Search Results (37,012)

Morphological traits, as core components of functional traits, are fundamental in determining environmental adaptability. However, under climate warming, the adaptive morphological changes and associated ecological risks of locust populations migrating to higher altitudes remain poorly understood. Here, we investigated Calliptamus italicus, the dominant locust species in the desert steppes of the Ili River Basin, to explore the response patterns of its morphological functional traits along an altitudinal gradient and their relationships with environmental factors. Morphological measurements revealed that forewing area, width, and length, as well as hindwing width, exhibited highly significant positive correlations with altitude (p < 0.01); in contrast, body length, head width, head height, pronotum length, pronotum width, hind femur length, and hind tibia length displayed significant negative correlations with altitude (p < 0.05). All morphological indicators presented highly significant sexual dimorphism (p < 0.001). Ratio analysis showed that the pronotum width-to-head width ratio (M/C), pronotum height-to-head width ratio (H/C), and forewing length-to-hind tibia length ratio (E/F) were significantly positively correlated with the altitudinal gradient (p < 0.05), with all ratios exhibiting significant sexual differences (p < 0.05). Random Forest analysis showed that PC1 (75.5% of variation) reflected traits for feeding, jumping, and reproduction, whereas PC2 (5.6%) represented flight-related traits, with significant sexual dimorphism. This study demonstrates that trait variation in C. italicus along an altitudinal gradient is closely linked to environmental factors. Our findings provide critical data for predicting habitat adaptation responses in locust populations, thereby enhancing the precision and efficacy of locust plague management and contributing to the conservation and restoration of desert steppe ecosystems. Full article

This study investigates shared molecular mechanisms between pyoderma gangrenosum (PG) and inflammatory bowel disease (IBD) and systematically evaluates the therapeutic potential of JAK inhibitors targeting this pathway. Despite the clear clinical comorbidity, the core inflammatory pathways driving cross-tissue associations between the two diseases remain unclear. Furthermore, systematic mechanistic evidence is lacking regarding whether JAK inhibitors act by regulating shared pathological pathways in patients with comorbidities. To address this, this study integrated PG skin and IBD intestinal transcriptome data, single-cell transcriptomic data, and genome-wide association study (GWAS) meta-data from public databases. It employed a multi-level computational biology approach combining Mendelian randomization, weighted gene co-expression network analysis, protein interaction network construction, molecular docking simulations, and system dynamics modeling. The results revealed that genetic analysis confirmed IBD as a causal risk factor for PG, precisely identifying six shared genetic loci. Transcriptomic analysis identified a cross-tissue conserved inflammatory module centered on the JAK-STAT pathway, with JAK2 and STAT3 identified as network hubs. Molecular docking predicted high affinity of baricitinib for both JAK1 and JAK2, while system dynamics modeling demonstrated that its intervention effectively suppresses signaling in the shared inflammatory network. This study reveals the molecular basis of the “gut–skin axis” comorbidity between PG and IBD from a multi-omics integration perspective. It provides predictive computational evidence for the use of JAK inhibitors in targeted comorbidity therapy. Baricitinib is identified as a particularly promising candidate. These findings advance the transition from empirical drug use to mechanism-guided precision treatment strategies. Although this study provides multiscale computational simulation evidence, the lack of direct experimental validation of these predicted results necessitates further confirmation through in vitro and in vivo experiments. Full article

Evaporation is a key component of air–sea coupling processes and understanding the uncertainty in its estimation is essential for climate research and prediction. Based on four widely used datasets (OAFlux, NCEP2, MERRA2 and ERA5), this study systematically analyzes the seasonal evolution of inter-dataset uncertainty in evaporation variation over the tropical Indian Ocean using an evaporation decomposition method. Our main contribution is to show that evaporation variation uncertainty is not seasonally uniform but organized into distinct seasonal regimes with different dominant controlling factors and sensitivity structures. The results reveal significant seasonal dependence of evaporation variation uncertainty: the uncertainty is relatively small in boreal spring and autumn but larger in boreal summer and winter. The evaporation variation is primarily controlled by the relative humidity term (RH^*) in boreal summer and by the wind speed term (U^*) in other seasons. More importantly, the sources of uncertainty differ fundamentally between seasons: the large uncertainty of RH^* in boreal summer mainly originates from the high and variable sensitivity of evaporation to relative humidity, whereas the large uncertainty of U^* in boreal winter primarily stems from substantial inter-dataset discrepancies in wind speed data itself. These findings reveal that evaporation variation uncertainty arises from both input data discrepancies and the nonlinear sensitivity of evaporation processes, with their relative contributions varying seasonally. This study provides a physically based explanation for evaporation uncertainty and offers a useful basis for evaporation dataset selection and climate model evaluation. Full article

