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In this study, we used tetraethylammonium chloride (TEAC), diethanolamine (DEA), and N-methyldiethanolamine (MDEA) to prepare ternary DES and binary DES to absorb CO2. We found that their formation was due to the hydrogen bond interaction between hydrogen bond acceptor and hydrogen bond donor (HBD). Surprisingly, TEAC/MDEA/DEA can react with CO2, but TEAC/MDEA cannot react with CO2. Unexpectedly, after adding DEA to TEAC/MDEA, the ternary TEAC/MDEA/DEA DES can react with CO2. Nuclear magnetic resonance spectroscopy and Fourier infrared spectroscopy results showed that the accidental CO2 absorption behavior mainly depended on the HBD DEA, because the imine group in DEA reacted with CO2 to form carbamate, thereby absorbing CO2, while the hydroxyl group on MDEA and the hydroxyl group of DEA did not interact with CO2. Through thermal stability analysis, TEAC/MDEA/DEA system with the molar ratio of 1:3:4 is more stable. We further studied the influence of molar ratio, temperature, water content, and other factors on the absorption of CO2 by ternary DES. In addition, TEAC/MDEA/DEA (1:3:4) was regenerated at 80°C, and the absorption capacity of DES was almost unchanged after five absorption–desorption cycles.

Diabetes mellitus (DM) presents a critical global health challenge, characterized by persistent hyperglycemia and associated with substantial economic and health-related burdens. This study employs advanced machine-learning techniques to improve the prediction and classification of antidiabetic peptides, with a particular focus on differentiating those effective against T1DM from those targeting T2DM. We integrate feature selection with analysis methods, including logistic regression, support vector machines (SVM), and adaptive boosting (AdaBoost), to classify antidiabetic peptides based on key features. Feature selection through the Lasso-penalized method identifies critical peptide characteristics that significantly influence antidiabetic activity, thereby establishing a robust foundation for future peptide design. A comprehensive evaluation of logistic regression, SVM, and AdaBoost shows that AdaBoost consistently outperforms the other methods, making it the most effective approach for classifying antidiabetic peptides. This research underscores the potential of machine learning in the systematic evaluation of bioactive peptides, contributing to the advancement of peptide-based therapies for diabetes management. [ABSTRACT FROM AUTHOR]

The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and the results show that the method has good prediction effect on stock price, can provide certain reference for investment.

Fire accidents often lead to tremendous losses and smoke has been reported to be the main cause of these heavy casualties. Accordingly, smoke movement can influence escape and smoke confinement is particular significant. Fire Dynamics Simulator software (FDS) was utilized to carry out complementary full scale numerical simulations of fire accident in a subway station. Different measure of smoke prevention and exhaust were adopted and the difference of smoke confinement among the measure was compared. According to the smoke temperature, hung wall had a good role during early stage of fire accident. However, the performance became worse with smoke accumulation. If the hung wall were adopted, smoke was exhausted from basement 2 and air was supplied into the basement 1 at the same time, the efficacy of smoke confinement was the best. The mechanical exhaust rate had obvious implications for the smoke diffusion.