Analysis of Statistically Predicted Rate Constants for Pyrolysis of High-Density Plastic Using R Software

The surge in plastic waste production has forced researchers to work on practically feasible recovery processes. Pyrolysis is a promising and intriguing option for the recycling of plastic waste. Developing a model that simulates the pyrolysis of high-density polyethylene (HDPE) as the most common polymer is important in determining the impact of operational parameters on system behavior. The type and amount of primary products of pyrolysis, such as oil, gas, and waxes, can be predicted statistically using a multiple linear regression model (MLRM) in R software. To the best of our knowledge, the statistical estimation of kinetic rate constants for pyrolysis of high-density plastic through MLRM analysis using R software has never been reported in the literature. In this study, the temperature-dependent rate constants were fixed experimentally at 420 °C. The rate constants with differences of 0.02, 0.03, and 0.04 from empirically set values were analyzed for pyrolysis of HDPE using MLRM in R software. The added variable plots, scatter plots, and 3D plots demonstrated a good correlation between the dependent and predictor variables. The possible changes in the final products were also analyzed by applying a second-order differential equation solver (SODES) in MATLAB version R2020a. The outcomes of experimentally fixed-rate constants revealed an oil yield of 73% to 74%. The oil yield increased to 78% with a difference of 0.03 from the experimentally fixed rate constants, but light wax, heavy wax, and carbon black decreased. The increased oil and gas yield with reduced byproducts verifies the high significance of the conducted statistical analysis. The statistically predicted kinetic rate constants can be used to enhance the oil yield at an industrial scale.     

New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete

Recently, artificial intelligence (AI) approaches have gained the attention of researchers in the civil engineering field for estimating the mechanical characteristics of concrete to save the effort, time, and cost of researchers. Consequently, the current research focuses on assessing steel-fiber-reinforced concrete (SFRC) in terms of flexural strength (FS) prediction by employing delicate AI techniques as well as to predict the raw material interaction that is still a research gap. In this study, the FS of SFRC is estimated by deploying supervised machine learning (ML) techniques, such as DT-Gradient Boosting, DT-XG Boost, DT-AdaBoost, and DT-Bagging. In addition to that, the performance model is also evaluated by using R², root mean square error (RMSE), and mean absolute error (MAE). Furthermore, the k-fold cross-validation method is also applied to validate the model’s performance. It is observed that DT-Bagging with an R² value of 0.95 is superior to DT-XG Boost, DT-Gradient Boosting, and DT-AdaBoost. Lesser error MAE and RMSE and higher R² values for the DT-Bagging model show the enhanced performance of the model compared to the other ensembled approaches. Considerable conservation of time, effort, and cost can be made by applying ML techniques to predict concrete properties. The evaluation of the outcome depicts that the estimated results of DT-Bagging are closer to the experimental results, indicating the accurate estimation of SFRC flexural strength. It is further revealed from the SHapley Additive exPlanations (SHAP) study that the volumetric content of steel fiber highly and positively influences the FS of SFRC.     

One-year outcome after severe pulmonary exacerbation in adults with cystic fibrosis

We retrospectively studied the outcomes of adult patients with cystic fibrosis (CF) hospitalized for severe pulmonary exacerbations (69 cases) between January 1997 and June 2001. Cases were treated either in the Pulmonary Department (n = 46) or in the intensive care unit (ICU) (n = 23) depending on severity. Noninvasive mechanical ventilation was used in 61% (14 of 23) and 33% (15 of 46) of cases treated in the ICU and the Pulmonary Department groups, respectively. Invasive ventilation was necessary in 4 of 23 cases treated in the ICU. The 1-year survival rate was 52% (12 of 23) and 91% (42 of 46) in the ICU and the Pulmonary Department groups, respectively. Lung transplantation was performed in two patients from the ICU group and in five patients from the Pulmonary Department group after hospital discharge. Factors predictive of death were prior colonization with Burkholderia cepacia and rapid decline in FEV1 before admission and severity of exacerbations (severity of hypoxemia and hypercapnia, simplified acute physiology score II and logistic organ dysfunction (LOD) scores, requirement of noninvasive mechanical ventilation, and hospitalization in the ICU) in the univariate analysis and were prior colonization with B. cepacia, the severity of hypoxemia at admission, and hospitalization in the ICU in the multivariate analysis. In 1-year survivors, pulmonary exacerbation did not affect the progression of the disease.     

Sickle cell retinopathy. A focused review

Graefe's Archive for Clinical and Experimental Ophthalmology - To provide a focused review of sickle cell retinopathy in the light of recent advances in the pathogenesis, multimodal retinal...     

A Snapshot of Social Risk Factors and Associations with Health Outcomes in a Community Sample of Middle Eastern and North African (MENA) People in the U.S.

Social factors (e.g. housing, food security, etc.) contribute significantly to health. The purpose of this study is to describe social risk and social exclusion factors in one of the largest Middle Eastern and North African (MENA) populations in the U.S. and their association with health outcomes. We conducted a cross-sectional study with a community convenience sample of 412 adults who self-identify as MENA. Weighted, adjusted linear regression models were used to examine relationships of interest. Prevalent social risks included transportation barriers to healthcare (33%), food insecurity (33%), and financial strain (25%). In adjusted models, perception of being treated unfairly (Estimate (SE) 0.08 (0.04), p?<?0.05) and fear of deportation (0.26 (0.06), p?<?0.001) were associated with more social risk factors. More social risk factors were associated with worse self-reported health (0.09 (0.03), p?<?0.01), more chronic conditions (0.11 (0.03), p?<?0.004), and more mental health symptoms (0.34 (0.14) p?<?0.01).Social risk is high among those perceiving unfairness and fear deportation. Those with more social risk factors reported worse health. These findings have implications for social needs screening and referral models that can best serve U.S. MENA sub-populations.