Agricultural Social Networks: An Agricultural Value Chain-Based Digitalization Framework for an Inclusive Digital Economy

Sustainable agriculture is the backbone of food security systems and a driver of human well-being in global economic development (Sustainable Development Goal SDG 3). With the increase in world population and the effects of climate change due to the industrialization of economies, food security systems are under pressure to sustain communities. This situation calls for the implementation of innovative solutions to increase and sustain efficacy from farm to table. Agricultural social networks (ASNs) are central in agriculture value chain (AVC) management and sustainability and consist of a complex network inclusive of interdependent actors such as farmers, distributors, processors, and retailers. Hence, social network structures (SNSs) and practices are a means to contextualize user scenarios in agricultural value chain digitalization and digital solutions development. Therefore, this research aimed to unearth the roles of agricultural social networks in AVC digitalization, enabling an inclusive digital economy. We conducted automated literature content analysis followed by the application of case studies to develop a conceptual framework for the digitalization of the AVC toward an inclusive digital economy. Furthermore, we propose a transdisciplinary framework that guides the digitalization systematization of the AVC, while articulating resilience principles that aim to attain sustainability. The outcomes of this study offer software developers, agricultural stakeholders, and policymakers a platform to gain an understanding of technological infrastructure capabilities toward sustaining communities through digitalized AVCs.

Social insecurity and varieties of family resilience strategies during the COVID-19 pandemic

PurposeThis study is aimed at developing an understanding of the consequences of the pandemic on families' socioeconomic resilience, and the strategies adopted by the families in overcoming social vulnerabilities amid uncertainty.Design/methodology/approachThe materials for this study consist of semi-structured interviews with 21 families spread across the South Sumatra Province, Indonesia. Families in the study represent four different income levels, namely very high, high, middle and low, and who also work in the informal sector. Each family has at least 1 or more members who fall into the vulnerable category (children, the elderly, people with disabilities unemployed or having potential economic vulnerability).FindingsTwo main findings are outlined. Regardless of their socioeconomic status, many of the families analyzed adopted similar strategies to remain resilient. Among the strategies are classifying the urgency of purchasing consumer goods based on financial capacity rather than needs, leveraging digital economic opportunities as alternative sources of income, utilizing more extensive informal networks and going into debt. Another interesting finding shows that the pandemic, to some extent, has saved poor families from social insecurity. This is supported by evidence showing that social distancing measures during the pandemic have reduced the intensity of sociocultural activities, which require invited community members to contribute financially. The reduction of sociocultural activities in the community has provided more potential savings for the poor.Research limitations/implicationsIn this study, informants who provided information about their family conditions represent a major segment of the workforce and tend to be technologically savvy and younger, due to the use of Zoom as a platform for conducting interviews. Therefore, there may be a bias in the results. Another limitation is that since the interviewees were recommended by our social network in the fields, there is a risk of a distorted selection of participants.Originality/valueThis study offers insights that are critical in helping to analyze family patterns in developing countries in mitigating the risks and uncertainties caused by COVID-19. In addition, the literature on social policy and development could benefit from further research on COVID-19 as an alternative driver to identify mechanisms that could bring about change that would result in "security.” Critical questions and limitations of this study are presented at the end of the paper to be responded to as future research agenda.

Interpersonal Distance Tracking with mmWave Radar and IMUs

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. © 2023 Owner/Author.

Path dependence or steps for major reforms? Pandemic-related social protection measures in ten OECD countries

This article examines with empirical evidence the social protection measures implemented in response to the COVID-19 pandemic in ten welfare states in the Global North. We analysed the potential similarities and differences in responses by welfare regimes. The comparative study was conducted with data from 169 measures, collected from domestic sources as well as from COVID-19 response databases and reports. In qualitative terms, we redeveloped Hall's theory on the distinction between first-, second- and third-order changes. In accordance with the path-dependence thesis, we show systematically that the majority of the studied changes (91%) relied on a pre-pandemic tool demonstrating flexibility within social security systems. The relative share of completely new instruments was notable but modest (9%). Thematically, the social protection measures converged beyond traditional welfare regimes, particularly among the European welfare states. Somewhat surprisingly, the changes to social security systems related not just to emergency aid to mitigate traditional risks but, to a greater extent, also to prevent new risks from being actualised.

