The reason of higher number of citations of some articles is discussed. Some considerations about the journals' impact factor, its merits and its pitfalls are also made. Scientific journals' impact factor, popularized by the Institute for Scientific Information, has become an objective parameter for authors' evaluation and also for institutions and other related circumstances. There is no reason for the impact factor's gap between some English journals and those written in other languages. English journals probably benefit of the "Mathew's effect", according to which eminent scientists are more rewarded by similar contributions than others less known. It is paradoxical that most of the major achievements of our age do not appear among the 100 most cited articles. There is no homogeneity among all the articles appearing in each scientific journal: half of the articles are cited ten times more than the other half. However, those articles cited 0 times are credited like the better ones. Each article should be evaluated by its own citations, which would be its impact factor; the authors should be evaluated by their H index.
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http://dx.doi.org/10.3305/nh.2015.32.6.10248 | DOI Listing |
Sci Rep
December 2024
Division of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India.
The mungbean yellow mosaic India virus (MYMIV, Begomovirus vignaradiataindiaense) causes Yellow Mosaic Disease (YMD) in mungbean (Vigna radiata L.). The biochemical assays including total phenol content (TPC), total flavonoid content (TFC), ascorbic acid (AA), DPPH (2,2-diphenyl-1-picrylhydrazyl), and FRAP (Ferric Reducing Antioxidant Power) were used to study the mungbean plants defense response to MYMIV infection.
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December 2024
Division of Pulmonary and Critical Care, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095-1690, USA.
Electronic cigarettes (e-cigs) fundamentally differ from tobacco cigarettes in their generation of liquid-based aerosols. Investigating how e-cig aerosols behave when inhaled into the dynamic environment of the lung is important for understanding vaping-related exposure and toxicity. A ventilated artificial lung model was developed to replicate the ventilatory and environmental features of the human lung and study their impact on the characteristics of inhaled e-cig aerosols from simulated vaping scenarios.
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December 2024
College of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
The mechanical responses of sandy soil under dynamic loading is closely related to protective engineering and geotechnical engineering, is still not fully understood. To investigate the energy attenuation law and propagation velocity of compressed waves in dry sandy soil, this paper focuses on the dynamic response of compression waves in the specimen under single impact and repetitive impact conditions using an improved split Hopkinson pressure bar (SHPB). The results reveal that the length of the specimen follows an exponential relationship with the attenuation of the peak stress.
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December 2024
Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, Riad El-Solh, PO Box 11-0236, 1107 2020, Beirut, Lebanon.
Fatigue is one of the most prevalent and disabling symptoms among patients with MS, but there is limited research investigating the longitudinal determinants of fatigue progression. This study aims to identify the sociodemographic, behavioral and clinical characteristics, and therapeutic regimens that are correlated with worsening fatigue over time in patients diagnosed with MS. This is a retrospective chart review of 483 patients.
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December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
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