Search Results (720)

This study aims to be the first phase of a comprehensive assessment of street canyon air quality in Beşiktaş, Istanbul. The main objective of this initial phase is to develop and systematically evaluate a high-resolution, integrated data infrastructure to support subsequent street-scale air quality modeling (Part B). The study period covers 2016–2023, based on long-term hourly observations. Air quality data (PM₁₀, PM_2.5, O₃, and nitrogen oxides reported separately as NO₂ and total NO_x) were obtained from the Beşiktaş air quality monitoring station. Missing values were filled using seasonal decomposition combined with linear interpolation for NO₂, NO_X, and PM₁₀; for PM_2.5, a multiple linear regression model incorporating PM₁₀, temperature, relative humidity, wind speed, and cyclical time variables was applied. Meteorological variables (temperature, rainfall, relative humidity, wind speed, and direction) were primarily sourced from the Şişli station and gap-filled using linear interpolation. Both datasets were cross-validated for temporal consistency with ground-based observations. Hourly vehicle counts were obtained from the Istanbul Metropolitan Municipality (IMM), while vehicle-type and fuel-category composition were derived from camera-based observations along the corridor. High missing rates in PM_2.5 data and uncertainties regarding vehicle composition are among the main limitations of this study. This study presents a coherent dataset to support street-level air quality modeling and related analyses in Türkiye. Beyond being the first long-term (2016–2023), hourly, street canyon dataset of its kind for Istanbul, this work provides a transferable framework for building model-ready air quality inputs and a practical basis for street-scale dispersion modeling, exposure assessment, and urban air quality management. Full article

Soil mesofauna play important roles in soil food webs, organic matter decomposition, and nutrient cycling. We quantified mesofauna responses to tillage, liming, fertilisation, and Geo2 biostimulant application at two Croatian experimental sites differing in soil type and land-use history: a long-term arable Stagnosol in Čačinci and a recently converted Gleysol in Križevci. Mesofauna were sampled in spring and autumn 2023 and analysed using generalised linear mixed-effects models (GLMMs). Seasonal dynamics exerted the strongest influence on mesofauna communities, with Collembola and Acari abundances approximately sixfold and sevenfold higher, respectively, in spring than in autumn. A significant Season × Location interaction for total Acari and Oribatida indicated a stronger spring increase at Križevci. Liming increased Mesostigmata (+94%) and total Acari (+33%), while recommended fertilisation increased Entomobryomorpha (+78%) and total Collembola (+46%). In contrast, tillage treatments did not significantly affect the abundance of Acari, Collembola, or their major subgroups. The Collembola:Acari ratio remained relatively stable (0.56–0.93), suggesting parallel responses of dominant taxa. Overall, site-specific conditions and seasonal variation exerted stronger effects on soil mesofauna communities than the tested management practices, emphasising the importance of local environmental factors and land-use history in shaping mesofauna community dynamics in agricultural soils. Full article

High-altitude rock slides frequently occur in the high-mountain canyon regions of the eastern Tibetan Plateau, posing significant disaster risks. The Baige landslide catastrophically failed in October 2018, blocking the Jinsha River and forming a major landslide-dammed lake. However, quantitative understanding of the spatiotemporal evolution and environmental control mechanisms remains insufficient, particularly regarding stage-dependent driving mechanisms. This study investigates the Baige landslide using mall Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), Seasonal-Trend decomposition based on Loess (STL) time-series decomposition, Principal Component Analysis–Independent Component Analysis (PCA-ICA) signal analysis, and slope-unit spatial statistics. Results indicate that: (1) deformation exhibited three stages separated by October 2018: slow pre-slide deformation, post-slide residual creep, and long-term sustained acceleration; (2) instability caused systematic restructuring of the deformation field, with valid pixels decreasing from 2766 to 560, deformation changing from slight positive line-of-sight (LOS) displacement to pronounced negative LOS displacement, and global standard deviation increasing from 21.40 mm to 40.55 mm, with stronger disturbances in the steep front zone; and (3) the driving mechanism shifted from short-term multi-factor control to a temperature-dominated long-term environmental control regime after failure, while gravity-driven creep and post-failure structural adjustment remained important background controls. Slope fragmentation and structural reorganization likely contributed to this transition. Full article

