Search Results (1,408)

This study focuses on developing a climate-flood model to investigate and interpret the relationship and impact of climate on runoff/flooding at a sub-regional scale using multiple linear regression (MLR) with 30 years of hydro-climatic data for the Cross River Basin, Nigeria. Data were obtained from Nigerian Meteorological Agency (NIMET) for the following climatic parameters: annual average rainfall, maximum and minimum temperatures, humidity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure. These hydro-meteorological data were analysed and used as parameters input to the climate-flood model. Results from multiple regression analyses were used to develop climate-flood models for all the gauge stations in the basin. The findings suggest that at 95% confidence, the climate-flood model was effective in forecasting the annual runoff at all the stations. The findings also identified the climatic parameters that were responsible for 100% of the runoff variability in Calabar (R² = 1.000), 100% the runoff in Uyo (R² = 1.000), 98.8% of the runoff in Ogoja (R² = 0.988), and 99.9% of the runoff in Eket (R² = 0.999). Based on the model, rainfall depth is the only climate parameter that significantly predicts runoff at 95% confidence intervals in Calabar, while in Ogoja, rainfall depth, temperature, and evaporation significantly predict runoff. In Eket, rainfall depth, relative humidity, solar radiation, and soil temperatures are significant predictors of runoff. The model also reveals that rainfall depth and evaporation are significant predictors of runoff in Uyo. The outcome of the study suggests that climate change has impacted runoff and flooding within the Cross River Basin. Full article

The translocation of cadmium (Cd) in the soil-rice system is complicated; therefore, most of the soil-plant models of Cd have not been extensively studied. Hence, we studied the back-propagation artificial neural network model (BP-ANN) and multiple regression model (MLR) to predict the cadmium (Cd) content in rice grain and soil through testing soil parameters. In this study, 486 pairs of rice grains and corresponding soil samples of 456 vectors were used for training + validation, and 30 vectors were collected from the southwestern karst area of Guangxi Province as a test data set. In this study, the Cd content in rice was successfully predicted by using the factors soil available cadmium (A_Cd), total soil cadmium (T_Cd), soil organic matter (SOM), and pH, which have a more significant impact on rice, as the main prediction variables. Root mean square error (RMSE), Relative Percent Difference (RPD), and correlation coefficient (R²) were used to assess the models. The R², RPD, and RMSE values for R_Cd medium obtained by the MLR model with pH, T_Cd, and A_Cd as entered variables were 0.551, 2.398, and 0.049, respectively. The R² and RMSE values for R_Cd medium obtained by the BP-ANN model with pH, T_Cd, and A_Cd as entered variables were 0.6846, 2.778, and 0.104, respectively. Therefore, it was concluded that BP-ANN was useful in predicting R_Cd and had better performance than MLR. Full article

Background: Homocysteine (Hcy) is a sulfur-containing amino acid crucial for various physiological processes, with elevated levels linked to cardiovascular and neurological adverse conditions. Various factors contribute to high Hcy, and past studies of impact factors relied on traditional statistical methods. Recently, machine learning (ML) techniques have greatly improved and are now widely applied in medical research. This study used four ML methods to identify key factors influencing Hcy in healthy elderly Taiwanese men, comparing their accuracy using multiple linear regression (MLR). The study seeks to improve Hcy prediction accuracy and provide insights into relevant impact factors. Methods: A total of 468 healthy elderly men were studied in terms of 33 parameters using four ML methods: random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and elastic net (EN). MLR served as a benchmark. Model performance was assessed using SMAPE, RAE, RRSE, and RMSE. Results: All ML methods demonstrated lower prediction errors than MLR, indicating higher accuracy. By averaging the importance scores from the four ML models, C-reactive protein (CRP) emerged as the leading impact factor for Hcy, followed by GPT, WBC, LDH, eGFR, and sport volume (SV). Conclusions: Machine learning methods outperformed MLR in predicting Hcy levels in healthy elderly Taiwanese men. CRP was identified as the most crucial factor, followed by GPT/ALT, WBC, LDH, and eGFR. Full article

