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Abstract Deep eutectic electrolytes (DEEs) show promise for future electrochemical systems due to their adjustable buffer capacities. This study utilizes machine learning algorithms to analyse the carbon dioxide reduction reaction (CO2RR) in DEEs with a buffer capacity of approximately 10.21 mol/pH. The objective is to minimize undesired hydrogen evolution reactions (HER) and render CO2RR dominant in a membrane cell. The CO2RR process was found to be non-adiabatic, as the time of nuclear motion for CO3 2− in K2CO3 product, through CO2 ●− trapping, is 0.368 femtoseconds shorter than the 1.856 × 10−3s charge transfer relaxation time. Microkinetic analysis reveals that the rate of CO2RR to CO2 ●− is 2.14 × 103 mol/cm2/s2 with a rate constant of 2.1 × 1010 cm/s. Our findings demonstrate that ensemble and k-Nearest Neighbours algorithms learn the CO2RR dataset, achieving a prediction accuracy of over 99%. The models were verified visually and quantitatively by overlaying predicted and experimental dataset. Diagnostic and SHAP analyses highlighted the gradient boost ensemble algorithm, predicting asymptotic current densities of -4.114 mA/cm2 or -13.340 mA/cm2, with high turnover frequencies (TOF) of 3.79 × 1010 h-1 or 12.30 × 1010 h-1 for CO2 ●− or K2CO3 generation on silver electrodes, respectively. These results consider both accuracy and robustness against overfitting, providing an opportunity to optimize future non-aqueous electrolytes for convenient TOF measurements at industrially relevant current densities.

Abstract This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.

Abstract Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable strength prediction reduces costs and time in design and prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Machine Learning (ML) models to enhance the prediction of CS, analyzing 1030 experimental CS data ranging from 2.33 to 82.60 MPa from previous research databases. The ML models included both non-ensemble and ensemble types. The non-ensemble models were regression-based, evolutionary, neural network, and fuzzy-inference-system. Meanwhile, the ensemble models consisted of adaptive boosting, random forest, and gradient boosting. There were eight input parameters: cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with the CS as the output. Comprehensive performance evaluations include visual and quantitative methods and k-fold cross-validation to assess the study’s reliability and accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted to understand better how each input variable affects CS. The findings showed that the Categorical-Gradient-Boosting (CatBoost) model was the most accurate prediction during the testing stage. It had the highest determination-coefficient (R2) of 0.966 and the lowest Root-Mean-Square-Error (RMSE) of 3.06 MPa. The SHAP analysis showed that the age of the concrete was the most critical factor in the predictive accuracy. Finally, a Graphical User Interface (GUI) was offered for designers to predict concrete CS quickly and economically instead of costly computational or experimental tests.

Abstract Non-alcoholic steatohepatitis (NASH) is the clinically aggressive variant of non-alcoholic fatty liver disease. Hippo pathway dysregulation can contribute to NASH development and progression. The use of probiotics is effective in NASH management. Our aim is to investigate the efficacy of kefir Milk in NASH management via modulation of hepatic mRNA-miRNA based panel linked to NAFLD/NASH Hippo signaling and gut microbita regulated genes which was identified using bioinformatics tools. Firstly, we analyzed mRNAs (SOX11, SMAD4 and AMOTL2), and their epigenetic regulator (miR-6807) followed by validation of target effector proteins (TGFB1, IL6 and HepPar1). Molecular, biochemical, and histopathological, analyses were used to evaluate the effects of kefir on high sucrose high fat (HSHF) diet -induced NASH in rats. We found that administration of Kefir proved to prevent steatosis and development of the inflammatory component of NASH. Moreover, Kefir improved liver function and lipid panel. At the molecular level, kefir down-regulated the expression of miR 6807-5p with subsequent increase in the expression of SOX 11, AMOTL2 associated with downregulated SMAD4, resulting in reduction in the expression of the inflammatory and fibrotic markers, IL6 and TGF-β1 in the treated and prophylactic groups compared to the untreated rats. In conclusion, Kefir suppressed NASH progression and improved both fibrosis and hepatic inflammation. The produced effect was correlated with modulation of SOX11, SMAD4 and AMOTL2 mRNAs) – (miR-6807-5p) – (TGFB, IL6 and, HepPar1) expression.

