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Multi-dimensional Open public Wellbeing A reaction to any COVID-19 Episode Amongst Meat-Processing Workers, Utah, March-June 2020.
COVID-19 has caused unprecedented health, economic and societal impacts across the world, including many low- and middle-income countries (LMICs). The pandemic and its fallout have laid bare deep-seated social and economic inequalities with marginalised groups being at greater risk of infection and being disproportionately affected by containment measures and their socioeconomic consequences. Stigma is a central element to such inequalities but remains largely overlooked in the debate on the response to COVID-19, including in LMICs. Yet we know from experiences with other infectious diseases such as HIV/AIDS and Ebola that disease-related stigma is detrimental to halting and controlling pandemics and achieving equitable development. Emerging evidence suggests that stigma associated with COVID-19 is already taking hold. This paper assesses potential driving factors of COVID-19-related stigma, and how this intersects with existing stigma fault lines and explores mechanisms through which COVID-19-related stigma may be counteracted, with a focus on LMICs.The global spread of COVID-19 represents a massive challenge for developing countries. Beyond the health crisis and the sudden stop of domestic economic activities, many countries face turmoil linked to commodity dependence. Commodity prices have reacted strongly to the crisis, reflecting changes in supply and demand due to policy measures to limit contagion. Commodity-dependent developing countries are therefore confronted with an unprecedented combination of shocks. However, the crisis has also exposed structural vulnerabilities of these countries linked above all to commodity price dynamics. In the context of a longstanding debate on commodities and development, we portray recent commodity price developments and underlying drivers and discuss implications for commodity-dependent countries, including the risks of depressed export earnings and of changing global production patterns in the long run. Responses to the crisis have to include measures to stabilize commodity prices as well as strategies for economic diversification.Cold chain maintaining is least stable at its end, where domestic storage often represents one of the most critical links because of storage time and inappropriate temperatures, increasing the risk of food-borne outbreaks in domestic households. Considering the time-temperature profile of refrigerators as a food safety indicator, the purpose of this study is to gain insight into refrigeration temperatures in parallel with refrigerator and household characteristics that could potentially influence the refrigeration temperatures. During a 24 h period in 15-min intervals, internal temperature of the test product, refrigerator air and ambient air temperatures were measured with one penetration and two air probes coupled with a data logger. The internal temperature of the test product was measured with pre-prepared "Karlsruhe Test Material", which had thermal properties similar to those of lean beef. Refrigerator and household characteristics were collected with a predefined observational sheet and short, structurconsumers to minimise food safety risks, improve food quality, and reduce food wastage.A novel approach to surrogate modeling motivated by recent advancements in parameter dimension reduction is proposed. selleck inhibitor Specifically, the approach aims to speed-up surrogate modeling for mapping multiple input variables to a field quantity of interest. Computational efficiency is accomplished by first identifying principal components (PC) and corresponding features in the output field data. A map from inputs to each feature is considered, and the active subspace (AS) methodology is used to capture their relationship in a low-dimensional subspace in the input domain. Thus, the PCAS method accomplishes dimension reduction in the input as well as the output. The method is demonstrated on a realistic problem pertaining to variability in residual stress in an additively manufactured component due to the stochastic nature of the process variables and material properties. The resulting surrogate model is exploited for uncertainty propagation, and identification of stress hotspots in the part. Additionally, the surrogate model is used for global sensitivity analysis to quantify relative contributions of the uncertain inputs to stress variability. Our findings based on the considered application are indicative of enormous potential for computational gains in such analyses, especially in generating training data, and enabling advancements in control and optimization of additive manufacturing processes.This study presents a methodology for estimating passenger's spatio-temporal trajectory with personalization and timeliness by using incomplete Wi-Fi probe data in urban rail transit network. Unlike the automatic fare collection data that only records passenger's entries and exits, the Wi-Fi probe data can capture more detailed passenger movements, such as riding a train or waiting on a platform. However, the estimation of spatio-temporal trajectories remains as a challenging task because a few unfavorable situations could result into deficient data. To address this problem, we first describe the Wi-Fi probe data and summarize their common defects. Then, the n-gram method is developed to infer missing spatio-temporal location information. Next, an estimation algorithm is designed to generate feasible spatio-temporal trajectories for each individual passenger by integrating multiple data sources, i.e., urban rail transit network topology, Wi-Fi probe data, train schedules, etc. This proposed method is tested on both simulated data in blind experiments and real-world data from a complex urban rail transit network. The results of case study show that 93% of passengers' unique physical routes can be estimated. Then, for 80% of passengers, the number of feasible spatio-temporal trajectories can be reduced to one or two. Potential applications of the trajectory estimation approach are also identified.The present work provides theoretical and experimental foundations for the ability of dinitrosyl iron complexes (DNICs) with thiol-containing ligands to be not only the donors of neutral NO molecules, but also the donors of nitrosonium cations (NO+) in living organisms ensuring S-nitrosation of various proteins and low-molecular-weight compounds. It is proposed that the emergence of those cations in DNICs is related to disproportionation reaction of NO molecules, initiated by their binding with Fe2+ ions (two NO molecules per one ion). At the same time, possible hydrolysis of iron-bound nitrosonium cations is prevented by the electron density transition to nitrosonium cations from sulfur atoms of thiol-containing ligands, which are included in the coordination sphere of iron. It allows supposing that iron in iron-nitrosyl complexes of DNICs has a d7 electronic configuration. This supposition is underpinned by experimental data revealing that a half of nitrosyl ligands are converted into S-nitrosothiols (RSNOs) when those complexes decompose, with the other half of those ligands released in the form of neutral NO molecules.
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