Background: Insulin resistance (IR) is an underlying pathophysiology for type 2 diabetes (T2D). The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) is the simplest method for evaluating IR. At the same time, volatile organic compounds (VOCs) detected in human respiration can be correlated with specific diseases. To date, machine learning (Mach-L) has yet to be used to examine potential relationships between VOCs and IR. The present study has two aims: (1) to identify the VOCs most relevant to HOMA-IR, and (2) to use Shapley addictive explanation (SHAP) to determine the impacts of the distributions and directions of each feature in Taiwanese women. Methods: A total of 1432 Taiwanese women between the ages of 19 and 84 years were enrolled, and 344 VOCs were measured. Traditional multiple linear regression (MLR) was used as a benchmark for comparison, applying three Mach-L methods. Finally, SHAP was used to evaluate the directions of impacts of the features on HOMA-IR. Results: Six VOCs were identified as important: dimethylfuran, propanamine, aniline, butoxyethanol, and isopropyltoluene, in order from most to least important. SHAP found that dimethylfuran, isopropyltoluene, and dodecane were positively correlated to HOMA-IR, while butoxyethanol, aniline, and propanamine were negatively correlated. Conclusions: Using three different Mach-L methods, six VOCs were selected to be related to IR in Taiwanese women. According to their importance, dimethylfuran, propanamine, aniline, butoxyethanol, and isopropyltoluene could be used to help diagnose HOMA-IR. Furthermore, by using SHAP, dimethylfuran, isopropyltoluene, and dodecane had a positive and the other three had a negative influence. Full article

Concrete in cold areas is often subjected to a freeze–thaw cycle period, and a harsh environment will seriously damage the structure of concrete and shorten its life. The frost resistance of concrete is primarily evaluated by relative dynamic elastic modulus and mass loss rate. To predict the frost resistance of concrete more accurately, based on the four ensemble learning models of random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost), this paper optimises the ensemble learning models by using a dynamic multi-stage optimisation algorithm (DMSOA). These models are trained using 7090 datasets, which use nine features as input variables; relative dynamic elastic modulus (RDEM) and mass loss rate (MLR) as prediction indices; and six indices of the coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and standard deviation ratio (SDR) are selected to evaluate the models. The results show that the DMSOA-CatBoost model exhibits the best prediction performance. The R² of RDEM and MLR are 0.864 and 0.885, respectively, which are 6.40% and 11.15% higher than those of the original CatBoost model. Moreover, the model performs better in error control, with significantly lower MSE, RMSE, and MAE and stronger generalization ability. Additionally, compared with the two mainstream optimisation algorithms (SCA and AOA), DMSOA-CatBoost also has obvious advantages in prediction accuracy and stability. Related work in this paper has a certain significance for improving the durability and quality of concrete, which is conducive to predicting the performance of concrete in cold conditions faster and more accurately to optimise the concrete mix ratio whilst saving on engineering cost. Full article

Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. The acquired LiDAR point clouds were processed to extract structural metrics such as canopy height, crown area, canopy density, and volume. A multistep variable selection procedure was applied to reduce collinearity and select the most informative predictors. Multiple linear regression (MLR) models were developed and validated using field inventory data. Random Forest (RF) models were also tested for E. globulus, enabling a comparative evaluation between parametric and machine learning regression models. The results show that the 25th height percentile, canopy cover density at two meters, and height variance demonstrated an accurate biomass estimation for E. globulus, with coefficients of determination (R²) varying between 0.86 for MLR and 0.90 for RF. Although RF demonstrated a similar predictive performance, MLR presented advantages in terms of interpretability and computational efficiency. For P. pinaster, only MLR was applied due to the limited number of field data, yet R² exceeded 0.80. Although absolute errors were higher for Pinus pinaster due to greater biomass variability, relative performance remained consistent across species. The results demonstrate the feasibility and efficiency of UAV LiDAR point cloud data for stand-level biomass estimation, providing simple and effective models for biomass estimation in these two species. Full article

This paper examines how demographic characteristics, institutional structures, and livelihood strategies shape household resilience to climate variability and drought in West Pokot County, one of Kenya’s most climate-vulnerable arid and semi-arid lands (ASALs). Using a mixed-methods approach, it combines household survey data with three statistical techniques: Multinomial Logistic Regression (MLR) assesses the influence of gender, age, and education on livestock ownership and livelihood choices; Multiple Correspondence Analysis (MCA) reveals patterns in institutional access and adaptive practices; and Stepwise Linear Regression (SLR) quantifies the relationship between resilience strategies and agricultural productivity. Findings show that demographic factors, particularly gender and education, along with access to veterinary services, drought-tolerant inputs, and community-based organizations, significantly shape resilience. However, trade-offs exist: strategies improving livestock productivity may reduce crop yields due to resource and labor competition. This study recommends targeted interventions, including gender-responsive extension services, integration of indigenous and scientific knowledge, improved infrastructure, and participatory governance. These measures are vital for strengthening resilience not only in West Pokot but also in other drought-prone ASAL regions across sub-Saharan Africa. Full article

