Study on the Comparison of Three Typical Gas Flow Standards for Calibrating Low Gas Flowmeter

A gas flowmeter for measuring low flow rate has been widely used in the field of medical, health, environmental protection, energy industry, aerospace, etc. To against Covid-2019, the requirement on the low flow rate has been increasing dramatically. At present, the typical standard devices for calibrating low gas flowmeter mainly include standard bell provers of gas flow, standard piston provers of low gas flow and standard laminar of low gas flow. Different measuring principles are adopted among these typical standard devices. To ensure the consistency of these typical standard devices, a comparison test is performed. The standard devices used in the comparison are of the same accuracy grade, with an extended uncertainty of 0.2%(k=2). The piston-type gas flow calibrator of grade 1.0 is selected as the transfer standard, and three flow points with high flow rate, medium flow rate and low flow rate are selected for test. The consistency of measurement results is evaluated by normalized deviation En. The comparison results are acceptable which show that three typical standard devices are accurate and reliable. © FLOMEKO 2022.All rights reserved

Stacking Classifier-based Automated Insurance Fraud Detection System

During the Covid-19 pandemic, the insurance industry's digital shift quickened, resulting in a surge in insurance fraud. To combat insurance fraud, a system that securely manages and monitors insurance processes must be built by combining a machine learning classification framework with a web application. Examining and identifying fraudulent features is a frequent method of detecting fraud, but it takes a long time and can result in false results. One of these issues is addressed by the proposed solution. By digitalizing the paper-based workflow in insurance firms, this paper intends to improve the efficiency of the existing approach. This method also aimed to improve the present approach's data management by integrating a web application with a machine learning stacking classifier framework experimented on a linear regression-based iterative imputed data for detecting fraud claims and making the entire claim processing and documentation process more robust and agile. © 2022 IEEE.

U-Net based Semantic Segmentation for Touchless Fingerprint Technology: A Reflective Review

Touch-based fingerprints are widely used in today's world;even with all the success, the touch-based nature of these is a threat, especially in this COVID-19 period. A solution to the same is the introduction of Touchless Fingerprint Technology. The workflow of a touchless system varies vastly from its touch-based counterpart in terms of acquisition, pre-processing, image enhancement, and fingerprint verification. One significant difference is the methods used to segment desired fingerprint regions. This literature focuses on pixel-level classification or semantic segmentation using U-Net, a key yet challenging task. A plethora of semantic segmentation methods have been applied in this field. In this literature, a spectrum of efforts in the field of semantic segmentation using U-Net is investigated along with the components that are integral while training and testing a model, like optimizers, loss functions, and metrics used for evaluation and enumeration of results obtained. © 2022 IEEE.

Monetary Policy Shocks and Resource Misallocations in the Periphery: Evidence from Chinese Provincial Bond Yields

We examine the effects of monetary expansions in the center on the excess yields of government bonds in the periphery. We show that the excess yields widen, and the widening is larger, the larger the misallocation of resources in the periphery. Specifically, we examine a panel of over 2500 local Chinese government bonds in 31 provinces, and find that the expansion of Chinese money supply surprisingly raises the excess yields of local bonds over Chinese sovereign bonds, and the gap is larger, the higher the degree of misallocation or other institutional failures in the province. By treating Chinese provinces as small emerging market open economies, we can draw implications for periphery countries in a common currency area, of an expansion in money supply in the center. Our results suggest that the increase in global liquidity post the 2009 and Covid-19 crises can lead to future fragility in the periphery countries.

Towards Intermediated Workflows for Hybrid Telemedicine

The growing platformization of health has spurred new avenues for healthcare access and reinvigorated telemedicine as a viable pathway to care. Telemedicine adoption during the COVID-19 pandemic has surfaced barriers to patient-centered care that call for attention. Our work extends current Human-Computer Interaction (HCI) research on telemedicine and the challenges to remote care, and investigates the scope for enhancing remote care seeking and provision through telemedicine workflows involving intermediation. Our study, focused on the urban Indian context, involved providing doctors with videos of remote clinical examinations to aid in telemedicine. We present a qualitative evaluation of this modified telemedicine experience, highlighting how workflows involving intermediation could bridge existing gaps in telemedicine, and how their acceptance among doctors could shift interaction dynamics between doctors and patients. We conclude by discussing the implications of such telemedicine workflows on patient-centered care and the future of care work. © 2023 Owner/Author.

The attachment of adult women to the Italian labour market in the shadow of COVID-19.

We investigate the attachment to the labour market of women in their 30s, who are combining career and family choices, through their reactions to an exogenous, and potentially symmetric shock, such as the COVID-19 pandemic. We find that in Italy a large number of women with small children, living in the North, left permanent (and temporary) employment and became inactive in 2020. Despite the short period of observation after the burst of the pandemic, the identified impacts appear large and persistent, particularly with respect to the men of the same age. We argue that this evidence is ascribable to specific regional socio-cultural factors, which foreshadow a potential long-term detrimental impact on female labour force participation.

The global financial cycle and capital flows during the COVID-19 pandemic.

We estimate the heterogeneous effect of the global financial cycle on exchange rates and cross-border capital flows during the COVID-19 pandemic, using weekly exchange rate and portfolio flow data for a panel of 59 advanced and emerging market economies. We estimate a global financial cycle (GFC) index at the weekly frequency with data through the end of 2021. We then estimate the country-specific sensitivities of exchange rates and capital flows to fluctuations in the GFC. The ability of the GFC to explain fluctuations in exchange rates and capital flows increased dramatically during the pandemic. There is significant cross-country heterogeneity in the response of exchange rates or capital flows to fluctuations in the GFC. During the pandemic, high-frequency indicators like weekly changes in Covid cases and vaccination rates were just as important as standard macroeconomic fundamentals like the current account, reserves, and net foreign assets in explaining this heterogeneity.

Predictive Information Workflow of Forecasting Number of COVID-19 Confirmed Cases

There are questions about how to accurately prepare with the correct number of resources for distribution in order to properly manage the healthcare resources (e.g., healthcare workers, Masks, ART-19 TestKit) required to tighten the grip on the COVID-19 pandemic. Mathematical and computational forecasting models have well served the means to address these questions, as well as the resulting advisories to governments. A workflow is proposed in this research, aiming to develop a forecasting simulation that makes accurate predictions on COVID-19 confirmed cases in Singapore. According to the analysis of the prior works, six candidate forecasting models are evaluated and compared in the workflow: polynomial regression, linear regression, SVM, Prophet, Holt's linear, and LSTM models. The study's goal is to determine the most suitable forecasting model for COVID-19 cases in Singapore. Two algorithms are also proposed to better compute the performance of two models: the order algorithm to determine optimal degree order for the polynomial regression model, and the optimizing algorithm for the Holt's linear model to calculate the optimal smoothing parameters. Observed from the experiment results with the COVID-19 dataset, the Prophet method model achieves the best performance with the lowest Root Mean Square Error (RMSE) score of 1557.744836 and Mean Absolute Percentage Error (MAPE) score of 0.468827, compared to the other five models. The Prophet method model achieving average accuracy range of 90% when forecasting the number of confirmed COVID-19 cases in Singapore for the next 87 days ahead. is chosen and recommended to be used as a system model for forecast the COVID-19 confirm cases in Singapore. The developed workflow will greatly assist the authorities in taking timely actions and making decisions to contain the COVID-19 pandemic. © 2022 IEEE.

The Italian quick survey on the effects of the COVID-19 health emergency on businesses: Sampling strategy and data editing

During the first phase of the COVID-19 pandemic, Istat performed the quick survey "Situation and perspectives of Italian enterprises during the COVID-19 health emergency,” with the aim of assessing the economic situation and the specific actions adopted by businesses to reduce the economic impacts of the emergency. To ensure the continuity in the information flow and to analyze the temporal evolution of the observed phenomena, the survey has been repeated in three different waves. The outcomes of each wave was released just after 2 months from the launch of the survey. The present work analyses the characteristics of the sampling strategy and describes the complexity of the data editing process, in the case of a survey planned to produce estimates able to ensure an acceptable level of accuracy in the maximum timeliness. © 2023 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons Ltd.

REGIONAL BUSINESS SUPPORT PROGRAMS IN THE CONTEXT OF COVID-19 AS AN EXAMPLE OF INTRODUCING BIG DATA IN PUBLIC ADMINISTRATION

The article puts forward a hypothesis about the difference between digitalization and management based on big data (hereinafter referred to as BD);it is assumed that these are stages of the same process. The transition from digitalization to BD-driven decisions is perceived as a kind of «leap» in public administration. Authors set the research task of identifying the key differences between these stages, as well as determining the reasons for the transition from one to the other. To do this, we carry out an analysis of the implementation of BD-driven solutions in public administration during the COVID-19 period, with a focus on the regional level. The analysis is based on the multiple streams theory. The case-study method was used to compare the selected regions. At this stage of the study, a gap in the development of the digital infrastructure in the Russian regions was revealed – a situation where only rich regions can carry out their original solutions, having the opportunity to invest in advance in the development of digital infrastructure, training and original management solutions. As a result of the conducted research, the absence of original digital solutions in the regions was noted. An alternative to using a BD-driven policy is simple digitalization, by which we mean either imitation solutions or blind copying of technological solutions offered by the federal center. In order to switch to BD-driven management, regional administrations need to accumulate expertise, experience in implementing successful digital projects, and make investments in the development of technological infrastructure and training. © HSE, 2022.

THE SPREAD OF THE COVID-19 INFECTION IN RUSSIA'S BALTIC MACRO-REGION: INTERNAL DIFFERENCES

This article explores the spread of the COVID-19 infection in Russia's Baltic macro-region. The monthly excess mortality rate in the Baltic region is analysed along with regional and municipal COVID-19 response acts to identify regional features affecting the spread of the disease. The spatial characteristics of Russia's Baltic regions, germane to the propagation of COVID-19, were distinguished by examining selected social and economic statistical indicators. Based on the space of places/space of flows dichotomy, Russia's Baltic regions can be divided into three spaces: 1) St. Petersburg, the Leningrad and Kaliningrad regions (dominated by spaces of flows;highly permeable space);2) the Republic of Karelia and the Murmansk region (the key factors are rotational employment and the introduction of the virus from without);3) the Novgorod and Pskov regions (lowly permeable spaces of places;the central role of local foci of the disease). The principal risk factor for the space of flows is the rapid spread of COVID-19 along transport arteries, whilst, within the space of places, the coronavirus spreads through spatial diffusion from isolated foci along short radii. In the former case, local authorities counteracted spatial diffusion by restricting movement in the local labour market;in the latter, by limiting travel between the centre and the periphery. The traditional ideas about positive (openness, centrality) and negative (closedness, peripherality) characteristics of space are reversed in the context of the pandemic: periphery gains the benefit of natural protection from the pandemic, whilst centres become acutely vulnerable © Alov, I. N., Pilyasov, A. N., 2023

The next phases of the Migrante Project: Study protocol to expand an observatory of migrant health on the Mexico-U.S. border.

Background: Mexican migrants traveling across the Mexico-United States (U.S.) border region represent a large, highly mobile, and socially vulnerable subset of Mexican nationals. Population-level health data for this group is hard to obtain given their geographic dispersion, mobility, and largely unauthorized status in the U.S. Over the last 14 years, the Migrante Project has implemented a unique migration framework and novel methodological approach to generate population-level estimates of disease burden and healthcare access for migrants traversing the Mexico-U.S. border. This paper describes the rationale and history of the Migrante Project and the protocol for the next phases of the project. Methods/design: In the next phases, two probability, face-to-face surveys of Mexican migrant flows will be conducted at key crossing points in Tijuana, Ciudad Juarez, and Matamoros (N = 1,200 each). Both survey waves will obtain data on demographics, migration history, health status, health care access, COVID-19 history, and from biometric tests. In addition, the first survey will focus on non-communicable disease (NCD), while the second will dive deeper into mental health and substance use. The project will also pilot test the feasibility of a longitudinal dimension with 90 survey respondents that will be re-interviewed by phone 6 months after completing the face-to-face baseline survey. Discussion: Interview and biometric data from the Migrante project will help to characterize health care access and health status and identify variations in NCD-related outcomes, mental health, and substance use across migration phases. The results will also set the basis for a future longitudinal extension of this migrant health observatory. Analyses of previous Migrante data, paired with data from these upcoming phases, can shed light on the impact of health care and immigration policies on migrants' health and inform policy and programmatic responses to improve migrant health in sending, transit, and receiving communities.

