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The release of emerging contaminants (ECs) into aquatic environments poses a significant risk to global water security. Advanced oxidation processes (AOPs), while effective in removing ECs, are often resource and energy-intensive. Here, we introduce a novel catalyst, CoFe quantum dots embedded in graphene nanowires (CoFeQds@GN-Nws), synthesized through anaerobic polymerization. It uniquely features electron-rich and electron-poor micro-regions on its surface, enabling a self-purification mechanism in wastewater. This is achieved by harnessing the internal energy of wastewater, particularly the bonding energy of pollutants and dissolved oxygen (DO). It demonstrates exceptional efficiency in removing ECs at ambient temperature and pressure without the need for external oxidants, achieving a removal rate of nearly 100.0%. The catalyst's structure-activity relationship reveals that CoFe quantum dots facilitate an unbalanced electron distribution, forming these micro-regions. This leads to a continuous electron-donation effect, where pollutants are effectively cleaved or oxidized. Concurrently, DO is activated into superoxide anions (O2•−), synergistically aiding in pollutant removal. This approach reduces resource and energy demands typically associated with AOPs, marking a sustainable advancement in wastewater treatment technologies.

DNA-binding proteins (DBP) play an essential role in the genetics and evolution of organisms. A particular DNA sequence could provide underlying therapeutic benefits for hereditary diseases and cancers. Studying these proteins can timely and effectively understand their mechanistic analysis and play a particular function in disease prevention and treatment. The limitation of identifying DNA-binding protein members from the sequence database is time-consuming, costly, and ineffective. Therefore, efficient methods for improving DBP classification are crucial to disease research. In this paper, we developed a novel predictor Hybrid _DBP, which identified potential DBP by using hybrid features and convolutional neural networks. The method combines two feature selection methods, MonoDiKGap and Kmer, and then used MRMD2.0 to remove redundant features. According to the results, 94% of DBP were correctly recognized, and the accuracy of the independent test set reached 91.2%. This means Hybrid_ DBP can become a useful prediction tool for predicting DBP.

The porosity defects in the ingot, which are caused by moisture absorption in slag during the electroslag remelting process, deserve the researcher’s attention in the summer wet season. The prediction of slag weight gain caused by moisture absorption is critical for developing slag baking and scheduling strategies and can assist workshop managers in making informed decisions during industrial production of electro slag remelting. The moisture absorption in slag under the conditions of different air humidity, experimental time, slag particle size, and CaO content in the slag are investigated by slag weight gain experiments. The purpose of this study is to predict the rate of weight gain in slag using observed weight gain data and machine learning (ML) models. The observation dataset includes features and rate of weight growth, which serve as independent and dependent variables, respectively, for ML models. Four machine learning models: linear regression, support vector regression, random forest regression, and multi-layer perceptron, were employed in this study. Additionally, parameters for machine learning models were selected using 5-fold cross-validation. Support vector regression outperformed the other three machine learning models in terms of root-mean-square errors, mean squared errors, and coefficients of determination. Thus, the ML-based model is a viable and significant method for forecasting the slag weight gain rate, whereas support vector regression can produce results that are competitive and satisfying. The results of slag weight gain data and ML models show that the slag weight gain increases with the increase of air humidity, experimental time, slag particle size, and CaO content in the slag. The porosity defect in the ingot during the ESR process often appears when the moisture in the slag exceeds 0.02%. Considering saving electric energy, the complexity of on-site scheduling, and 4 h of scheduling time, the slag T3 (CaF2:CaO:Al2O3:MgO = 37:28:30:5) is selected to produce H13 steel ESR ingot in the winter, and slag T2 (CaF2:CaO:Al2O3:MgO = 48:17:30:5) is selected to produce H13 steel ESR ingot in the summer.

Water scarcity is a growing threat to humankind. At university campuses, there is a need for shared shower room managers to forecast the demand for bath water accurately. Accurate bath water demand forecasts can decrease the costs of water heating and pumping, reduce overall energy consumption, and improve student satisfaction (due to stability of bath water supply and bathwater temperature). We present a case study conducted at Capital Normal University (Beijing, China), which provides shared shower rooms separately for female and male students. Bath water consumption data are collected in real-time through shower tap controllers to forecast short-term bath water consumption in the shower buildings. We forecasted and compared daily and hourly bath water demand using the autoregressive integrated moving average, random forests, long short-term memory, and neural basis expansion analysis time series-forecasting models, and assessed the models’ performance using the mean absolute error, mean absolute percentage error, root-mean-square error, and coefficient of determination equations. Subsequently, covariates such as weather information, student behavior, and calendars were used to improve the models’ performance. These models achieved highly accurate forecasting for all the shower room areas. The results imply that machine learning methods outperform statistical methods (particularly for larger datasets) and can be employed to make accurate bath water demand forecasts.