Search Results (2,308)

This study investigated the long-term relationship between land-use change and land-cover-based ecological quality patterns in Ordos City, a typical coal resource-based city in northern China. To explicitly capture land-use transitions driven by coal exploitation, mining areas were classified as an independent land-cover type. An improved U-Net semantic segmentation model integrating multispectral information and land surface temperature was subsequently employed to generate multi-temporal land-cover maps. In the internal semantic segmentation validation, the proposed model achieved a mean Intersection over Union (mIoU) of 69.51%, a mean accuracy (mAcc) of 81.65%, and a pixel-level overall accuracy (aAcc) of 83.16%. An independent point-based accuracy assessment of the final land-cover map yielded an overall accuracy (OA) of 66.00% and a Kappa coefficient of 0.6033, indicating acceptable classification reliability in complex mining areas. Based on the classification results, three indicators, namely the land-use transition matrix, ecological environmental quality index (EQI), and ecological contribution index, were adopted to analyze spatiotemporal land-use dynamics and associated land-cover-based ecological quality patterns in Ordos City over the past 25 years. The results indicate that: (1) Marked land-use changes occurred in the land-use pattern of the study area during 2000–2025, with grassland and unused land consistently remaining the dominant land-use types. The area classified as Mining increased from 105.83 km² to 1184.17 km² in 2020, exhibiting distinct characteristics of phased expansion and subsequent adjustment. Built-up land continued to expand, whereas the water area decreased by 47.55%. (2) The land-cover-based EQI exhibited a pattern of decline, recovery, and stabilization, decreasing from 0.529 in 2000 to 0.489 in 2015 before recovering to 0.522 in 2025. This pattern was associated with changes in land-cover composition over the study period, including mining expansion and restoration-related transitions. The findings provide a scientific basis for ecological restoration planning and high-quality transformation in Ordos and other coal resource-based cities with similar arid and semi-arid environmental conditions. Full article

To reduce the communication and centralized-computation burden associated with frequent intraday upper-level updates under renewable-energy uncertainty, this paper proposes an edge-autonomous multi-objective energy management strategy with an offline–online architecture. In the day-ahead stage, Gaussian Copula modeling and trajectory screening generate temporally correlated photovoltaic (PV) scenarios. The conventional Strength Pareto Evolutionary Algorithm 2 (SPEA2) first constructs a base library C0. A dual-space coordinated selection mechanism then retains every C0 strategy exactly and adds complementary trajectories according to their objective responses and differences in energy storage system power trajectories. In the intraday stage, the edge controller combines current local measurements with preloaded PV, load, and price profiles, propagates every stored candidate over a receding horizon, and applies the first action of the strict minimum-composite-cost candidate. The case-study results show that, without relying on intraday upper-level communication, the proposed method achieves a composite-objective value close to that of the perfect-information centralized reference. Full article

Most predictive approaches in educational data mining rely on complex models whose opacity limits practical adoption by classroom teachers, creating a gap between model sophistication and classroom usability. This gap is particularly acute at the class-group level, where institutional gradebook data are routinely aggregated for teacher-level planning but rarely modelled with an explicit account of when model complexity is actually justified. This paper addresses that gap: its novelty is to provide a structural explanation, grounded in group-level academic dynamics, for why linear models are highly competitive, rather than merely adequate, for this type of data, and to test this account empirically. An eight-year longitudinal dataset (2013/2014–2020/2021) from a Spanish secondary school—1070 class-group records across 32 subjects—was used to compare linear regression and Random Forest for final grade prediction, a Random Forest classifier against an XGBoost classifier for academic risk detection, and SHAP (SHapley Additive exPlanations)-based explainability, validated through Leave-One-Course-Out (LOCO) cross-validation. Within this dataset, linear regression consistently matches or outperforms Random Forest in both scenarios (R² = 0.857 with two assessments; R² = 0.740 with one), explained by stable cohort dynamics—baseline grades, teaching continuity, group composition—that produce a linear temporal structure (Spearman ρ > 0.81) leaving little predictive return for ensemble complexity in this setting. For the passing class, the Random Forest classifier achieves F1 = 0.972 with high inter-cohort stability (LOCO F1 ∈ [0.944, 0.984]); for the minority at-risk class, it outperforms XGBoost (F1 = 0.69 vs. 0.57), a gap consistent with the benefit of explicit class-imbalance handling, though fully disentangling this from a possible ensemble-family effect is left for future work. The 2019/2020 cohort is statistically anomalous (Mann–Whitney U, p < 0.001), reflecting an exogenous shift in the grade-generating process under emergency evaluation rather than evidence against the linearity account under normal conditions. Simple, transparent models operating on routinely collected gradebook data deliver actionable early-warning signals within the digital competence of most practising teachers; group-level prediction additionally protects student identity by ensuring no individual is labelled at-risk, combining predictive utility with ethical design. Full article

