The Application of PageRank model in urban epidemic prevention and control

The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.

Quantitative Identification Study of Epidemic Risk in the Spatial Environment of Harbin City

Global pandemics pose a threat to the sustainable development of urban health. As urban spaces are important places for people to interact, overcrowding in these spaces can increase the risk of disease transmission, which is detrimental to the sustainable development of urban health. Therefore, it is crucial to identify potential epidemic risk areas and assess their risk levels for future epidemic prevention and the sustainable development of urban health. This article takes the main urban area of Harbin as the research object and conducts a cluster spatial analysis from multiple perspectives, including building density, functional density, functional mix, proximity, intermediacy, and thermal intensity, proposing a comprehensive identification method. The study found that (1) functional density is the most significant influencing factor in the formation of epidemic risks. Among various urban functions, commercial and public service functions have the strongest impact on the generation and spread of epidemic risks, and their distribution also has the widest impact range. (2) The spaces with higher levels of epidemic risk in Harbin are mainly distributed in the core urban areas, while the peripheral areas have relatively lower levels of risk, showing a decreasing trend from the center to the periphery. At the same time, the hierarchical distribution of urban space also has an impact on the spatial distribution of the epidemic. (3) The method proposed in this study played an important role in identifying the spatial aggregation of epidemic risks in Harbin and successfully identified the risk levels of epidemic distribution in the city. In spatial terms, it is consistent with high-risk locations of epidemic outbreaks, which proves the effectiveness and feasibility of the proposed method. These research findings are beneficial for measures to promote sustainable urban development, improve the city's epidemic prevention capabilities and public health levels, and make greater contributions to the sustainable development of global public health, promoting global health endeavors.

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation: IMS

PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implicationsThanks to the abnormal risk panel;human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/valueThe monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products' waste avoidance.

Assessing the Risk of Spreading COVID-19 in the Room Utilizing Low-Cost Monitoring System

High hygiene standards were established during the COVID-19 epidemic, and their adherence was closely monitored. They included the need to regularly wash one's hands and the requirement to cover person's upper airways or keep at least a two-meter space between individuals. The ITS (Information Technology Systems) community made a big contribution to this by developing methods and applications for the ongoing observation of people and the environment. Our major objective was to create a low-cost, straightforward system for tracking and assessing the danger of spreading COVID-19 in a space.The proposed system collects data from various low-cost environmental sensors such as temperature, humidity, CO2, the number of people, the dynamics of speech, and the cleanliness of the environment with a significant connection to elements of wearable electronics and then evaluate the level of contamination and possible risks and, in the event of a high level of risk, alerts the person to take actions that can reduce or eliminate favourable conditions for the spread of the virus. The system was created at the Laboratory of industrial control systems of the University of Žilina, Slovakia. The experiment demonstrates the ability and feasibility to control the number of people in a space depending on particular symptoms like fever, coughing, and hand hygiene. On the other hand, the laboratory's temperature, humidity, and air quality should be controlled to reduce the spread of illness.

A Smart CO2-Based Ventilation Control Framework to Minimize the Infection Risk of COVID-19 In Public Buildings

This study aims to present a smart ventilation control framework to reduce the infection risk of COVID-19 in indoor spaces of public buildings. To achieve this goal, an artificial neural network (ANN) was trained based on the results from a parametric computational fluid dynamics (CFD) simulation to predict the COVID-19 infection risk according to the zone carbon dioxide (CO2) concentration and other information (e.g., zone dimension). Four sample cases were analyzed to reveal how the CO2 concentration setpoint was varied for a given risk level under different scenarios. A framework of smart ventilation control was briefly discussed based on the ANN model. This framework could automatically adjust the system outdoor airflow rate and variable air volume (VAV) terminal box supply airflow rate to meet the needs of reducing infection risk and achieving a good energy performance. © International Building Performance Simulation Association, 2022

A Digital Twins Model for Analyzing and Simulating Cold Chain Risks

Due to the outbreak of COVID-19, increasing attention has been paid to designing a cold chain logistics mechanism to ensure the quality of vaccine delivery. In this study, a cold chain digital twins-based risk analysis model is constructed to handle and monitor the vaccine delivery process with a high level of reliability and traceability. The model integrates the Internet of Things (IoT) and digital twins to acquire data on environmental conditions and shipment movements and connect physical cold chain logistics to the digital world. Through the simulation of cold chain logistics in a virtual environment, the risk levels relating to physical operations at a certain forecast horizon can be predicted beforehand, to prevent a 'broken' cold chain. The result of this investigation will reshape the cold chain in the digital age, benefit society in terms of sustainability and environmental impact, and hence contribute to the development of cold chain logistics in Hong Kong. © 2023 IEEE.