As China’s primary grain-producing area, the Luxi Plain is rich in groundwater resources, which serves as the main water supply source in this region. Investigating the evolution of hydrochemical characteristics and influencing factors of groundwater in this region is crucial for maintaining the safety of groundwater quality and ensuring the high-quality development of the water supply. This study took Liaocheng City in the hinterland of the Luxi Plain as the study area. To clarify the hydrochemical characteristics evolution trend of groundwater in the area, the hydrochemical characteristics of shallow groundwater in recent years were systematically analyzed. The methods of ion ratio, correlation analysis, Gibbs and Gaillardet endmember diagrams, as well as the application of the absolute principal component scores–multiple linear regression (APCS-MLR) receptor model were used to determine the contribution rates of different ion sources to groundwater and to elucidate the driving factors behind the evolution of groundwater chemistry. Results showed significant spatiotemporal variations in the concentrations of major ions such as Na⁺, SO₄²⁻, and Cl⁻ in groundwater in the study area, and these variations demonstrated an overall increasing trend. Notably, the increases in total hardness (THRD), SO₄⁻, and Cl⁻ concentrations were particularly pronounced, while the variations in Na⁺, Mg²⁺, Ca²⁺ and other ions were relatively gradual. APCS-MLR receptor model analysis revealed that the ions such as Na⁺, Ca²⁺, Mg²⁺, SO₄²⁻, Cl⁻, HCO₃⁻ and NO₃⁻ all have a significant influence on the hydrochemical composition of groundwater due to the high absolute principal component scores of them. The hydrochemical characteristics of groundwater in the study area were controlled by multiple processes, including evaporites, silicates and carbonates weathering, evaporation-concentration, cation alternating adsorption and human activities. Among the natural driving factors, rock weathering had a greater influence on the evolution of groundwater hydrochemical characteristics. Moreover, mining activities were the most important anthropogenic factor, followed by agricultural activities and living activities. Full article

Background: Systemic inflammatory markers have emerged as accessible and reproducible tools for oncologic risk stratification, yet their prognostic value in rectal cancer remains incompletely defined, particularly in acute surgical settings. This study aimed to assess six inflammation-based indices—NLR, PLR, MLR, SII, SIRI, and AISI—in relation to tumor stage, recurrence, and outcomes among patients undergoing emergency versus elective resection for rectal cancer. Methods: We retrospectively evaluated 174 patients treated between 2018 and 2024. Pre-treatment blood counts were used to calculate inflammatory indices. Clinical and pathological parameters were correlated with biomarker levels using univariate and multivariate analyses. Results: Pre-treatment inflammation markers were significantly elevated in patients requiring emergency surgery (e.g., NLR: 3.34 vs. 2.4, p = 0.001; PLR: 204.1 vs. 137.8, p < 0.001; SII: 1008 vs. 693, p = 0.007), reflecting advanced tumor biology and immune activation. Notably, these patients also had higher rates of stage IV disease (p = 0.029) and permanent stoma (p = 0.002). Post-treatment, recurrence was paradoxically associated with significantly lower levels of SII (p = 0.021), AISI (p = 0.036), and PLR (p = 0.003), suggesting a potential role for immune exhaustion rather than hyperinflammation in early relapse. Conclusions: Inflammatory indices provide valuable insights into both tumor local invasion and host immune status in rectal cancer. Their integration into perioperative assessment could improve prognostication, particularly in emergency presentations. Post-treatment suppression of these markers may identify patients at high risk for recurrence despite initial curative intent. Full article

Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil moisture retrieval by integrating Sentinel-1 and Sentinel-2 remote sensing data. Dual-polarization parameters (VV + VH and VV × VH) were constructed and tested. Pearson correlation analysis showed that these polarization combinations carried the most useful information for soil moisture estimation. We then applied Shapley Additive exPlanations (SHAP) for feature selection, and a Stacking model was used to perform soil moisture inversion based on the selected features. SHAP values derived from the coupled support vector regression (SVR) and random forest regression (RFR) models were used to select an additional six key features for model construction. Building on this framework, a comparative analysis was conducted to evaluate the predictive performance of multivariate linear regression (MLR), RFR, SVR, and a Stacking model that integrates these three models. The results demonstrate that the Stacking model outperformed other approaches in soil moisture retrieval, achieving a higher R² of 0.70 compared to 0.52, 0.61, and 0.62 for MLR, RFR, and SVR, respectively. This process concluded with the use of the Stacking model to generate a county-level farmland soil moisture distribution map, which provides an objective and practical approach to guide agricultural management and the optimized allocation of water resources in arid regions. Full article

