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(searched for: 10.29328/journal.ijcv.1001035)
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Raja Anupam, Pk Saifuddin, Shekhar Nishant, Singh Harvinder, Sarma Phulen, Prakash Ajay,
International Journal of Clinical Virology, Volume 5, pp 047-053; https://doi.org/10.29328/journal.ijcv.1001035

Abstract:
More than 200 countries have been devastated by the SARS-CoV-2 pandemic. The health workers exposed to SARS patients have been confirmed to be infected with coronavirus (SARS-CoV-2), regardless of the degree of their exposure. The increasing complexity of virus existence and heterogeneity has cast doubt on disinfectants as a viable choice. Hence, the present systematic review aims to achieve the comparative analysis of established disinfectants against enveloped and non-enveloped viruses including SARS-CoV and MERS-CoV. Three databases (Pubmed, Google scholar, and Medline) were searched to frame the systematic review. Our comparative analyses with 34 studies have found that 85% ethanol-based hand sanitizers and 7.5% Povidine Iodine based soaps/surgical scrub could be used to deter the SARS-CoV-2 virus as preferred hand sanitizers. For surface eradication, 0.5% sodium hypochlorite or a mixture of glutaraldehyde, Quaternary Ammonium Compounds (QAC), and isopropanol could have more efficacies as compared to hydrogen peroxide, phenol, and QAC alone. Moreover, the accelerated hydrogen peroxide as an active ingredient in the automatic quick surface disinfectant (tunnel system), maybe a positive indication for quick whole-body sanitation. Additionally, the alternative method for avoiding the rapidly increasing chain of infection with SARS and restarting regular life has been exclusively discussed.
, Soma Shirakabe, Yun He, Shunya Ueta, Teppei Suzuki, Kaori Abe, Asako Kanezaki, Shin'ichiro Morita, Toshiyuki Yabe, Yoshihiro Kanehara, et al.
Published: 20 July 2017
Abstract:
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In 2015 and 2016, we thoroughly study 1,600+ papers in several conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV.
Yun R. Fu
Manifold Learning for 3D Shape Description and Classification; https://doi.org/10.21236/ada606874

Abstract:
Periodically, the US Army conducts detailed measurement surveys of its soldiers as a way to understand the impact that changes in soldier body size have for the design, fit and sizing of virtually every piece of clothing and equipment that Soldiers wear and use in combat. Recently finished US Army Anthropometric Survey (ANSUR II) has collected 3D body scan data of soldiers at the Natick Solider Center (NSC), as shown in Figure 1. By applying new techniques for shape analysis and classification to these 3D body scan data will help designers of clothing and personal protection equipment to understand and fit Army population. The overall research goal of this proposal is to create a new manifold learning framework for large-scale graph decomposition and approximation problems by low-rank approximation and guarantee computable, stable and fast optimizations for 3D shape description and classification. The PI's group has published (or accepted for publication) 1 book through Springer and 13 scientific papers partially supported by this grant. In particular, these papers are in top journals and conference proceedings such as TPAMI, IJCV, TCSVT, ICCV, AAAI, SDM, ACM MM, etc. One paper, 1 out of 384, receives the Best Paper Award in SDM 2014. The PI, Dr. Y. Raymond Fu has received the 2014 INNS Young Investigator Award, from International Neural Networks Society (INNS), 2014. Leveraged by this grant, the PI has been granted an ARO Young Investigator Program (YIP) Award and a Defense University Research Instrumentation Program (DURIP) award.
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