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To study the spreading trend and risk of COVID-19, according to the characteristics of COVID-19, this paper proposes a new transmission dynamic model named SLIR(susceptible-low-risk-infected-recovered), based on the classic SIR model by considering government control and personal protection measures. The equilibria, stability and bifurcation of the model are analyzed to reveal the propagation mechanism of COVID-19. In order to improve the prediction accuracy of the model, the least square method is employed to estimate the model parameters based on the real data of COVID-19 in the United States. Finally, the model is used to predict and analyze COVID-19 in the United States. The simulation results show that compared with the traditional SIR model, this model can better predict the spreading trend of COVID-19 in the United States, and the actual official data has further verified its effectiveness. The proposed model can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures. Copyright ©2023 Control and Decision.
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The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their "onboarding"period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces. © 2023 ACM.
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PurposeThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).Design/methodology/approachThe authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.FindingsAccording to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.Originality/valueThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.
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This chapter illustrates how the change to a virtual setting challenged students' social connectedness and sense of belonging. It demonstrates how students found a way to build social connectedness in a virtual setting that reinforced their sense of community. The chapter discusses how Students of Color experienced the COVID-19 interruption. It offers insights into whether thriving in college is even possible for students when their means of creating community have been disrupted. At the University of Utah, the detachment was experienced by students in their interactions with faculty and their relationships with their friends, peers, and classmates. The closing of campus and the shift to online learning also limited students' social connectedness with friends, classmates, and peers. Students also relied on new communities to gain motivation and achieve academically. University employees were also a part of students' relational communities. Some students created a strong emotional connection with staff members, such as advisors and student affairs professionals. The pandemic and the resulting educational changes added another layer of complexity to the academic experiences of Students of Color. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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The current respiratory infectious disease has expanded over the world, posing a serious threat to people's physical and mental health, as well as their lives. Science and technology immediately united to fight against such deadly infectious disease in the past 100 years. Mathematical models have proved invaluable to understand and help control infectious disease epidemics. By simplifying real world phenomena, these models describe, analyze, and predict disease transmission patterns, producing tractable solutions in the face of quickly changing situations. In this Chapter, we firstly summarized the history and development of the mathematical models in infectious diseases. Afterwards, the specific transmission dynamics models with different model structures used in fitting and forecasting the situation of the current respiratory infectious disease were introduced, aiming different analytical objectives including but not limited to parameter estimation, trend prediction and early warning, prevention and control measures effectiveness evaluation, and transmission uncertainty exploration. Summary in values of transmission dynamics models is followed to illustrate their contribution in understanding and combating infectious disease outbreaks. Despite their utility, however, mathematical models are facing several important challenges which, if ignored, would result in biased estimation of the crucial epidemiological parameters, bad fitting of the data, or misinterpretation of the results. In conclusion, mathematical modeling should be one of the most valuable tools to reflect such huge uncertainties or, on the other hand, warn of the worst situation. An appreciation of models' shortcomings not only clarifies why they cannot do but helps anticipate what they can. © People's Medical Publishing House, PR of China 2022.
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In spite of the sudden onset of the COVID-19 pandemic, many instructors who used team-based pedagogies shifted them online rather than suspending them entirely, but with limited time and resources. To examine the difference in team dynamics and outcomes for courses in Spring 2019 and Spring 2020 of over 1500 first-year engineering students per semester, Wilcoxon signed-rank tests and random forests method were used. Results show that students reported less improvement in team-member effectiveness, lower psychological safety, and less satisfaction in the semester with the emergency transition. However, students also reported lower conflict. The most important factor predicting project grades shifted from 'Interacting with teammates' to 'Having relevant knowledge, skills, and abilities' amid the emergency shift, accompanied by a reduction in team interdependence. In spite of the collection of data during an emergency transition, the foundation of face-to-face interaction before moving to virtual cooperation represents a useful contribution to research that has focused exclusively on virtual learning circumstances.