This study examines the dynamic interactions among gross domestic product (GDP), external public debt, and domestic public debt in Iraq and discusses their implications for fiscal sustainability. The observed annual data cover the period 2005–2024, while the values for 2025 are based on IMF projections. Because continuous official monthly observations were unavailable, the annual series were temporally disaggregated using cubic-spline interpolation to construct an analytical monthly series. These interpolated values do not represent additional independent observations; therefore, the empirical findings are interpreted as exploratory. A recursive Structural Vector Autoregression (SVAR) model was estimated using Cholesky identification with the ordering GDP, external debt, and domestic debt. The variables were transformed into second differences in their natural logarithms, and a three-lag specification was employed. Impulse-response functions and forecast-error variance decomposition were used to examine the transmission and relative importance of the identified innovations. At the 24-month forecast horizon, GDP shocks explained 93.3% of GDP variation, while external debt was predominantly explained by its own shocks (86.3%). Domestic-debt variation was explained by its own shocks (47.3%), GDP shocks (44.1%), and external-debt shocks (8.6%). These results suggest that domestic debt is more closely associated with changes in domestic economic activity, whereas external debt follows a comparatively persistent path. The findings emphasize the importance of debt composition, non-oil revenue diversification, expenditure management, and coordination between domestic and external borrowing. Because oil revenues and government expenditure are not included as separate endogenous variables, the model does not directly identify oil-revenue or government-spending shocks. Future research should employ genuinely observed quarterly or monthly data and incorporate these fiscal variables explicitly. Full article

Background/Objectives: Body composition may influence functional capacity and long-term outcomes after thoracic organ transplantation. However, direct comparisons of body composition patterns between heart transplant (HTx) and lung transplant (LTx) recipients remain limited. This study compared body composition parameters and their relationship with time since transplantation in HTx and LTx recipients using bioelectrical impedance analysis (BIA). Methods: This cross-sectional study included 79 clinically stable transplant recipients (38 HTx and 41 LTx) with a median time of 9 months after transplantation (range, 1 month–9 years). Body composition was assessed using the SECA mBCA 515 analyzer. Absolute fat mass (AFM), relative fat mass (RFM), fat-free mass (FFM), skeletal muscle mass (SMM), phase angle (PA), and extracellular water-to-total body water ratio (ECW/TBW) were measured. Associations between body composition parameters and time since transplantation were modelled using adjusted generalized additive models with separate smooth functions for HTx and LTx recipients. Results: Positive cross-sectional associations between AFM and time since transplantation were observed during the 3–9-month post-transplant interval in both groups (HTx: 0.72 kg/month, p = 0.042; LTx: 0.83 kg/month, p = 0.021). In contrast, significant positive associations between SMM and time since transplantation were observed exclusively among LTx recipients during the 1–3-month (0.59 kg/month, p = 0.039) and 3–9-month (0.49 kg/month, p = 0.003) intervals, whereas no significant associations were observed after HTx. In LTx recipients, ECW/TBW was negatively associated and PA positively associated with time since transplantation during the 3–9-month interval. Conclusions: HTx and LTx recipients exhibited different cross-sectional patterns of body composition according to time since transplantation. HTx recipients were characterized by greater adiposity without a significant association between SMM and time since transplantation, whereas LTx recipients showed positive associations between SMM and time since transplantation during the early post-transplant intervals. Full article

