New Understandings of Ethanol Oxidation Reaction Mechanism on Pd/C and Pd2Ru/C Catalysts in Alkaline Direct Ethanol Fuel Cells

Ethanol oxidation reaction (EOR) on Pd2Ru/C and Pd/C catalysts in alkaline media is studied comprehensively by cyclic voltammetry, chronoamperometry, in situ FTIR, single fuel cell test and electrochemical impedance spectroscopy measurements. The results show that, as compared to Pd/C, Pd2Ru/C favors acetaldehyde formation and hinders its oxidation. Based on X-ray absorption data, which evidence that Ru promotes a larger electronic vacancy of the Pd 4d band, it is expected that the formation of adsorbed ethoxy is favored on Pd2Ru/C and followed by its oxidation to acetaldehyde facilitated by oxygenated species provided by Ru. In contrast, acetaldehyde oxidation is more difficult on Pd2Ru/C than on Pd/C likely because the adsorption energy of the reactive species is increased. We also show that the performance of Pd2Ru/C anode in alkaline direct ethanol fuel cell (ADEFC) is initially better but degrades much more rapidly than that with Pd/C anode under the same test conditions. The degradation is demonstrated to result from the accumulation of large amounts of acetaldehyde, which in alkaline media forms dimers by the aldol condensation reaction. The dimers tend to be responsible for blocking the active sites for further ethanol oxidation. This comprehensive study provides new understandings of the roles of Ru in Pd2Ru/C for EOR in alkaline media, unveils the causes of the performance degradation of fuel cells with Pd2Ru/C and demonstrates that initial good performances are not necessarily a valid criterion for selecting appropriate anode catalysts for ADEFC applications

Treatment Effects of Ischemic Stroke by Berberine, Baicalin, and Jasminoidin from Huang-Lian-Jie-Du-Decoction (HLJDD) Explored by an Integrated Metabolomics Approach

Berberine, baicalin, and jasminoidin were major active ingredients of Huang-Lian-Jie-Du-Decoction (HLJDD), a famous prescription of traditional Chinese medicine (TCM), which has been used for the treatment of ischemic stroke. The aim of the present study was to classify their roles in the treatment effects of ischemic stroke. A rat model of middle cerebral artery occlusion (MCAO) was constructed to mimic ischemic stroke and treatment effects of berberine, baicalin, and jasminoidin, and HLJDD was assessed by neurologic deficit scoring, infarct volume, histopathology, immunohistochemistry, biochemistry, quantitative real-time polymerase chain reaction (qRT-PCR), and Western blotting. In addition, the 1H NMR metabolomics approach was used to assess the metabolic profiles, which combined with correlation network analysis successfully revealed metabolic disorders in ischemic stroke concerning the treatment of the three principal compounds from HLJDD for the first time. The combined results suggested that berberine, baicalin, and jasminoidin are responsible for the effectiveness of HLJDD on the treatment of ischemic stroke by amelioration of abnormal metabolism and regulation of oxidative stress, neuron autophagy, and inflammatory response. This integrated metabolomics approach showed its potential in understanding the function of complex formulae and clarifying the role of its components in the overall treatment effects

3D-IDS: Doubly Disentangled Dynamic Intrusion Detection

Network-based intrusion detection system (NIDS) monitors network traffic for malicious activities, forming the frontline defense against increasing attacks over information infrastructures. Although promising, our quantitative analysis shows that existing methods perform inconsistently in declaring various unknown attacks (e.g., 9% and 35% F1 respectively for two distinct unknown threats for an SVM-based method) or detecting diverse known attacks (e.g., 31% F1 for the Backdoor and 93% F1 for DDoS by a GCN-based state-of-the-art method), and reveals that the underlying cause is entangled distributions of flow features. This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme. Specifically, we first disentangle traffic features by a non-parameterized optimization based on mutual information, automatically differentiating tens and hundreds of complex features of various attacks. Such differentiated features will be fed into a memory model to generate representations, which are further disentangled to highlight the attack-specific features. Finally, we use a novel graph diffusion method that dynamically fuses the network topology for spatial-temporal aggregation in evolving data streams. By doing so, we can effectively identify various attacks in encrypted traffics, including unknown threats and known ones that are not easily detected. Experiments show the superiority of our 3D-IDS. We also demonstrate that our two-step feature disentanglements benefit the explainability of NIDS.Comment: Accepted and appeared in the proceedings of the KDD 2023 Research Trac

