Computational AI to predict and optimize the relationship between dye removal efficiency and Gibbs free energy in the adsorption process utilizing TiO2/chitosan-polyacrylamide composite.

Building a model that can accurately anticipate and optimize the dynamics of dye removal and Gibbs free energy within the framework of an adsorption process is the main goal of this research. Furthermore, it has been determined that a correlation exists between the efficacy of dye removal and the behavior of Gibbs free energy throughout the process of adsorption. The study utilized a composite material consisting of chitosan-polyacrylamide/TiO2 as an adsorbent to remove anionic dye from a mainly aqueous solution. The parameters have been analyzed using response surface methodology (RSM), artificial neural networks (ANN), and machine learning (ML) techniques in this particular context. The obtained F-value of 814.62 for the RSM model, which assesses dye removal efficiency, suggests that the model under examination is statistically significant. Furthermore, based on the RSM data, the proposed model demonstrates a significant level of accuracy in predicting the performance of the TiO2/chitosan-polyacrylamide composite as an adsorbent during the dye removal adsorption process. The ANN model achieved a high level of accuracy, as evidenced by its R2 value of 0.999455. Through the utilization of neural networks and machine learning, the intended objective of forecasting dye removal efficiency and Gibbs free energy behavior in the adsorption process was effectively accomplished.

Vitamin D status as a predictor for liver transplant outcomes.

It is well known that vitamin D plays a pivotal role in immune system modulation; however, its role in liver transplantation (LT) has not yet been well elucidated. This study aimed to assess the association between vitamin D status and LT outcomes. This retrospective cohort study was conducted on 335 registered cirrhotic patients with end-stage liver disease (ESLD) who underwent LT during 2019-2021 and had measurement of serum vitamin D before LT. The association of vitamin D levels before LT with the odds of acute cellular rejection (ACR) and risk mortality was assessed by applying logistic and cox regression, respectively. The mean MELD-Na and serum level of vitamin D were 20.39 ± 9.36 and 21.52 ± 15.28 ng/ml, respectively. In the final adjusted model, there was a significant association between vitamin D deficiency in the pre-transplant period and odds of ACR (odds ratio [OR] 2.69; 95% confidence interval [CI] 1.50-4.68). Although in the crude model, vitamin D deficiency in the pre-transplant period was significantly associated with an increased risk of mortality after two years of follow-up (Hazard ratio (HR) = 2.64, 95% CI 1.42-4.33), after adjustment for potential confounders, the association of vitamin D status and mortality became non-significant (HR = 1.46, 95% CI 0.71-3.00). The present study provides evidence that pre-transplant serum vitamin D levels may be a predictor for ACR in patients with cirrhosis undergoing LT.

Curve fitting model of Polycarbonate Al2O3-nanoparticle membranes for removing emerging contaminants from wastewater: Effect of temperature and nanoparticles.

The purpose of this paper is to demonstrate the use of the phase separation procedure in order to synthesize ultrafiltration polycarbonate containing aluminum oxide (Al2O3) nanoparticles (NPs) to remove emerging contaminants from wastewater at varying temperatures and nanoparticle contents. In the membrane structure, Al2O3-NPs are loaded at rates of 0≤φ≤1% volume. Fourier transform infrared (FTIR), atomic force microscopy (AFM), and scanning electron microscopy (SEM) were used to characterize the fabricated membrane containing Al2O3-NPs. Nevertheless, volume fractions ranged from 0 to 1% during the experiment, which was conducted between 15 and 55 °C. An analysis of the ultrafiltration results was conducted by using a curve-fitting model to determine the interaction between these parameters and the effect of all independent factors on the emerging containment removal. Shear stress and shear rate for this nanofluid are nonlinear at different temperatures and volume fractions. Viscosity decreases with increasing temperature at a specific volume fraction. In order to remove emerging contaminants, a decrease in viscosity at a relative level fluctuates, resulting in more porosity in the membrane. NPs become more viscous with an increasing volume fraction at any given temperature on the membrane. For example, a maximum relative viscosity increases of 34.97% is observed for a 1% volume fraction at 55 °C. A novel model is then used to measure the viscosity of nanofluid. This indicates that the results and experimental data are in very close agreement, as the maximum deviation is 2.6%.

Machine learning modeling of polycarbonate ultrafiltration membranes at different temperatures, Al2O3 nanoparticle volumes, and water ratios.

