Exploring Social Media for Early Detection of Depression in COVID-19 Patients

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.

Dynamic Safety Assessment and Enhancement of Port Operational Infrastructure Systems during the COVID-19 Era

Seaports function as lifeline systems in maritime transportation, facilitating critical processes like shipping, distribution, and allied cargo handling. These diverse subsystems constitute the Port Infrastructure System (PIS) and have intricate functional interdependencies. The PIS is vulnerable to several external disruptions, and the impact of COVID-19 is severe and unprecedented in this domain. Therefore, this study proposes a novel general port safety framework to cope with recurring hazards and crisis events like COVID-19 and to augment PIS safety through a multi-state failure system. The PIS is divided into three critical subsystems: shipping, terminal, and distribution infrastructure, thereby capturing its functional interdependency and intricacy. A dynamic input-output model is employed, incorporating the spatial variability and average delay of the disruption, to determine the PIS resilience capacity under the stated disruptions. This study simulates three disruption scenarios and determines the functional failure capacity of the system by generating a functional change curve in Simulink. This study offers viable solutions to port managers, terminal operators, and concerned authorities in the efficient running of intricate interdependent processes and in devising efficient risk control measures to enhance overall PIS resilience and reliability. As part of future studies, given the difficulty in obtaining relevant data and the relatively limited validation of the current model, we aim to improve the accuracy and reliability of our model and enhance its practical applicability to real-world situations with data collected from a real-world case study of a PIS system.

Negotiation of psycho-social risks of remote working. An enterprise-level comparison in Italy and France

The article focuses on how the actors of industrial relations acted at firmlevel in multi-national company, Danone, in two different institutional contexts (Italy and France), in order to set rules and procedures aimed to mitigate the negative psycho-social consequences of remote working adopted during Covid-19 pandemic. In particular, it examines what was the role of firm-level industrial relations in setting specific oriented actions and what were the relations between these, global policies of the company and national level policies about health and safety and in general the institutional context. It emerges the importance of informality of relations between actors in helping to face an emergency together with the role of global policies of the company that seems to overcomes the different national institutional contexts.

Modeling of Physical Activity Behavioral Interventions Relying on MPC Strategy

Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.

Design for an Intelligent Waste Classifying System: A Case Study of Plastic Bottles

The use of plastic bottles has become a significant environmental concern, and recycling them has become a priority. Small and medium-sized recycling companies must collect and categorize large volumes of plastic bottles and sell them to larger recycling firms, a process that is time-consuming, costly, and labor-intensive. This manual sorting process can pose health risks, particularly during the COVID-19 pandemic, and can affect worker productivity. To address these issues, this study proposes the development of an automated conveyor belt system that can rapidly and accurately separate plastic bottles by type. The system utilizes an opaque and transparent plastic bottle separation platform, which saves time, cost, and manpower. This system design provides recycling SMEs with a competitive advantage by serving as a practical application model and a prototype with an easy-to-use concept. Key tools employed in this research include product design development (PDD), Kansei engineering, manufacturing process design, controlling system, and fault tree analysis (FTA). The light sensors are critical components in the separation process, detecting the opacity or transparency of the bottles' surfaces. The proposed prototype's reliability will be assessed by FTA, which considers all potential failures. This study contributes to the body of knowledge surrounding the integration of conveyor systems and provides valuable information for businesses seeking to optimize their sorting processes. The guidelines developed in this study can serve as a starting point for further research on the integration of conveyors in waste sorting plants. © 2013 IEEE.

Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago's "L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods' to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that "L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users' risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods' access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.

Development of a Decentralized Web App (D-App) for Covid-19 using AI

A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.

Low-level laser therapy for treatment of severe acute respiratory syndrome 2 infection

SARS-CoV-2 is a new threat to public health due to its increased transmissibility and immune evasion. Angiotensin-converting enzyme 2 (ACE2) plays a critical role in SARS-CoV-2 infection as its serve as the virus's major entry receptor in humans. Vaccines have been authorized for emergency use to control the current pandemic and they have greatly reduced the spread of SARS-CoV-2 and mortality rates, nevertheless this coronavirus has shown the ability to endure crucial mutations that increases its infectivity which makes it likely that the virus will continue to mutate and disseminate. There is a need to find and introduce alternative and effective methods of controlling SARS-CoV-2. Notably, low-level laser therapy (LLLT) is a method of exposing cells or tissue to low levels of red and near infrared light which has a high success rate for treatment of other ailments. The aim of the study is to determine for the first time, the effects of LLLT on SARS-CoV-2 infected HEK293/ACE2 cells and compare them to uninfected ones. Both infected and uninfected HEK293/ACE2 cells were irradiated at a wavelength of 640 nm, at different doses. Then, the effects of laser irradiation on the cells and the virus were evaluated using luciferase, cytotoxicity, and cell viability assays. Preliminary results showed that irradiated uninfected cells had no changes in cell viability and cytotoxicity, while there were changes in irradiated infected cells. In addition, laser irradiation caused cell membrane damage in infected cells. Lastly, uninfected irradiated cells showed no luciferase activity while laser irradiation reduced luciferase activity in infected cells. © 2023 SPIE.