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After the outbreak of COVID-19, it is of great significance to find an appropriate dynamic model of COVID-19 epidemic in order to master its transmission law, predict its development trend, and provide corresponding prevention and control basis. In this paper, the SEIRV chamber model is adopted, and the dynamics model of infectious disease is established by combining the fractional derivative of Conformable. The fractional derivative differential equation of Conformable is discretized by numerical method and its numerical solution is obtained. In addition, numerical simulation was carried out on the confirmed data of Wuhan city from January 23, 2020 to February 11, 2020. At the same time, consider that the Wuhan municipal government revised the epidemic data on February 12, 2020, adding nearly 14,000 people. The order α value of SEIRV model is modified, and then the revised data is simulated. The simulation results are in good agreement with the published data. The results show that compared with the traditional integer order model, the fractional order model can simulate the modified data. This reflects the advantages of fractional infectious disease dynamics model, and can provide certain reference value for the prediction of COVID-19 model. © 2022 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.
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The European Air Transportation Network was significantly impacted by the COVID-19 pandemic, resulting in an unprecedented loss of flight connections. Utilizing a combination of graph representation learning and time series analysis, this paper studies the evolution of both the global connectivity as well as the structure of the European Air Transportation Network from January 2020 to December 2022. Specifically, it finds strong differences in recovery rates for flights across six different market segments. In terms of network structure, the study finds that structural roles that are present in the pre-covid network have seen a loss in performance over the course of the pandemic, but have recovered to pre-covid levels. Using regional changes in structural roles, this study identifies Italy as the region with the strongest increase and the United Kingdom as the region with the strongest decrease in structural role, finding substantial differences in recovery rates per market segment. Lastly, this study pays special attention on the effect of the Russia-Ukrainian war on the European Air Transportation Network. © 2023 IEEE.
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Broiler chicken eggs are one of the main and strategic foods for the people of Indonesia and contribute to regional and national inflation. Broiler egg production in Indonesia differs between regions. Areas with a surplus of eggs tend to have lower prices than areas with a deficit. This research is to measure the transmission of broiler egg prices between markets in surplus and deficit areas, using weekly price time series data for the period January 2018-December 2021. Areas of surplus broiler eggs, East Java Province (the highest broiler egg production in Indonesia) which become one of the main suppliers to the Province of East Nusa Tenggara as a deficit area. Using the Johannsen cointegration test it is found that there is no cointegration or there is no relationship between the surplus and deficit regions in the long term but not in the short term. Factors of marketing infrastructure, market information systems, and geographical conditions can be obstacles to the absence of cointegration. The VAR (Vector Auto-Regressive) Vector Error Correction model (VECM) test, found that price transmission occurred between surplus and deficit areas, meaning that between the two regions, there was market integration prior to Covid. The transmission has weakened, and due to the Covid situation, there have been restrictions on the movement of people and goods. The government and other market players need to study the response of the broiler egg market, in the short and long term so that market players can make the right policies.
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This chapter discusses the format of the International Program of Art Therapy in Thailand (IPATT) program before and during the COVID pandemic. A survey will contrast the strengths and weaknesses of online learning as experienced by IPATT students who participated in the program in-person as well as online. The IPATT studio is equipped with audio-visual aids for instructors to present art therapy theories and a studio working area with a wide assortment of art materials that allow students to work experientially. In addition to art therapy education, having students in the studio also gives them opportunities to learn relational literacy inside and outside class time, such as confronting or allying, deepening or skipping, and encouraging or letting go. The student survey that contrasted in-person and online classroom experiences revealed that face-to-face teaching gets a higher score in 3 out of 9 quality sub-categories;most aspects remain the same in both classroom settings. There are advantages and disadvantages to sharing art and personal experiences online, where group dynamics can be different, benefiting some group members but not others. While there is a preference for face-to-face education, the small student survey shows that most IPATT students accept online education as an alternative. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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The aim of this study was to evaluate the impact of the most frequent Asn501 polar uncharged amino acid mutations upon important structural properties of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) Surface Glycoprotein RBD–hACE2 (human angiotensin-converting enzyme 2) heterodimer. Mutations N501Y, N501T and N501S were considered and their impact upon complex solubility, secondary motifs formation and intermolecular hydrogen bonding interface was analyzed. Results and findings are reported based on 50 ns run in Gromacs molecular dynamics simulation software. Special attention is paid on the biomechanical shifts in the receptor-binding domain (RBD) [499-505]: ProThrAsn(Tyr)GlyValGlyTyr, having substituted Asparagine to Tyrosine at position 501. The main findings indicate that the N501S mutation increases SARS-CoV-2 S-protein RBD–hACE2 solubility over N501T, N501 (wild type): (Formula presented.), (Formula presented.). The N501Y mutation shifts (Formula presented.) -helix S-protein RBD [366-370]: SerValLeuTyrAsn into π-helix for t > 38.5 ns. An S-protein RBD [503-505]: ValGlyTyr shift from (Formula presented.) -helix into a turn is observed due to the N501Y mutation in t > 33 ns. An empirical proof for the presence of a Y501-binding pocket, based on RBD [499-505]: PTYGVGY (Formula presented.) 's RMSF peak formation is presented. There is enhanced electrostatic interaction between Tyr505 (RBD) phenolic -OH group and Glu37 (hACE2) side chain oxygen atoms due to the N501Y mutation. The N501Y mutation shifts the (Formula presented.) hydrogen bond into permanent polar contact;(Formula presented.);(Formula presented.). © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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Why do some populations display a higher attack and mortality rate from the current coronavirus disease 2019 (COVID-19) pandemic than others? Are there geographic, environmental, behavioral, genetic, and comorbidity differences that influence spatial dynamics of COVID-19 transmission and outcomes? Where are the regional and country-level hotspots, and what drives those hotspots? These are some of the questions the current chapter strives to answer. The dynamics of transmission and consequences of COVID-19 are not homogeneous but instead have a geographical and spatial clustering. Population-level genetic, vaccination rates, health care disparities, SARS-CoV-2 variants, and meteorological factors are all underlying determinants of the disease dynamics globally, regionally, nationally, and locally. Disease surveillance frameworks to control, mitigate, and prevent the SARS- CoV-2 infections, particularly in low- and middle-income countries, are critical. Lastly, we highlight the spatial differences in the consequences of the pandemic focusing on behavioral and post-acute sequelae of SARS-CoV-2 infection. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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The necessity for portable cooling devices to prevent thermal-related diseases in workers wearing protective clothing in hot outdoor weather conditions, such as COVID-19 quarantine sites, is increasing. Coolers for such purposes require a compact design and low-power consumption characteristics to maximize wearability and operating time. Therefore, a thermoelectric device based on the Peltier effect has been widely used rather than a relatively bulky system based on a refrigeration cycle accompanying the phase change of a refrigerant. Despite a number of previous experimental and numerical studies on the Peltier cooling device, there remains much research to be conducted on the effect and removal of motor-related internal heat sources deteriorating the cooling performance. Specifically, this paper presents thermo-electro-fluidic simulations on the impact of heat from an air blower on the coefficient of performance of a Peltier cooler. In addition, a numerical study on the outcome of heat source removal is also evaluated and discussed to draw an improved design of the cooler in terms of cooling capacity and coefficient of performance. The simulation results predicted that the coefficient of performance could be raised by 10.6% due to the suppression of heat generation from a blower motor. Accordingly, the cooling capacity of the specific Peltier cooler investigated in this study was expected to be considerably improved by 80.6% from 4.68 W to 8.45 W through the design change.
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Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.
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The non-orthogonal local submatrix method applied to electronic structure–based molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32-mixed floating-point arithmetic when using 4400 NVIDIA A100 GPUs of the Perlmutter system. This is enabled by a modification of the original method that pushes the sustained fraction of the peak performance to about 80%. Example calculations are performed for SARS-CoV-2 spike proteins with up to 83 million atoms. [ FROM AUTHOR] Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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This work utilizes Sentinel-2A L1C remote sensing photographs from the years 2018, 2020, and 2022 to identify the different land use categories in the study area using the support vector machine (SVM) technique. The accuracy of categorization is greater than 90%. This research explores four factors of the dynamic change in land use in Hongta District from 2018 to 2022: the proportion of various types of land;the extent of something like the changing land usage;land use transfer;and the dynamic degree of the change in land use. According to the study's results, the proportion of cultivated and grassland land grew, while the quantity of barren and construction land fell by 1.90 percent, 0.03 percent, and 0.69 percent, respectively. The water system land portion of total area increased by 2.58 percent and 0.13 percent, respectively. After comparing the two research periods, the entire dynamic degree of the second stage is determined to be 3.5 percent lower than that of the first stage, and the pace of land use change is quite sluggish, which may be associated with the worldwide COVID-19 outbreak in 2020. The outcomes of the research may give the natural resources department the knowledge it needs to manage land resources properly. © 2023 SPIE.
