Functional carbon dots-hydrogel complex for selective antibacterial and detection applications.

The carbon dots (CDs) with excellent optical properties and their hydrogel complex are of great significance in biomedicine, healthcare and biochemical detection fields. This paper reports the preparation of green-emitting CDs (MA-CDs) through one-step hydrothermal route with citric acid as reducing agent, L-malic acid as carbon source and N-(2-hydroxyethyl)ethylenediamine as nitrogen source. To expand its application in biology, MA-CDs were coupled with vancomycin to obtain multifunctional CDs (VMA-CDs). The prepared VMA-CDs exhibit selective antibacterial behavior to Gram-positive bacteria, and it could be used as a fluorescent probe to selectively label Staphylococcus aureus (S. aureus). Moreover, thanks to the excellent optical properties of VMA-CDs, it has been used as a fluorescent sensor to detect Au3+ with detection range of 6.50 nM-21.93 µM and detection limit 3.98 nM. By introducing the fluorescence of CDs as the reference signal, and VMA-CDs as a response signal, the hydrogel (V-SP) was prepared and realized the detection of Au3+ in microfluidics with assistance of a smartphone to collect and analyze data.

Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network.

With the development of information technology construction in schools, predicting student grades has become a hot area of application in current educational research. Using data mining to analyze the influencing factors of students' performance and predict their grades can help students identify their shortcomings, optimize teachers' teaching methods and enable parents to guide their children's progress. However, there are no models that can achieve satisfactory predictions for education-related public datasets, and most of these weakly correlated factors in the datasets can still adversely affect the predictive effect of the model. To solve this issue and provide effective policy recommendations for the modernization of education, this paper seeks to find the best grade prediction model based on data mining. Firstly, the study uses the Factor Analyze (FA) model to extract features from the original data and achieve dimension reduction. Then, the Bidirectional Gate Recurrent Unit (BiGRU) model and attention mechanism are utilized to predict grades. Lastly, Comparing the prediction results of ablation experiments and other single models, such as linear regression (LR), back propagation neural network (BP), random forest (RF), and Gate Recurrent Unit (GRU), the FA-BiGRU-attention model achieves the best prediction effect and performs equally well in different multi-step predictions. Previously, problems with students' grades were only detected when they had already appeared. However, the methods presented in this paper enable the prediction of students' learning in advance and the identification of factors affecting their grades. Therefore, this study has great potential to provide data support for the improvement of educational programs, transform the traditional education industry, and ensure the sustainable development of national talents.