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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.
Leading with purpose: Unraveling the impact of responsible leadership on employee green behavior in the workplace

Xiao Y, Tao X, Chen P, Hung Kee DM

Heliyon, 2024 May 15;10(9):e30096.
PMID: 38707323 DOI: 10.1016/j.heliyon.2024.e30096

Although sustainability has been a priority for organizations, there is still a lack of research on how leaders with a stakeholder perspective can motivate employees to adopt green behavior for sustainability in a complex and changing environment. This paper introduced social cognitive theory to describe two mechanisms by which responsible leadership predicts employee green behavior. Our research considers felt obligation for constructive change and stakeholder value as mediations with cognitive perspective in this process. Additionally, we consider the moderating effects of positive emotion and the superior-subordinate relationship. Our model received support from the investigation and research. By emphasizing the significance of perceived responsible leadership and proposing a new way of perceiving employee green behavior that ensures guidance from responsible leadership along the cognition perspective, the present research contributes to our understanding of the incentive effect of responsible leadership on employee green behavior.
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 Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)

Acharya M, Deo RC, Tao X, Barua PD, Devi A, Atmakuru A, et al.

Comput Methods Programs Biomed, 2025 Feb;259:108506.
PMID: 39581069 DOI: 10.1016/j.cmpb.2024.108506

BACKGROUND AND OBJECTIVES: Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impair the cognitive functions, memory, and daily activities. They affect millions of individuals worldwide, posing a significant challenge for its diagnosis and management, leading to detrimental impacts on patients' quality of lives and increased burden on caregivers. Hence, early detection of MCI and AD is crucial for timely intervention and effective disease management.
METHODS: This study presents a comprehensive systematic review focusing on the applications of deep learning in detecting MCI and AD using electroencephalogram (EEG) signals. Through a rigorous literature screening process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the research has investigated 74 different papers in detail to analyze the different approaches used to detect MCI and AD neurological disorders.
RESULTS: The findings of this study stand out as the first to deal with the classification of dual MCI and AD (MCI+AD) using EEG signals. This unique approach has enabled us to highlight the state-of-the-art high-performing models, specifically focusing on deep learning while examining their strengths and limitations in detecting the MCI, AD, and the MCI+AD comorbidity situations.
CONCLUSION: The present study has not only identified the current limitations in deep learning area for MCI and AD detection but also proposes specific future directions to address these neurological disorders by implement best practice deep learning approaches. Our main goal is to offer insights as references for future research encouraging the development of deep learning techniques in early detection and diagnosis of MCI and AD neurological disorders. By recommending the most effective deep learning tools, we have also provided a benchmark for future research, with clear implications for the practical use of these techniques in healthcare.
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.
Fulltext Engineering of Impact Ionization Characteristics in GaAs/GaAsBi Multiple Quantum Well Avalanche Photodiodes

Tao X, Jin X, Gao S, Yi X, Liu Y, Rockett TBO, et al.

ACS Photonics, 2024 Nov 20;11(11):4846-4853.
PMID: 39584037 DOI: 10.1021/acsphotonics.4c01343

The presence of large bismuth (Bi) atoms has been shown to increase the spin-orbit splitting energy in bulk GaAsBi, reducing the hole ionization coefficient (β) and thereby reducing the excess noise seen in avalanche photodiodes. In this study, we show that even very thin layers of GaAsBi introduced as quantum wells (QWs) in a GaAs matrix exhibit a significant reduction of β while leaving the electron ionization coefficient, α, largely unchanged. The optical and avalanche multiplication properties of a series of GaAsBi/GaAs multiple quantum well (MQW) p-i-n structures with nominally 5 nm thick, 4.4% Bi GaAsBi QWs, varying from 5 to 63 periods and corresponding barrier widths of 101 to 4 nm were investigated. From photoluminescence, ω-2θ X-ray diffraction, and cross section transmission electron microscopy measurements, the material was found to be of high quality despite the strain introduced by the Bi in all except the samples with 54 and 63 QW periods. Photomultiplication measurements undertaken with different wavelengths showed that α in these MQW structures did not change appreciably with the number of QWs; however, β decreased significantly, especially at lower values, the noise factor, F, is reduced by 58% to 3.5 at a multiplication of 10, compared to a similar thickness bulk GaAs structure without any Bi. This result suggests that Bi-containing QWs could be introduced into the avalanching regions of APDs as a way of reducing their excess noise.