Search Results (327)

The Gaizi River Basin, an alpine region in China crossed by the Karakoram Highway, is highly prone to glacier-related debris flows (GDF). Accurate debris flow susceptibility assessment in this high-altitude area remains challenging due to complex terrain, active tectonics, and dynamic glacial processes. This study develops a hybrid model integrating statistical methods and machine learning-based anomaly detection for debris flow susceptibility mapping. To address data noise, certainty factor (CF) distributions of debris flow predisposing factors (DFPFs) were derived via Locally Weighted Scatterplot Smoothing (LOWESS). The strength of the association between DFPFs and GDF susceptibility was evaluated using the mean residual between the raw and LOWESS-smoothed CF values. Multiple anomaly detection algorithms, including distance-based (L2 Norm), density-based (One-Class SVM), ensemble (Isolation Forest, RandNet), and GAN-based (WBiGAN-GP) methods, were tested on raw and CF-transformed data, using only the GDF inventory as the label. The CF-WBiGAN-GP model delivers the most balanced performance, excelling at identifying both high- and low-susceptibility zones. Results show that distance to stream, slope, and the topographic roughness and wetness indices are strongly associated with GDF susceptibility. Distance to glacier and precipitation appear less informative for direct susceptibility inference under our specific dataset and analytical setup. Full article

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with $O\left(N\right)$ computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO₂ detection ($N=1$) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO₂ concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points ($\mathrm{HI}\ge 1.0$) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing. Full article

The water–energy–food (WEF) system acts as a critical nexus of social–ecological systems. However, rapid urbanization has intensified the regional imbalance in the supply and demand of ecosystem services (ESs). Clarifying the spatiotemporal matching of ecosystem services supply and demand (ESSD) within the WEF framework and revealing the driving mechanisms behind such imbalances are essential to formulating reasonable zoning schemes and targeted optimization strategies for the coordinated development of the regional WEF system. Taking Zhejiang Province as a case study, this research uses water yield (WY), carbon sequestration (CS), and grain production (GP) to characterize the WEF nexus system. It uses the InVEST model to assess WY and CS, applies spatial allocation methods to characterize GP, and integrates socioeconomic data to quantify the demand for the above three ESs. All indicators were standardized and integrated with equal weights to further clarify the comprehensive levels of ESSD. By integrating the Geodetector and K-Means clustering methods, the study analyzes the supply–demand matching of ecosystem services and its driving mechanisms in Zhejiang Province during this period, thereby exploring ecological management zoning and optimization strategies within the WEF system. The study findings indicate that: (1) From the supply perspective, Zhejiang Province’s WY services demonstrate a trend of elevated activity in the southwest and diminished presence in the northeast; high values for CS services are predominantly found in the vegetation-rich areas of the northwest, while high values for GP services are clustered in the northern Zhejiang Plain; from the demand perspective, high values for all three ESs in Zhejiang Province are primarily located in economically active, densely populated urban areas. (2) The correlation between ESSD within Zhejiang Province’s WEF system exhibits significant spatial heterogeneity and is driven by the combined effects of natural and socioeconomic factors, with the interaction between these two factors often producing a synergistic effect. Specifically, annual average precipitation and population density are the dominant factors influencing WY services, NDVI and human footprint are the dominant factors influencing CS services, and population density and GDP are the dominant factors influencing GP services. (3) From 2000 to 2020, the supply–demand ratio for comprehensive ESs in Zhejiang Province generally followed a pattern of being lower in the east and higher in the west. The supply–demand imbalance of ESs intensified in the core areas of eastern cities, whereas the western regions maintained a relatively sound supply–demand balance. (4) The study classifies the counties in Zhejiang Province into four ecological management zones—ecological stable zones, ecological conservation zones, ecological control zones, and ecological restoration zones—and explores differentiated approaches to optimizing these zones and implementing control strategies. Full article