Mountain flood susceptibility in complex mountainous basins is strongly influenced by terrain–climate interactions; however, the linkage between spatial susceptibility patterns and hydrological processes remains poorly understood. This study proposes a process-oriented framework that explicitly links flood susceptibility patterns with hydrological processes, moving beyond conventional approaches that rely on independent model integration. The Baishuijiang River Basin, located in Wenxian County, southern Gansu Province, China, is selected as a representative mountainous watershed for this analysis. The specific conclusions are as follows: (1) Flood susceptibility was mapped using a Particle Swarm Optimization (PSO)-enhanced Maximum Entropy (MaxEnt) model based on multi-source environmental variables, including climatic, terrain, soil, land cover, and vegetation factors. The model achieved high predictive accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.912), identifying precipitation of the driest month (bio14), elevation, and land use as dominant controlling factors. Medium-to-high-susceptibility areas account for approximately 22% of the basin and are mainly distributed along river valleys and flow convergence areas. These patterns are strongly associated with reduced infiltration capacity under dry antecedent conditions and enhanced flow concentration in steep terrain, and they exhibit clear nonlinear responses and threshold effects. (2) Hydrological simulations using Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) show good agreement with observed runoff (Nash–Sutcliffe Efficiency (NSE) = 0.74−0.85). Sensitivity analysis indicates that runoff dynamics are primarily controlled by the Curve Number (CN), recession constant, and ratio to peak, corresponding to infiltration capacity, recession processes, and peak discharge amplification. The spatial consistency between high-susceptibility areas and areas of strong runoff response demonstrates that susceptibility patterns can be physically explained through hydrological processes, providing a process-based interpretation rather than a purely statistical prediction. (3) Future projections indicate that medium–high-susceptibility areas remain generally stable but show a gradual expansion (+5.2% ± 0.8%) and increasing concentration along river corridors under climate change scenarios. This reflects intensified precipitation variability and enhanced runoff concentration processes, suggesting a climate-driven amplification of flood risk in hydrologically connected areas. Overall, this study goes beyond conventional susceptibility assessment by establishing a physically interpretable framework that provides a consistent linkage between environmental controls, susceptibility patterns, and hydrological responses. The proposed approach is transferable to similar mountainous basins with strong terrain–climate interactions, although uncertainties related to data limitations and single-basin application remain and require further investigation. Full article

Under long-term cyclic loading, the cumulative plastic deformation of unsaturated sandy subgrade is a key control factor for the pavement’s service performance. However, its evolution mechanism and quantitative characterization still lack a universal model. In this study, based on the GDS dynamic triaxial system, a series of cyclic tests were conducted under different conditions: matric suction from 0 to 90 kPa, net confining pressure from 30 to 120 kPa, dynamic stress amplitude from 60 to 240 kPa, and compaction degrees of 87–96%, reaching a total of 10,000 cycles. The results reveal that the permanent deformation of unsaturated sandy subgrade material evolves through three stages: fast, slow, and stable. The deformation is exponentially negatively correlated with matric suction, net confining pressure, and compaction degree, and exponentially positively correlated with dynamic stress amplitude. A coupling prediction model was developed by embedding matric suction and compaction degree factors into the Karg model. This model incorporates net confining pressure, dynamic stress amplitude, matric suction, and compaction degree. By using a normalized master curve method, the permanent deformation curves under different working conditions were compressed into a unique dimensionless function. The parameters have clear physical significance and allow for a unified description across stress, suction, state, and soil types. Experimental data, along with data from the literature, were used to validate the model, showing prediction errors of less than 10% and R² > 0.95. The model provides a simple, high-precision, and transferable theoretical tool for long-service-life subgrade deformation control. Full article

