers. The framework is embedded within a hierarchical model predictive control (MPC) architecture, where the upper layer performs hourly dynamic partition updates and the lower layer executes a five-minute rolling dispatch for EVs. Simulations conducted on a modified IEEE 33-bus system demonstrate that, compared to uncoordinated charging, the proposed method reduces total daily network losses by 4991.3 kW, corresponding to a decrease of 3.9%. Furthermore, it markedly shrinks the low-voltage area and generally raises node voltages throughout the day. The method effectively enhances voltage uniformity, reduces network losses, and improves renewable energy accommodation capability. Full article

The increasing penetration of solar photovoltaic (PV) generation into power systems poses significant operational and planning integration challenges due to the high variability in solar irradiance, which makes PV power forecasting difficult—particularly in the short term. These fluctuations originate from atmospheric dynamics that are only partially captured by numerical weather prediction (NWP) models. In this context, probabilistic forecasting has emerged as a state-of-the-art approach, providing central estimates and additional quantification of uncertainty for decision-making under risk conditions. This work proposes a novel hybrid methodology for day-ahead, hourly resolution point, and probabilistic PV power forecasting. The approach integrates a multiple linear regression (LM) model to predict global tilted irradiance (GTI) from NWP-derived variables, followed by support vector quantile regression (SVQR) applied to the residuals to correct systematic errors and derive GTI quantile forecasts and a linear mapping to PV power quantiles. Robust data preprocessing procedures—including outlier filtering, smoothing, gap filling, and clustering—ensured consistency. The hybrid model was applied to a 960 kWp PV plant in southern Italy and outperformed benchmarks in terms of interval coverage and sharpness while maintaining accurate central estimates. The results confirm the effectiveness of hybrid risk-informed modeling in capturing forecast uncertainty and supporting reliable, data-driven operational planning in renewable energy systems. Full article

Flooding is a recurrent problem in many Brazilian cities, resulting in significant losses that affect health, assets, finance, and the environment. The uncertainty regarding extreme rainfall events due to climate change makes this challenge even more severe, compounded by inadequate urban planning and the occupation of risk areas, particularly for the municipality of Catu, in the state of Bahia, which also suffers from recurrent floods. Critical hotspots include the Santa Rita neighborhood and its surroundings, the main supply center, and the city center—the municipality’s commercial hub. The focus of this research is the unprecedented quantification of the socioeconomic impact of these floods on the low-income population and the region’s informal sector (street vendors). This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu, Bahia, and its effects on the local economy across different recurrence intervals. A hydrological simulation software suite based on computational and geoprocessing technologies—specifically HEC-RAS 6.4, HEC-HMS 4.11, and QGIS— 3.16 was utilized. Two-dimensional (2D) modeling was applied to assess the flood-prone areas. For the socioeconomic impact assessment, a loss procedure based on linear regression was developed, which correlated the different return periods of extreme events with the potential losses. This methodology, which utilizes validated, indirect data, establishes a replicable framework adaptable to other regions facing similar socioeconomic and drainage challenges. The results revealed that the area becomes impassable during flood events, preventing commercial activities and causing significant economic losses, particularly for local market vendors. The total financial damage for the 100-year extreme event is approximately US $30,000, with the loss model achieving an R² of 0.98. The research concludes that urgent measures are necessary to mitigate flood impacts, particularly as climate change reduces the return period of extreme events. The implementation of adequate infrastructure, informed by the presented risk modeling, and public awareness are essential for reducing vulnerability. Full article

Accurate quantification of methane (CH₄) emissions from individual point sources is essential for understanding localized greenhouse gas dynamics and supporting mitigation strategies. This study employs satellite-based point-source emission rate data from the Carbon Mapper initiative, combined with ERA5 meteorological reanalysis, to simulate near-surface CH₄ dispersion using a Gaussian plume model coupled with Monte Carlo simulations. This approach captures local dispersion characteristics around each emission source. Simulations driven by these emission inputs reveal a highly skewed, heavy-tailed concentration distribution (consistent with log-normal characteristics), where the 95th percentile (1292.1 ppm) significantly exceeds the mean (475.9 ppm), indicating the dominant influence of a small number of super-emitters. Sectoral analysis shows that coal mining contributes the most high-emission sites, while the solid waste and oil & gas sectors present higher per-source intensities, averaging 1931.1 ppm and 1647.6 ppm, respectively. Spatially, emissions are concentrated in North and Northwest China, particularly Shanxi Province, which hosts 62 high-emission sites with an average maximum of 1583.9 ppm. Sensitivity analysis reveals that emission rate perturbations produce nearly linear responses in concentration, whereas wind speed variations induce an inverse and asymmetric nonlinear response, with sensitivity amplified under low wind speed conditions (a ±30% change in wind speed results in more than ±25% variation in concentration). Under stable atmospheric conditions (Class E), concentrations are approximately 1.3 times higher than those under weakly unstable conditions (Class C). Monte Carlo simulations further indicate that output uncertainty peaks within 150–300 m downwind of emission sources. These results provide a quantitative basis for improving uncertainty characterization in satellite-based methane inversion and for prioritizing risk-based monitoring strategies. Full article

