An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO ₄ ), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R ² ), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R ² , RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

The human epidermal growth factor receptor 2 (HER-2) protein is a member of the epidermal growth factor receptor (EGFR or ErbB) family and is a transmembrane tyrosine kinase receptor.

A new platform has been developed for environmentally friendly C–C cross-coupling reactionsviafiltration of reactants through a portable Pd^(II)-salen complex-embedded filter paper.

Osteoporosis (OP) has been observed to have a deleterious effect on postmenopausal women’s life quality by increasing the risk of fragility fractures. The current research was adopted to verify the role of serum adiponectin, a cytokine released by adipose tissue, as a marker for OP across different body mass index groups, for a better understanding of fatty tissue role in OP. A case-control study recruited 210 eligible postmenopausal women and subgrouped into three groups based on their DEXA scan results: osteoporotic group, osteopenia group, and healthy controls; each includes 70 patients. Three datasets were collected: anthropometric, age, menopause duration, weight, height, body mass index (BMI), waist circumference, and fat percentage. Radiological examination estimated the bone mineral density (BMD) for the femoral neck and lumbar spines with their respective T-score. From blood, we measured alkaline phosphatase and calcium by a spectrophotometer and serum adiponectin, phosphate, CTX, and PICP by ELIZA. Total BMD, T-score, serum phosphate, and PICP were significantly higher among healthy controls. Serum adiponectin, CTX, and ALP scored higher levels among OP cases. A strong inverse relationship was proved between serum adiponectin and T-score in osteoporotic and osteopenia groups (−0.427, −0.301). A strong negative relationship was found between serum adiponectin and total BMD in healthy controls (−0.204). All correlations were statistically significant, $P$ value <0.001. Serum adiponectin can be a valuable marker for reduced bone mineral density among the general populace, irrespective of the body mass index. Further research is warranted to explore therapeutic and preventive applications for this adipocytokine.