The accumulation of training and competition loads represents a critical determinant of musculoskeletal injury risk in professional team sports, yet contemporary monitoring systems remain limited by their reliance on single-domain temporal analysis that overlooks the multi-scale rhythmic patterns inherent in athletic workload signals. This study introduces FDTM (frequency-aware dual-stream temporal model), a deep learning framework that jointly encodes time-domain dependencies and frequency-domain spectral signatures from digital athlete monitoring streams to predict individual injury risk over a forward-looking seven-game horizon. The framework integrates a stacked bidirectional long short-term memory branch augmented with temporal self-attention pooling, a spectral encoding branch employing discrete Fourier transform decomposition across high-frequency (weekly), mid-frequency (bi-weekly), and low-frequency (seasonal) bands, and a cross-modal gated attention fusion module that adaptively balances temporal and spectral representations conditioned on player context. We evaluate FDTM on three heterogeneous public sports datasets spanning basketball (NBA game-log corpus 2013–2023), Australian rules football (AFL Player Workload Dataset), and soccer (SoccerMon open monitoring corpus), comprising 612 athletes and 247,830 player-game observations across ten competitive seasons. FDTM achieves AUC-ROC values of 0.858, 0.833, and 0.821 on the three datasets respectively, outperforming the strongest deep-learning baseline (FEDformer) by 2.0 to 3.3 percentage points and the strongest non-spectral baseline (TCN) by 3.2 to 4.5 percentage points while maintaining a Brier score below 0.04. Ablation studies confirm that the spectral branch contributes 5.1 percent to overall discriminative performance. SHAP attribution analyses identify high-frequency weekly components as the dominant injury-relevant signal, followed by low-frequency seasonal trends and the cumulative acute-to-chronic workload temporal feature, with gating-weight visualizations revealing dynamic modality contributions consistent with established sports science theory. Direct spectral analysis of the raw workload signal confirms that injury-preceding windows exhibit significantly elevated weekly-band power across all three datasets (Mann–Whitney U test, p < 1 × 10⁻⁷), and the architectural advantage is shown to be robust across 30 independent training seeds. These findings suggest that frequency-aware modeling may serve as a transferable methodology for sports engineering applications in injury prevention, return-to-play planning, and individualized rehabilitation, pending further external validation in female athletes and additional team sports. Full article

We present a 42-year (1984–2025) Landsat consistent satellite vegetation trajectory for coastal wetlands in the Shell Beach area in the Breton Sound estuary, Louisiana. We applied the Controlled Interrupted Time Series (CITS) analysis to the satellite record to quantify the causal effect of the 2009 Mississippi River Gulf Outlet (MRGO) closure on the coastal wetland vegetation. The analysis used NDVI, kNDVI, and NDII across 88 vegetation transect plots located within five Coastal Reference and Monitoring Systems (CRMS) stations in the Shell Beach wetlands. Vegetation communities identified included Saline, Brackish, Freshwater, and Intermediate marsh. Sentinel-2 data from 2015 to 2025 were retained as an independent parallel record for NDRE analysis only. Quarterly median composites were decomposed using the Seasonal-Trend decomposition using LOESS (STL) to isolate de-seasonalized vegetation anomalies. The CITS design used segmented Ordinary Least Squares (OLS) regression with Newey–West HAC standard errors (lag = 3) at the study area. Northern Barataria Bay was used as an untreated regional control site to remove concurrent climate and sea level rise confounders. Whilst Hurricane Katrina and subsequent years (2005–2008) were excluded from the models, the single group ITS identified significant negative post-closure slope change across three indices. These were NDVI (β3 = −0.0034 yr⁻¹, p = 0.000), NDII (β3 = −0.0032 yr⁻¹), and kNDVI (β3 = −0.0016 yr⁻¹). These values indicated continued site-level decline relative to the pre-closure trend. Community-stratified ITS analysis showed a distinct divergent pattern with Freshwater marshes demonstrating significant recovery, with NDVI β3 = +0.0190 yr⁻¹, p = 0.000, whilst Saline, Brackish, and Intermediate communities continued to decline. CITS Difference-in-Differences (DiD) confirmed that site-level NDII and kNDVI declines were MRGO-specific. The DiD findings were that NDII = −0.00313 yr⁻¹, p < 0.001; kNDVI = −0.00123 yr⁻¹, p = 0.008. These findings isolated that physiological water stress and the non-linear biomass losses were a result of the MRGO-closure. The Freshwater DiD for NDVI (+0.02071 yr⁻¹, p = 0.000) was the strongest evidence of MRGO-specific recovery. Barataria Freshwater declined, whilst the Shell Beach Freshwater recovered. The results demonstrated that multi-index decadal Landsat monitoring with seasonal decomposition and full inter-sensor harmonization is essential for restoration trajectory assessment in managed coastal wetlands. Full article

