Microenvironment Restruction of Emerging 2D Materials and their Roles in Therapeutic and Diagnostic Nano-Bio-Platforms

Engineering advanced therapeutic and diagnostic nano-bio-platforms (NBPFs) have emerged as rapidly-developed pathways against a wide range of challenges in antitumor, antipathogen, tissue regeneration, bioimaging, and biosensing applications. Emerged 2D materials have attracted extensive scientific interest as fundamental building blocks or nanostructures among material scientists, chemists, biologists, and doctors due to their advantageous physicochemical and biological properties. This timely review provides a comprehensive summary of creating advanced NBPFs via emerging 2D materials (2D-NBPFs) with unique insights into the corresponding molecularly restructured microenvironments and biofunctionalities. First, it is focused on an up-to-date overview of the synthetic strategies for designing 2D-NBPFs with a cross-comparison of their advantages and disadvantages. After that, the recent key achievements are summarized in tuning the biofunctionalities of 2D-NBPFs via molecularly programmed microenvironments, including physiological stability, biocompatibility, bio-adhesiveness, specific binding to pathogens, broad-spectrum pathogen inhibitors, stimuli-responsive systems, and enzyme-mimetics. Moreover, the representative therapeutic and diagnostic applications of 2D-NBPFs are also discussed with detailed disclosure of their critical design principles and parameters. Finally, current challenges and future research directions are also discussed. Overall, this review will provide cutting-edge and multidisciplinary guidance for accelerating future developments and therapeutic/diagnostic applications of 2D-NBPFs

Intelligent Prediction of Customer Churn with a Fused Attentional Deep Learning Model

In recent years, churn rates in industries such as finance have increased, and the cost of acquiring new users is more than five times the cost of retaining existing users. To improve the intelligent prediction accuracy of customer churn rate, artificial intelligence is gradually used. In this paper, the bidirectional long short-term memory convolutional neural network (BiLSTM-CNN) model is integrated with recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in parallel, which well solves the defective problem that RNNs and CNNs run separately, and it also solves the problem that the output results of a long short-term memory network (LSTM) layer in a densely-connected LSTM-CNN (DLCNN) model will ignore some local information when input to the convolutional layer. In order to explore whether the attention bidirectional long short-term memory convolutional neural network (AttnBLSTM-CNN) model can perform better than BiLSTM-CNN, this paper uses bank data to compare the two models. The experimental results show that the accuracy of the AttnBiLSTM-CNN model is improved by 0.2%, the churn rate is improved by 1.3%, the F1 value is improved by 0.0102, and the AUC value is improved by 0.0103 compared with the BLSTM model. Therefore, introducing the attention mechanism into the BiLSTM-CNN model can further improve the performance of the model. The improvement of the accuracy of the user churn prediction model can warn of the possibility of user churn in advance and take effective measures in advance to prevent user churn and improve the core competitiveness of financial institutions

Study on Exchange Rate Forecasting with Stacked Optimization Based on a Learning Algorithm

The time series of exchange rate fluctuations are characterized by non-stationary and nonlinear features, and forecasting using traditional linear or single-machine models can cause significant bias. Based on this, the authors propose the combination of the advantages of the EMD and LSTM models to reduce the complexity by analyzing and decomposing the time series and forming a new model, EMD-LSTM-SVR, with a stronger generalization ability. More than 30,000 units of data on the USD/CNY exchange rate opening price from 2 January 2015 to 30 April 2022 were selected for an empirical demonstration of the model’s accuracy. The empirical results showed that the prediction of the exchange rate fluctuation with the EMD-LSTM-SVR model not only had higher accuracy, but also ensured that most of the predicted positions deviated less from the actual positions. The new model had a stronger generalization ability, a concise structure, and a high degree of ability to fit nonlinear features, and it prevented gradient vanishing and overfitting to achieve a higher degree of prediction accuracy

Research on Pork Jerky Obtained Through Fermentation with Pediococcus acidilactici

Pediococcus acidilactici was used to ferment fresh pork. After fermentation, the pork jerky was subjected to sensory evaluation and the levels of pH, free amino acids, and volatile compounds were measured. The results showed that the fermented pork jerky had a better sensory evaluation score (score: 93.2), lower pH value (3.54), and more free amino acids (39.24 mg/100 g). Furthermore, in the fermented pork jerky, the content of three acids (18.552%) was high, which lowered the pH of the pork jerky and inhibited growth of pathogens. Moreover, some new compounds produced, including 3-hydroxy-2-butanone (49.095%), 2,3-butanediol (2.790%), 2-ethyl-1-hexanol (2.400%), oxalic acid isobutyl hexyl ester (2.280%), phenylethyl alcohol (0.953%), and eucalyptol (0.659%), contributed to the flavour of pork jerky. Overall, our results demonstrated that P. acidilactici can be used for the production as well as improvement of the quality and flavour of fermented pork jerky