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

In this study, we analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensified the spillover effects across international stock markets. Our results demonstrate that DE, UK, EU, and US acted as net transmitters of dynamic connectedness;however, Japan, China, India, and Hong Kong acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

Managing Building Water Disruptions in a Post-COVID World: Water Quality and Safety Risk Assessment Tool for Academic Institutions and School Settings

Fluctuating building occupancy during the COVID-19 pandemic contributed to poor water quality and safety conditions in building water distribution systems (BWDSs). Natural disasters, man-made events, or academic institutional calendars (i.e., semesters or holiday breaks) can disrupt building occupant water usage, which typically increases water age within a BWDS. High water age, in turn, is known to propagate poor water quality and safety conditions, which potentially exposes building occupants to waterborne pathogens (e.g., Legionella) associated with respiratory disease or hazardous chemicals (e.g., lead). Other influencing factors are green building design and municipal water supply changes. Regardless of the cause, an increasing number of water management policies require building owners to improve building water management practices. The present study developed a Water Quality and Safety Risk Assessment (WQSRA) tool to address gaps in building water management for academic institutions and school settings. The tool is intended to assist with future implementation of water management programs as the result of pending policies for the built environment. The WQSRA was modeled after water management practices created for controlling water contaminants in healthcare facilities. Yet, a novel WQSRA tool was adapted specifically for educational settings to allow building owners to evaluate risk from water hazards to determine an appropriate level of risk mitigation measures for implementation. An exemplar WQSRA tool is presented for safety, facility, industrial hygiene, and allied professionals to address current gaps in building water management programs. Academic institutions and school settings should examine the WQSRA tool and formulate an organization-specific policy to determine implementation before, during, and after building water-disruptive events associated with natural or man-made disasters.

Prevention and Control of COVID-19 Infection and Property Management: Evidence from Wuhan, China

Property enterprise has contributed significantly to the prevention and control of COVID-19, and its functions received positive feedback from the urban residents via a survey. Detailed data on confirmed COVID-19 cases in 446 communities in Wuhan were collected and the property fee of each community was used to assess the quality of the property services provided. Both binary logit and ordered logit models were used to measure the impact of property fees on the pandemic prevention and control efficiency of each community. The results showed that a higher property fee corresponded to a better property service and a higher probability that the residential community would be free of COVID-19. Furthermore, where property fees were higher, pandemic prevention and control efficiency increased and the community achieved a lower pandemic risk level. In conclusion, the promotion of high-quality property services is conducive to community disease prevention and control in the case of a pandemic.

Data-Driven COVID-19 Vaccine Development for Janssen

The COVID-19 pandemic has spurred extensive vaccine research worldwide. One crucial part of vaccine development is the phase III clinical trial that assesses the vaccine for safety and efficacy in the prevention of COVID-19. In this work, we enumerate the first successful implementation of using machine learning models to accelerate phase III vaccine trials, working with the single-dose Johnson & Johnson vaccine to predictively select trial sites with naturally high incidence rates ("hotspots"). We develop DELPHI, a novel, accurate, policy-driven machine learning model that serves as the basis of our predictions. During the second half of 2020, the DELPHI-driven site selection identified hotspots with more than 90% accuracy, shortened trial duration by six to eight weeks (approximately 33%), and reduced enrollment by 15,000 (approximately 25%). In turn, this accelerated time to market enabled Janssen's vaccine to receive its emergency use authorization and realize its public health impact earlier than expected. Several geographies identified by DELPHI have since been the first areas to report variants of concern (e.g., Omicron in South Africa), and thus DELPHI's choice of these areas also produced early data on how the vaccine responds to new threats. Johnson & Johnson has also implemented a similar approach across its business including supporting trial site selection for other vaccine programs, modeling surgical procedure demand for its Medical Device unit, and providing guidance on return-to-work programs for its 130,000 employees. Continued application of this methodology can help shorten clinical development and change the economics of drug development by reducing the level of risk and cost associated with investing in novel therapies. This will allow Johnson & Johnson and others to enable more effective delivery of medicines to patients.

Optimal Capacity-Constrained COVID-19 Vaccination for Heterogeneous Populations

COVID-19 and the ensuing vaccine capacity constraints have emphasized the importance of proper prioritization during vaccine rollout. This problem is complicated by heterogeneity in risk levels, contact rates, and network topology which can dramatically and unintuitively change the efficacy of vaccination and must be taken into account when allocating resources. This paper proposes a general model to capture a wide array of network heterogeneity while maintaining computational tractability and formulates vaccine prioritization as an optimal control problem. Pontryagin's Maximum Principle is used to derive properties of optimal, potentially highly dynamic, allocation policies, providing significant reductions in the set of candidate policies. Extensive numerical simulations of COVID-19 vaccination are used to corroborate these findings and further illicit optimal policy characteristics and the effects of various system, disease, and population parameters. © 2022 IEEE.

