Hardisoncarson4965

India reported its first case of COVID-19 on January 30, 2020. Six months since then, COVID-19 continues to be a growing crisis in India with over 1.6 million reported cases. In this communication, we assess the quality of COVID-19 data reporting done by the state and union territory governments in India between July 12 and July 25, 2020. We compare our findings with those from an earlier assessment conducted in May 2020. We conclude that 6 months into the pandemic, the quality of COVID-19 data reporting across India continues to be highly disparate, which could hinder public health efforts.This paper examines the role of entrepreneurs in advancing sustainable lifestyles (SLs) to address climate change and social inequity. It is based on empirical study of eight U.S.-based sustainable entrepreneurs, focused on reducing material consumption. While business has a key role to play, many large companies are unwilling to promote SLs as this is contrary to their current business models which are focused on growing consumption and sales. This presents an opportunity for entrepreneurial companies with innovative business models who are passionate about sustainability and social impact, and better positioned to take risks and innovate. The research examined emerging business models for advancing SLs, key success factors and challenges reported by the entrepreneurs, the social and environmental impacts of their actions, and the future opportunities for scaling up such practices. The study found that entrepreneurs are well positioned to address simultaneously environmental and social issues, however, they s, but help educate and influence key stakeholders, develop informal sustainability ecosystem, and thus create momentum for policy changes.The COVID-19 virus outbreak has affected most of the world in 2020. This paper deals with artificial intelligence (AI) methods that can address the problem of predicting scale, dynamics and sensitivity of the outbreak to preventive actions undertaken with a view to combatting the epidemic. In our study, we developed a cellular automata (CA) model for simulating the COVID-19 disease spreading. The enhanced infectious disease dynamics  S E I R (Susceptible, Exposed, Infectious, and Recovered) model was applied to estimate the epidemic trends in Poland, France, and Spain. We introduced new parameters into the simulation framework which reflect the statistically confirmed dependencies such as age-dependent death probability, a different definition of the contact rate and enhanced parameters reflecting population mobility. To estimate key epidemiological measures and to predict possible dynamics of the disease, we juxtaposed crucial CA framework parameters to the reported COVID-19 values, e.g. length of infection, mortality rates and the reproduction number. Moreover, we used real population density and age structures of the studied epidemic populations. #link# The model presented allows for the examination of the effectiveness of preventive actions and their impact on the spreading rate and the duration of the disease. It also shows the influence of structure and behavior of the populations studied on key epidemic parameters, such as mortality and infection rates. Although our results are critically dependent on the assumptions underpinning our model and there is considerable uncertainty associated with the outbreaks at such an early epidemic stage, the obtained simulation results seem to be in general agreement with the observed behavior of the real COVID-19 disease, and our numerical framework can be effectively used to analyze the dynamics and efficacy of epidemic containment methods.The COVID-19 pandemic highlights the need for fast and simple assays for nucleic acid detection. As an isothermal alternative to RT-qPCR, we outline the development of a detection scheme for SARS-CoV-2 RNA based on reverse transcription recombinase polymerase amplification (RT-RPA) technology. RPA uses recombination proteins in combination with a DNA polymerase for rapid amplification of target DNA at a constant temperature (39-42 °C) within 10 to 20 minutes and can be monitored in real-time with fluorescent probes.Exposure to information about genetics is at an all-time high, while a full understanding of the biocultural complexity of human difference is low. This paper demonstrates the value of an "anthropological approach" to enhance genetics education in biology, anthropology, and other related disciplines, when teaching about human differences such as race/ethnicity, sex/gender, and disability. As part of this approach, we challenge educators across social and natural sciences to critically examine and dismantle the tacit cultural assumptions that shape our understanding of genetics and inform the way we perceive (and teach about) human differences. It calls on educators from both social and natural science disciplines to "de-silo" their classrooms and uses examples from our biological anthropology and sociocultural anthropology classrooms, to demonstrate how educators can better contextualize the "genetics" of human difference in their own teaching. Numerous opportunities to transform our teaching exist, and we are doing a disservice to our students by not taking these critical steps.Social science inquiries of American agriculture have long recognized the inextricability of farm households and farm businesses. Efforts to train and support farmers, however, often privilege business realm indicators over social issues. Such framings implicitly position households as disconnected from farm stress or farm success. This article argues that systematically tracing the pathways between farm households and farm operations represents a potentially powerful inroad towards identifying effective support interventions. We argue childcare arrangements are an underrecognized challenge through which farm household dynamics directly influence agricultural production. We draw on interviews and focus group data with farmers in the Northeastern United States to understand