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This study utilizes the enclosed and stable environment of underground space for long-term sustainable planning for urban epidemics and disasters. Owing to the COVID-19 epidemic, cities require long-term epidemic-disaster management. Therefore, this study proposed a strategy for integrating multiple functions to plan a comprehensive Underground Resilience Core (URC). A planning and assessment methods of URC were proposed. With this methodology, epidemic- and disaster- URCs were integrated to construct a comprehensive-URC in underground spaces. The results show: (1) Epidemic-resilient URCs adopting a joint progressive approach with designated hospitals can rapidly suppress an epidemic outbreak. (2) The regularity of the morphology of underground spaces determines the area of the URC. Bar-shaped underground spaces have the potential for planning disaster-URCs. (3) The URC planning efficiency ranking is as follows: Bar shapes lead overall, T shapes are second under seismic resilience, and Cross shapes are second under epidemic resilience. (4) The potential analysis of planning a comprehensive-URC in the underground parking in Chinese cities showed that the recovery time can be advanced from 29% to 39% and the comprehensive resilience can be improved by 37.63%. The results of this study can serve as sustainable urban planning strategies and assessment tools for long-term epidemic-disaster management.
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As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.
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Understanding particle settlement in channeled fluids has wide applications, such as fine particulate matter, coronavirus particle transport, and the migration of solid particles in water. Various factors have been investigated but few studies have acknowledged the channel's effect on settlement dynamics. This study developed a coupled interpolated bounce-back lattice Boltzmann-discrete element model and examined how a channel's width affects particle settlement. A factor k denoting the ratio of the channel's width and the particle diameter was defined. The terminal settling velocity for a single particle is inversely proportional to k, and the time that the particle takes to reach the terminal velocity is positively related to k. When k is greater than 15, the channel width's effects are negligible. For dual particles of the same size, the drafting-kissing-tumbling (DKT) process occurs infinitely in a periodic pattern, with the two particles swapping positions and settling around the channel's centerline. The smaller the k, the sooner the DKT process occurs. The particles collide with the channel wall when k <= 10. For dual particles of different sizes, the DKT process occurs once so that the bigger particle leads the settlement. Both particles settle along the channel's centerline in a steady state. The bigger the k, the bigger the difference in their terminal settling velocities until k = 15. The small particle collides with the channel wall if released under the big particle when k = 6. The findings of this study are expected to inform channeling or pipeline design in relevant engineering practices.
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In the last 20 years, the world has been threatened with coronavirus (CoV) pandemic threats from severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and finally COVID-19 due to SARS-CoV-2 in 2019. These viruses posed serious global pandemic threats, with estimated case fatality rates of 15% for SARS-CoV, 34% for MERS-CoV, and 1-3% for SARS-CoV-2. With the current pandemic still far from over there is an urgent need to find new drug treatments for COVID-19. We can assume that this will not be the last coronavirus to threaten humanity, so we need better tools to identify drugs active against past but also future coronavirus threats. In this Chapter we describe in silico computer modeling and screening approaches that can rapidly identify drugs from existing drug libraries that could be repurposed to treat COVID-19 infections. We also describe how this computational screening pipeline can be expanded in the future to identify drugs with broad spectrum activity against a wide diversity of coronaviruses. A significant concern is that the protection against CoVs provided by single drugs protection may be short-lived because viruses rapidly mutate to develop drug resistance. We know from other viruses such as HIV that drugs hitting multiple targets within the virus provide better protection against the development of resistance. This Chapter describes the current state of development of in silico CoV drug repurposing, the challenges and pitfalls of these approaches, and our predictions of how these methods could be used to develop drugs for future pandemics before they occur. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.