Using daily data from Investing.com South Africa, this study investigates the forecasting performance of four Rand currency rate markets (EUR/ZAR, CHF/ZAR, BRL/ZAR, and CNY/ZAR) from 13 February 2018 until 24 February 2025. The predictive fitness of three competing models, Support Vector Regression (SVR), Principal Component Regression (PCR), and eXtreme Gradient Boosting (XGBoost), is explored between 80%/20% and 95%/5% training-testing splits. Forecasting accuracy is evaluated based on evaluation errors, i.e., Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The Diebold–Mariano test is employed to check for statistical significance. Empirical results show that the linear SVR model outperforms PCR across all markets, while XGBoost achieves competitive predictive accuracy on average; the trade-offs between SVR and XGBoost are often very small. The data indicate that linear kernel methods provide a robust prediction pipeline, especially when macroeconomic factors (gold, oil, platinum prices, and the USD/ZAR exchange rate) and calendar-based factors are taken into account, and offer a strong framework for predicting daily exchange rate fluctuations. The results of this research provide practitioners (traders, risk managers, and policymakers) with insights into the relative efficiency of the kernel vs. ensemble learning approaches for forecasting the value of emerging-market currencies in the presence of structural volatility. Full article

Background/Objectives: Diabetes is a chronic metabolic disorder affecting global health, where early prediction can significantly reduce disease severity. Methods: This research proposes an interpretable multi-metric fuzzy distance-based ensemble (MMFDE) that integrates multi-variant gradient-boosting classifiers (GBM, LightGBM, XGBoost, and AdaBoost) through a novel fuzzy fusion mechanism designed for intrinsic interpretability. Unlike conventional ensembles relying on opaque averaging or voting, MMFDE transforms base classifier predictions into a high-dimensional fuzzy space quantified via a weighted hybrid distance incorporating Euclidean, Manhattan, Chebyshev, and cosine metrics against ideal diabetic and non-diabetic reference vectors. These distances are translated into membership degrees with the help of exponentially decaying functions, which give clinicians calibrated confidence scores for every prediction. Comprehensive SHAP analysis identifies important clinical risk factors (glucose, BMI, and diabetes pedigree function), which show concordance with the medical literature, thereby giving greater clinical trust. Results: Experimental evaluations on two publicly available datasets, Hospital Frankfurt Germany Diabetes Dataset (HFGDD) and Pima Indians Diabetes Dataset (PIDD), show that MMFDE outperforms all base models with a significant accuracy of 94.83% and Area Under the Curve (AUC) of 97.66% on HFGDD and three different levels of interpretability: geometric transparency via distance-based decisions, confidence-calibrated uncertainty estimates, and feature-level explanations via SHAP. The confidence thresholds enabled in the framework support risk stratification clinical workflows with high-confidence predictions for automated screening and cases with moderate/low confidence flagged out for review by the clinician. Conclusions: By demonstrating that high performance and interpretability need not be mutually exclusive, MMFDE advances trustworthy AI for clinical decision support, addressing the critical need for transparent and clinically actionable diabetes prediction systems. Full article

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article

Existing studies of street-related emotional perception mainly rely on static scene evaluations, which cannot capture the cumulative effects of environmental exposure during continuous walking. To address this limitation, this study proposes a method for predicting pedestrian physiological responses in sequential audiovisual street environments. Four real-world walking routes were selected, with outbound and return directions treated as independent paths, yielding eight paths and 32 valid samples. EEG, ECG, sound pressure level, first-person video, and GPS data were synchronously collected to construct a 1 s multimodal time-series dataset. Pearson correlation, Kendall correlation, and mutual information analyses were used to examine linear, monotonic, and nonlinear relationships between environmental variables and physiological indicators, and the resulting weights were incorporated into a Long Short-Term Memory (LSTM) model for multi-step prediction. Visual elements and noise exposure were the main factors influencing physiological responses. Among the models, the mutual-information-weighted LSTM performed best, achieving an R² of 0.77 for heart rate variability (RMSSD), whereas prediction of the EEG ratio (β/α and θ/β) remained limited. An additional independent street sample outside the training set was then used to generate a dual-dimensional EEG-ECG physiological response map, demonstrating the model’s potential for identifying emotional risk segments and supporting street-level micro-renewal. Full article