Abstract This study intends to give qualitative results toward the understanding of different slip mechanisms impact on the natural heat transfer performance of nanofluids. The slip mechanisms considered in this study are Brownian diffusion, thermophoretic diffusion, and sedimentation. This study compares three different Eulerian nanofluid models; Single-phase, two-phase, and a third model that consists of incorporating the three slip mechanisms in a two-phase drift-flux. These slip mechanisms are found to have different impacts depending on the nanoparticle concentration, where this effect ranges from negligible to dominant. It has been reported experimentally in the literature that, with high nanoparticle volume fraction the heat transfer deteriorates. Admittingly, classical nanofluid models are known to underpredict this impairment. To address this discrepancy, this study focuses on the effect of thermophoretic diffusion and sedimentation outcome as these two mechanisms turn out to be influencing players in the resulting heat transfer rate using the two-phase model. In particular, the necessity to account for the sedimentation contribution toward qualitative modeling of the heat transfer is highlighted. To this end, correlations relating the thermophoretic and sedimentation coefficients to the nanofluid concentration and Rayleigh number are proposed in this study. Numerical experiments are presented to show the effectiveness of the proposed two-phase model in approaching the experimental data, for the full range of Rayleigh number in the laminar flow regime and for nanoparticles concentration of (0% to 3%), with great satisfaction.

Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable strength prediction reduces costs and time in design and prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Machine Learning (ML) models to enhance the prediction of CS, analyzing 1030 experimental CS data ranging from 2.33 to 82.60 MPa from previous research databases. The ML models included both non-ensemble and ensemble types. The non-ensemble models were regression-based, evolutionary, neural network, and fuzzy-inference-system. Meanwhile, the ensemble models consisted of adaptive boosting, random forest, and gradient boosting. There were eight input parameters: cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with the CS as the output. Comprehensive performance evaluations include visual and quantitative methods and k-fold cross-validation to assess the study's reliability and accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted to understand better how each input variable affects CS. The findings showed that the Categorical-Gradient-Boosting (CatBoost) model was the most accurate prediction during the testing stage. It had the highest determination-coefficient (R2) of 0.966 and the lowest Root-Mean-Square-Error (RMSE) of 3.06 MPa. The SHAP analysis showed that the age of the concrete was the most critical factor in the predictive accuracy. Finally, a Graphical User Interface (GUI) was offered for designers to predict concrete CS quickly and economically instead of costly computational or experimental tests. [ABSTRACT FROM AUTHOR]

Abstract Worldwide, stroke is a leading cause of long-term disability in adults. Alteplase is the only approved treatment for acute ischemic stroke (AIS) and results in an improvement in a third of treated patients. We evaluated the post-stroke unfavourable outcome predictors in alteplase-treated patients from Egypt and Saudi Arabia. We assessed the effect of different risk factors on AIS outcomes after alteplase in Egypt and Saudi Arabia. Our study included 592 AIS alteplase-treated patients. The relationship between risk factors, clinical presentation, and imaging features was evaluated to predict factors associated with poor outcomes. An mRS score of three or more was used to define poor outcomes. Poor outcome was seen in 136 patients (23%), and Patients with unfavourable effects had significantly higher admission hyperglycaemia, a higher percentage of diabetes mellitus, cardioembolic stroke, and a lower percentage of small vessel stroke. Patients with higher baseline NIHSS score (OR 1.39; 95% CI 1.12–1.71; P = 0.003), admission hyperglycaemia (OR 13.12; 95% CI 3.37–51.1; P