Price volatility in fresh food commodities can weaken supply-chain coordination, disturb market expectations, and increase short-term risks to food availability and affordability. This issue is more pronounced for specialty crops with seasonal production, concentrated supply, limited storability, and high sensitivity to climate, trade, energy, and online-attention shocks. This study develops a trajectory-regularized physics-informed multi-source forecasting framework for daily wholesale prices of garlic, scallion, and ginger in China from 2014 to 2024. The framework, denoted as STL–ETO–EMA–PILSTM, integrates Seasonal-Trend decomposition using LOESS (STL), Efficient Multi-scale Attention (EMA), Long Short-Term Memory (LSTM), an economically motivated physics-informed trajectory residual constraint, and Exponential-Trigonometric Optimization (ETO), using production, climate, macroeconomic, trade, crude-oil, and online-attention indicators. In this framework, the physics-informed component is implemented as a trajectory residual constraint inspired by price-adjustment inertia and local continuity, rather than as a conventional PINN based on strict governing physical equations. In one-step-ahead forecasting, the model outperformed conventional machine learning baselines and additional time-series baselines, including naive persistence, Transformer Encoder, and PatchTST, with MAE values of 0.0853, 0.0581, and 0.1409 for garlic, scallion, and ginger, respectively, and R² values above 0.996. Leakage-prevention procedures, walk-forward validation, multi-horizon forecasting, and Diebold–Mariano tests were used to strengthen result credibility. Multi-step forecasting showed clear performance degradation as the horizon increased, supporting the positioning of the framework as a short-term market-monitoring tool rather than a long-horizon structural projection model. Permutation-based feature-importance and interaction analyses revealed crop-specific price drivers. The framework provides an interpretable tool for fresh food price forecasting, market stability monitoring, and short-term operational risk monitoring in fresh food supply chains. Full article

Natural lakes in urbanizing regions face compounding climatic and anthropogenic pressures. Despite their socio-ecological importance, the dual vulnerability of these urban lakes to both long-term areal shrinkage and the shifting frequencies of extreme water events remains a critical research gap, often overlooked in favor of large, remote lake systems. We investigated surface area dynamics, extreme events, and climatic attribution of 7320 natural lakes across China’s five major urban agglomerations (Jing-Jin-Ji, Yangtze River Delta, Greater Bay Area, Chengdu-Chongqing, and Middle Yangtze) from 2001 to 2023. Using a satellite area product, we assessed long-term trends via Seasonal-Trend decomposition by Loess (STL). Regional climate shifts were detected via multi-scale Standardized Precipitation–Evapotranspiration Index (SPEI) breakpoint analysis, and climate attribution was performed by correlating detrended lake areas with SPEI. Results show 59.4% of lakes exhibit significant trends, with shrinkage (50%) vastly outpacing expansion (9.4%), most severely in Jing-Jin-Ji (−0.28%/year). Despite all agglomerations transitioning toward wetter conditions (2008–2013), extreme event responses diverged markedly regionally. Climate-driven lakes (14.5%) displayed stronger shrinkage and greater sensitivity to extremes than lakes with low climate sensitivity, particularly in Jing-Jin-Ji and Chengdu-Chongqing. These findings reveal pronounced spatial heterogeneity in urban lake vulnerability, providing an evidence base for sensitivity-stratified management strategies. Full article

