PurposeThis paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.Design/methodology/approachA systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.FindingsOver the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.Research limitations/implicationsThis review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.Originality/valueThis paper provides a systematic insight into research trends in ML in both logistics and the supply chain.

The study aims to investigate to survey students of Can Tho University (CTU) about the understanding of same-sex marriage, thereby analyzing students’ perceptions of same-sex marriage. Since then, the study pointed out the factors that affect students’ perception of this issue. Data of the study were collected from 400 students who are majoring in Social Humanities, Natural Science and Engineering Sciences at CTU. Descriptive statistics are used mainly in this study to clarify the purpose of the study. The research results showed that CTU students have understandings of same-sex marriage, most of them realized the positive and negative effects of this relationship.

Healthcare workers are among the most at-risk groups to get infected by SARS-CoV-2 due to the exposure to disease sources in specific working environment. However, after experiencing four waves of epidemics, there was a certain proportion of healthcare workers who have ever been diagnosed to be infected with SARS-CoV-2, regardless of whether they have a history of contact with or an unknown history of contact with an unidentified source of infection. This research aimed to identify the characteristics of healthcare workers who have not diagnosed with SARS-CoV-2 or have never been infected by SARS-CoV-2. 79 staffs of Hanoi Medical University were selected at the time of April 2022. 16 out of 79 individuals were infected with SARS-CoV-2 but had not been identified. 50% of this group were physicians working at primary care unit. Subjects who had been infected by SARS-CoV-2 showed greater levels of anti-receptor-binding domain (RBD) of SARS-CoV-2 antibodies than those who had never been infected (p=0.001). Anti-RBD antibody levels in 93.67% of research individuals were above the FDA-recommended threshold of 4.8 U/mL for the use of convalescent plasma. In the group that had never been infected by SARS-CoV-2, the antibody levels against RBD-SARS-CoV-2 were lower in those vaccinated above 90 days than those vaccinated below 90 days (p=0.02). 20.25% of research subjects who had never been diagnosed had infected by SARS-CoV-2, with greater levels of anti-RBD-SARS-CoV-2 antibodies than the uninfected group. In the uninfected group, antibody levels gradually decreased over time.

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