Automated Face Mask Detection using Artificial Intelligence and Video Surveillance Management

Surveillance camera has become an essential, ubiquitous technology in people's daily lives, whether applicable for home surveillance or extended to public workplace detection. The importance of the camera is irreplaceable in terms of the agent for an enclosed system to function correctly. The goal of ubiquitous computing is to keep different devices or technology communicating seamlessly, allowing them to expand to other areas instead of limiting it to one device. However, many research papers have been released on how the camera can aid in the current situation where COVID-19 is still raging worldwide, especially in crowded places. This paper aims to suggest a method by which surveillance cameras on the university campus can automatically detect student face mask status and notify them. Alongside that, this concept of applying a video management system within the university campus will assist in the automation of invigilating the student's daily mask status from the number of embedded surveillance cameras around the campus. © 2023 IEEE.

Reported shocks, households' resilience and local food commercialization in Thailand

PurposeThe authors examine the factors affecting households' resilience capacities and the impacts of these capacities on household consumption and crop commercialization.Design/methodology/approachThe authors use panel data of 1,648 households from Thailand collected in three years, 2010, 2013 and 2016. The authors employ an econometric model with an instrumental variable approach to address endogenous issues.FindingsThe study results show that the experience of shocks in previous years positively correlates with households' savings per capita and income diversification. Further, a better absorptive capacity in the form of better savings and a better adaptive capacity in the form of higher income diversification have a significant and positive influence on household expenditure per capita and crop commercialization.Practical implicationsDevelopment policies and programs aiming to improve income, increase savings and provide income diversification opportunities are strongly recommended.Originality/valueThe authors provide empirical evidence on the determinants of resilience strategies and their impacts on local food commercialization from a country in the middle-income group.

Study on the Distribution of Fresh Food Support System—An Example of Shanghai during the Epidemic Closure of 2022

One of the key indicators to measure the sustainability and resilience of a city during a public health crisis is how well it can meet the daily needs of its residents. During the COVID-19 lockdown in Shanghai in 2022, e-commerce shopping and delivery became the most important method for ensuring the city's material supplies. This article uses the distribution data of a fresh e-commerce platform's pre-warehouse and static population distribution data to establish a basic material supply system evaluation model for the city and explore its resilience potential. Focusing on the central urban area of Shanghai, this study uses a population heat map with geographic coordinates to reflect the static distribution of residents and obtains the distribution data of the e-commerce pre-warehouses. Using kernel density analysis, the relationship between the pre-warehouses and the residents' needs is established. Through analysis, it was found that the supply capacity of fresh food in different areas of Shanghai during the lockdown could be categorized as insufficient, adequate, or excessive. Based on these three categories, improvement strategies were proposed. Finally, this article suggests establishing a scientific supply security system to promote urban sustainability and prepare for future challenges.

The reform of social protection in Morocco following the coronavirus pandemic

The COVID-19 pandemic has affirmed the importance of social protection. To combat the effects of the pandemic, countries have taken exceptional measures to preserve health and have introduced or adapted measures to provide income support to people who have lost their sources of income. The pandemic has also highlighted the weaknesses of the social protection system in Morocco, introduced in 1940, which is composed of a contributory system whose financing depends on social security contributions and regulations, and a subsidiary system which takes into covers people who do not have access to contributory basic social insurance. The kick-off for the implementation of the social protection reform in Morocco was given in April 2021 and should be spread over five years. The objective of this reform is to reorganize and improve the operation of the various social protection instruments with a view to greater effectiveness and increased efficiency and also to create new components likely to extend coverage. This large-scale reform initiated by Morocco requires an annual envelope estimated at 51 billion dirhams, which constitutes a major challenge for the country's public finances, which have been hit by the COVID-19 crisis.

A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models

The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Development of a Video Surveillance web Application for the use of Biosafety Equipment with Image Recognition for Workspaces

With the arrival of Covid-19, several preventive measures were implemented to limit the spread of this virus. Among these measures is the use of masks, both in open and closed public spaces. This measure has forced commercial establishments, workplaces, schools, hospitals, to maintain constant vigilance, upon entering their facilities, of the proper use of the mask, which should completely cover the nose, mouth and chin. However, this manual control is tedious and ineffective since most of the population is not able to correctly identify when a person has the mask on properly, with high error rates in the manual detection of the correct use of the mask according to surveys carried out. For this reason, this work proposes the automation of the detection of the proper use of the mask at the entrance to the work areas, also providing a follow-up panel of the recorded incidents. The effectiveness of the proposal was evaluated through the detection and categorization of a data set of more than 3000 images, resulting in an accuracy of 98.6%. © 2022 IEEE.