how farmer-parents access and negotiate childcare. Farmer-parents value raising children on farms, but express reluctance to expect current or future labor from them. Years with young children thus represent an especially vulnerable phase during a farm's trajectory. We identify and analyze social, economic, and cognitive pathways through which childcare impacts farm operations. Social pathways include relationship tensions and gendered on-farm divisions of labor; economic pathways include farm layout and structure; cognitive pathways include how farmers think about and plan for their operations. Explicitly acknowledging such issues can better equip farmer-parents to anticipate and plan for conflicting demands on their time.Modelling and simulation (M&S) techniques are frequently used in Operations Research (OR) to aid decision-making. With growing complexity of systems to be modelled, an increasing number of studies now apply multiple M&S techniques or hybrid simulation (HS) to represent the underlying system of interest. A parallel but related theme of research is extending the HS approach to include the development of hybrid models (HM). HM extends the M&S discipline by combining theories, methods and tools from across disciplines and applying multidisciplinary, interdisciplinary and transdisciplinary solutions to practice. In the broader OR literature, there are numerous examples of cross-disciplinary approaches in model development. However, within M&S, there is limited evidence of the application of conjoined methods for building HM. Where a stream of such research does exist, the integration of approaches is mostly at a technical level. In this paper, we argue that HM requires cross-disciplinary research engagement and a conceptual framework. The framework will enable the synthesis of discipline-specific methods and techniques, further cross-disciplinary research within the M&S community, and will serve as a transcending framework for the transdisciplinary alignment of M&S research with domain knowledge, hypotheses and theories from diverse disciplines. The framework will support the development of new composable HM methods, tools and applications. Although our framework is built around M&S literature, it is generally applicable to other disciplines, especially those with a computational element. The objective is to motivate a transdisciplinarity-enabling framework that supports the collaboration of research efforts from multiple disciplines, allowing them to grow into transdisciplinary research.This work aimed at studying the potentiality of interactions between kaolinite surfaces and a protein-fragment (350-370 amino acid units) extracted from the glycoprotein E1 in the transmembrane domain (TMD) of hepatitis C virus capsid. A computational work was performed for locating the potential electrostatic interaction sites between kaolinite aluminol and siloxane surfaces and the residues of this protein-fragment ligand, monitoring the possible conformational changes. This hydrated neutralized kaolinite/protein-fragment system was simulated by means of molecular modeling based on atomistic force fields based on empirical interatomic potentials and molecular dynamic (MD) simulations. read more indicated that the studied protein-fragment interacted with the kaolinite surfaces with an exothermic process and structural distortions were observed, particularly with the hydrophilic aluminol surface by favorable adsorption energy. The viral units isolation or trapping by the adsorption on the kaolinite nanoparticles producing structural distortion of the peptide ligands could lead to the blockage of the entry on the receptor and hence a lack of viral activity would be produced. Therefore, these findings with the proposed insights could be an useful information for the next experimental and development studies in the area of discovering inhibitors of the global challenged hepatitis and other pathogenic viruses based on the phyllosilicate surface activity. These MD studies can be extended to other viruses like the COVID-19 interacting with silicate minerals surfaces.In the wake of the unprecedented global scientific output boom, are top-tier journals such as the FT50 journals following suit? If these prestigious journals consistently increase their publication volumes, will their impact factors be affected? Drawing on the Mann-Kendall trend test method, this study analysed time series trends of the FT50 journals' annual publication volumes and impact factor ratios (IFR) over a 15 year period. link3 The results indicate that half of the FT50 journals have consistently increased their publication volumes over the years. Although to increase publication volumes is riskier than to stay put, it has a significantly higher probability of increasing the IFR, and therefore keeping pace with other top journals. However, the expanding of publication volumes must be carried out cautiously, as the study also finds that growing too fast may lead to opposite effects.This paper presents a non-linear model to simulate and predict the spreading of the newly discovered disease caused by a new series of a Novel Coronavirus (COVID-19). The mathematical modeling in this study is based on the Susceptible Infected Recovery (SIR) model, where key controlling parameters are considered, namely human contact factor b, transmit factor (a), health medication factor (m) and initial infected (I0). The simulation results show the effect of these parameters, and their role in spreading the COVID-19. The results also show that by keeping a high medication factor and a low contact factor, the spreading of COVID-19 will slow down. The medication health factor depends on the infrastructure of a country, and it is difficult to improve it instantly. On the other hand, the contact factor can be easily controlled. Enforcing the physical social distancing, drastically decreases the contact factor. Hence, slow down the spreading of the virus. Also, the effect of medication factor on the number deaths caused by COVID-19 is studied.