Non-traditional Security: A Risk-Centric View

As described in Saha and Chakrabarti (South Asian Survey 28(1):111-132, 2021: 112), "COVID-19 has firmly established itself as the single largest security disrupter of this century in the non-traditional sense. It has necessitated a recalibration of securitisation framework.…" The impact of the COVID-19 pandemic certainly illustrates the transnational nature of today's security landscape. Similarly, events such as the WannaCry cyber-attack, global terrorism, serious and organized crime, disease vectors, and natural disasters create challenges that affect both global and national security interests. Such events are shaping the security calculus across dimensions such as health security, economic security, food security, and energy security emerging as interrelated concepts that characterize the security landscape as complex. The increased transnational flow of people, goods, money, and information as products of "globalization” has also changed the security landscape in terms of the "globalization” of risks. This transnational/transborder nature of security challenges our traditional views of national security characterized by state-based, military dimensions. The non-traditional security calculus thereby emerges as part of the security landscape that can often have significant national security impacts through the implications associated with systemic risks. Outstanding scholarly work has been presented on the topic of non-traditional security through the lens of International Relations and Contemporary Security Studies (e.g., see Collins, Contemporary security studies. Oxford University Press, Oxford, 2013). This chapter presents non-traditional security through a risk-centric lens and explores the notion of systemic risk as part of the security calculus. COVID-19 will be used as an illustrative example of a shock to societal systems that reveals systemic risks, vulnerabilities, and impacts across the non-traditional security domains. © Springer Nature Switzerland AG 2022.

Evolutionary game analysis of crowding risk control strategies during the COVID-19 Pandemic

To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies, and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic. © 2023 SPIE.

LLDSNet: Limited labeled data segmentation network for COVID-19 CT lesion segmentation

With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks. © 2023 SPIE.

Managing risk in commercial property development projects during the COVID-19 pandemic: Evidence from China

The COVID-19 pandemic is the biggest global health crisis in years. China is the first market primarily affected by the COVID-19 pandemic, with unprecedented lockdown measures bringing real estate and other economic activities to a standstill. This study has two objectives: (1) to identify the risks critical to the risk management of commercial real estate (CRE) development projects based on the project life cycle stages and (2) to identify the stages most affected by the COVID-19 pandemic and the risk factors at different stages. Three rounds of the Delphi study were conducted with nine experts involved in the construction project. The findings indicate that the construction, lease and sale phases are prone to significant risks. Additionally, the analytic hierarchy process (AHP) identified ‘health and safety risk' as the most critical risk factor during the construction phase and ‘marketing and payback risk' as the most critical risk factor during the lease and sale phase. This study enhanced the effectiveness of risk management practices for implementing CRE development projects in China.

Risk Mitigation in Agriculture in Support of COVID-19 Crisis Management

The main focus of this article is the problem of exacerbating agricultural risks in the context of the COVID-19 crisis, which started against the background of the novel coronavirus (COVID-19) pandemic. The motivation for conducting the research presented in this article was the desire to increase the resilience of agricultural companies to economic crises. This paper is aimed at studying the Russian experience of changing the production and financial risks of agricultural companies during the COVID-19 crisis, substantiating the important role of innovations in reducing these risks, and determining the prospects for risk management in agriculture based on innovations to increase its crisis resilience. Using the structural equation modelling (SEM) method, we modelled the contribution of innovations to the risk management of agriculture during the COVID-19 crisis. The advantages of the SEM method, compared to other conventional methods (e.g., independent correlation analysis or independent regression analysis), include the increased depth of analysis, its systemic character, and the consideration of multilateral connections between the indicators. Using the case-study method, a "smart" vertical farm framework is being developed, the risks of which are resistant to crises through the use of datasets and machine learning. The originality of this article lies in rethinking the risks of agriculture from the standpoint of "smart" technologies as a new risk factor and a way to increase resilience to crises. The theoretical significance of the results obtained is that they make it possible to systematically study the changes in the risks of agriculture in the context of the COVID-19 crisis, while outlining the prospects for increasing resilience to crises based on optimising the use of "smart" technologies. The practical significance of the article is related to the fact that the authors' conclusions and applied recommendations on the use of datasets and machine learning by agricultural companies can improve the efficiency of agricultural risk management and ensure successful COVID-19 crisis management by agricultural companies.

