Search Results (2,018)

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

Amazonian large trees act as central elements of forest ecosystems, storing a disproportionate fraction of aboveground biomass. However, these trees are not randomly distributed across the landscape, and it is expected that edaphic attributes influence floristic composition, forest structure, and vegetation biomass. In this study, we investigated how variation in soil chemical and physical properties affects the diversity and biomass of large trees. Forest inventories were conducted at five sites within protected areas in the states of Pará and Amapá. Aboveground biomass was estimated using allometric equations, while soil samples were analyzed for their physical and chemical properties. Diversity indices, rarefaction, Redundancy Analysis, and Generalized Additive Models were applied. Edaphic variables such as soil pH, organic matter, phosphorus, and aluminum were associated with floristic composition and the biomass of these individuals. Trees with a diameter at breast height greater than or equal to 70 cm accounted for up to 80% of total biomass, revealing a pattern of biomass hyperdominance. The results indicate that the occurrence of large trees is related to edaphic and structural attributes, such as tree density and size distribution, suggesting that these individuals are not randomly distributed along soil gradients. Understanding these patterns is essential for improving ecological models, biomass extrapolations, and management strategies aimed at conserving the Amazon rainforest. Full article

Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) and operational metrics, such as fill rate and stockouts. Methods: The workflow integrates daily, store-level transactions with external covariates, constructs temporal and lag features, and trains a Random Forest (RF) model using chronological splitting and time-series validation. Daily forecasts are then aggregated to the monthly level and used as inputs to an inventory simulation and an ROI-based economic model. Building on this simulation, a Genetic Algorithm (GA) optimizes the parameters of a monthly replenishment policy, incorporating minimum-coverage constraints. Results: In testing, the forecasting model achieved a mean absolute percentage error (MAPE) below 13%, and the RF+GA scheme outperformed the 28-day moving average baseline (MA28) in ROI across all five stores, with an average improvement of 4.52 percentage points; statistical significance was confirmed using the Wilcoxon test. Conclusions: Overall, the RF+GA approach serves as a decision-support tool that generates monthly order quantities consistent with demand and operational constraints, delivering verifiable improvements in both economic and service metrics. Full article

Landslide hazard potential is high across the St. Lawrence lowlands of Québec, Canada, due to sensitive glaciomarine clay deposits and the presence of moderate seismic activity, causing slope failures in the region. The main objectives of the study are to develop a working database for landslides in the region and use that database to improve regional landslide susceptibility analysis. Using high-resolution (1 m by 1 m cells) digital terrain models dated from 2009 and validated with satellite photogrammetry from 2012, a landslide inventory of 263 cases related to the 2010 Val-des-Bois earthquake (moment magnitude 5.0) is created. Relationships between landslide susceptibility factors, such as slope angle, and seismic conditioning factors, such as peak ground acceleration, are examined through machine learning methods. For landslide detection, an overall accuracy of approximately 85% (AUC 0.914) is achieved using random forest and logistic regression models cross-validated through 5-fold analysis, showing improvement over the currently employed Hazus method, which achieves an accuracy of approximately 67%. From a regional perspective, the developed inventory and resultant susceptibility models are unique and form the foundation for future studies to improve the understanding of earthquake-induced landslides in the Western Québec Seismic Zone, which historically lacks detailed landslide inventories. Full article

Fire suppression in the upper U.S. Midwest has led to the expansion of flammable coniferous ladder fuels, necessitating precise tracking of conifer species basal area (BA) for fire risk management. This study benchmarks four subset-selection pipelines—xPLS, GA-xPLS, RF-xPLS, and SVR-xPLS—to optimize the fusion of high-dimensional, collinear data from Sentinel-2, Landsat-9, and LiDAR sensors. Using 141 field plots in Minnesota’s Kawishiwi Ranger District of the Superior National Forest, we evaluated 175 predictors against eight BA response variables. Results show that RF-xPLS provided the superior accuracy–parsimony trade-off, achieving the highest pooled R² (≈0.86) and lowest error with a compact 27-predictor block. GA-xPLS ranked second, excelling for specific species such as Pinus resinosa. The most effective predictors combined SWIR-based moisture indices, red-edge/NIR structure, and a single LiDAR-derived surface of vertical-structure (quadratic mean height). Our findings demonstrate that integrating machine learning selection engines with multi-sensor fusion substantially enhances the scalability and precision of forest inventory and fuels monitoring. This comparative framework offers practical insights for sustainable management and fire risk mitigation in northern temperate–boreal forests. Full article

