Evaluating the potential severity of biogas toxic release, fire and explosion: consequence modeling of biogas dispersion in a large urban treatment plant.

Objectives. Biogas production in treatment plants for energy generation has increased in recent years. This study aimed to model the consequence of biogas release in a large urban treatment plant. Methods. The study modeled biogas storage tank consequences in a large urban treatment plant in Iran. Due to potential for biogas harmfulness, three consequences of toxic release, fire and explosion were evaluated. Scenarios were evaluated in the worst-case situation. All modeling steps were performed using PHAST version 7.2. Results. In the case of catastrophic reservoir rupture in summer, distances of 3788.94, 128.86 and 91.72 m from the reservoir in the wind direction will be in the range of 100, 500 and 1000 ppm biogas, respectively. Study of pressure values due to explosion in the catastrophic rupture scenario revealed that distances of 57.19, 14.70 and 115.84 m from the biogas reservoir were in the range of 0.02, 0.13 and 0.2 bar pressure increase, respectively. Conclusion. Due to the treatment plant's location in a dense urban area, biogas dispersion could lead to exposure of many people to high-risk areas. Therefore, taking control measures comparable with the consequence modeling output can be a practical step toward reducing vulnerability against such incidents.

Meta-Analysis of Body Concentration of Polychlorinated Biphenyls and Prostate Cancer.

Prostate Cancer (PCa) is the second most common hormone-sensitive neoplasm among men and the fifth cause of death due to malignancy in developed countries. Moreover, studies have shown the links between polychlorinated biphenyls (PCBs) and hormone-related cancers such as prostate cancer. Hence, we conducted a systematic review and meta-analysis to evaluate the potential relationship between the PCBs and developing PCa. In this meta-analysis study, the relevant databases such as Web of Science, PubMed, and Scopus were studied for English research. The Newcastle-Ottawa Scale was applied to evaluate the quality of the selected publications. The GRADE method was used to assess the risk of bias studies. After reviewing the relevant studies, a cohort and seven case-control studies entered the meta-analysis. These articles were published during 2003-2021 with 2989 participants and 1212 PCa cases. The heterogeneity among the studies was significant (p = 0.001, I2 = 70.61). Using a random-effects model, the association between the serum and plasma levels of PCBs and the risk of PCa was not shown to be significant (OR = 1.12; 95% CI: 0.90-1.39). The results of Egger's test showed no trace of publication bias in the studies (P of bias = 0.573). This systematic review and meta-analysis was presented based on relatively strong evidence and has confirmed negatively significant associations between PCa risk and some PCBs congeners (PCB 44, 52, and 101). This study does not provide strong evidence that total PCB exposure is a risk factor for PCa development in humans.

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies.

Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(ε-caprolactone) (PCL)/gelatin (Gt) nanofibers. Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFMMLP is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving. According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFMMLP in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning.