The impact of positive thinking on anxiety level of corona virus/ covid-19 infection during the lockdown among UNRWA staff in Jordan.

The current study aimed to identify the impact of positive thinking on the anxiety of Coronavirus infection through the lockdown among UNRWA staff in Jordan. To achieve the objectives of the study, three measures were developed, the Positive Thinking Scale, the anxiety level of Coronavirus Infection Scale, and the lockdown Scale. A sample of (2036) employees responded to the measures. The results showed that the level of anxiety for COVID -19 infection among the sample was high, and statistically significant differences were found in the level of Covid-19 infection anxiety according to the sex variable for females, also statistically differences were found in the level of anxiety in COVID-19 infection according to age in favor of (31-40), and statistically differences for the social status in favor of married couples. Results also showed a direct negative impact of positive thinking variable on the level of anxiety associated with COVID-19 infection, and an effect of positive thinking on the level of anxiety during activating the lockdown. The study recommends designing training programs for employees to help them adapt to different circumstances and enable them to continue performing their assigned work. [ FROM AUTHOR] Copyright of An-Najah University Journal for Research, B: Humanities is the property of An-Najah National University 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.)

Subsidy strategy for reserving flexible capacity of emergency supply production

This paper investigates a government's subsidy strategy for motivating a manufacturer to set up a flexible production line for emergency supplies. Four subsidy strategies are proposed to ensure a desired service level in case of an emergency: zero subsidy, a fixed subsidy, a marginal subsidy, and a hybrid subsidy. We develop a game theoretical model to examine how the government can induce a manufacturer to set up a flexible production line that can respond promptly to an emergency, based on the manufacturer's cost structure (fixed and marginal costs). We find that when the marginal profit of an emergency product is higher than that of the manufacturer's regular product, a fixed (marginal) subsidy is the dominant strategy if the manufacturer's fixed (marginal) cost is high, while a hybrid subsidy strategy is dominant if both costs are high. When the marginal profit of an emergency product is lower than that of the manufacturer's regular product, neither a fixed subsidy nor a zero subsidy will be the dominant strategy. We also find that a marginal subsidy can ensure the effectiveness of the strategy, while a fixed subsidy helps improve strategy efficiency. We use government subsidy strategies implemented for Chinese COVID-19 emergency supplies as examples to demonstrate the effectiveness and efficiency of the subsidy strategies under the proposed framework. We also extend the discussion by considering the manufacturer's social consciousness.

New development: A ‘journey of personal and professional emotions'—emergency ambulance professionals during Covid-19

IMPACTThis article explores the consequences of emotional labour on UK NHS ambulance staff and their response to the Covid-19 pandemic. It highlights the challenges faced by ambulance crews while dealing with their emotional labour within the context of organizational settings. Research findings also explain the importance of emergency responders' psychosocial wellbeing. The article has clear relevance as to how frontline staff manage their emotional labour in other emergency service settings, such as the police and fire and rescue services.Alternate :Managing emotions are essential aspect of many jobs, and frontline healthcare workers have to manage and control their emotions while caring for critically ill patients and working in an emotionally-charged dynamic environment;this was particularly the case during Covid-19. Ambulance workers are an important group in this respect but they are currently under-researched. Evidence behind this article comes from data collected from an NHS ambulance trust in England. One of the key contributions of this article is to highlight how frontline ambulance professionals manage their emotional labour while working within the stipulations of organizational constraints.

A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients' suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP;(2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

Lessons for the Age of Consequences: COVID-19 and the Macroeconomy

Comparative empirical evidence for 22 OECD countries shows that country differences in cumulative mortality impacts of SARS-CoV-2 are caused by weaknesses in public health competences, pre-existing variances in structural socio-economic and public health vulnerabilities, and the presence of fiscal constraints. Remarkably, the (fiscally non-constrained) U.S. and the U.K. stand out, as they experience mortality outcomes similar to those of fiscally-constrained countries. High COVID19 mortality in the U.S. and the U.K. is due to pre-existing socio-economic and public health vulnerabilities, created by the following macroeconomic policy errors: (a) a deadly emphasis on fiscal austerity (which diminished public health capacities, damaged public health and deepened inequalities);(b) an obsessive belief in a trade-off between ‘efficiency' and ‘equity', which is mostly used to justify extreme inequality;(c) a complicit endorsement by mainstream macro of the unchecked power over monetary and fiscal policy-making of global finance and the rentier class;and (d) an unhealthy aversion to raising taxes, which deceives the public about the necessity to raise taxes to counter the excessive liquidity preference of the rentiers and to realign the interests of finance and of the real economy. The paper concludes by outlining a few lessons for a saner macroeconomics.

Collaboration in humanitarian operations in the context of the COVID-19 pandemic

This chapter provides an overview of collaboration in humanitarian operations, focusing on the logistical aspects. Humanitarian logistics and operations has emerged as a subfield of supply chain and operations management and studies how humanitarian organizations can be more efficient in the delivery of humanitarian aid. We review the key characteristics of this subfield compared to traditional logistics and supply chain management. Collaboration is particularly important in the humanitarian context, in which only the collective performance of humanitarian organizations should count. We therefore discuss key factors of collaboration using the humanitarian SCOR model. Finally, we analyze how far the COVID-19 pandemic has forced humanitarian organizations to collaborate differently in their operations and what can be learned from it for the future of humanitarian operations. © Springer Nature Switzerland AG 2023. All rights reserved.

Access to special COVID-19 relief from distress grant and livelihood outcome of livestock farming households in Eastern Cape Province, South Africa

Unexpected events and shocks constitute greater threats to the attainment of zero hunger targets in Africa and the world over, and in the extreme case, lead to total collapse of the global food system and food supply chain. Consequently, this causes significant loss of critical income sources, renders individuals vulnerable, and further deteriorates households' livelihood outcome and welfare state. Therefore, the need for social protection programs to mitigate the impact of distress and unexpected events, as well as extreme occurrences cannot be over emphasized. This research used dataset from the 1499 households captured in the 2021 South African General Household Survey to investigate whether access to a special relief from distress grant has effect on the livestock farming households' food security status in Eastern Cape Province of South Africa. Descriptive statistics, cross-tabulation, a two-sample t-test, a food insecurity experience-based scale technique, and a fractional outcome model were used to analyze the datasets. Based on access to the grant, households in the non-beneficiary group are significantly distinguishable from the beneficiary counterparts, such that the beneficiary households out-performed the non-beneficiary households in the food break-even and food surplus categories. The findings further indicated the possibility of transition of the beneficiary households' population under the transitory food insecurity category to either the chronic food insecurity status or food break-even status, subject to the effectiveness of the food security policy to which they are exposed. The fractional outcome model also indicated that non -metropolitan resident households (p < 0.05), access to the special grant (p < 0.01), access to health facilities (p < 0.01), age of households' heads (p < 0.01), colored, indian and white population groups (both at p < 0.01), as well as access to remittance (p < 0.01) made significant contributions to the households' food security status. The Wald test indicated that access to the special relief grant had a significant effect on the households' food security status in the study area. The study therefore recommends accelerated investments in various social investment programs as sustained responses to expected and unexpected shocks and occurrences to be able to induce progress and realize more resilient food systems.

An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19

In this proposal, a study based on deep-learned features via transfer learning was developed to obtain a set of features and techniques for pattern recognition in the context of COVID-19 images. The proposal was based on the ResNet-50, DenseNet-201 and EfficientNet-b0 deep-learning models. In this work, the chosen layer for analysis was the avg pool layer from each model, with 2048 features from the ResNet-50, 1920 features from the DenseNet0201 and 1280 obtained features from the EfficientNet-b0. The most relevant descriptors were defined for the classification process, applying the ReliefF algorithm and two classification strategies: individually applied classifiers and employed an ensemble of classifiers using the score-level fusion approach. Thus, the two best combinations were identified, both using the DenseNet-201 model with the same subset of features. The first combination was defined via the SMO classifier (accuracy of 98.38%) and the second via the ensemble strategy (accuracy of 97.89%). The feature subset was composed of only 210 descriptors, representing only 10% of the original set. The strategies and information presented here are relevant contributions for the specialists interested in the study and development of computer-aided diagnosis in COVID-19 images. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Aid, Trade and Development: The Future of Globalization, Second Edition

This volume presents a broad sweep of modern economic history underpinning aid, trade, development and globalization in the last half century and the salient challenges facing the global community today. The author draws on his long years as an academic and development practitioner to recommend what needs to be done to cope with the backsliding of the fight against global poverty, fractured geopolitics and the threats to the multilateral economic order. The new, revised edition analyses how unilateralism, rising protectionism and the Covid-19 pandemic seriously threaten global sustainable development. It concludes with recommendations on the policy changes needed to make globalization more equitable and development more sustainable. This book will be of interest to researchers and students of economic development and economic history, as well as all those concerned about global inequality and sustainability. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Food Bank Led Federal Food Disbursement and Pantry Sourced Food Quality Over Time During COVID-19 Pandemic in Minnesota

Federally funded Emergency Food Assistance Program (TEFAP) aims to improve nutrition equity through hunger relief setting. However, little is known about TEFAP disbursement method (choice, modified choice, and no choice) to pantries or the quality received by pantries. Food inventory data from 239 pantries in 2020 were used to assess the association between TEFAP quality and type of disbursement methods longitudinally. While no significant differences were observed in quality across disbursement types and months. Our study suggests there are tradeoffs across different methods. TEFAP for the most part is a healthy food source that should be maximized by food pantries.Copyright © 2023 Taylor & Francis Group, LLC.

Housing inequalities and resilience: the lived experience of COVID-19

COVID-19 policy responses have intensified the use of housing as a spatial and material defence against community spread of infection. In so doing, they have focussed attention upon pre-existing inequalities and the effects of socio-economic management of COVID-19. This paper draws upon individual households' accounts to explore these effects on housing inequalities, and then adapts a critical resilience framework from disaster response in order to examine the implications for policymaking. The empirical work centres upon a case study of lived experiences of COVID-19-constrained conditions, based on a longitudinal-style study combining semi-structured interviews with 40 households, photographs and household tours at two datapoints (before/during COVID-19) in Victoria, Australia. The study reveals how these households were impacted across four domains: (1) employment, finances, services, and mobilities;(2) homemaking including comfort and energy bills, food and provisioning, and home-schooling/working from home;(3) relationships, care and privacy, and;(4) social, physical and mental health. The interviews also indicate how households coped and experienced relief payments and other related support policies during COVID-19. Drawing upon literature on disaster response, we highlight the centrality of vulnerability and resilience in recognising household exposure and sensitivity to COVID-19, and capabilities in coping. From this analysis, gaps in COVID-19 housing and welfare policy are exposed and guide a discussion for future housing policy interventions and pandemic planning.

Opening industry data: The private sector's role in addressing societal challenges

This commentary explores the potential of private companies to advance scientific progress and solve social challenges through opening and sharing their data. Open data can accelerate scientific discoveries, foster collaboration, and promote long-term business success. However, concerns regarding data privacy and security can hinder data sharing. Companies have options to mitigate the challenges through developing data governance mechanisms, collaborating with stakeholders, communicating the benefits, and creating incentives for data sharing, among others. Ultimately, open data has immense potential to drive positive social impact and business value, and companies can explore solutions for their specific circumstances and tailor them to their specific needs.

Navigating crisis, navigating ICTs: A study of Indian migrant workers during the pandemic

In this note, we present the preliminary findings from a qualitative interview-based study among migrant workers in India who went through much hardship in the wake of one of most strict pandemic-induced lockdown in 2020. Through this study, we narrate the four ways in which digital technologies enabled the relief and crisis mitigation efforts targeted to migrant workers and how that in turn shaped the workers' experience of the crisis and associated relief efforts. We argue that more flexible use of familiar digital tools and channels, collaboration across state and non-state actors and assistance from human intermediaries in navigating ICTs make for more effective and inclusive relief measures. © 2022 ACM.

Optimization-driven distribution of relief materials in emergency disasters.

The distribution of relief materials is an important part of post-disaster emergency rescue. To meet the needs of the relief materials in the affected areas after a sudden disaster and ensure its smooth progress, an optimized dispatch model for multiple periods and multiple modes of transportation supported by the Internet of Things is established according to the characteristics of relief materials. Through the urgent production of relief materials, market procurement, and the use of inventory collection, the needs of the disaster area are met and the goal of minimizing system response time and total cost is achieved. The model is solved using CPLX software, and numerical simulation and results are analyzed using the example of the COVID-19 in Wuhan City, and the dispatching strategies are given under different disruption scenarios. The results show that the scheduling optimization method can meet the material demand of the disaster area with shorter time and lower cost compared with other methods, and can better cope with the supply interruptions that occur in post-disaster rescue.

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients.

Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning-based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg's method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.

Collecting and communicating perishable data in a post-disaster context: rapid research and rapid dissemination.

Context: Puerto Rico experienced four natural disasters in 4 years (2017-2021): Hurricanes Irma and Maria, thousands of earthquakes reaching 6.4 magnitude, and the COVID-19 pandemic. In this context, our team sought to understand the impact of disaster aid distribution on poverty and economic inequality, and their relationship to the spread of COVID-19 across Puerto Rico. Rapid research was required to ensure we could collect perishable data within this ever-changing context. Challenges: Our mixed methods design relied on both secondary and primary data. Because analyses of the former were to inform where and how to collect the latter, timing was of the essence. The data sources identified were not readily available to the public, and thus required gaining access through direct requests to government agencies. The requests coincided with a transition between administrations after an election. This resulted in unexpected delays. Once in the field, the team had to balance the rapid nature of the research with the mindful work to avoid compounding traumas experienced by participants, heightened risk for re-traumatization and fatigue, the risk of COVID-19, the digital divide, and intermittent electrical and telecommunication services. Adaptations: In response to the delayed access to secondary data, we adjusted our research question. We continued to collect data as they became available, incorporating some immediately into analyses, and cleaning and storing others for future research opportunities. To overcome ongoing trauma challenges and prevent fatigue, we recruited and hired a large temporary team, including members of communities where we collected data. By recruiting participants and co-researchers at the same time and place, we both collapsed time between these activities and increased our team's contextual competency. To adapt to challenges presented by the pandemic, we created hybrid data collection procedures where some data were collected online, and some in person, while maintaining COVID-19 protections. We used similar adaptations for dissemination. Lessons: Rapid research needs to be agile. Working within a convergence framework to investigate wicked problems had the unexpected added benefit of providing our team with a variety of disciplinary approaches which proved helpful in adapting to the changing conditions in the field. In addition to the resourcefulness of a transdisciplinary team, it is important to be willing to pivot in response to changes and to collect data where and when you can. To increase participation, opportunities need to be designed with flexibility, mindful of competing demands faced by individuals willing to collaborate. Collecting and analyzing data iteratively and utilizing local resources can enable rapid research that is rigorous and yields rich data. Contributions: Our team applied the lessons learned to structure a rapid and iterative dissemination plan. We combined member-checking with community-level dissemination, enabling us to hone findings further before presenting to policy makers and media. Rapid research creates opportunities to make data-informed program and policy adjustments when they can be most impactful. Both the media and policy makers pay closer attention to research on current events. Hence, our recommendation is to do more rapid research! The more we do, the better we will get at it, and the more accustomed community leaders, policy makers, and program designers will become to using data to inform decisions.