Water diversion projects can alleviate the uneven spatiotemporal distribution of water resources, but they may also impact functions of aquatic ecosystems in the waters along the route. Despite their importance, the temporal and spatial changes in multi-domain microbial community structure and function along the route remain poorly understood. Metagenomics was employed to investigate the community structure and function of bacteria, archaea, and fungi in the aquatic environments along the Yangtze-to-Huaihe water diversion project in winter and summer seasons. The results showed that seasonal variations may drive a trade-off in the species diversity of bacterial and fungal communities. Seasonal variations altered microbial communities (especially for the bacteria), and exerted a greater influence on community structure than spatial factors. Microbial community composition was more sensitive to seasonal fluctuations than functional genes. The species spatial turnover played a dominant role in shaping microbial communities (especially for winter) in both seasons. Archaea, bacteria, fungi and KEGG functional genes all exhibited a positive correlation with some environmental factors in summer but not in winter. PLS-SEM indicated that water quality and microbial composition directly significantly impacted functional genes. This study offers a theoretical basis for maintaining the stability of water ecological microorganisms in water transfer projects. Full article

Purpose: Host metabolic status may affect outcomes in lung cancer, but longitudinal changes in body composition and systemic metabolism remain incompletely understood. We examined whether paired ¹⁸F-FDG PET/CT-derived host features were associated with progression-free survival (PFS) during systemic therapy. Methods: Fifty-three patients with confirmed lung cancer who underwent baseline and post-treatment ¹⁸F-FDG PET/CT were retrospectively included. Tumor metabolic burden, body composition, organ metabolism, and brain metabolism were quantified. Delta values were defined as post-treatment minus baseline measurements. Associations with PFS were assessed using Kaplan–Meier analysis, adjusted Cox regression and exploratory metabolic network analysis. Results: During follow-up, 31 patients experienced disease progression, and median PFS was 7 months. SM volume (p = 0.001) decreased, and MedF volume (p < 0.001) increased at follow-up after FDR adjustment, whereas the numerical increases in SAT and TF volumes did not remain statistically significant after correction for multiple comparisons. Higher delta torso fat (TF) volume was associated with a lower risk of progression after adjustment (HR = 0.64, 95% CI: 0.43–0.97, p = 0.033). In the temporally aligned analysis of 29 patients whose follow-up PET/CT preceded progression, delta TF volume was not associated with subsequent PFS (HR, 1.84; 95% CI, 0.91–3.68; p = 0.087). Total network perturbation strength decreased after treatment (p = 0.002) but was not associated with PFS. Conclusions: Paired ¹⁸F-FDG PET/CT characterized longitudinal changes in body composition during systemic therapy. Delta TF volume showed an adjusted association with PFS in the exploratory full-cohort model, but its prognostic relevance requires further validation. Full article

Floating plastic debris in urban canals is a management-relevant precursor to downstream microplastic pollution. This structured narrative review synthesizes conventional field surveys, camera-, UAV-, and satellite-based image analysis and AI-assisted image-based monitoring. Emphasis is placed on Southeast Asian engineered canals, where monsoon-driven flow, tides, gates, turbidity, glare, occlusion, and organic debris challenge continuous observation. Conventional surveys provide verifiable composition data but limited temporal coverage. Camera systems increase observation frequency, while deep learning can automate detection and tracking; however, reported performance depends strongly on the dataset, site, target size, and validation design. Published studies show substantial losses under cross-site transfer and condition-specific gains from preprocessing rather than a universal accuracy threshold. The synthesis therefore develops a decision-oriented framework linking camera calibration, conditional preprocessing, site-separated validation, uncertainty reporting, and hydrological data to operational triggers for cleanup or interception. Current evidence supports monitoring and pilot decision support, while broader autonomous operation requires further field validation. Priorities include transparent evidence reporting, shared Southeast Asian datasets, standardized metrics and environmental descriptors, cross-site testing, and life-cycle evaluation of deployment cost and maintenance. Full article

