Microbial desalination cells (MDCs) are an emerging concept for simultaneous wastewater treatment and water desalination. This work presents a mathematical model to simulate dynamic behavior of MDCs for the first time through evaluating multiple factors such as organic supply, salt loading, and current generation. Ordinary differential equations were applied to describe the substrate as well as bacterial concentrations in the anode compartment. Local sensitivity analysis was employed to select model parameters that needed to be re-estimated from the previous studies. This model was validated by experimental data from both a bench-and a large-scale MDC system. It could fit current generation fairly well and simulate the change of salt concentration. It was able to predict the response of the MDC with time under various conditions, and also provide information for analyzing the effects of different operating conditions. Furthermore, optimal operating conditions for the MDC used in this study were estimated to have an acetate flow rate of 0.8 mL· min −1 , influent salt concentration of 15 g·L −1 and salt solution flow rate of 0.04 mL·min −1 , and to be operated with an external resistor less than 30 Ω. The MDC model will be helpful with determining operational parameters to achieve optimal desalination in MDCs. ■ INTRODUCTIONWater shortage is a global issue that influences a large population, especially in the developing nations. 1 To alleviate this problem, desalinating seawater or brackish water appears to be an effective approach to provide alternative source of fresh water. However, the high energy consumption associated with desalination processes makes desalinated water prohibitively expensive. For example, reverse osmosis (RO), which is the most widely applied desalination technology, requires 3−7 kWh of energy to produce 1 m 3 of freshwater. 2 Recent development of the microbial desalination cell (MDC) provides a potentially energy-efficient desalination method. 3,4 MDCs use electricity generated from low-grade substrates such as wastewater to drive the desalination process, so that it requires little external energy input (e.g., to drive pumping systems). Therefore, it holds great promise to significantly reduce energy consumption in desalination processes. MDC research is still in an early stage, and studies have been carried out to improve the understanding of MDCs by investigating the key factors, such as the anode organic loading rates, 5 salt loading rates, 6,7 external resistance, 8,9 hydraulic retention time, 9 new functions, 10,11 membrane fouling, 12,13 intermembrane distance, 14 and system configuration. 15 Given complex desalination processes and strong interactions between biological, electrochemical, and engineering factors in MDCs, a proper mathematic model will be essential for the optimization and the scaling up of MDCs. MDCs derive from microbial fuel cells (MFCs) by adding a third compartment between the anode and the cathode, separated by anion or cation exchange membranes for ...

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Background The use of machine learning (ML) in predicting disease prognosis has increased, and scientists have adopted different methods for cancer classification to optimize the early screening of cancer to determine its prognosis in advance. In this study, we aimed at improving the prediction accuracy of gastric cancer in postoperation patients by constructing a highly effective prognostic model. Methods The study used postoperative gastric cancer patient data from the SEER database. The LASSO regression method was used to construct a clinical prognostic model, and four machine learning methods (Boruta algorithm, neural network, support vector machine, and random forest) were used to screen and recombine the features to construct an ML prognostic model. Clinical information on 955 postoperative gastric cancer patients collected from the Affiliated Tumor Hospital of Harbin Medical University was used for external verification. Results Experimental results showed that the AUC values of 1, 3 and 5 years in the training set, validation set and external validation set of clinical prognosis model and ML prognosis model directly established by LASSO regression are all around 0.8. Conclusion Both models can accurately evaluate the prognosis of postoperative patients with gastric cancer, which may be helpful for accurate and personalized treatment of postoperative patients with gastric cancer.

The past four decades have witnessed an enormous increase in modern contraception in most low‐ and middle‐income countries. We examine the extent to which this change can be attributed to changes in fertility preferences versus fuller implementation of fertility preferences, a distinction at the heart of intense debates about the returns to investments in family planning services. We analyze national survey data from five major survey programs: World Fertility Surveys, Demographic Health Surveys, Reproductive Health Surveys, Pan‐Arab Project for Child Development or Family Health, and Multiple Indicator Cluster Surveys. We perform regression decomposition of change between successive surveys in 59 countries (330 decompositions in total). Change in preferences accounts for little of the change: less than 10 percent in a basic decomposition and about 15 percent under a more elaborate specification. This is a powerful empirical refutation of the view that contraceptive change has been driven principally by reductions in demand for children. We show that this outcome is not surprising given that the distribution of women according to fertility preferences is surprisingly stable over time.

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