Balanced Development of Tourism and Resort and Recreational Sphere: International, National and Regional Vectors

The subject of the study is international, national and regional vectors of balanced development of tourism and resort and recreational sphere. Methodology. The study used general scientific methods, in particular: theoretical generalization;methods of positive and normative analysis and statistical analysis. The aim is to substantiate the process of balanced development of tourism and resort and recreational sphere using international, national and regional vectors in modern conditions. Conclusion of the study. International experience of balanced tourism development has shown that among the leading countries in tourism are France, USA, China, Spain, Italy, Turkey, Mexico, Thailand. It is emphasized that these countries are annually visited by 30 to 100 million tourists. It was noted that France ranks first among the countries that attract tourists, the tourist flow to which in 2019 amounted to 217.8 million people, in 2020 it decreased to 117.1 million people, or almost 1.86 times;in second place among the leading countries is the United States of America, where in 2019 the tourist flow amounted to 167.4 million people, and in 2020 it decreased by almost 3.7 times and amounted to 44.7 million people;China ranks third in terms of tourist visits in 2019 - 167.5 million people, in 2020 it decreased to 30.4 million people, or 5.3 times. Analysis of the national experience of tourism development showed that the pandemic of viral infection also had the most significant impact on the reduction of tourist flows in Ukraine. It is noted that in 2019, the reduction of inbound tourists was almost 4 times, while the reduction of tourists in 2014 at the beginning of military aggression was 1.8 times. The authors consider it appropriate to predict a slight reduction in tourist flows during a full-scale war between Russia and Ukraine and to ensure the development of an innovative model of balanced development of national tourism. The creation of an innovative model of balanced development of tourism and resort and recreational sphere with the use of international, national and regional vectors is quite relevant in modern conditions. It is proved that such a model should ensure the achievement of public interests, short-term and long-term goals in solving alternative tasks of obtaining quick and maximum profit by the tourism business and meeting the needs of international tourist traffic and international capital in solving the problem of geographical promotion of tourist flows to new territories.

The Impact of Foreign Trade on International Equity Investments During Global Financial Crisis and Great Lockdown Period

Countries more invest to their close trade partners and this relationship between trade and financial investments differs during the global crisis processes. This study analyzes the effect of commodity trade on bilateral equity investments and how this effect changed in the context of the 2008 Global Financial Crisis and the Great Lockdown with the gravity model. The gravity model has been estimated with the instrumental variable method to avoid the endogeneity between two flows. The results show that trade reduces equity investment. While this relationship did not change in the 2008 crisis, it is seen that the import increased the equity investments in the Covid-19 crisis.

System Dynamics Modeling for Assessing the Impact of COVID-19 on Food Supply Chains: A Case Study of Kenya and Rwanda

Disruptions in the food supply chains caused by the COVID-19 pandemic have destabilized the balance between production, supply, transport, distribution, and consumption. Consequently, these disruptions have affected food and nutritional security all over the world. This study proposes a framework for investigating the impact of COVID-19 on food supply chains, considering Eastern Africa as a focus region with Kenya and Rwanda as case studies. A systems thinking approach with three systemic components (food and nutrition, COVID-19 contagion, and human health) was applied. The contagion component was characterized by the susceptible, exposed, infected, recovered, and deceased (SEIRD) epidemiological modeling method. We then applied a causal loop diagram and stock and flow diagrams to map the links and interactions between variables from the contagion, health, and food supply chain components of the whole system. The results reveal that COVID-19 has adversely affected food and nutritional security in Eastern African countries. Key response measures to COVID-19 such as lockdowns, closure of borders, isolation, and quarantining have resulted in labor shortages, increased unemployment rates, loss of income, and the subsequent contraction of economies. The disruption of the food supply chain has negatively impacted the main pillars of food and nutrition security, which are availability, accessibility, utilization, and stability. We suggest direct food supply from local producers to the consuming communities to shorten the food supply chain and therefore enhance food self-sufficiency to reduce the severe effects of COVID-19 on food and nutrition security. Overall, our study provides a useful framework to help design better policies and build more resilient and inclusive food systems during COVID-19 and similar pandemics in the future.

Regional Business Support Programs in the Context of Covid-19 as an Example of Introducing Big Data in Public Administration

The article puts forward a hypothesis about the difference between digitalization and management based on big data (hereinafter referred to as BD);it is assumed that these are stages of the same process. The transition from digitalization to BD-driven decisions is perceived as a kind of << leap >> in public administration. Authors set the research task of identifying the key differences between these stages, as well as determining the reasons for the transition from one to the other. To do this, we carry out an analysis of the implementation of BD-driven solutions in public administration during the COVID-19 period, with a focus on the regional level. The analysis is based on the multiple streams theory. The case-study method was used to compare the selected regions. At this stage of the study, a gap in the development of the digital infrastructure in the Russian regions was revealed - a situation where only rich regions can carry out their original solutions, having the opportunity to invest in advance in the development of digital infrastructure, training and original management solutions. As a result of the conducted research, the absence of original digital solutions in the regions was noted. An alternative to using a BD-driven policy is simple digitalization, by which we mean either imitation solutions or blind copying of technological solutions offered by the federal center. In order to switch to BD-driven management, regional administrations need to accumulate expertise, experience in implementing successful digital projects, and make investments in the development of technological infrastructure and training.