Corporate Social Responsibility in Times of Epidemic Outbreaks: A Case Study from the Hospitality Industry in Spain

Corporate social responsibility (CSR) is increasingly seen as a key aspect of business management. The rapid spatial spread of the COVID-19 outbreak led to border closures and mandatory mass quarantine. In this context, sectors such as the hospitality industry have been actively involved in various CSR activities, providing infrastructure and resources to help governments and societies cope with the pandemic. This study examines the different effects of CSR activities in the context of the COVID-19 outbreak, both from a business and institutional perspective. A qualitative research design has been chosen for this purpose, based on the analysis of in-depth qualitative interviews with the CEO and senior executives of three international hotel chains in Spain. The results reveal that CSR activities have contributed to containing the pandemic by helping to reduce the collapse of healthcare, as well as improving the organization's performance in terms of reputation and image. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

The Role of Political Connections in COVID Policy Response: Effectiveness of Firm-Level Government Support in Egypt

The COVID-19 pandemic saw two sets of policy responses: lockdown to limit spread of the virus, which was a huge demand and supply shock, and government support to firms and individuals to offset the effects of this policy-induced shock. This paper explores the allocation and effectiveness of government support to firms in Egypt. We consider both financial support measures which were by and large already being implemented pre-COVID, as well as tax- and loan-related exemptions and deferments. After controlling for the endogeneity of government support, our main findings show that the latter has helped mitigate the effects of COVID-19, with a significantly larger, favorable impact on smaller, younger and private firms. There is no equity-effectiveness trade-off. However, although these firms apparently make better use of government support, they receive a disproportionately smaller share of it. In line with the emerging ‘unsocial' social contract, government support has been chiefly determined by political connections and a captured industrial policy. This ‘misallocation' reinforces the ‘missing middle' phenomenon which acts as a constraint as SMEs are unable to grow. Nevertheless, the crisis has presented a chance for the pattern of support to slowly shift towards the more vulnerable through the more frequent use of ‘exemptions and deferments'. © 2023 German Institute of Development and Sustainability.

Corporate Restructuring Laws Under Stress

Bail-outs by way of loan have a similar effect (on the debtor: plainly, the cost of delivering relief is allocated differently as between a bail-out and a bail-in) in that they enable the debtor to meet current fixed costs through borrowing, in effect swapping shorter-term liabilities with a longer-term liability. The authors acknowledge the support of the Oxford Law Faculty in funding the Conference "Corporate Restructuring Laws Under Stress" (St Hugh's College, Oxford, 10 October 2022) at which the papers in this special issue were first presented, and the support of the Covid-19 Research Response Fund at Oxford University, which provided funding for the wider project of which the Conference formed one part. Most authors, however, express some concerns in relation to Covid-19 bail-out design, and in particular query whether some bail-outs may have been too generous. [Extracted from the article] Copyright of European Business Organization Law Review is the property of Springer Nature 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.)

India's lockdown: an interim report.

The world has continued to change rapidly since the last version of this article was written on May 20, 2020. Yet, as this article goes to press, we are aware of two realities; first, that we cannot perennially chase a moving target, but second, that nothing about the fundamental trends that we have identified appear to have changed. India is firmly in the throes of a vicious pandemic that we can only hope will abate with the development of an effective vaccine. Our plea for the widespread provision of adequate health and medical facilities, adequate protection for the elderly, and transfers to those severely affected by the lockdown are absolutely unchanged in the face of the latest data. In contrast, the brutal enforcement of a lockdown with none of these accompanying measures can only worsen outcomes for the poorest and most vulnerable among the population.