Landslides are characterized by strong suddenness and a wide range of damage; accurate prediction of their susceptibility is an important prerequisite for regional risk prevention and control. To address the difficulties in acquiring landslide inventories in complex terrain areas and the insufficient interpretability of existing prediction models, this study proposes a landslide susceptibility assessment (LSA) framework that integrates automated sample detection and interpretability analysis. The proposed framework is applied to Moxi Town, a typical alpine valley area in Sichuan Province, China. A Mask R-CNN instance segmentation model was introduced to achieve automated detection of landslide samples, resulting in a high-quality inventory containing 923 landslides. Based on the spatial relationships between the landslide inventory and influencing factors, a convolutional neural network (CNN) landslide susceptibility assessment model incorporating Shapley Additive exPlanations (SHAP) interpretability analysis was constructed. The CNN model was further compared with random forest (RF) and extreme gradient boosting (XGBoost) machine learning models. The results show that the AUC value of the CNN model has increased by 4.3% and 3.2% compared with the RF and XGBoost models, respectively, and it significantly reduces the pretzel effect of landslide susceptibility mapping (LSM). The results validate the reliability of the proposed framework, which can provide technical support for landslide disaster prevention and monitoring. Full article

The need for effective inventory management in the transition from “Just-in-Time” to “Just-in-Case” supply chain strategies was addressed by developing a machine learning model to predict inventory backorders. Using a large store keeping unit dataset, five supervised learning algorithms, namely, logistic regression, random forest, k-nearest neighbours, Naïve Bayes, and gradient boosting, were implemented with Python 3.13 Data imbalance was managed using the synthetic minority over-sampling technique, while power transformation was applied to improve data distribution and model performance. Among the models, random forest demonstrated the highest prediction accuracy at 98% and a strong receiver operating characteristic score of 0.897, making it the best model for backorder prediction. This approach enhances supply chain resilience and proactive inventory control, enabling manufacturers to mitigate risks of stockouts and optimize resource planning. It is necessary to incorporate advanced balancing techniques, hyperparameter tuning, and cross-validation methods to improve predictive performance further. Full article

Assessment of landslide risk is crucial given the substantial related economic losses and infrastructure damage in mountain areas every year. Particularly, the Sagarmatha National Park (SNP), a key destination for Himalayan glacier tourism, remains relatively understudied in this context. Existing studies primarily focus on regional inventories or simply inventory landslides and lack tourism-specific hazard assessment. This study evaluates landslide distribution, its controlling factors, and the exposure of infrastructure to varying degrees of landslide susceptibility in SNP. A blind inventory of 680 landslides and twelve conditioning factors, including six topographic and six non-topographic variables, were analyzed using Frequency Ratio (FR), Logistic Regression (LR), and Random Forest (RF) models. In addition, spatial overlay analysis was employed to assess the degree of infrastructure exposure. Results indicate that Land Surface Temperature (LST) is the most dominant factor influencing landslides occurrence, followed by rainfall, elevation, and slope, along with specific aspects like south and west and, land cover class like Barren land and Alpine meadows. Random Forest achieved the highest predictive accuracy (91%), outperforming both Logistic Regression (87%) and Frequency Ratio (84%). Exposure assessment of key tourism infrastructure indicates that trekking routes, helipads, buildings, campsites, and bridges are subject to varying levels of landslide risk. Although only 2.73 km (0.52%) of trekking routes intersect active landslide scars, 147 km (28%) lie within high-exposure zones. Consequently, both typical and paraglacial landslides threaten access to glacier tourism destinations, highlighting significant implications for Nepal’s tourism. Full article