Wildfire monitoring using synthetic aperture radar (SAR) provides critical capabilities under challenging atmospheric conditions where optical sensors are limited by smoke and cloud cover. We evaluated Sentinel-1 C-band SAR interferometric coherence for Burned-area detection of the 2017 “Las Máquinas” megafire (Maule, Chile), comparing Ascending (Asc) and Descending (Dsc) orbital geometries processed with the AMSTer InSAR software. Multi-temporal RGB Coherent Change Detection composites were constructed using two interferometric pairs per orbit: the Normalised Differential Activity Index (NDAI, R channel), pre-fire coherence (G channel), and co-event coherence (B channel), clearly delineating the fire scar through red and orange signatures reflecting fire-induced vegetation loss and soil exposure. Seven machine-learning classifiers (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and XGBoost) were trained on the three-band coherence feature space. For the Ascending orbit, XGBoost achieved the highest performance (OA = 0.9328; F1 = 0.9195) and mapped 143,950 ha (76.2%) as Burned. For the Descending orbit, XGBoost also performed best (OA = 0.9221; F1 = 0.9055) and mapped 144,475 ha (76.5%) as Burned. In this case study, the Ascending geometry performed marginally better than the Descending one; however, the leading classifiers were statistically indistinguishable, indicating that the Burned and Unburned classes are close to linearly separable in the coherence feature space. These results confirm the effectiveness of coherence-based SAR analysis for large-scale wildfire mapping under adverse atmospheric conditions. Full article

Aims: Although the Mediterranean diet (MD) is widely recognized as a sustainability-aligned dietary model, its relationship with sustainable eating behaviors and its cross-sectional associations with adiposity, cooking and food skills, and macrovascular complications in type 2 diabetes mellitus (T2DM) remain insufficiently characterized. Methods: This cross-sectional study included 120 adults with T2DM (median age, 60 years) consecutively recruited from an endocrinology outpatient clinic. Mediterranean diet adherence (MDA), Sustainable and Healthy Eating Behaviors (SHEB), and Cooking Skills and Food Skills (CSFS) were assessed using validated scales. Dietary intake was evaluated using a single 24-h dietary recall, and anthropometric and body composition measures were obtained using standardized procedures. Adjusted group differences were examined using ANCOVA. To account for multiplicity across prespecified families of exploratory outcomes, nominal p-values were corrected using the Benjamini–Hochberg false discovery rate (FDR) procedure. Results: Higher MDA was associated with a higher total SHEB score (primary outcome; p < 0.001) and with higher scores for quality labels, seasonal foods and food waste avoidance, local food, and reduced meat consumption, after FDR correction (all q < 0.05). Across SHEB tertiles, waist-to-hip ratio differed significantly after FDR correction (p = 0.001, q = 0.010), whereas the nominal difference in neck circumference did not remain significant (p = 0.046, q = 0.230). In analyses according to macrovascular complication status, lower adjusted protein intake remained significant after FDR correction (p = 0.002, q = 0.032). Nominal differences in food skills, total CSFS, MDA, total and saturated fat intake, monounsaturated fat intake, and dietary total antioxidant capacity did not remain significant after multiplicity correction (all q > 0.05). Conclusions: Greater adherence to the Mediterranean diet was cross-sectionally associated with more sustainable and healthier eating behaviors, whereas higher SHEB scores were associated with a lower waist-to-hip ratio. Most exploratory differences according to macrovascular complication status did not remain significant after correction for multiple testing, underscoring the need for cautious interpretation. Prospective studies are needed to establish temporal relationships, and intervention studies are required to determine whether modifying adherence to the Mediterranean diet, sustainable eating behaviors, or cooking and food skills affect subsequent cardiometabolic and vascular outcomes in T2DM. Full article