Intensive mining over recent decades has caused severe ground subsidence in mining regions, threatening safety and long-term sustainability. High-precision, continuous monitoring and prediction of subsidence are therefore urgently needed. Traditional methods—terrestrial surveying and GPS—offer limited coverage, sparse measurement points, high costs, and poor scalability, making them unsuitable for large-scale, long-term surface deformation monitoring. InSAR is widely used for ground deformation monitoring due to its wide-area coverage, long-term sampling, high spatial resolution, and millimeter-scale precision. However, conventional InSAR often fails in vegetated areas and under steep deformation gradients—common in mining zones. To overcome these limitations, this study applied SBAS-InSAR, a method better suited for large-magnitude, continuous subsidence monitoring in mining areas. This study proposed an enhanced hierarchical spatiotemporal dependency graph neural network (HSDGNN) integrated with a Long Short-Term Memory (LSTM) module to improve temporal feature representation. Using this model, this study predicted surface subsidence at the Dexing Copper Mine under environmental drivers. Key findings are as follows: (1) Surface subsidence exhibited pronounced spatial heterogeneity and strong temporal nonlinearity; major subsidence zones were localized in open-pit excavation areas and waste rock dumps, with peak subsidence rates reaching −126.121 mm/yr. (2) Precipitation and soil moisture emerged as the dominant environmental controls on subsidence, displaying distinct seasonal modulation and quantifiable lagged responses—up to several months—relative to subsidence onset. (3) The HSDGNN model achieved high predictive accuracy for both Mine 1 and Mine 2, attaining R² values of up to 0.9950. This work establishes a robust, scalable, and operationally viable framework for high-precision subsidence monitoring and forecasting in geologically and anthropogenically complex mining environments. Full article

Since the 1960s, research on sea-level rise (SLR) and land subsidence has grown significantly; however, comprehensive syntheses remain limited. This study presents a systematic review of 2171 publications spanning 1964–2025, combining a global perspective with a regional focus on the Gulf of Guinea, a critically underrepresented region within the African continent. The results show a steady increase in publications, exceeding 80 per year since 2015. A combined bibliometric and content analysis approach was adopted, integrating large-scale metadata analysis with an in-depth evaluation of 166 full-text studies corresponding to 311 study sites. Bibliometric analyses highlight four main themes: (1) factors driving SLR and subsidence, including climate, geophysical, and human effects; (2) monitoring methods such as tide gauges, GPS, and InSAR-based land motion tracking; (3) impacts on coastal communities, and ecosystems; and (4) strategies for adaptation and mitigation. A comparative assessment of global research output and Low-Elevation Coastal Zone (LECZ) exposure reveals a marked spatial mismatch, with critically vulnerable regions, such as the Gulf of Guinea, remaining significantly underrepresented (44 studies). The synthesis identifies key conceptual, methodological, and practical research gaps. Addressing these gaps requires holistic frameworks that integrate SLR and subsidence, long-term monitoring networks, advanced modeling, and evidence-based adaptation strategies. By linking bibliometric evidence with the interpretation of research trends and gaps, this study provides an analytical basis for supporting monitoring strategies, coastal planning, and adaptive responses. Additionally, the results highlight priority directions for future research directions in the Gulf of Guinea region. Full article

This study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to investigate surface deformation dynamics in the hyper-arid Eastern Province of Saudi Arabia, with emphasis on quantifying sand dune migration and identifying areas susceptible to wind erosion. Utilizing Sentinel-1 SAR data and the MintPy toolbox, ground deformation was quantified with millimeter-scale precision. Results reveal significant subsidence, up to 15 cm/year in landfills, linked to waste compaction and groundwater depletion. Localized uplift of ~4 cm/year on northern peripheries is directly attributed to aeolian sand accumulation from seasonal Shamal winds, providing quantitative evidence of dune migration. While direct measurement of wind erosion (net deflation) remains challenging due to the dominance of depositional signals and the spatial heterogeneity of erosion processes, areas of potential erosion are inferred from negative displacement patterns outside landfill zones and from coherence characteristics indicative of surface instability. The integration of SBAS-InSAR with GPS and ERA5 wind reanalysis resolves the combined influence of aeolian deposition, hydrogeological changes, and anthropogenic activity, offering insights into both components of aeolian dynamics and a replicable model for sustainable land management in arid environments. Full article

Shape optimization of arch dams is essential for balancing structural safety and economic efficiency, yet remains computationally intensive due to costly finite element analyses and strict geometric constraints. Conventional sampling techniques often yield infeasible designs that undermine surrogate model fidelity. This study proposes an integrated framework combining Stratified Conditional Latin Hypercube Sampling (SC-LHS), automated modeling, and Gaussian Process (GP) surrogate models. SC-LHS incorporates hierarchical constraints to eliminate infeasible samples during generation, while a Python-driven workflow automates the process from parameterization to simulation. Coupling the GP surrogate with NSGA-II enables efficient Pareto front exploration. The results indicate that SC-LHS is superior to standard LHS, Constrained LHS, and Sobol sequences with rejection in terms of feasibility rate and space-filling metrics. The optimal compromise solution reduces dam volume by 10.4% and tensile zone volume by 15.2% compared to the initial design. This framework effectively reconciles economic and safety objectives, offering a robust methodology for complex hydraulic structure design. Full article