Due to climate change, drought periods are becoming more frequent and more intense, posing substantial stress to Central European forest stands, especially climatically sensitive conifer forests. The early detection and accurate spatial delineation of forest damage are essential for supporting adaptive forest management decisions. This study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation. In the first phase, potential damaged forest patches were identified in two sample areas of the Sopron Mountains using double-difference maps derived from monthly window NDVI maxima calculated from Sentinel-2 data. In the second phase, UAV surveys were carried out over the selected forest compartments, resulting in individual-tree-level canopy segmentation and object-based NDVI analysis. The photogrammetric point clouds were combined with ground points derived from airborne laser scanning to enable the accurate generation of canopy height models. The results confirmed that NDVI double-difference analysis is suitable for the spatial detection of both gradual drought-related damage and sudden disturbances—such as forest fire—even under sequences of drought and moderate years occurring in a sporadic pattern. The UAV-based analysis corroborated the satellite observations in detail and enabled an accurate inventory of damaged trees as well as the exploration of their spatial distribution. The proposed methodology provides an efficient, cost-effective, and operational tool for multi-scale monitoring of forest damage, contributing to the timely recognition of climate-change impacts and to the substantiation of targeted forest management interventions. Full article

As a crucial pillar industry in the country, the automotive industry continues to evolve with the increasing number of vehicles in operation, leading to a continual rise in the need for aftermarket parts and repair services. Fluctuations in automotive spare part requirements are influenced by various complex factors, which significantly impact production costs. The intermittent distribution of such requirements and strict limitations highlights the importance of automotive spare part management to enhance production efficiency and reduce costs. To improve demand forecasting accuracy, this study summarizes and synthesizes trends in automotive spare parts; proposes a tree-based machine learning forecasting model, based on a two-stage random forest (RF) structure that separately models demand occurrence probability and conditional demand size; and compares the outcomes with benchmarks to validate model effectiveness. The empirical study is conducted using an industrial dataset consisting of monthly demand records for approximately 2500 spare parts over a four-year period. This forecasting approach enables companies to rationalize inventory storage, ensure the quality of automotive repairs, and elevate service standards. Simultaneously, by improving the efficiency of inventory planning and allocation decisions, companies can enhance the quality of after-sales services, reduce inventory costs, and maximize the value of the automotive industry chain. Through reducing spare parts wastage and further lowering enterprise costs and industrial emissions, companies can achieve the goals of automotive supply chain resilience. Notably, this study focuses on automotive spare parts management and provides a feasible, reliable, and interpretable forecasting solution for automotive manufacturers to address intermittent demand challenges in spare parts management. Full article

Accurate estimation of forest aboveground carbon (AGC) is crucial for understanding the carbon cycle and formulating climate policies, yet it remains challenging in complex mountainous regions. This study used machine learning framework to estimate the spatiotemporal dynamics of AGC in the Three Parallel Rivers region of China from 2003 to 2024. By integrating China’s National Forest Continuous Inventory (NFCI) data with multispectral satellite imagery, we employed a two-stage feature selection strategy to identify key predictor variables. Among three ensemble algorithms tested, the Random Forest model achieved the optimal performance (R² = 0.74). The results indicated a net increase of 67.05 Tg in total AGC over the two decades, with a spatial pattern characterized by higher densities in the west and north. Geographical Detector analysis revealed that the driving forces were synergistic, with the interaction between temperature and population density exhibiting the most prominent explanatory capacity. This study provides a high-resolution (30 m) benchmark for AGC in a global biodiversity hotspot and underscores the critical role of ecological protection policies in enhancing carbon sequestration, offering valuable insights for managing similar mountain ecosystems worldwide. Full article