1H NMR-Based Metabolomics Reveals Refined-Huang-Lian-Jie-Du-Decoction (BBG) as a Potential Ischemic Stroke Treatment Drug With Efficacy and a Favorable Therapeutic Window

Huang-Lian-Jie-Du-Decoction (HLJDD) is a traditional Chinese medicine (TCM) used to treat ischemic stroke. However, the complexity of its chemical composition makes quality control difficult. Berberine, baicalin, and geniposide are the three main ingredients in HLJDD. Here, a formula of BBG comprised of berberine, baicalin, and geniposide, known as Refined-Huang-Lian-Jie-Du-Decoction, was investigated for its efficacy, therapeutic window, and mechanisms of action. BBG was assessed on two major types of ischemic stroke, cerebral ischemia-reperfusion (I/R) injury, and continuous ischemia injury, respectively. BBG showed efficacy comparable to HLJDD in the treatment of cerebral I/R injury within 5 h after injury initiation but did poorly in treating continuous ischemia injury. BBG exhibited neuroprotective effects on cerebral I/R injury by regaining the balance in energy metabolism, oxidative stress, amino acid metabolism, inflammation, and nucleic acid metabolism. These results suggested that BBG could be a good alternative to HLJDD, with high efficacy and a long therapeutic window, which shows great potential for drug development to treat stroke

One Day of Yimin

This case presents a day in the life of WANG Yimin, a typical female white-collar worker in Shanghai. By portraying her outward behaviors and inner decision-making processes as a consumer, this case attempts to portray features of the contemporary Chinese market and Chinese consumers. It discusses some important cultural phenomena in China, such as face, guanxi, family, and nationalism, and their impact on consumers. The descriptions of the interactions between WANG Yimin and the people around her reflect the everyday lives of Chinese individuals from different social classes, cultural backgrounds, generations, and geographic locations. This case will help MBA students interpret consumer behavior and mentality from different cultural perspectives, understand the business implications behind cultures, and become aware of both the diversities and consistencies of the Chinese market

Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network

A healthy and sustainable higher education system plays an important role in social development. The evaluation and prediction of such a system are vital for higher education. Existing models are usually constructed based on fewer indicators and original data are incomplete; thus, evaluation may be inefficient. In addition, these models are generally suitable for specific countries, rather than the whole universe. To tackle these issues, we proceed as follows: Firstly, we select a series of evaluation indicators that cover most aspects of higher education to establish a basic evaluation system. Then, we choose several representative countries to illustrate the system. Next, we use the analytic hierarchy process (AHP) to calculate a weight matrix of the indicators according to their importance. Furthermore, we obtain authoritative data from these countries. Then, we apply the indicators to the technique for order preference by similarity to an ideal solution (TOPSIS) algorithm to ascertain their relative levels. Finally, we combine the weight matrix with the relative levels to achieve a comprehensive evaluation of higher education. So far, a theoretical establishment of a higher education evaluation model has been generally completed. For better practical application, we add a predictive function to our evaluation model. Starting with China, we predict the development of national higher education for the next 20 years. We adopt a long short-term memory (LSTM) neural network as a method of prediction. Considering the significant influences of national policies on higher education, we address the issues under two circumstances: with or without policy influences. At last, we compare our model with existing models. Experimental results show that our model better reflects national higher education levels and provides more reasonable and robust prediction results

Malicious JavaScript Detection Based on Bidirectional LSTM Model

JavaScript has been widely used on the Internet because of its powerful features, and almost all the websites use it to provide dynamic functions. However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS), Cross-site Request Forgery (CSRF), and drive-by download attack. Traditional malicious script detection relies on expert knowledge, but even for experts, this is an error-prone task. To solve this problem, many learning-based methods for malicious JavaScript detection are being explored. In this paper, we propose a novel deep learning-based method for malicious JavaScript detection. In order to extract semantic information from JavaScript programs, we construct the Program Dependency Graph (PDG) and generate semantic slices, which preserve rich semantic information and are easy to transform into vectors. Then, a malicious JavaScript detection model based on the Bidirectional Long Short-Term Memory (BLSTM) neural network is proposed. Experimental results show that, in comparison with the other five methods, our model achieved the best performance, with an accuracy of 97.71% and an F1-score of 98.29%