Winter wheat is a cornerstone of China’s grain production, contributing substantially to national food security and overall cereal output. This study modeled the nonlinear associations between multitemporal remote sensing variables and winter wheat yield. To produce high-spatiotemporal-resolution inputs, we used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to integrate Sentinel-2 normalized difference vegetation index (NDVI) data with MODIS NDVI data, generating NDVI composites at 8-day intervals with a 10-m spatial resolution. The NDVI, actual evapotranspiration (ET), land surface temperature (LST), precipitation (PRE), and soil moisture (SM) were selected as predictors for yield estimation because they are closely associated with winter wheat growth and yield formation during primary growth stages. By integrating the local temporal feature-learning capacity of a one-dimensional convolutional neural network (1-D CNN) with the strength of a bidirectional long short-term memory (BiLSTM) model in capturing temporal dependencies within time series, a BiLSTM–CNN model was constructed for wheat yield estimation and prediction. The BiLSTM–CNN model showed higher estimation accuracy than individual BiLSTM and 1-D CNN models, with an R² of 0.69 and root mean square error (RMSE) of 478.68 kg/hm². The use of all the parameters produced the best estimation performance among all the parameter combinations. Approximately two months before harvest, the model still provided satisfactory yield prediction accuracy. This study provides an important theoretical basis for high-accuracy regional winter wheat yield estimation and pre-harvest forecasting. Full article

Background/Objectives: Sleeve gastrectomy induces substantial changes in body composition, energy metabolism, and systemic inflammation, but their temporal patterns during early postoperative follow-up remain incompletely characterized. This pilot study primarily characterized longitudinal changes in body composition, resting energy metabolism, and inflammatory biomarkers during the first nine months following sleeve gastrectomy. A secondary exploratory objective examined associations between the dietary omega-6/omega-3 ratio and these outcomes. Methods: Thirty-seven adults (67.6% female; mean age 35.1 ± 11.3 years) undergoing sleeve gastrectomy were prospectively assessed at baseline and 3, 6, and 9 months postoperatively. Dietary intake was assessed using three-day food records. Body composition, resting metabolic rate (RMR), respiratory quotient, and inflammatory biomarkers (CRP, IL-6, and IL-10) were evaluated longitudinally. Linear mixed models adjusted for age, sex, and comorbidity were used to assess temporal changes and dietary associations. Results: Distinct postoperative trajectories were observed across outcomes. BMI declined progressively from 43.76 ± 6.44 to 28.49 ± 3.77 kg/m² and total body fat from 52.32 ± 5.76% to 39.62 ± 5.71% (both p < 0.001 for time). In contrast, RMR showed a marked early decline by 3 months, followed by relative stabilization (p < 0.001). CRP progressively decreased from 12.84 ± 12.60 to 2.96 ± 2.95 mg/L (p = 0.001), whereas IL-6 and IL-10 showed no significant temporal changes. Notably, no consistent associations were observed between the dietary PUFA measures and the evaluated outcomes. Conclusions: Postoperative adaptation following sleeve gastrectomy differed across physiological domains, with progressive reductions in adiposity and CRP, an early decline followed by relative stabilization of RMR, and no detectable longitudinal changes in IL-6 or IL-10. Dietary PUFA measures showed no significant associations with postoperative outcomes. Full article