Background: The Global Positioning System (GPS) and wearable monitoring technologies are increasingly applied in sport science to quantify training load; however, data from female cross-country skiers in nations with emerging competitive programs remain scarce. This case series covering the complete national team roster analyzed the complete annual training cycle of the Korean women’s national cross-country skiing team (KCF) using GPS and heart rate-based wearable sensors. Methods: All three national team members were monitored throughout the 2022–2023 season (52 weeks), structured into General Preparation Period 1 (April–July), General Preparation Period 2 (August–November), and Competition Period (December–March). Individualized five-zone intensity thresholds were established through graded exercise testing on a roller ski treadmill with ventilatory threshold and blood lactate determination, independently assessed by two exercise physiologists (PhD level). Results: The total annual training volume was 667.72 h, comprising roller/on-snow skiing (54.0%), running (23.3%), and strength training (22.7%). The endurance-only intensity distribution demonstrated a polarized pattern (Zones 1–2: 91.5%). The total annual training distance reached 4673.30 km. The mean FIS points were 108.46 ± 38.60, and the mean VO₂max was 60.17 ± 6.11 mL·kg⁻¹·min⁻¹. Conclusions: When benchmarked against world-class female (WCF) standards (800–950 h annually), the overall training volume was approximately 18–30% lower. The relative strength training allocation (22.7%) exceeded typical WCF values (10–15%). These observations should be interpreted cautiously given the small sample size and cross-study comparison design, using published literature-based benchmarks. Full article

Heavy-duty trucks, especially those used in urban areas, are responsible for a disproportionally large share of the external costs of the transportation sector. Policy instruments that target these trucks could thus be efficient measures to reduce negative impact from the traffic sector. This paper presents how heavy-duty trucks operated in Sweden’s two largest cities, Gothenburg and Stockholm, in the year 2022 and how zero-emission zones or environmental zones with an entrance fee targeting heavy-duty trucks could affect not only urban traffic but all trucks on Swedish roads. The analysis is based on GPS data from 69,000 trucks in operation in Sweden in the year 2022. Of these trucks, 4% visited the two cities for more than 100 days (frequent visitors) and 40% visited at least once during the year. Although zero-emission zones would have the strongest impact, environmental zones with an entrance fee could be a more flexible way to create a strong enough incentive for frequent visitors to electrify. An entrance fee of 100 Euro per day in combination with current investment subsidies would make electric trucks competitive for frequent visitors and still allow for others to continue using conventional trucks during a transition period. Full article

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

Background: Depression remains a major global health burden, yet fragmented care often leads to waiting times and unmet needs. Therefore, the Belgian collaborative Integrated Depression Care (IDECA) project strengthened primary care depression management by introducing a Reference Person Mental Wellbeing (RPMW) who functions as a case manager, supported by shared-care tools, structured psychoeducation modules, and targeted training for general practitioners (GPs). This study examines normalization in primary care practice. Methods: A single-arm, mixed-method study was implemented over 18 months in two Flemish Primary Care Zones (PCZ). Implementation outcomes were assessed every four months using the NoMAD questionnaire and analyzed using Wilcoxon signed-rank tests. Peer review sessions with professionals and interviews with patients were analyzed thematically. Caseload and service delivery were assessed using process evaluation logs. Results: Twenty-two professionals (17 GPs, two RPMWs, and three PCZ staff members) completed the NoMAD questionnaire. Intervention familiarity increased during the first eight months (T0–T1: p < 0.001; T1–T2: p = 0.022) and continued to rise thereafter (T3–T4: p = 0.008). Integration into daily practice and perceived impact on professional work improved progressively, reaching near-ceiling scores. Peer review sessions highlighted the RPMW’s central role in trust-building and care coordination. Over 12 months, one full-time equivalent RPMW supported 175 patients (mean age 40.7 years; 75% female), with an average of five consultations per patient. Patients reported high satisfaction, emphasizing accessibility, empathy, and practical support. Conclusions: Sustained results suggest successful normalization and support the potential of collaborative, low-threshold depression care. Future work will assess clinical and economic outcomes. Full article