The Impact of the COVID-19 Pandemic on the Economic Security of Russian Regions: Assessment of Resistance to the Spillover Effects of Epidemic Risks

The research aims to identify the spillover effects of epidemic risks for the economic security of Russian regions and propose a system of measures for their leveling at the stage of post-pandemic recovery. The research substantiates the hypothesis that epidemic risks of economic security have spillover effects on the economy of Russian regions, increasing the impact of a combination of traditional risks and provoking the emergence of new risks. The authors proposed a system of indicators and a methodology for assessing the stability of regional economic systems to the spillover effects of epidemic risks based on the use of a statistical method for calculating integral indicators. The results of the calculations made it possible to rank the regions of Russia by the level of resistance to the spillover effects of epidemic risks to economic security. It is proved that the Southern Federal District has the greatest resistance to spillover effects. Ranking of regions by the indicator of resistance to spillover effects allowed the authors to propose a model of economic security management for two types of regional economic systems. For regions resistant to spillover effects, it is recommended to implement a set of measures aimed at strengthening internal potential. For regions unstable to the spillover effect, a sequence of actions is proposed based on leveling the negative impact of the spillover effects and the subsequent implementation of measures to strengthen internal potential. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Meeting the Hospital Oxygen Demand with a Decentralized Autonomous PV System: Effect of PV Tracking Systems

When it comes to supplying oxygen, current standard hospitals in Iran have proven inadequate in the face of the COVID-19 pandemic, particularly during infection peaks. Power disruptions drastically reduce the oxygen pressure in hospitals, putting patients' health at risk. The present study is the first to attempt to power an oxygen concentrator with a solar-energy-based system. The HOMER 2.81 package was used for technical–economic–environmental–energy analysis. The most notable aspects of this work include evaluating different available solar trackers, using up-to-date equipment price data and up-to-date inflation rate, considering the temperature effects on solar cell performance, sensitivity analysis for the best scenario, considering pollution penalties, and using a three-time tariff system with price incentives for renewable power. The study has been carried out at Hajar Hospital, Shahrekord, Chaharmahal and Bakhtiari Province, Iran. The study showed that, by supplying 60% of the power demand, the dual-axis solar tracking system offered the highest annual power output (47,478 kWh). Furthermore, generating power at—$0.008/kWh due to selling power to the grid, the vertical-axis tracker was found to be the most economical design. Comparing the configuration with a vertical-axis tracker with the conventional scenario (relying on the power distribution grid), the investment is estimated to be recovered in three years with $234,300 in savings by the end of the 25th year. In the best economic scenario, 6137 kg CO2 is produced, and the analysis revealed the negative impact of a temperature rise on the performance and solar power output.

COVID-19's Impulse to Intensify Neo-industrial Digital Technologies 4.0 in the Legal Regulation of Foreign Economic Activity of Economic Entities of the EAEU and the BRICS Member States

The chapter contains a study of the impact of the new coronavirus pandemic 2019–2020 on the business activity of economic entities of the EAEU and BRICS jurisdictions in the implementation of foreign trade activities. The authors note the need and highlight the opportunities for wider and more intensive use of digital technologies of Industry 4.0 in the legal regulation of export–import transactions between the parties-residents of the member states of integration associations with the participation of the Russian Federation. The authors substantiate that in modern realities, infectious epidemiological risks are considered not as a force majeure circumstance but as restrictions on the collective work of employees of companies, including foreign trade commercial organizations. The authors argue that this pandemic set of legal procedures for the wider and more intensive use of digital technologies in Industry 4.0 should be enshrined in the provisions of the international convention, which countries participating in integration associations will be able to join, thereby ensuring the entry into force of the relevant regulatory provisions in their jurisdictions. The authors propose several legal procedures for wider and more intensive use of digital technologies of Industry 4.0 in the implementation of foreign economic transactions, which launched and implemented after the announcement of the next pandemic by the World Health Organization. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

From least cost to least risk: Producing climate change mitigation plans that are resilient to multiple risks

Our plans to tackle climate change could be thrown off-track by shocks such as the coronavirus pandemic, the energy supply crisis driven by the Russian invasion of Ukraine, financial crises and other such disruptions. We should therefore identify plans which are as resilient as possible to future risks, by systematically understanding the range of risks to which mitigation plans are vulnerable and how best to reduce such vulnerabilities. Here, we use electricity system decarbonization as a focus area, to highlight the different types of technological solutions, the different risks that may be associated with them, and the approaches, situated in a decision-making under deep uncertainty (DMDU) paradigm, that would allow the identification and enhanced resilience of mitigation pathways. Copyright © 2023 Gambhir and Lempert.

Pandemic techno-politics in the global South

Brazil has been standing out as one of the worst places on Earth to be during a global health crisis, especially for those whose struggle for basic humanitarian rights is already routine. How do the political environment and historical inequalities in countries like Brazil affect the ways in which public policy and technologies are framed as responses for the pandemic crisis? In this paper we aim to present the sequence of actions and omissions in the fight against sars-cov2 in Brazil, concentrating on measures based on the use of digital technologies and the sociotechnical arrangements unfolding in materialities that give shape to such measures. We will also discuss possible repercussions of the widespread adoption of surveillance technologies as a quick fix to the effects of the pandemic. Our focus is to explain how the materiality of the virus and its political as well as territorial effects are combined with digital technologies as responses (or lack of them) in the fields of healthcare, education, communication and labour in the context of the global South. [ FROM AUTHOR] Copyright of Information Polity: The International Journal of Government & Democracy in the Information Age is the property of IOS Press 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.)

"Fortune amidst misfortune”: The impact of Covid-19 city lockdowns on air quality

Air pollution is one of the major causes of health risks as it leads to widespread disease and death each year. Countries have invested heavily in fighting air pollution, arguably without convincing results. The outbreak of the highly infectious disease COVID-19 in December 2019 has been declared a pandemic and a worldwide health crisis by World Health Organization (WHO). Countries resorted to city lockdowns that sternly curtailed personal mobility and economic activities to control the spread of this deadly coronavirus disease. This paper examines the impact of Covid-19 city lockdowns on air quality. The researchers adopted a comprehensive interpretative document analysis for this study, which guided the careful but rigorous examination of air quality and coronavirus data. This method affirmed the authenticity of the information examined and interpreted in the US, Italy and China, the study areas. The study found that Covid-19 city lockdowns have contributed to a significant improvement in air quality within the first four months of the outbreak of Covid-19. National Aeronautics and Space Administration (NASA) had reported that NO2 concentrations in the study areas had reduced significantly using evidence from their Sentinel-5P instrument. Air quality in Covid-19 cities' lockdowns also improved because of the enforcement of other types of measures enacted to battle the virus. WHO still believes that the amount of NO2 concentration in the atmosphere is still high per their standards and regulations. Based on this, the researchers recommend that governments and other stakeholders put in much effort in terms of legislation to "win the war” against air pollution.

Importance of meaningful engagement: how and why older adults volunteer in uncertain times

PurposeMany older adults engage in volunteer activities, drawing meaning and purpose through such efforts. Social distancing restrictions, put in place during Covid-19 surges to reduce the risk of transmission, disrupted older adult volunteers' lives and volunteer experiences. Social distancing measures provide a unique opportunity to explore what happened when the choices around pausing or stopping volunteering were not entirely within the control of older adults. This paper aims to explore the experiences of older adult volunteers as they navigated uncertainties and made difficult decisions around balancing their safety and their desire to continue volunteering.Design/methodology/approachThe authors conducted interviews with 26 community-dwelling older adults, age 50+, who had engaged in volunteer activities for at least 1 h a week prior to the start of the pandemic. The interviews were conducted on the phone or via Zoom. The authors used thematic analysis to help us analyze the data and identify patterns from participants' experiences.FindingsDespite the risk presented by Covid-19, most participants volunteered during the pandemic. They continued some or all of their previous activities with safety-related adjustments, with some seeking new or different opportunities. Participants' discussions highlight the challenges of volunteering during the pandemic and the importance of engagement to their resiliency and subjective well-being.Originality/valueThis paper provides original contributions to understanding how and why older adults volunteered during the Covid-19 pandemic. The social distancing measures provide a novel opportunity to enrich our understanding of the meaningfulness and value of volunteerism to older adults' lives and subjective well-being.