As the participation of photovoltaic–storage systems (PVSS) in the energy and frequency regulation ancillary service markets continues to increase, the market risks caused by photovoltaic output uncertainty will directly affect photovoltaic integration efficiency and the provision of system flexibility, thereby having a significant impact on the sustainable development of power systems. Therefore, studying the risk decision-making of PVSS in the energy and frequency regulation markets is of great importance for supporting the sustainable development of power systems. First, to address the issue where the existing studies regard PVSS as a price taker and fail to reflect the impact of bids on clearing prices and awarded quantities, this paper constructs a market bidding framework in which PVSS acts as a price-maker. Second, in response to the revenue volatility and tail risk caused by PV uncertainty, and the fact that existing CVaR-based bidding studies focus mainly on a single energy market, this paper introduces CVaR into the price-maker (Stackelberg) bidding framework and constructs a two-stage bi-level risk decision model for PVSS. Finally, using the Karush–Kuhn–Tucker (KKT) conditions and the strong duality theorem, the bi-level nonlinear optimization model is transformed into a solvable single-level mixed-integer linear programming (MILP) problem. A simulation study based on data from a PV–storage power generation system in Northwestern China shows that compared to PV systems participating only in the energy market and PVSS participating only in the energy market, PVSS participation in both the energy and frequency regulation joint markets results in an expected net revenue increase of approximately 45.9% and 26.3%, respectively. When the risk aversion coefficient, β, increases from 0 to 20, the expected net revenue decreases slightly by about 0.4%, while CVaR increases by about 3.4%, effectively measuring the revenue at different risk levels. Full article

Autonomous navigation in cluttered and dynamic industrial environments remains a major challenge for mobile robots. Traditional occupancy-grid and geometric planning approaches often struggle in such unstructured settings due to partial observability, sensor noise, and the frequent presence of moving agents (machinery, vehicles, humans). These limitations seriously undermine long-term reliability and safety compliance—both essential for Industry 4.0 applications. This paper introduces S3PM, a lightweight entropy-regularized framework for simultaneous mapping and path planning that operates directly on dense 3D point clouds. Its key innovation is a dynamics-aware entropy field that fuses per-voxel occupancy probabilities with motion cues derived from residual optical flow. Each voxel is assigned a risk-weighted entropy score that accounts for both geometric uncertainty and predicted object dynamics. This representation enables (i) robust differentiation between reliable free space and ambiguous/hazardous regions, (ii) proactive collision avoidance, and (iii) real-time trajectory replanning. The resulting multi-objective cost function effectively balances path length, smoothness, safety margins, and expected information gain, while maintaining high computational efficiency through voxel hashing and incremental distance transforms. Extensive experiments in both real-world and simulated settings, conducted on a Raspberry Pi 5 (with and without the Hailo-8 NPU), show that S3PM achieves 18–27% higher IoU in static/dynamic segmentation, 0.94–0.97 AUC in motion detection, and 30–45% fewer collisions compared to OctoMap + RRT* and standard probabilistic baselines. The full pipeline runs at 12–15 Hz on the bare Pi 5 and 25–30 Hz with NPU acceleration, making S3PM highly suitable for deployment on resource-constrained embedded platforms. Full article

The European forest-based sector faces a perfect storm of demographic, geopolitical, climatic, and policy-driven challenges. These multipronged, oftentimes interlinked factors are particularly consequential for export-oriented, forest-rich economies like Sweden. This study provides a qualitative scenario analysis to assess potential futures for the Swedish forest sector towards 2050, focusing on the impacts of key drivers: geopolitical alignment, European Union (EU) policy implementation, economic and demographic trends, technological progress, and climate change. Two critical uncertainties—Europe’s geopolitical positioning and the policy balance between wood use and forest conservation—form the axes for four contrasting scenarios. Results indicate that, across all futures, volume-based manufacturing in Sweden is expected to stagnate or decline due to high costs and weak EU demand, with bulk production shifting to the Global South. Long-term viability hinges on a strategic shift to high-value segments (e.g., specialty packaging solutions, biochemicals, construction components) and the adoption of advanced technologies. Concurrently, the sector must adapt to increased forest disturbances and diversify tree species, despite industry processes being optimized for current conifers. The study concludes that without a decisive transition from commodity production to innovative, value-added strategies, the Swedish forest sector’s competitiveness and resilience are at serious risk. Full article