Background: Wearable sensors and global positioning systems (GPS) can enable objective monitoring of training loads in outdoor endurance sports. In biathlons, comparing training characteristics across developmental stages can help identify structural gaps and support evidence-informed progression within long-term athlete development (LTAD). This study aimed to quantitatively compare the annual training characteristics of Korean male biathlon national team (NT) and university (UNV) athletes. Methods: Annual physical training data (2022–2024) from NT (n = 6) and UNV (n = 6) athletes were collected using Catapult Vector S7 GPS devices and Polar H10 heart rate monitors. Training volume, intensity distribution (zones 1–3 based on %HRmax), modality (skiing vs. running), and periodization were compared using Mann–Whitney U tests with rank-biserial correlation (r_rb). Results: NT athletes accumulated a higher annual training time and distance than UNV athletes (812 vs. 606 h; 6359 vs. 4130 km; p = 0.002, r_rb = 1.000 for both). The NT athletes spent a lower proportion of time on low-intensity training and a higher proportion on mid and high intensities than UNV athletes (p ≤ 0.015). During high-intensity training, NT athletes maintained a higher proportion of ski-specific training, whereas UNV athletes relied more on running (skiing: 78.5% vs. 46.4%; running: 21.5% vs. 53.6%; both p < 0.001, r_rb = 1.000). The UNV group also showed a more concentrated structure during competition periods than NT athletes (COMP: 28.3% vs. 14.6%; p < 0.05). The absolute annual strength training time did not differ, but UNV athletes showed a higher strength ratio (23.3% vs. 16.8%; p < 0.001, r_rb = 1.000). Conclusion: UNV athletes exhibited a lower total volume, more low-intensity-skewed distribution, and reduced ski-specific exposure during high-intensity training compared with NT athletes. These observed structural gaps can provide empirical benchmarks that may help coaches plan stage-appropriate progression, and they illustrate the practical value of GPS- and wearable-based monitoring for identifying training divergences across developmental stages. Full article

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with Global Positioning System (GPS) tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system’s effectiveness in detecting elevated pollutant levels in rural areas with solid fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m³ in forested areas to as high as 160 μg/m³ in industrial zones, while CO₂ levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement. Full article

Electrospinning and electrospraying nanotechnologies were used to valorise agro-industrial residues into biohybrid controlled-release polyphenol (CRP) scaffolds. Four polyhydroxybutyrate ± polycaprolactone (PHB±PCL) architectures were fabricated that differed in polymer phase, Klason lignin from hazelnut shell (HS-KL) presence vs. absence, and co-location with grape-pomace polyphenols (GP-PPs), as well as in distribution between fibres and bead-like depots. Scaffolds were characterised using optical microscopy/stereomicroscopy/SEM, FTIR, UV–Vis spectroscopy, and dynamic water contact angle (absorption). GP-PP release was monitored for 14 days at ~25 °C and 37 °C, the latter representing shallow-soil hot-spell conditions in Mediterranean zones. All matrices exhibited multimodal release, with modest initial bursts and three phases (burst, mid, and late tail), analogous to controlled-release fertiliser profiles. At ~25 °C, the PHB/PCL matrix with HS-KL confined to PHB fibres and GP-PP in large PCL beads showed the highest total GP-PP release, whereas the architecture with HS-KL and GP-PP co-located in both PHB and PCL fibres and in PCL depots combined high total release with a smoother, well-metered late phase. At 37 °C, this HS-KL-GP-PP co-located scaffold was the most robust, retaining the highest total and late tail release. These results identify HS-KL-GP-PP co-located PHB/PCL architectures as promising carriers for temperature-resilient delivery of bioactive polyphenols in Mediterranean agrosystems. Full article

The influence of minute-scale preaging (MSP) at 200 °C on the microstructures and formability of 2195-T34 Al-Li alloy in the creep-aging (CA) process was systematically investigated. The elongation of the alloy before CA first increased and then decreased with MSP time (2 to 15 min), reaching a maximum of 17.86% at 5 min; the yield ratio exhibited the opposite trend, attaining a minimum of 65.55% at 5 min. When the true strain was greater than 1.4%, the strain hardening rate of the samples after MSP treatment for less than 8 min was similar to and higher than that of the samples preaged for 15 min. Compared with the non-preaged specimens, the ultimate creep deformation of samples preaged for less than 8 min was improved, reaching a maximum improvement of 29.5% at 5 min. Simultaneously, the peak aging was retarded without reducing the final peak strength (T6 level), thereby broadening the formability window. HAADF-STEM observations revealed that MSP for 5 min markedly reduces the density of G.P. zones, which delays T₁ precipitation and reduces the resistance to dislocation motion. When the preaging time was less than 8 min, the number of movable dislocations increased. However, exceeding 8 min led to obvious precipitation of the T₁ phase, reducing the plasticity of AA2195-T34 sheets. Full article