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1.
International Journal of Applied Metaheuristic Computing ; 13(1):25, 2022.
Article in English | Web of Science | ID: covidwho-1979479

ABSTRACT

GSK algorithm is based on the concept of how humans acquire and share knowledge through their lifespan. Discrete binary version of GSK named novel binary gaining sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. One of these practical applications is to optimally schedule the flights for residual stranded citizens due to COVID-19. The problem is defined for a decision maker who wants to schedule a multiple stepped trip for a subset of candidate airports to return the maximum number of residuals of stranded citizens remaining in listed airports while comprising the minimization of the total travelled distances for a carrying airplane. A nonlinear binary mathematical programming model for the problem is introduced with a real application case study. The case study is solved using DBGSK.

2.
PLoS One ; 17(7): e0251771, 2022.
Article in English | MEDLINE | ID: covidwho-1933198

ABSTRACT

Cave-dwelling bats widely use anthropogenic structures such as temples in south Asia as roosting and nursery sites. Such roosts are constantly under threat, even more so after the COVID-19 pandemic. Despite the importance of such roosts, there is no detailed understanding of what makes temples favorable for bats and the critical factors for their persistence. Here we relate temple microhabitat characteristics and land use around ancient temples (>400 years) to bat species richness and abundance in the Tamiraparani river basin of south India. Temples were selected for sampling along the river basin based on logistics and permission to access them. We counted bats at the roost in the mornings and late afternoons from inside the temples. Temple characteristics such as dark rooms, walkways, crevices, towers, and disturbances to the roosts were recorded. Based on European Space Agency land use classifications, we recorded land use such as crops, trees, scrub, grassland, urban areas, and water availability within a 5 km radius of the temple. Generalized Linear Mixed Models were used to relate the counts in temples with microhabitats and land use. We sampled 59 temples repeatedly across 5 years which yielded a sample of 246 survey events. The total number of bats counted was 20,211, of which Hipposideros speoris was the most common (9,715), followed by Rousettus leschenaultii (5,306), Taphozous melanopogon (3,196), Megaderma lyra (1,497), Tadarida aegyptiaca (303), Pipistrellus sp. (144) and Rhinopoma hardwickii (50). About 39% of the total bats occurred in dark rooms and 51% along walkways. Species richness and total abundance were related to the availability of dark rooms and the number of buildings in the temple. Land use elements only had a weak effect, but scrub and grassland, even though they were few, are critical for bats. We conclude that retaining undisturbed dark rooms with small exits in temples and other dimly lit areas and having natural areas around temples are vital for bat conservation.


Subject(s)
COVID-19 , Chiroptera , Agriculture , Animals , Humans , Pandemics , Trees
3.
Journal of Scientometric Research ; 11(1):47-54, 2022.
Article in English | Web of Science | ID: covidwho-1897066

ABSTRACT

This study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. The PubMed, ScienceDirect, and ResearchGate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed "all articles" and ScienceDirect published articles. Analyzed databases were (1A) PubMed (01/12/2019-12/06/2020) "all_articles" (16) PubMed Review articles) and (1C) PubMed Clinical Trials (2) ScienceDirect all publications (01/12/2019- 25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020 - 30/4/2020). Total publications in the observation period for PubMed, ScienceDirect, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, ScienceDirect and RG were 70.0 +/- 128.6, 77.6 +/- 125.3 and 255.6 +/- 205.8 respectively. PubMed shows an avalanche in the number of publications around May 10, the number of publications jumped from 6.0 +/- 8.4/day to 282.5 +/- 110.3/ day. The average doubling time for PubMed, ScienceDirect, and RG was 10.3 +/- 4 days, 20.6 days, and 2.3 +/- 2.0 days respectively. The average number of publications per author for PubMed, ScienceDirect, and RG was 1.2 +/- 1.4, 1.3 +/- 0.9, and 1.1 +/- 0.4 respectively. Subgroup analysis, PubMed review articles mean review <0 vertical bar 17 +/- 17 vertical bar 77> days: and reducing at a rate of -0.21 days (count)/day. The number of publications related to the COVID-19 until now is huge and growing very fast with time. It is essential to rationalize and limit the publications.

