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Fulltext Bibliometric Analysis and Visualization of Academic Procrastination

Tao X, Hanif H, Ahmed HH, Ebrahim NA

Front Psychol, 2021;12:722332.
PMID: 34733204 DOI: 10.3389/fpsyg.2021.722332

Numerous students suffer from academic procrastination; it is a common problem and phenomenon in academic settings. Many previous researchers have analyzed its relationships with other factors, such as self-regulation and academic success. This paper aims to provide a full outline of academic procrastination and explore the current hot spots and trends. Bibliometrix and VOSviewer were used to conduct quantitative analysis. The data was collected from the Web of Science core collection database, which contains 1,240 articles from the years 1938 to 2021. The analysis shows that the publication of articles on academic procrastination has been rapidly increasing since 1993. In terms of the most influential countries and institutions, the United states took a prominent lead among all countries, and the most productive institutions in this area were the University of Washington and University of California, Los Angeles. By analyzing the authors, we see that most authors like working with a few collaborators, leading to main groups of authors, such as Murat Balkis and June J. Pilcher. The most frequently cited author was Esther D. Rothblum. Based on the co-citation journals network, Personality and Individual Differences was the prolific and influential journal referring to the number of citations and articles it received. The VOSviewer tool identified the hot spots of academic procrastination, which were mainly distributed as follows: (a) procrastination, (b) academic procrastination, (c) self-regulation, (d) academic performance, and (e) motivation. Therefore, this paper is helpful for scholars and practitioners to know the trend of academic procrastination research comprehensively.
Fulltext Transition Metal Dichalcogenides for the Application of Pollution Reduction: A Review

Zhang X, Teng SY, Loy ACM, How BS, Leong WD, Tao X

Nanomaterials (Basel), 2020 May 26;10(6).
PMID: 32466377 DOI: 10.3390/nano10061012

The material characteristics and properties of transition metal dichalcogenide (TMDCs) have gained research interest in various fields, such as electronics, catalytic, and energy storage. In particular, many researchers have been focusing on the applications of TMDCs in dealing with environmental pollution. TMDCs provide a unique opportunity to develop higher-value applications related to environmental matters. This work highlights the applications of TMDCs contributing to pollution reduction in (i) gas sensing technology, (ii) gas adsorption and removal, (iii) wastewater treatment, (iv) fuel cleaning, and (v) carbon dioxide valorization and conversion. Overall, the applications of TMDCs have successfully demonstrated the advantages of contributing to environmental conversation due to their special properties. The challenges and bottlenecks of implementing TMDCs in the actual industry are also highlighted. More efforts need to be devoted to overcoming the hurdles to maximize the potential of TMDCs implementation in the industry.
Fulltext Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

Xu Y, Yu S, Zou JW, Hu G, Rahman NA, Othman RB, et al.

PLoS One, 2015;10(11):e0144171.
PMID: 26636321 DOI: 10.1371/journal.pone.0144171

The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew's correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.
The genetic characteristics and evolution of Tembusu virus

Lei W, Guo X, Fu S, Feng Y, Tao X, Gao X, et al.

Vet Microbiol, 2017 Mar;201:32-41.
PMID: 28284620 DOI: 10.1016/j.vetmic.2017.01.003

BACKGROUND: Since the turn of the 21st century, there have been several epidemic outbreaks of poultry diseases caused by Tembusu virus (TMUV). Although multiple mosquito and poultry-derived strains of TMUV have been isolated, no data exist about their comparative study, origin, evolution, and dissemination.
METHODOLOGY: Parallel virology was used to investigate the phenotypes of duck and mosquito-derived isolates of TMUV. Molecular biology and bioinformatics methods were employed to investigate the genetic characteristics and evolution of TMUV.
PRINCIPAL FINDINGS: The plaque diameter of duck-derived isolates of TMUV was larger than that of mosquito-derived isolates. The cytopathic effect (CPE) in mammalian cells occurred more rapidly induced by duck-derived isolates than by mosquito-derived isolates. Furthermore, duck-derived isolates required less time to reach maximum titer, and exhibited higher viral titer. These findings suggested that poultry-derived TMUV isolates were more invasive and had greater expansion capability than the mosquito-derived isolates in mammalian cells. Variations in amino acid loci in TMUV E gene sequence revealed two mutated amino acid loci in strains isolated from Malaysia, Thailand, and Chinese mainland compared with the prototypical strain of the virus (MM1775). Furthermore, TMUV isolates from the Chinese mainland had six common variations in the E gene loci that differed from the Southeast Asian strains. Phylogenetic analysis indicated that TMUV did not exhibit a species barrier in avian species and consisted of two lineages: the Southeast Asian and the Chinese mainland lineages. Molecular traceability studies revealed that the recent common evolutionary ancestor of TMUV might have appeared before 1934 and that Malaysia, Thailand and Shandong Province of China represent the three main sources related to TMUV spread.
CONCLUSIONS: The current broad distribution of TMUV strains in Southeast Asia and Chinese mainland exhibited longer-range diffusion and larger-scale propagation. Therefore, in addition to China, other Asian and European countries linked to Asia have used improved measures to detect and monitor TMUV related diseases to prevent epidemics in poultry.
Fulltext Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science

Jahmunah V, Sudarshan VK, Oh SL, Gururajan R, Gururajan R, Zhou X, et al.

Int J Imaging Syst Technol, 2021 Jun;31(2):455-471.
PMID: 33821093 DOI: 10.1002/ima.22552

In 2020 the world is facing unprecedented challenges due to COVID-19. To address these challenges, many digital tools are being explored and developed to contain the spread of the disease. With the lack of availability of vaccines, there is an urgent need to avert resurgence of infections by putting some measures, such as contact tracing, in place. While digital tools, such as phone applications are advantageous, they also pose challenges and have limitations (eg, wireless coverage could be an issue in some cases). On the other hand, wearable devices, when coupled with the Internet of Things (IoT), are expected to influence lifestyle and healthcare directly, and they may be useful for health monitoring during the global pandemic and beyond. In this work, we conduct a literature review of contact tracing methods and applications. Based on the literature review, we found limitations in gathering health data, such as insufficient network coverage. To address these shortcomings, we propose a novel intelligent tool that will be useful for contact tracing and prediction of COVID-19 clusters. The solution comprises a phone application combined with a wearable device, infused with unique intelligent IoT features (complex data analysis and intelligent data visualization) embedded within the system to aid in COVID-19 analysis. Contact tracing applications must establish data collection and data interpretation. Intelligent data interpretation can assist epidemiological scientists in anticipating clusters, and can enable them to take necessary action in improving public health management. Our proposed tool could also be used to curb disease incidence in future global health crises.