Accelerating Biosafety Capacity Building to Ensure National Biosecurity

Biosafety is an essential part of the national security system, which is related to people's lives and health, the country's longterm stability, and sustainable development, which is the bottom line that must be guaranteed. The international biosafety situation is grim and complex, while domestic biosafety faces challenges. Therefore, biosafety capacity building has become an international hot spot, among which scientific and technological innovation, talent training, and infrastructure platform construction are the top priorities. Although China has achieved strategic results in the rapid identification of pathogens, research, and development of specific vaccines and medicine in fighting against COVID-19 by relying on scientific research, it has shown the urgency for scientific and technological innovation in biosafety. Therefore,China has developed a strategic plan on "promoting the modernization of the national security system and capabilities, resolutely safeguarding national security and social stability" included in the 20th National Congress of the Communist Party of China. Hence, it is suggested to promote biosafety capacity building further to improve China's biosecurity system, protect people's health, ensure national security, and maintain long-term peace and stability by improving the layout of scientific and technological frontiers, promoting the construction of biosafety discipline, training of more special talents, and infrastructure platform construction.

An IoT Based Smart Vault Security and Monitoring System with Zero UI

Recent advances have introduced IoT as one of the key technologies globally. As safety remains a critical issue for those who spend much time outside. Automated security systems are very useful where safety is an important issue. With a prospect of a Zero User Interface (UI) model this work represents a novel IoT based smart vault security system. The system is built and designed based on IoT combining with Arduino-Uno and Bluetooth module. This system involves LDR sensor, IR sensor and Sonar sensor for monitoring. The vault provides security on three levels. Password protected entry to connect with the smartphone using Bluetooth module, IR sensor array to use 'secret gesture pattern' to unlock the door, tracking number of transactions from the vault using Sonar sensor and LDR was used as a switch. To avoid the replication of physical unlocking of objects IR sensor array was used to introduce 'secret gesture pattern' unlocking system through touchless interfaces for the avoidance of transmissive diseases like COVID-19. This novel system has substantial possibility as a security vault system for industrial and residential use in a contactless manner. © 2023 IEEE.

Systematization of Threats to Financial Security of Individual, Society, Business and the State in Terms of the Pandemic

The article highlights the need to monitor factors, risks and threats to financial security at different levels of the social hierarchy. The tools are examined for identifying threats to financial and socio-economic security. The study generalizes international experience of monitoring the financial security of the state and business in terms of the COVID-19 pandemic. Threats to Ukraine's financial security have been identified with the help of modern approaches adapted to the conditions of the pandemic. Reserves of digitalization of business are investigated. The identification of risks and threats to social security of Ukraine by its components has been carried out. The article analyzes the impact of the pandemic on rising unemployment. The number of households in crisis conditions caused by the pandemic and quarantine measures is estimated as an indicator of socio-economic security. The impact of the pandemic on financial and social security is summarized at different levels of the social hierarchy. It is proved that updating the list of indicators and qualitative enrichment of the analytical system of threat identification with dynamic indicators of digitalization of the economy will enable identifying additional threats to financial security at different levels of the social hierarchy. Additional risks for the national financial system related to globalization and digitalization of the state financial system are identified, which are not taken into account by the current methodological recommendations for calculating the level of economic security of Ukraine. Additional risks for the national social system connected with intrastate machinery, social and political changes are identified, which are not taken into account by the current methodological recommendations for calculating the level of economic security of Ukraine. It is proved that due to the slow implementation of reforms in the social and economic spheres of security activities, the existing socio-economic security system turned out to be vulnerable to an intense crisis event, i.e. the COVID-19 pandemic, which has led to a number of threats. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Smart Home and Machine Learning as a Sustainable Healthcare Solution: Review and Perspectives