Redefining investable infrastructure in developing nations in a postpandemic era: the case of the 15-Minute City

The impacts of the COVID-19 pandemic have led developing nations to increasing debt ceilings, with an inability to invest in infrastructural developments that can benefit communities and societies, in favor of direct economic investment flows. While direct investment flows can lead to increasing employment, they do not have indirect benefits that can increase urban quality of life. An emerging concept of 15-Minute City showcases that how urban regeneration, through the precepts of chrono-urbanism, allows for proximity-based planning principles that increase the sustainability, resilience, and economic prosperity of neighborhoods. The direct beneficiaries of the funding of such a concept lead to higher quality of life, which indirectly can lead to higher economic returns;hence aligning with the precept that funding such programs can lead to higher economic resilience and vibrancy. This chapter explores the need to redefine "investable infrastructure” in line with the Sustainable Development Goals and in a postpandemic era. © 2023 Elsevier Inc. All rights reserved.

The impact of Covid-19 pandemic on the value relevance of cash flows: evidence from banks

Purpose: This paper is motivated by the ongoing debate on the value relevance of cash flow statement (CFS) between bank regulators, on one hand, and accounting standard setters, on the other hand. The importance and usefulness of cash flows during economic turmoil exacerbated by the Covid-19 pandemic has recently gained attention. Hence, this paper investigates the impact of Covid-19 pandemic on the value relevance of cash flows (CF) beyond accounting earnings and book equity. Design/methodology/approach: A panel of 236 bank-year observations is examined for value relevance in emerging capital markets of Gulf Cooperation Council (GCC) countries. Using value relevance regression, operating and non-operating CF items are tested for value relevance beyond earnings and book value of equity during the periods 2018–2019 (pre-pandemic) and 2020–2021 (pandemic). Findings: There is limited value relevance of CF beyond accounting earnings and the book value of equity. The distinction between operating and non-operating CF is not informative. However, the value relevance is significant during the pandemic, indicating that investors rely on CF for valuation purposes at times of uncertainty, corroborating further research on CF distress predictive ability. Originality/value: This paper provides novel evidence on value relevance of CF and its superiority to accounting earnings and equity values during times of uncertainty. It extends a small body of research in the banking sector in emerging markets. Hence, it complements prior literature and has practical implications to accounting standard setters and bank regulators in emerging markets. © 2023, Emerald Publishing Limited.

Whither critical Management and Organization Studies? For a performative critique of capitalist flows in the wake of the COVID-19 pandemic

Over the last few months, COVID-19 has entered our own consciousness as a moment of profound disruption, leading in too many cases to misery and death, but also, forcing us more mundanely to reorganize our lives, work and social relations. The disruption of capitalist flows by the pandemic has exacerbated the cleavages and power inequalities. Addressing them can help critical MOS to reconnect the places and times of production and paid work to the circulation and consumption of goods and services and, more broadly, to social reproduction. This is necessary to identify the contradictions at the heart of capitalism, denaturalize it as a mode of organizing the economy and society, and envision more just flows and novel subjectivities. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

COVID-19 and Beyond: Taming the Virtual Healthcare Tiger with Colored Petri Nets based Modeling and Simulation

The Coronavirus crisis has forced hospitals globally to develop new virtual service portals and systems to: 1) triage, diagnose, and manage new patients virtually for every clinical specialty at home as their symptoms emerge, avoiding COVID-19 exposure to patient or physicians, and hospitalization, as much as possible, and 2) discharge, track, and support recovered patients via homecare and virtual visits to free as many critical care beds as possible. This paper focuses on simulating and modeling an episode of care with innovative initial patient contact and triage processes using the Colored Petri Net (CPN) formalism to help optimize workflow, patient throughput, and overall system efficacy. The two patient triage programs under consideration are a health system in Australia and an orthopedic surgical program in the US. We describe our model for the US program. Our presented results establish a desired stratification of patients through a virtual musculoskeletal triage. © 2022 IEEE Computer Society. All rights reserved.