Tropical mountain watersheds contain heterogeneous land cover and land use (LCLU) mosaics, yet the relationship between these mosaics and landslide morphometry and occurrence at the watershed scale remains unclear. We compiled landslide inventory from 2002 to 2023 for the 152.3 km² Upper Ciliwung Watershed, West Java, Indonesia. We mapped morphometry for a subset of 84 landslides, classified the events into seven LCLU classes, and compared landslide size–frequency distributions across vegetation groups. Principal component analysis (PCA) revealed that LCLU type influences landslide size and mobility. Forested terrain produced narrower, longer-runout landslides on steeper slopes, whereas agricultural and other herbaceous-dominated terrain generated wider landslides on gentler slopes. Clarifying landslides by vegetation characteristics as either tree- or herbaceous-dominated areas (including urban areas) revealed distinct size–frequency patterns, especially for small landslides (tree-dominated: 133 m², herbaceous-dominated and other: 97 m²; overall 112 m²), which are consistent with the contrasting vegetation structures and hydrological responses. PCA supported these patterns, with PC1 describing a morphometric axis and PC2 capturing gradients in event rainfall and antecedent wetness. Together, these results support the conclusion that vegetation structure and land-use conditions influence slope stability by affecting soil reinforcement and hydrological responses. This provides a foundation for land–use–specific geohazard mitigation and vegetation-based slope stability planning. Full article

Healthcare service providers face recurrent systemic disruptions (e.g., pandemics, reimbursement delays, supply shortages, and regulatory shocks), yet firm-level resilience monitoring remains underdeveloped due to limited explainability and weak out-of-time validation in prior work. We develop an explainable machine learning pipeline to predict firm-level financial resilience (a financial health/robustness proxy) for outpatient healthcare providers. Using annual data for 2600 Romanian firms (Nomenclature of Economic Activities - NACE 8622) over 2014–2023, resilience is operationalised as an ordered three-class label derived from a Principal Component Analysis (PCA)-based composite score built from eight capital structure and asset composition ratios, with train-only frozen thresholds and a strict anti-leakage protocol. We evaluate multinomial logistic regression (baseline), Random Forest (RF), and HistGradientBoosting (HGB) (primary) on a prospective 2023 hold-out using Accuracy, Balanced Accuracy, and Macro-F1, with bootstrap uncertainty for key contrasts. The primary model achieves Balanced Accuracy = 0.943 and Macro-F1 = 0.944 in 2023, outperforming the linear baseline and RF; errors concentrated between adjacent classes. Model-faithful permutation importance on HGB highlights working-capital disciplines (receivables, cash, inventory, asset structure), while RF–SHAPley Additive Explanations (SHAP) is used only for auxiliary pattern exploration and stability checks, with Individual Conditional Expectation (ICE)/Partial Dependency Plot (PDP) confirming key nonlinear regimes on HGB. Overall, the results support governance-ready, interpretable resilience monitoring while maintaining a clear separation between predictive explanations and causal claims. Full article

This study evaluates the capability of multi-temporal vegetation indices derived from Sentinel-2 imagery to indicate groundwater discharge in a forested mountainous sector of the Northern Apennines (Italy). The NDVI was computed from Level-2A surface reflectance data (10 m resolution) and analyzed over five growing seasons (2017–2021), encompassing recurrent summer droughts. Aridity conditions were quantified using the Standardized Precipitation–Evapotranspiration Index (SPEI) derived from long-term meteorological records. The methodological framework integrates cloud-masked satellite observations, drought characterization, and spatial statistical comparison between known spring discharge zones and randomly distributed forested control points. NDVI values extracted within 100 m radius buffers, centered on spring outlets, were systematically compared with those from control areas located outside the shallow-water-table influence zone. During periods of negative SPEI (moderate-to-severe drought), spring-centered buffers consistently exhibited higher NDVI values than control sites, with the NDVI contrast increasing under severe arid conditions. This pattern indicates enhanced vegetation resilience supported by shallow groundwater availability. The results demonstrate that vegetation health anomalies, when constrained by homogeneous land cover and a consistent hydrogeological setting, can serve as indicators of the groundwater discharge likelihood. The proposed workflow provides a reproducible and cost-effective tool to support hydrogeological reconnaissance and spring inventorying in rugged mountainous environments where field-based surveys are logistically demanding. Full article