This article presents a multi-indicator approach to forecasting the monthly workload of a military road freight transport system in support of operational planning. The empirical basis of the study consisted of real-world operational data from 2020 to 2025, aggregated into regular monthly time series. Four complementary workload indicators were analysed: the number of transport tasks, the number of vehicles assigned to task execution, the mass of transported cargo, and transport work expressed in tonne-kilometres. The research procedure comprised data preprocessing, indicator construction, seasonality analysis, time-series decomposition, comparison of classical forecasting models, and assessment of forecast uncertainty using prediction intervals. The forecasting models considered included the naive model, the moving-average model, Brown’s and Holt’s exponential smoothing models, ETS, and ARIMA. Model performance was evaluated using a rolling-origin validation procedure with an expanding training window, based on MAE, RMSE, MAPE, MASE, and Bias metrics. The results showed that the recommended model depends on the forecasted workload dimension: Brown’s model performed best for the number of transport tasks, ETS for the number of vehicles and transport work, whereas the 12-month moving-average model was most effective for transported cargo mass. All recommended models achieved MASE values below 1, indicating improved predictive performance compared with the naive benchmark. The study demonstrated that point forecasts supplemented with 80% and 95% prediction intervals can support monthly planning of fleet resources, transport capacity reserves, and future workload levels. Although the empirical analysis concerns a military transport system operating under peacetime conditions, the proposed framework may be adapted to support monthly workload forecasting and operational planning in other freight transport systems. Full article

Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1–2 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability. Full article

Remote sensing change detection (RSCD) aims to produce pixel-accurate change maps from bi-temporal images yet is fundamentally challenged by radiometric pseudo-changes (season, illumination, and atmosphere) that cause structure–environment entanglement in deep features. We propose CSD-Net, a framework centered on content–style decoupling (CSD): a physics-inspired feature decomposition mechanism that encourages separation between intrinsic geometric content and extrinsic environmental style. In the CSD module, learnable pseudo-change tokens estimate a spatially invariant global style proxy through cross-attention and broadcast, and subtraction performs feature-level radiometric-bias compensation, yielding pseudo-change-robust content features for change prediction. CSD-Net (Base) alone achieves state-of-the-art performance across four benchmarks (LEVIR-CD, LEVIR-CD+, CDD, and WHU) with favorable accuracy–efficiency trade-off (14.49M parameters and 15.26G FLOPs). We further explore an optional extension, CSD-Net⁺, that employs an MLLM (Qwen2.5-3B, LoRA-tuned) as a semantic refiner and SAM for instance mask refinement, coupled with uncertainty-aware three-way softmax fusion. This exploratory Stage 2 brings modest but consistent IoU improvements of 0.45–2.20% at the cost of significant computational overhead and is designed for offline, quality-critical scenarios. We provide a comprehensive account of both the effectiveness and the limitations of the proposed approach, including the marginal benefit–cost ratio of foundation model integration. Full article

Energy-efficiency retrofit assessment for old residential buildings commonly relies on energy benchmarks, but such benchmarks cannot reveal household-level disparities in energy use. This study integrates energy-consumption benchmarks with distributional-fairness indicators to support retrofit prioritization. Monitored electricity data from 1024 households in four representative old residential building types in Chongqing were analyzed using the Dagum Gini coefficient decomposition method. The results show clear seasonal and typological differences in energy-use imbalance. The annual Gini coefficients for Types A–D were 0.34, 0.42, 0.45, and 0.40, respectively, while the overall level of imbalance generally followed the order winter > summer > transition seasons > annual average. Median energy use intensity (EUI) did not correspond directly to distributional fairness. Type B had the highest annual median EUI (3.89 kWh/m²) but not the highest Gini coefficient, whereas Type C had the lowest median EUI (3.28 kWh/m²) and the highest Gini coefficient (0.45). These findings show that benchmark-based assessment alone may misidentify retrofit priorities. A dual-benchmark diagnostic framework is therefore proposed to integrate energy-use level and distributional fairness, supporting more precise retrofit prioritization, fairer resource allocation, and sustainable renewal of old residential communities. Full article

Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect operational decision-making. To address this issue, this paper proposes an integrated anomaly detection framework named PEL, which combines Prophet-based seasonal-trend decomposition, ensemble empirical mode decomposition (EEMD), and a multilayer long short-term memory (LSTM) network. Prophet is first employed to decompose the original series into trend, seasonal, holiday, and residual components. Sample entropy analysis and white noise tests are then adopted to evaluate whether the residual component still contains complex structured information requiring secondary decomposition. Next, EEMD is applied to the residual component to extract multi-scale intrinsic mode functions. Finally, all decomposed components are normalized and fed into a multilayer LSTM model for anomaly detection. Experiments on a real-world power load dataset demonstrate that the proposed PEL framework achieves an accuracy of 99.92%, a precision of 97.33%, a recall of 100%, an F1-score of 98.65%, and an AUC of 0.9996, outperforming or matching several baseline and hybrid models. Full article