This study introduces a predictive model for estimating indoor room temperatures in vernacular building using external environmental factors such as air temperature, humidity, sunshine duration, and wind speed. The dataset was sourced from the Meteorology, Climatology, and Geophysics Agency and supplemented with direct measurements collected from four rooms within a vernacular building in Aceh Province, Indonesia. A Generative Adversarial Network (GAN)-based imputation technique was implemented to address missing data during preprocessing. The prediction model adopts a hybrid framework that integrates Multiple Linear Regression (MLR) and Artificial Neural Networks (ANNs), with both models optimized using Support Vector Regression (SVR) to better capture the nonlinear dynamics between inputs and outputs. The evaluation results show that the ANN-SVR model achieved the lowest average $\overline{MAE}$ and $\overline{RMSE}$ values, at 0.164 and 0.218, respectively, and the highest average $\overline{R}$ and $\overline{R^2}$ values, at 0.785 and 0.618. Evaluation results indicate that the ANN-SVR model consistently achieved the lowest error rates and the highest correlation coefficients across all four rooms, identifying it as the most effective model for forecasting indoor thermal conditions. These results validate the combined use of ANN-SVR for prediction and GAN for preprocessing as a powerful strategy to enhance data quality and model performance. The findings offer a scientific basis for architectural planning to improve thermal comfort in vernacular buildings such as the Rumoh Aceh. Full article

Microcystins (MCs) are the most toxic and abundant cyanotoxins found in natural waters during harmful cyanobacterial blooms. These toxins pose a significant threat to plant, animal, and human health due to their toxicity. Degradation of MCs by MC-degrading bacteria is a promising method for controlling these toxins, demonstrating safety, high efficiency, and cost-effectiveness. In this study, we isolated potential MC-degrading bacteria (strains TA13, TA14, and TA19) from Lake Kasumigaura in Japan and found that they possess a high capacity for MC degradation. Based on 16S rRNA gene sequencing, all three isolated strains were identified as belonging to the Klebsiella species. These bacteria effectively degraded MC-RR, MC-YR, and MC-LR under various temperature and pH conditions within 10 h, with the highest degrading activity and degradation rate observed at 40 °C. Furthermore, the isolated strains efficiently degraded MCs not only under neutral pH conditions, but also in alkaline environments. Additionally, we detected the MC-degrading gene (mlrA) in all three isolated strains, marking the first report of the mlrA gene in Klebsiella species. The copy number of the mlrA gene in the strains increased after exposure to MCs. These findings indicate that strains TA13, TA14, and TA19 significantly contribute of MC bioremediation in Lake Kasumigaura during cyanobacterial blooms. Full article

Background/Objectives: Anorexia nervosa (AN) is a chronic eating disorder with the highest mortality rate among psychiatric conditions. Malnutrition and starvation lead to long-term impairments in metabolic processes, hormonal regulation, and immune function, offering potential diagnostic and prognostic value. This study aimed to identify immune–metabolic–hormonal markers associated with treatment response and nutritional rehabilitation. Methods: Fifty hospitalized female patients with AN were included. Anthropometric measurements and venous blood samples were collected at admission and discharge, following partial nutritional recovery. Blood analyses included complete blood count, serum levels of total cholesterol, LDL and HDL, triglycerides, glucose, NT-pro-BNP, TSH, free thyroxine (fT4), sodium, chloride, potassium, calcium, iron, and vitamin D. Composite immune-inflammatory indices calculated were neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR); neutrophil-to-high-density lipoprotein (NHR), monocyte-to-high-density lipoprotein (MHR), platelet-to-high-density lipoprotein (PHR) and lymphocyte-to-high-density lipoprotein (LHR) ratios; systemic immune-inflammation (SII), and systemic inflammation response (SIRI) indexes. Results: Responders (R) and non-responders (NR) differed significantly at baseline in levels of sodium, chloride, fT4, monocyte count, MCV, NLR, MLR, SII, and SIRI (all: R < NR; p < 0.05). Predictive ability for treatment response was confirmed by AUC values (95%CI): sodium = 0.791 (0.622–0.960), chloride = 0.820 (0.690–0.950), fT4 = 0.781 (0.591–0.972), monocytes = 0.785 (0.643–0.927), MCV = 0.721 (0.549–0.892), NLR = 0.745 (0.578–0.913), MLR = 0.785 (0.643–0.927), SII = 0.736 (0.562–0.911), SIRI = 0.803 (0.671–0.935). The lower levels of inflammation and chloride are particularly predictive of better nutritional recovery, accounting for 26% of the variability in treatment response. Conclusions: The study demonstrated important insights into the hematological, metabolic, hormonal, and immune-inflammatory mechanisms associated with nutritional recovery in AN. Full article