Medicine and Food Homology (MFH) substances are often limited by bitter off-flavors that may limit consumer acceptance. This study employed a multimethod approach integrating electronic nose (E-nose), sensory evaluation, electronic tongue (E-tongue), gas chromatography-ion mobility spectrometry (GC-IMS), and electroencephalography (EEG) to investigate bitterness perception in five representative MFH extracts (Chenpi, Lotus leaf, Sichuan pepper, Gardenia, and American ginseng). Sensory and E-tongue analyses revealed discrepancies between perceived and chemically measured bitterness. GC-IMS identified distinct volatile fingerprints, highlighting differences in aroma composition among samples. EEG results showed that several conventionally bitter extracts were primarily associated with low-frequency scalp-EEG activity (δ–α bands), whereas Sichuan pepper showed an apparent delayed broadband increase extending into the β–γ range. Descriptive scalp EEG patterns varied across MFH stimulation conditions, including differences at frontal and temporal electrodes. Overall, this study provides an exploratory multimethod characterization of the sensory, instrumental, and scalp-EEG responses elicited by different MFH extracts. Full article

Alzheimer’s disease (AD) is the most prevalent progressive neurodegenerative disorder in older adults, marked by insidious cognitive decline and gradual loss of functional independence. Growing evidence suggests that handgrip strength (HGS) assessment may provide a simple, noninvasive approach for early screening of AD-related functional changes. This review summarizes current evidence on the association between HGS and AD, while distinguishing evidence obtained directly in AD/MCI populations from evidence derived from general aging, cognitive decline, dementia, or non-AD disease cohorts. Longitudinal and cohort studies have reported associations between reduced grip strength and a higher risk of MCI and AD; however, HGS is influenced by age, sex, body composition, frailty, physical activity, nutritional status, comorbidities, and measurement procedures, and therefore should not be interpreted as an AD-specific diagnostic biomarker. Beyond maximal grip force, dynamic features such as force variability, temporal instability, fatigue-related change, contraction smoothness, and bilateral asymmetry may provide additional information on motor control. The review further discusses measurement standardization, disease specificity, residual confounding, reverse causality, and the methodological requirements of AI-assisted analysis. Recent advances in machine learning, deep learning, and multimodal analysis are considered as tools for integrating multidimensional functional information within an early-screening framework rather than as established diagnostic solutions. In community and home settings, HGS-based assessment may serve as a first-stage screening approach to identify individuals who warrant further clinical evaluation; definitive assessment should remain based on appropriate cognitive/behavioral and neuropsychological examinations and, when clinically indicated, neuroimaging or established AD biomarkers. Future studies should prioritize standardized protocols, prospective longitudinal designs, direct comparison with conventional clinical variables, external validation, calibration, and clinically meaningful screening outcomes. Full article

Background/Objectives: Antimicrobial resistance (AMR) and virulence represent co-evolving dimensions of microbial pathogenic potential whose ecological organization in building-scale wastewater systems remains poorly understood. Methods: Using shotgun metagenomic sequencing, we characterized the temporal dynamics and ecological associations of antimicrobial resistance genes (ARGs) and virulence factors (VFs) in 12 wastewater grab samples (2 per semester) collected from a university residence hall designated for COVID-19 quarantine between 2021 and 2023. Results: The wastewater microbiome was anchored by a stable core of gut-associated anaerobic bacteria, with community composition exhibiting significant Spring-versus-Fall structuring and a year × semester interaction that explained 60% of the community variation. A marked shift toward opportunistic taxa, particularly Acinetobacter, during Fall 2023 represented the most pronounced temporal perturbation. Total ARG abundance remained stable across semesters, while resistome composition shifted significantly, indicating that temporal dynamics were driven by compositional turnover rather than changes in overall resistance burden. VF functional categories were broadly conserved across sampling periods, consistent with their structural embedding within the persistent fecal core microbiome. Correlation and network analyses revealed modular ecological coupling between resistance and virulence functional categories, with metal/co-resistance and fosfomycin classes showing the strongest associations with virulence functions. At the community level, a Benjamini–Hochberg–corrected co-occurrence network resolved into taxa-anchored resistance modules and separate virulence-function clusters, with Acinetobacter and fluoroquinolone resistance as the principal connectors. Conclusions: These findings indicate that building-scale wastewater metagenomics can capture ecologically structured functional gene dynamics, highlighting its potential as a surveillance tool for monitoring AMR and virulence in built environments. Full article