[Eating in public transportation: A behavior to avoid for health and sanitary purposes?]

Variations in Citations Across Databases: Implications for Journal Impact Factors

The Journal Impact Factor (JIF) is a widely used metric for ranking journals based on the number of citations garnered by papers published over a specific timeframe. To assess the accuracy of JIF values, I compared citation counts for 30 of my own publications across six major bibliography databases: CrossRef, Web of Science, Publisher records, Google Scholar, PubMed and Scopus. The analysis revealed noteworthy variations in citation counts, ranging from 10% to over 50% between the lowest and highest citation counts. Google Scholar records the highest citation numbers, while PubMed reported the lowest. Notably, Web of Science, whose citation data are used in JIF calculations, tend to underestimate citation counts compared to other databases. These observations raise concerns about the accuracy of JIF calculation based on Web of Science's citation data. The real JIF values for most journals would differ from those annually reported by Clarivate's journal citation reports (JCR). These citation discrepancies underscore the importance of comprehensive data collection and the necessity to include additional citation sources. Not because a paper is cited in one journal rather than another should it have a less or more citation weight. Ultimately, one citation remains one citation, regardless of its origin. Clarivate Analytics may thus need to consider integrating all citation sources for more accurate JIF values. Alternatively, Google Scholar could potentially develop its own journal or citation impact based on its extensive journal citation records. However, while making adjustments to how the Journal Impact Factor is calculated can make it more mathematically precise, it doesn't address the fundamental biases built into the metric. Even with refinements, the Journal Impact Factor will remain skewed due to how it's defined and used.

Make good use of big data: A home for everyone

The ongoing COVID-19 pandemic should teach us some lessons at health, environmental and human levels toward more fairness, human cohesion and environmental sustainability. At a health level, the pandemic raises the importance of housing for everyone particularly vulnerable and homeless people to protect them from the disease and against other similar airborne pandemics. Here, I propose to make good use of big data along with 3D construction printers to construct houses and solve some major and pressing housing needs worldwide. Big data can be used to determine how many people do need accommodation and 3D construction printers to build houses accordingly and swiftly. The combination of such facilities- big data and 3D printers- can help solve global housing crises more efficiently than traditional and unguided construction plans, particularly under environmental and major health crises where health and housing are tightly interrelated.

Environmental and Health Effects of Ventilation in Subway Stations: A Literature Review

Environmental health in subway stations, a typical type of urban underground space, is becoming increasingly important. Ventilation is the principal measure for optimizing the complex physical environment in a subway station. This paper narratively reviews the environmental and health effects of subway ventilation and discusses the relevant engineering, environmental, and medical aspects in combination. Ventilation exerts a notable dual effect on environmental health in a subway station. On the one hand, ventilation controls temperature, humidity, and indoor air quality to ensure human comfort and health. On the other hand, ventilation also carries the potential risks of spreading air pollutants or fire smoke through the complex wind environment as well as produces continuous noise. Assessment and management of health risks associated with subway ventilation is essential to attain a healthy subway environment. This, however, requires exposure, threshold data, and thereby necessitates more research into long-term effects, and toxicity as well as epidemiological studies. Additionally, more research is needed to further examine the design and maintenance of ventilation systems. An understanding of the pathogenic mechanisms and aerodynamic characteristics of various pollutants can help formulate ventilation strategies to reduce pollutant concentrations. Moreover, current comprehensive underground space development affords a possibility for creating flexible spaces that optimize ventilation efficiency, acoustic comfort, and space perception.