Introduction: Work life quality (WLQ) in nursing is a critical factor that influences both staff well-being and the quality of care provided to patients. Spiritual intelligence (SI) and servant leadership (SL) have been identified as potential positive predictors of WLQ, as they facilitate resilience, job satisfaction, and stress management in highly demanding hospital environments. However, the specific relationship between these constructs in the Peruvian nursing context has not yet been thoroughly explored. Objective: We aimed to examine the impact of spiritual intelligence and servant leadership on the work life quality of Peruvian nurses, assessing their predictive role through a structural equation modeling approach. Methods: A cross-sectional and explanatory study was conducted with a sample of 134 Peruvian nurses (M = 36.29 years, SD = 7.3). Validated Spanish-language instruments were used to measure SI, SL, and WLQ. Structural equation modeling (SEM) with a robust maximum likelihood estimator (MLR) was employed to evaluate the relationships between the variables. Results: Spiritual intelligence showed a positive correlation with WLQ (r = 0.40, p < 0.01) and with servant leadership (r = 0.44, p < 0.01). Likewise, servant leadership had a significant relationship with WLQ (r = 0.53, p < 0.01). The structural model demonstrated a good fit (χ² = 1314.240, df = 970, CFI = 0.96, TLI = 0.96, RMSEA = 0.05, SRMR = 0.08). The hypothesis that SI positively predicts WLQ was confirmed (β = 0.41, p < 0.001), as was the significant effect of SL on WLQ (β = 0.26, p < 0.001). Conclusions: The results indicate that both spiritual intelligence and servant leadership are key predictors of work life quality in Peruvian nurses. SI contributes to developing a transcendent perspective on work and greater resilience, while SL fosters a positive and motivating organizational environment. It is recommended to implement training programs and leadership strategies focused on these constructs to enhance work life quality in the healthcare sector. Full article

Machine learning (ML) models are widely used to analyze the spatiotemporal impacts of agricultural practices on environmental sustainability, including the contribution to global greenhouse gas (GHG) emissions. Management practices, such as organic amendment applications, are critical pillars of Climate-smart agriculture (CSA) strategies that mitigate GHG emissions while maintaining adequate crop yields. This study investigated the critical threshold of soil moisture level associated with soil CO₂ emissions from organically amended plots using the classification and regression tree (CART) algorithm. Also, the study predicted the short-term soil CO₂ emissions from organically amended systems using soil moisture and weather variables (i.e., air temperature, relative humidity, and solar radiation) using multilinear regression (MLR) and generalized additive models (GAMs). The different organic amendments considered in this study are biochar (2268 and 4536 kg ha⁻¹) and chicken and dairy manure (0, 224, and 448 kg N/ha) under a sweet corn crop in the greater Houston area, Texas. The results of the CART analysis indicated a direct link between soil moisture level and the magnitude of CO₂ flux emission from the amended plots. A threshold of 0.103 m³m⁻³ was calculated for treatment amended by biochar level I (2268 kg ha⁻¹) and chicken manure at the N recommended rate (CXBX), indicating that if the soil moisture is less than the 0.103 m³m⁻³ threshold, then the median soil CO₂ emission is 142 kg ha⁻¹ d⁻¹. Furthermore, applying biochar at a rate of 4536 kg ha⁻¹ reduced the soil CO₂ emissions by 14.5% compared to the control plots. Additionally, the results demonstrate that GAMs outperformed MLR, exhibiting the highest performance under the combined effect of chicken and biochar. We conclude that quantifying soil moisture thresholds will provide valuable information for the sustainable mitigation of soil CO₂ emissions. Full article