Background/Objectives: The trajectory of influenza hospitalization burden from pre-COVID-19 pandemic baseline through post-pandemic recovery remains poorly characterized at the national level. This study characterized phase-stratified burden and seasonal structure, quantified racial and ethnic disparities, and assessed whether post-pandemic seasons represent anomalous departures from pre-pandemic expectations. Methods: Sixteen complete seasons of FluSurv-NET surveillance data (2009–2010 through 2024–2025; 509 observation weeks) were analyzed across pre-pandemic, disruption, and recovery phases using OLS regression with effect-size estimation, bootstrapped age-adjusted rate ratios, seasonal-trend decomposition (STL), Prophet time-series forecasting, and Isolation Forest anomaly detection. Results: Mean peak weekly hospitalization rate nearly doubled from pre-pandemic to recovery (5.1 to 11.1 per 100,000), cumulative seasonal burden increased from 46.3 to 87.0 per 100,000, and median peak timing advanced from MMWR week 9 to week 50. STL decomposition revealed a marked shift from weak pre-pandemic seasonality (Fs = 0.14) to substantially stronger annual regularity (Fs = 0.98) across three recovery seasons, with threefold amplitude increase. Non-Hispanic Black persons had rate ratios of 1.72, 2.16, and 1.99 relative to White persons across phases; American Indian and Alaska Native persons showed the highest disruption-phase ratio (2.24, 95% CI 1.90–3.53), based on two contributing seasons. A flat-growth Prophet model detected first exceedance in February 2020, outperforming a linear-growth specification on held-out validation. Isolation Forest identified 2017–2018, 2023–2024, and 2024–2025 as robust anomalies across all contamination thresholds. Conclusions: Post-COVID-19 pandemic influenza recovery is characterized by intensified and restructured seasonality, persistent racial and ethnic disparities, and anomalous burden exceeding pre-pandemic projections, identified independently by time-series forecasting and unsupervised anomaly detection. Full article

With the rapid expansion of agricultural e-commerce in China, inefficient cloud warehouse consolidation and high environmental costs have hindered the sustainability of supply chains. To address the challenges of low vehicle loading rates and high carbon emissions, this study proposes an optimization model for collection path planning that integrates sales forecasting and three-dimensional loading constraints. First, STL decomposition is employed to identify seasonal sales patterns, and a hybrid SARIMA and ARIMA-BPNN model is constructed to achieve precise forecasting of future orders to provide data support for dynamic demand. Second, a single-objective path planning model is formulated to minimize the fixed vehicle costs, fuel consumption, and carbon emissions while maximizing the load utilization rates. To solve this complex problem, a two-stage solution framework, consisting of path planning and three-dimensional loading verification, was designed. This framework integrates an improved genetic–hill-climbing hybrid algorithm with a constructive heuristic to handle real-time spatial constraints and achieve the efficient optimization of distribution paths. Finally, a case study on the HLYX agricultural cloud warehouse in Harbin, China, demonstrated that the proposed approach significantly enhances space utilization and reduces transportation and carbon emission costs. This study provides a sustainable development path for the cost reduction, economic efficiency improvement, and carbon emission reduction of smart agricultural logistics. Full article

International agri-food trade and climate change interact in ways that have significant implications for supply chain resilience and food sovereignty, yet these interactions remain insufficiently understood at the level of specific traded commodities. This paper analyses European fresh orange imports over 2012–2022 using a unit value decomposition applied to FAOSTAT and Eurostat bilateral trade data, alongside a seasonal supply analysis of monthly import flows from the main exporting regions. The analysis documents a pronounced geographic reorientation of global orange production toward developing and emerging economies in North Africa, Southern Africa, and South America, many of which face documented climate-related stressors. The unit value decomposition identifies how exporter-level unit values and import share reallocations contribute to changes in regional import unit value indices. The seasonal supply analysis shows that the European orange supply depends on a tight sequence of regional exporters operating in largely non-overlapping seasonal windows, leaving limited redundancy if disruptions occur in any single supplying region. These findings provide a descriptive, origin-disaggregated account of Europe’s trade-side exposure in fresh orange supply chains. They underscore the need for product-specific monitoring tools and policy approaches that consider seasonal import dependence, supplier concentration, and the climate vulnerability of major origin regions, while recognising that the present analysis does not estimate causal climate effects. Full article