4.
2021 ASEE Virtual Annual Conference, ASEE 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1695438

ABSTRACT

Aspects of society and culture that encompass the response to COVID-19 have impacted all lives, including those of K-12 students and their families. The ongoing COVID-19 pandemic offers a complex context in which students can experience ambiguity with an engineering design challenge as an iterative process of divergent-convergent thinking while focusing on the big picture. Students can learn with an emphasis on systems thinking, making decisions in a collaborative team environment;and managing uncertainty in social processes [1]. The conversations around how schools could function during the pandemic offered a unique opportunity to engage students in problem solving about a situation that they are experiencing themselves. In the US Southwest, three state universities came together during the early stages of the 2020 pandemic lockdown to create a virtual design competition for high school students. The TriU Partnership, including engineering college deans, faculty, and college recruitment and outreach staff from Arizona State University, Northern Arizona University, and the University of Arizona, was formed as an outgrowth of a National Science Foundation, INCLUDES project [2]. One of the aims of this project was to increase engineering awareness and interest amongst a broad population of the state and thereby enhance entry into the state's four-year university engineering programs. The TriU Partnership served 96 high school students from 4 different states in a virtual educational event offered in June 2020. Twenty-five teams of students were asked to consider the challenges their high schools faced in achieving a safe reopening in a pandemic. Over six days, participants attended online seminars, consulted with experts and worked with engineering undergraduate mentors to come up with creative engineering solutions for protective equipment, hallway traffic patterns, bell schedules and social distancing in various high school settings. Final submissions included a detailed engineering notebook, a live online presentation, and interviews with a team of expert judges. The expert judge panel was composed of engineering faculty and industry partners. Teams also submitted prototypes and, in some cases, complete CAD drawings. In this paper, we tell the story of the TriU engineering partnership, share the logistics of the virtual design challenge, talk about lessons learned and share results. Data sources include student survey responses, daily exit tickets, and materials produced such as their final presentation, notebooks, and solutions. The TriU Partnership will continue each summer with each university taking the lead, in turn to offer the design challenge as part of their normal outreach efforts. © American Society for Engineering Education, 2021

5.
Studies in Systems, Decision and Control ; 358:215-228, 2021.
Article in English | Scopus | ID: covidwho-1340304

ABSTRACT

The aim of this paper is to introduce an improved strategy for controlling COVID-19 and other pandemic episodes as an environmental disinfection culture for public places. The scheduling aims at achieving the best utilization of the available working day-time hours, which is calculated as the total consumed disinfection times minus the total loosed transportation times. The proposed problem in network optimization identifies a disinfection group who is likely to select a route to reach a subset of predetermined public places to be regularly disinfected with the most utilization of the available day-working hours. A Nonlinear Binary Model is introduced with a detailed real application case study involving improving the scheduling of Coronavirus disinfection process for some Educational Institutions as an example of crowded public places in Cairo, Egypt. The case study mathematical model is solved using a novel Discrete Binary Gaining-Sharing knowledge-based optimization algorithm (DBGSK). The results of this study show that the novel optimization algorithm can efficiently solve the proposed Problem. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

6.
EAI/Springer Innovations in Communication and Computing ; : 135-157, 2021.
Article in English | Scopus | ID: covidwho-1231879

ABSTRACT

Many application problems are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. One of these practical applications is to optimally distribute protective materials for the newly emerged COVID-19. It is defined for a decision-maker who wants to choose a subset of candidate hospitals comprising the maximization of the distributed quantities of protective materials to a set of chosen hospitals within a specific time shift. A nonlinear binary mathematical programming model for the problem is introduced with a real application case study;the case study is solved using a novel discrete binary gaining-sharing knowledge-based optimization algorithm (DBGSK). The solution algorithm proposes a novel binary adaptation of a recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge through their life span. Discrete binary version of GSK named novel binary gaining-sharing knowledge-based optimization algorithm (DBGSK) depends mainly on two binary stages: binary junior gaining-sharing stage and binary senior gaining-sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. © Springer Nature Switzerland AG 2021.

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