Green supply chain management, risk-taking, and corporate value—Dual regulation effect based on technological innovation capability and supply chain concentration

With the promotion of carbon-peak and carbon-neutral strategies and the increase in green awareness, green development is gradually gaining attention, and the green supply chain management (GSCM) derived from traditional supply chain management is gradually becoming a path to promote green development. At the same time, enterprise, as an important source of pollution, how to consider social responsibility, such as environmental protection, in the process of ensuring efficiency improvement has become an important issue. To study the impact of GSCM on enterprise value and its path of action, this paper examines the impact of GSCM on enterprise value, explores the moderating effect of the risk-taking level, and further analyzes the dual moderating effect played by technological innovation capability and supply chain concentration. Based on the micro data of 131 Chinese listed enterprises from 2014 to 2021, a panel-regression model is used to illustrate how GSCM affects enterprise value, and the results show that: (1) GSCM can promote enterprise value;(2) the level of risk-taking strengthens the promoting effect of GSCM on enterprise value enhancement;and (3) the technological innovation capability negatively regulates the moderating effect of risk-taking, while the supply chain concentration positively regulates the moderating effect of risk-taking. The research results of this paper enrich the path of the effect of implementing of GSCM on enterprise value enhancement, i.e. the process of GSCM to enhance enterprise value is regulated by the level of enterprise risk-taking, while technological innovation capability and supply chain concentration will also regulate the level of enterprise risk-taking and thus promote enterprise value enhancement. This research not only extends the research perspective and enriches the existing research, but also provides a theoretical basis for enterprises to implement GSCM to promote value enhancement and improve the level of GSCM implementation and the green development of enterprises.

Risk preference, risk perception, and purchase recovery period: Empirical evidence from salmon contamination of COVID-19 in China

This paper investigates the recovery period of consumer salmon purchase intention after food scares at the Xinfadi wholesale market in China during the COVID-19 pandemic and examines the impact mechanism of risk preference and risk perception on the period duration. Our empirical analysis is based on a survey of 655 salmon consumers in Beijing, Tianjin, and Shanghai. We estimate that the purchase intention recovery period lasts 21 weeks among the surveyed consumers after the shock. Although the epidemic risk levels of the three cities are different, there is a significant difference only in the recovery period from 5 to 7th weeks. The Cox proportional hazards model results further show that consumers with less risk-averse are more active in resuming purchase intention, and the effect of risk perception is just the opposite. Moreover, risk perception has a moderating effect on risk preference and recovery period. Finally, we put forward three possible policy implications: attaching nucleic acid detection certificate, strengthening cold chain management, and diversifying cooking methods.

Anosmia Association with Covid-19 Risk Level among Staff: Validating the Covid-19 Health Risk Assessment System in A Malaysian University

Introduction: Anosmia has been increasingly recognized as one of the most important clinical symptom to be screened for the COVID-19 Objective: This study was conducted to determine the prevalence of anosmia and its association with COVID-19 risk level among staff in a higher education institution in Malaysia. Method(s): The data was obtained from COVID-19 risk assessment system implemented in the institution within four-month surveillance period from May to September 2020. The risk level was categorised into three levels namely low, moderate, and high depending on the staff reported symptoms and presence of epidemiological link. Pearson Chi Square analysis and Binary Logistic Regression were applied to assess possible association between anosmia and COVID-19 risk level. Result(s): A total of 1787 staff were involved in the analysis;1455 (81.4%) were categorized as low risk, 316 (17.7%) were medium risk and 16 (0.9%) were at high risk of contracting COVID-19. Out of 1787, 65 (3.6%) staff presented with anosmia. There was a statistically significant association between anosmia and COVID-19 risk level. Those with anosmia were 8.31 times more likely to be categorised under higher risk group (medium and high risk) compared to those without anosmia (Odds Ratio (OR): 8.31, beta =2.117, 95% Confidence Interval (CI): 4.94-13.99, p < 0.001). Conclusion(s): The COVID-19 risk assessment system is proven to be valid as it demonstrated anosmia association with higher COVID-19 risk level which is consistent the current epidemiological evidence on anosmia. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Emotion Analysis to Identify Risk of Committing Suicide Using Statistical Learning

Given the alarming numbers of incidences of suicide in today's society, and especially after social distancing and confinement prevention measures brought by the COVID-19 pandemic, mental health experts require tools to support the identification of individuals at risk of committing suicide. This paper proposes a new methodology to detect suicidal tendencies in Twitter users relying on analysis of emotions. Using statistical learning models, the proposed methodology identifies the risk level through emotion analysis in the text. Results show that supervised non-parametric and unsupervised methods detected extreme levels of suicide risk on the dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Multi-Criteria Recommender System for Determining Risk Level of Covid-19

The coronavirus pandemic is a global disease outbreak causing countless loss of lives and also threatening the economic, social, religious, education, and other key sectors of nations. This highly infectious virus continues to spread rapidly and therefore, the need to develop innovative strategies and policies to curb the growing effects becomes very crucial. One significant approach is the introduction of lockdown measures, although this instrument is not completely dependable, due to possible adverse effects on societal activities. Prior to deployment, a number of criteria are taken into account, including demographic conditions, healthcare options, and Covid-19 case data. Depending on the influencing factors, a lockdown decision is typically made by assessing the different danger levels of a certain place. Consequently, this research propose a multi-criteria recommender system to determine the worth and risk of various regions, based on several constraints and databases. The model, which utilized the analytical network process (ANP) to discover interconnectedness and feedback, also included the weighting technique. In this study carried out in 27 districts and cities in West Java, Indonesia, 15% of the selected locations were categorized as high-risk levels. Meanwhile, 63% and 22% were associated with medium and low risk, respectively. © 2022 IEEE.

Proposed Empirical Assessment of Remote Workers' Cyberslacking and Computer Security Posture to Assess Organizational Cybersecurity Risks

Cyberslacking is conducted by employees who are using their companies' equipment and network for personal purposes instead of working during work hours. Cyberslacking has a significant adverse effect on overall employee productivity., however, recently, due to COVID19 move to remote working also pose a cybersecurity risk to organizations networks and infrastructure. In this work-in-progress research study, we are developing, validating, and will empirically test a taxonomy to assess an organization's remote workers' risk level of cybersecurity threats. This study includes a three-phased developmental approach in developing the Remote Worker Cyberslacking Security Risk Taxonomy. In collaboration with cybersecurity Subject Matter Experts (SMEs) use the taxonomy to assess organization's remote workers' risk level of cybersecurity threats by using actual system indicators of productivity measures to estimate their cyberslacking along with assessing via organizational information the computer security posture of the remote device being used to access corporate resources. Anticipated results from 125 anonymous employees from one organization will then be assessed on the cybersecurity risk taxonomy where recommendation to the organization's cybersecurity leadership will be provided. © 2022 IEEE.

Modeling COVID-19 infection in high-risk settings and low-risk settings.

In this research paper we present a mathematical model for COVID-19 in high-risk settings and low-risk settings which might be infection dynamics between hotspots and less risky communities. The main idea was to couple the SIR model with alternating risk levels from the two different settings high and low-risk settings. Therefore, building from this model we partition the infected class into two categories, the symptomatic and the asymptomatic. Using this approach we simulated COVID-19 dynamics in low and high-risk settings with auto-switching risk settings. Again, the model was analyzed using both analytic methods and numerical methods. The results of this study suggest that switching risk levels in different settings plays a pivotal role in COVID-19 progression dynamics. Hence, population reaction time to adhere to preventative measures and interventions ought to be implemented with flash speed targeting first the high-risk setting while containing the dynamics in low-risk settings.

The Impact of COVID-19 on the Cybersecurity in Civil Aviation: Review and Analysis

The significance of cybersecurity and cyber resilience in the aviation sector cannot be ignored, and this fact has been highlighted time and again due to frequent cyberattacks prior and during the COVID-19 pandemic. This paper presents an analysis of the relevant studies that addressed the impact of the COVID-19 pandemic on cybersecurity generally, and in the aviation sector in particular, including identifying the most common increased cyber-attacks associated with the outbreak of COVID-19. Our analysis shows that commonly increased cyber-attacks associated with the outbreak of COVID-19 in aviation are phishing and ransomware. Furthermore, the likelihood of phishing and ransomware attacks is very likely during the pandemic than before it while the impact on flight operations is low, and the subsequent risk level is tolerable during the pandemic. Additionally, we propose a set of possible mitigation measures for those cyber-attacks. © 2022 IEEE.

Special Issue on Big Data for eHealth Applications

[...]they should leverage the huge amount of information buried under these Big Data [7], exploiting, in this way, their full potential. In this Special Issue, some innovative applications, tools, and techniques specifically tailored to address issues related to the eHealth domain by leveraging BDA methodologies are presented. [...]these techniques are also presented in this Special Issue, given the definition of complex systems and architectures for the eHealth domain fundamentally based on the combination of Internet of Things (IoT) devices and Artificial Intelligence (AI) methods. [...]the Cyber Security (CS) for eHealth topic is also addressed given the significant increase in cyber threats in the healthcare sector during the last few years. 2. [...]areas with a certain level of risk in different periods of time were determined.