Background/Objectives: Candidates with cardiometabolic risk are considered for living kidney donation more frequently because of the global organ shortage. The 2017 Kidney Disease: Improving Global Outcomes (KDIGO) guidelines introduced individualized risk assessment based on composite donor profiles rather than categorical exclusion, but the long-term implications of accepting donors with potential risk factors require careful evaluation. This review synthesizes current evidence on outcomes of living kidney donors with obesity, prediabetes, hypertension, and smoking. Methods: A literature search was conducted in PubMed/MEDLINE for studies published between 1 January 2000 and 28 February 2026, including cohort studies, registry analyses, meta-analyses, and clinical guidelines evaluating living kidney donors with obesity, smoking, prediabetes, or hypertension. Priority was given to large cohorts with long-term follow-up. Over 70 publications were included in the final synthesis. Findings were synthesized narratively by risk factors and outcomes. Results: Obesity was associated with an 86% increased end-stage kidney disease (ESKD) risk and 32% increased 20-year mortality. Central adiposity measures outperformed body mass index (BMI) for predicting estimated glomerular filtration rate (eGFR) decline. Post-donation weight gain increased the risk for developing hypertension and diabetes. Smoking conferred a 7.5-fold chronic kidney disease (CKD) risk, with impaired compensatory renal adaptation after donation. Prediabetic donors showed comparable outcomes to normoglycemic donors, with 57.8% reverting to normoglycemia at 10 years. Pre-donation hypertension increased 15-year ESKD risk 3-fold, but absolute risk remained low. At 15 years post-donation, over 50% of the donors developed hypertension. Glucagon-like peptide-1 (GLP-1) receptor agonists reduce diabetes progression by 73–94% in at-risk populations, but prospective studies in donors are lacking. Conclusions: Each risk factor carries quantifiable risks for individualized stratification. These risk factors usually coexist and interact. Refinement of risk prediction models, strategies for metabolic optimization and prospective evaluation of emerging pharmacologic therapies are key priorities. Full article

Super oxidized water is a disinfectant generated by electrolysis whose effectiveness depends mainly on oxidation–reduction potential and pH. In this study, a 2² factorial Design of Experiments was applied to evaluate the influence of applied potential (8.2–12.2 V) and NaCl concentration (0.05–0.25 wt.%) on the redox properties of SOW, aiming to produce solutions with targeted disinfection profiles. The obtained models showed excellent predictive capacity (R² > 0.99), identifying NaCl concentration as the most influential factor affecting both oxidation–reduction potential and pH. The system enabled the controlled generation of SOW with ORP values ranging from approximately 950 to 1100 mV and pH between ~3.8 and 5.0, with experimental errors below 1.5%. Stability tests demonstrated that oxidation–reduction potential and pH remained within ±25 mV and ±0.15 units, respectively, over 24 weeks of storage. Microbiological evaluation revealed effective antimicrobial activity against Escherichia coli, Staphylococcus aureus, methicillin-resistant Staphylococcus aureus, and Candida albicans, with inhibition halos of up to ~5 mm depending on ORP and microorganism. The results demonstrate that Design of Experiments enables precise adjustment of SOW redox properties, allowing optimization of antimicrobial performance for specific applications. This positions super oxidized water as a flexible, stable, and scalable disinfection technology for industrial and clinical use. Full article

Background/Objectives: Reinforced radiculoplasty (RRP) is effective for symptomatic sacral cysts, yet postoperative recovery varies significantly. This study aimed to systematically identify preoperative predictors of delayed short-term symptomatic recovery, with a specific focus on the prognostic impact of concomitant bladder–bowel dysfunction. Methods: A retrospective analysis was conducted on a cohort of 148 consecutive patients who underwent RRP. Comprehensive clinical, high-resolution imaging, and detailed surgical data were collected. The primary outcome was defined as symptom resolution within 9 months postoperatively. Independent prognostic factors were identified using univariate and subsequent multivariate logistic regression analysis. Results: Short-term symptom resolution was achieved in 86 patients (58.1%). Multivariate analysis identified three independent risk factors for prolonged recovery: a history of prior cyst surgery (OR 8.389, 95% CI 2.328–30.230), multi-segment cyst involvement (OR 2.682, 95% CI 1.066–6.744), and preoperative bladder–bowel dysfunction (OR 7.859, 95% CI 3.478–17.759). The predictive model demonstrated good discriminative ability (sensitivity 83.7%, specificity 61.3%). Conclusions: Prior surgical history, multi-segment cyst involvement, and preoperative bladder–bowel dysfunction are independent predictors of delayed short-term recovery after RRP for sacral cysts. Full article

