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User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations.

Physical exercise can significantly impact our bodies, affecting our functional capacity, structure establishment, and molecular makeup. The magnitude of these changes depends on the specific exercise protocols used. For instance, low-to-moderate-intensity exercise can activate important molecular targets in the short term, such as BDNF-mediated signaling, while high-intensity exercise can maintain these signaling molecules in the active state for a longer term. This makes it challenging to recommend specific exercises for obtaining BDNF-induced benefits. Additionally, exercise-induced molecular signaling targets can have positive and negative effects, with some exercises blunting these targets and others activating them. For example, increasing BDNF concentration through exercise can be beneficial for brain health, but it may also have a negative impact on conditions such as bipolar disorder. Therefore, a deeper understanding of a specific exercise-mediated mechanistic approach is required. This review will delve into how the sprint exercise-mediated activation of BDNF could help maintain brain health and explore potential molecular interventions.

With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes.

Butyrate, produced by gut microbe during dietary fiber fermentation, has anti-inflammatory and antioxidant effects on chronic inflammation diseases, yet it remains to be explored whether butyrate has protective effects against viral infections. Here, we demonstrated that butyrate alleviated tissue injury in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected golden hamsters supplemented with butyrate before and during the infection. Butyrate-treated hamsters showed augmentation of type I interferon (IFN) response and activation of endothelial cells without exaggerated inflammation. In addition, butyrate regulated redox homeostasis by enhancing the activity of superoxide dismutase (SOD) to inhibit excessive apoptotic cell death. Therefore, butyrate exhibited effective prevention against SARS-CoV-2 by upregulating antiviral immune responses and promoting cell survival.

To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model. The proposed ensemble CNN attention-based BILSTM model (ECA-BILSTM) combines shallow convolutional neural networks (CNNs), attention mechanisms, and bidirectional long short-term memory (BILSTM). Moreover, additional variables are selected according to the characteristics of teleconsultation demand and added to the inputs of forecasting models. To verify the superiority of ECA-BILSTM and the effectiveness of additional variables, two actual teleconsultation datasets collected in the National Telemedicine Center of China (NTCC) are used as the experimental data. Results showed that ECA-BILSTMs can significantly outperform corresponding benchmark models. And two key additional variables were identified for teleconsultation demand prediction improvement. Overall, the proposed ECA-BILSTM model with effective additional variables is a feasible promising approach in teleconsultation demand forecasting.