A Review on COVID-19 Related Research in Leading Information Systems Journals

To identify the key challenges, issues and opportunities affecting individuals, organizations, and society on coping with COVID-19, we reviewed extant research related to the COVID-19 pandemic in the leading information systems journals. Six major research themes and representative literature for each theme are identified by content analysis. The six major research themes include digital transformation, data visualization and artificial intelligence, infodemic and cybersecurity, IT governance, digital divide, and IS research direction in the post-pandemic period. Moreover, we discussed the challenges, current research, and opportunities related to each of the themes. The review provides a snapshot of IS literature on the COVID-19 pandemic and can enable scholars to evaluate possible opportunities for conducting research and development activities related to the pandemic

Statistics of α - μ random variables and their applications in wireless multihop relaying and multiple scattering channels

Exact results for the probability density function (PDF) and cumulative distribution function (CDF) of the sum of ratios of products (SRP) and the sum of products (SP) of independent α-μ random variables (RVs) are derived. They are in the form of 1-D integral based on the existing works on the products and ratios of α-μ RVs. In the derivation, generalized Gamma (GG) ratio approximation (GGRA) is proposed to approximate SRP. Gamma ratio approximation (GRA) is proposed to approximate SRP and the ratio of sums of products (RSP). GG approximation (GGA) and Gamma approximation (GA) are used to approximate SP. The proposed results of the SRP can be used to calculate the outage probability (OP) for wireless multihop relaying systems or multiple scattering channels with interference. The proposed results of the SP can be used to calculate the OP for these systems without interference. In addition, the proposed approximate result of the RSP can be used to calculate the OP of the signal-to-interference ratio (SIR) in a multiple scattering system with interference

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning

Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications, such as social networks and e-commerce, involve temporal graphs where nodes and edges are dynamically evolving. Temporal graph neural networks (TGNNs) have progressively emerged as an extension of GNNs to address time-evolving graphs and have gradually become a trending research topic in both academics and industry. Advancing research and application in such an emerging field necessitates the development of new tools to compose TGNN models and unify their different schemes for dealing with temporal graphs. In this work, we introduce LasTGL, an industrial framework that integrates unified and extensible implementations of common temporal graph learning algorithms for various advanced tasks. The purpose of LasTGL is to provide the essential building blocks for solving temporal graph learning tasks, focusing on the guiding principles of user-friendliness and quick prototyping on which PyTorch is based. In particular, LasTGL provides comprehensive temporal graph datasets, TGNN models and utilities along with well-documented tutorials, making it suitable for both absolute beginners and expert deep learning practitioners alike.Comment: Preprint; Work in progres

The relationship between health-promoting behaviors and negative emotions in college freshmen: a cross-lagged analysis

BackgroundThe prevalence of mental health issues has been gradually increasing among college students in recent years. Improvements in mental health can be achieved through changes in daily behavior and the use of psychological counseling. This study aims to investigate the relationship between health-promoting behaviors and negative emotions among college freshmen as they enter the university. It also examines the impact of various sub-dimensions of health-promoting behaviors and other factors on the negative emotions (stress, anxiety, and depression) experienced by college freshmen.MethodsUsing the Negative Emotion and Health-Promoting Behavior scales, a 7-month longitudinal study was conducted on 4,252 college freshmen, with collection of data at two time points (T1: November 12, 2021; T2: June 17, 2022). Out of this longitudinal study, 3,632 valid samples were obtained. This research aimed to explore the association and impact between negative emotions and the level of health-promoting behaviors among college students during their time at the university.Results① There were significant differences in the levels of health-promoting behaviors and negative emotions over the course of 7 months (P < 0.05). Health-promoting behaviors were found to have a significant negative correlation with negative emotions (P < 0.05). ② Negative emotions at T1 significantly negatively predicted health-promoting behaviors at T2 (β = −0.11, P < 0.01), while health-promoting behaviors at T1 significantly negatively predicted negative emotions at T2 (β = −0.12, P < 0.001). ③ Stress management (β = −0.104, P < 0.05; β = −0.087, P < 0.05), self-actualization (β = −0.282, P < 0.01; β = −0.260, P < 0.05), health responsibility (β = −0.057, P < 0.05; β = −0.088, P < 0.05), and interpersonal relations (β = 0.068, P < 0.01; β = 0.138, P < 0.05) were important components in improving stress and anxiety. Self-actualization (β = −0.437, P < 0.001), exercise (β = 0.048, P < 0.001), nutrition (β = 0.044, P < 0.001), and interpersonal relations (β = 0.065, P < 0.001) were important components in improving depression. ④ Gender, place of household registration, and whether the individual is the only child were significant factors affecting negative emotions in college freshmen.ConclusionThe level of health-promoting behaviors is an important indicator for assessing the negative emotional states of college freshmen. Enhancing health-promoting behaviors across various dimensions can help alleviate different types of negative emotions. Gender, place of household registration, and being the only child are significant factors that influence negative emotions

Melittin promotes dexamethasone in the treatment of adjuvant rheumatoid arthritis in rats

Background: Rheumatoid arthritis (RA) is an erosive-destructive inflammation of the joints, and the chronic, long-term stiffness and deformation induced by RA are some of the symptoms of arthritis that are difficult to treat. Dexamethasone (DEX) and melittin (MLT) are two interesting anti-inflammatory substances, both of which possess anti-inflammatory effects exerted through the suppression of the immune system. The purpose of this study was to explore the role of MLT in the treatment of RA by DEX as well as to clarify the influence of MLT on the efficacy and side effects of DEX.Method: The rats were injected with Complete Freund’s Adjuvant (CFA) to induce arthritis, followed by treatment with different doses of DEX and/or MLT. The relevant indexes of paw inflammation were determined, and the appetite, growth status, arthritis status, cytokine levels, and organ coefficient of the rats were evaluated. In addition, the paraffin sections of the joint tissues were prepared to analyze the pathological changes.Result: DEX exhibited side effects, notably hindering feed intake and growth, and inducing immune organ lesions in the rats. MLT significantly reduced the side effects of DEX and promoted its efficacy. DEX in combination with MLT demonstrated a synergistic efficacy in RA treatment, showing advantages in detumescence reduction, pro-inflammatory cytokine inhibition, and joint internal pathological improvement.Conclusion: Thus, MLT promoted the efficacy of DEX in adjuvant RA treatment in rats, offering an approach to reduce the use dosage and side effects of DEX