Accurate prediction of effluent water quality is essential for the intelligent and sustainable operation of wastewater treatment plants (WWTPs). However, this task remains challenging due to the strong nonlinearity, long-term temporal dependencies, and severe fluctuations inherent in influent characteristics. In this study, a novel data-driven framework termed the Multi-Scale Temporal Representation-Enhanced Informer (MTRE-Informer), is proposed to predict key effluent quality indicators, including total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). To ensure data quality and computational efficiency, a generative recurrent learning framework is first employed for anomaly detection and correction, followed by variance inflation factor (VIF)-based feature selection to mitigate multicollinearity. Furthermore, feature contribution analysis is conducted to improve model interpretability. Subsequently, the core MTRE-Informer architecture utilizes hierarchical multi-scale temporal representation learning to simultaneously capture local patterns and long-term dependencies within the complex dynamics of the wastewater treatment process. Experimental results demonstrate that the MTRE-Informer achieves robust and stable predictive performance across diverse operational datasets. For TN prediction, the proposed framework attains a coefficient of determination () of 0.9637 and a mean absolute percentage error (MAPE) of 3.39%. Compared with baseline approaches, the improvement ranges from 3.8% to 14.2%, validating its superior capability. To further enhance model robustness, an anomaly detection and correction strategy based on a generative recurrent learning framework is employed. In addition, feature contribution analysis and VIF-based feature selection are conducted to improve interpretability, mitigate multicollinearity, and enhance computational efficiency. Overall, this framework provides a reliable and practical solution for real-time effluent quality prediction, facilitating the intelligent management of WWTPs. Full article

Soil electrical resistivity (SER) is widely used as an indirect indicator of soil physical, chemical, and hydrological properties and plays an important role in applications such as grounding system design, geotechnical site characterization, agricultural soil monitoring, and environmental contamination assessment. However, SER is strongly influenced by environmental variables including soil moisture content, temperature, salinity, and soil texture, which makes accurate prediction challenging under heterogeneous field conditions. A systematic review was conducted following the PRISMA 2020 protocol using the Scopus database to identify peer-reviewed studies published between 2018 and 2026 related to predictive models of soil electrical resistivity based on environmental parameters. After applying defined inclusion and exclusion criteria, a set of relevant studies was selected for qualitative and comparative analysis. The reviewed studies consistently identify soil moisture content as the most frequently reported influential factor affecting SER, followed by temperature, salinity, and soil texture. This observation reflects the predominant focus of the analyzed literature within the selected time frame rather than a definitive representation of all controlling physical processes. Similarly, the reviewed literature suggests that empirical and statistical models remain valuable due to their simplicity and interpretability, whereas machine learning approaches such as artificial neural networks, support vector regression, and ensemble methods are often reported to achieve higher predictive accuracy in complex soil environments. The predictive SER modeling represents a rapidly evolving research field, and future work should focus on hybrid physics-informed machine learning models, the development of standardized datasets, and the integration of predictive algorithms with emerging sensing technologies and IoT-based monitoring systems. Full article

Three–dimensional excitation–emission matrix (3D–EEM) fluorescence spectroscopy is widely applied for the rapid characterization of dissolved organic matter (DOM) in aquatic environments. However, conventional decomposition based on parallel factor analysis (PARAFAC) requires multiple spectra and manual intervention, limiting its applicability for rapid analysis and future online implementation. The purpose of this study is to develop an efficient data–driven method capable of decomposing fluorescence components from a single 3D–EEM spectrum. We propose a conditional single–spectrum decomposition network (CSSD–Net) based on the encoder–decoder model. The encoder extracts fluorescence features from the input spectrum, while the decoder combines these features with conditional information on component count to generate up to five component maps. The component count can be automatically predicted by CSSD–Net or manually specified to support flexible application scenarios. CSSD–Net was trained using publicly available component spectra from the OpenFluor database without PARAFAC preprocessing. Validation on natural water samples demonstrates that the results obtained from CSSD–Net using a single sample are highly consistent with those from PARAFAC using multiple parallel samples, with a mean Tucker’s congruence coefficient (TCC) of 0.9615. These results show that CSSD–Net provides a fast and practical solution for decomposing single 3D–EEM spectra under constrained aquatic scenarios, and it has potential for future near–real–time and in situ applications. Full article