The efficacy of novel polycarbonate ultrafiltration, aluminum oxide nanoparticle (Al2O3-NPs) volume fraction, temperature, and water/ethylene glycol (EG) ratio were evaluated to determine the thermophysical properties of the membrane. 5%-10% of Al2O3-NPs have been added to the PC. A machine learning approach was used to compare the volume fraction of Al2O3-NPs, the temperature, and the water-to-ethylene glycol (EG) ratio. To determine the impact of Al2O3-NPs loading on the Response Surface Method (RSM), DOE, ANOVA, ANN, MLP, and NSGA-II, the number of aluminum oxide nanoparticles (Al2O3-NPs), temperature, and water/ethylene glycol (EG) on membranes in PC ultrafiltration are evaluated. Based on the Relative Thermal Conductivity Model (RSM), the regression coefficient of Al2O3 in water and EG was 0.9244 and 0.9170 with adjusted regression coefficients. A higher concentration of EG enhances the thermal conductivity of the membrane when the effective parameters are considered. The effect of temperature on the relative viscosity of the membrane led to the conclusion that Al2O3 water/EG can cool at high temperatures while providing no viscosity change. When Al2O3 is dissolved in water and EG, more EG is necessary to optimize the mode of reactivity. Using the MLP model, the calculated R-value is 0.9468, the MSE is 0.001752989 (mean square error), and the MAE is 0.01768558 (mean absolute error). RSM predicted the average thermal conductivity behavior of nanofluid better. The ANN model, however, has proven to be more effective than the RSM in simulating the relative viscosity of nanofluids. The NSGA-II optimized results showed that the minimum relative viscosity and maximum coefficient of thermal conductivity occurred at the lowest water ratio and maximum temperature.

Factors Associated With Length of Hospital Stay Following Liver Transplant Surgery.

OBJECTIVES: Length of stay is considered an important surrogate for transplant survival rate and resource utilization. Therefore, in the present study, our aim was to determine factors affecting length of hospital stay. MATERIALS AND METHODS: We retrospectively analyzed records of patients who underwent liver transplant at the Tehran University of Medical Sciences Liver Transplantation Center from March 2014 to March 2016. RESULTS: For our final analyses, there were 161 adult recipients, including 106 males (65.8%) and 55 females (34.1%). Univariate analyses showed that body mass index, Modelfor End-Stage Liver Disease score, duration of surgery, number of administered packed red blood cells and fibrinogen during surgery, reoperation, retransplant, bacterial infection, pleural effusion, ascites, renal failure that required dialysis, and wound infection were risk factors for length of hospital stay. After multivariate linear regression analysis, only body mass index (ß = 0.016; P = .028), Model for End-Stage Liver Disease score (ß = 0.017; P = .002), surgical duration (ß = 0.002; P = .001), reoperation (ß = 0.016; P < .001), presence of pleural effusion (ß = 0.212; P = .042), and management of bacterial infection (ß = 0.21; P = .03) and psychiatric problems after liver transplant (ß = 0.213; P = .025) were independent risk factors for length of hospital stay. CONCLUSIONS: The present study showed that multiple preoperative, intraoperative, and postoperative variables could have an impact on length of hospitalization. Therefore, methods for assessing these factors could improve patient outcomes and resource savings in liver transplant centers.

Optimization of the removal Pb (II) and its Gibbs free energy by thiosemicarbazide modified chitosan using RSM and ANN modeling.

In this research, the removal of Pb (II) by thiosemicarbazide modified chitosan (TSFCS) using RSM and ANN modeling was studied. Also, Gibbs free energy changes of adsorption process based on changes in initial concentration and temperature of solution was investigated. Optimization of these two objectives was performed using NSGA-II and RSM. The regression coefficients of the RSM model for the removal percentage and Gibbs free energy changes were 0.9776 and 0.9864, respectively. Also, the F-values of RSM for the removal efficiency and Gibbs free energy were 81.72365 and 93.78053, respectively, show the proper accuracy of model. The best structure of the neural network with 5 hidden layers, which has 3, 3, 6, 4, 2 neurons in each layers, respectively. Also the transfer function was tansig, tansig, logsig, tansig, tansig for each layer. The initial population of the study for the purpose of optimization with NSGA-II algorithm was consist of 50 samples. The results of two methods NSGA-II and RSM show that the maximum removal efficiency (92%) and minimum ΔGo (-5 Kj/mol) are achieved at the highest temperature (55 °C) and lowest initial concentration of solution (10 ppm). The desirability degree for the RSM optimization obtained 0.981.