Prior research shows that highly religious consumers are more stable through times of uncertainty, in part due to religious support networks. However, several situations (e.g., pandemics, epidemics, natural disasters, mass shootings) represent unique changes where routine large gatherings are restricted due to uncertainty. In such situations, highly religious consumers may experience the greatest disruption to life, potentially resulting in stability-seeking consumption behaviors. Three studies test and confirm this relationship in the coronavirus pandemic context. Specifically, study 1 shows that priming awareness of restricted in-person religious gatherings increases consumption in comparison to a general religious prime or control condition. Study 2 confirms that consumers with higher (lower) levels of religiosity are the most (least) likely to increase consumption, and that situational concern and stability found through purchasing sequentially mediate this relationship. Study 3 provides practical implications revealing that stability-based messaging reduces consumption in comparison to standard social distancing messaging.Quasi-zero stiffness (QZS) vibration isolators can provide better isolation performance in the low frequency range than linear vibration isolators. Currently, most of the designed QZS isolators perform vibration isolation only in one direction and few papers are focused on simultaneously isolating the vibrations in two directions. In this paper, an integrated translational-rotational QZS vibration isolator is designed by using the cam-roller mechanism. The proposed QZS system is able to provide the high-static-low-dynamic stiffness in two directions simultaneously. The excitations in both translational and rotational directions are considered independent but with mutual interaction to their induced vibration response. The workable ranges of the QZS system and its limitations are first numerically identified. Then the static characteristics and typical nonlinear dynamic response with jump phenomena are theoretically investigated. The jump-down frequencies for small amplitude oscillations are determined from their amplitude-frequency relationships. Furthermore, the force transmissibility and moment transmissibility of the proposed QZS system are compared with those of the corresponding linear system without the cam-roller mechanism, which clearly demonstrate better isolation performance in both translational and rotational directions.COVID-19 has affected millions of people across the world but disproportionately and severely affects persons with metabolic disorders such as obesity, diabetes mellitus and hypertension. In this brief review, we discuss the pathways of immune dysregulation that may lead to severe COVID-19 in persons with metabolic conditions.In recent decades, several dozen colleges and universities have instituted loan-reduction initiatives (LRIs), such as "no-loan" programs. Institutions frequently cast such initiatives as efforts to increase socioeconomic diversity on campus. Using a difference-in-differences analytic strategy with national institution-level data, we examine the effect of LRI adoption at 54 institutions on three sets of outcomes student borrowing, admission metrics, and campus diversity. Our analysis suggests LRIs decreased institution-level borrowing rates at private institutions, with no detected change at public institutions. https://www.selleckchem.com/products/molidustat-(bay85-3934).html Consistent with stated program goals, LRI adoption increased the number of Pell Grant recipients at both public and private institutions. However, adopting LRIs at public institutions reduced racial/ethnic diversity, suggesting possible trade-offs for LRI adoption in terms of student body diversity.Many U.S. households lack access to healthful food and rely on inexpensive, processed food with low nutritional value. Surveying access to healthful food is costly and finding the factors that affect access remains convoluted owing to the multidimensional nature of socioeconomic variables. We utilize machine learning with census tract data to predict the modified Retail Food Environment Index (mRFEI), which refers to the percentage of healthful food retailers in a tract and agnostically extract the features of no access-corresponding to a "food desert" and low access-corresponding to a "food swamp." Our model detects food deserts and food swamps with a prediction accuracy of 72% out of the sample. We find that food deserts and food swamps are intrinsically different and require separate policy attention. Food deserts are lightly populated rural tracts with low ethnic diversity, whereas swamps are predominantly small, densely populated, urban tracts, with more non-white residents who lack vehicle access. Overall access to healthful food retailers is mainly explained by population density, presence of black population, property value, and income. We also show that our model can be used to obtain sensible predictions of access to healthful food retailers for any U.S. census tract.As the COVID-19 pandemic expands, the shortening of medical equipment is swelling. A key piece of equipment getting far-out attention has been ventilators. The difference between supply and demand is substantial to be handled with normal production techniques, especially under social distancing measures in place. The study explores the rationale of human-robot teams to ramp up production using advantages of both the ease of integration and maintaining social distancing. The paper presents a model for faster integration of collaborative robots and design guidelines for workstations. The scenario is evaluated for an open source ventilator through continuous human-robot simulation and amplification of results in a discrete event simulation.Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classification and region-level functional difference detection tasks between ASD and healthy control (HC) groups is difficult. Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data. We investigate the potential of including mutual information (MI) loss (Infomax), which is an unsupervised term encouraging large MI of each nodal representation and its corresponding graph-level summarized representation to learn a better graph embedding. Specifically, this work developed a pipeline including a GNN encoder, a classifier and a discriminator, which forces the encoded nodal representations to both benefit classification and reveal the common nodal patterns in a graph.