Over the past two years, the world has witnessed one of the worst pandemics due to the outbreak of coronavirus (covid19), which has infected hundreds of millions and claimed the lives of millions across the globe. If we have learned anything from this pandemic, it is that the actual healthcare systems are unreliable under situations of enormous pressure. Accordingly, the present investigation tackles smart healthcare paradigm as a solution to transform the classical healthcare model into a sustainable one. Therefore, this paper reviews the most advances on remote healthcare monitoring technologies and introduces a novel smart home architecture combined with cloud computing and machine learning to create a sustainable solution for healthcare. Furthermore, a case study of a patient with heart disease is suggested to highlight the importance of using machine learning to automate medical monitoring at home. Additionally, an investigation of human behavior using neural network transformers is suggested as a perspective of the research in hand to examine patients' activities at home using surveillance camera thus constructing a resilient remote healthcare model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution

With the growing importance of preventing the COVID-19 virus in cyber-manufacturing security, face images obtained in most video surveillance scenarios are usually low resolution together with mask occlusion. However, most of the previous face super-resolution solutions can not efficiently handle both tasks in one model. In this work, we consider both tasks simultaneously and construct an efficient joint learning network, called JDSR-GAN, for masked face super-resolution tasks. Given a low-quality face image with mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some competitive methods. IEEE

Food Security and Agricultural Crises in a "Financialized Food Regime"

The paper explores the impact of finance's penetration into agriculture and the global food system. The authors analyze the causes of the recent global food crises, unveiling the key role played by financial speculation and explaining why this phenomenon is likely to affect food security more than the problems related to the supply and demand dynamics taking place in the "real economy". Financial markets, the authors argue, are engendering pricing mechanisms and dynamics of wealth distribution that have consequences on the agrarian structures, but also on everyday life of both producers and consumers. While creating new profit opportunities for speculators and the agribusiness, the penetration of finance into food systems increase uncertainty and imply new risks for local actors, to the point of compromising their capability to respond to exogenous shocks, such as the COVID-19 pandemic. In any case, to make sense of these phenomena they must be linked to the broader transformation of the global food system and to the long-term trajectories of capitalist development. This operation is here made with the support of the analytical tools provided by some approaches inspired by the world-system analysis, bringing to light the roots of what can be defined as a "financialized food regime" and discussing some of its important ecological and socio-economic contradictions.

A Framework for Developing Tabletop Cybersecurity Exercises

As remote work increases in adoption, partly pushed by the 2020 COVID-19 pandemic, conducting and offering security training to employees is ever more challenging, due to physical constraints. Cyber-security training is ever more critical as both digitalization of controls and services increases, and remote working increases the risks of cyber-threats, due to vulnerable communication channels and lack of security practices from remote location working. As physical presence and coordination of large groups of employees becomes more challenging, it is necessary to offer more flexible, adaptable and lightweight training and exercise solutions for cyber-security training. For this reason, in this work we propose a lightweight tabletop framework for conducting cybersecurity exercises. The framework has been developed taking into consideration personalized learning theory concepts and feedback from academic and industrial stakeholders. Evaluation of the framework was conducted through a series of exercises with industrial personnel and university students. According to the results of the experiments, the framework is effective at developing a great range of table-top exercises for both students, security professionals and technical operators. By focusing on flexibility, ease of implementation, remote accessibility and other key attributes, the exercises developed with the framework have been reported to be successful in achieving the goals, and found engaging and motivating by participants. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Social Distance Monitoring Method Based on Improved YOLOv4 for Surveillance Videos

Social distance monitoring is of great significance for public health in the era of COVID-19 pandemic. However, existing monitoring methods cannot effectively detect social distance in terms of efficiency, accuracy, and robustness. In this paper, we proposed a social distance monitoring method based on an improved YOLOv4 algorithm. Specifically, our method constructs and pre-processes a dataset. Afterwards, our method screens the valid samples and improves the K-means clustering algorithm based on the IoU distance. Then, our method detects the target pedestrians using a trained improved YOLOv4 algorithm and gets the pedestrian target detection frame location information. Finally, our method defines the observation depth parameters, generates the 3D feature space, and clusters the offending aggregation groups based on the L2 parametric distance to finally realize the pedestrian social distance monitoring of 2D video. Experiments show that the proposed social distance monitoring method based on improved YOLOv4 can accurately detect pedestrian target locations in video images, where the pre-processing operation and improved K-means algorithm can improve the pedestrian target detection accuracy. Our method can cluster the offending groups without going through calibration mapping transformation to realize the pedestrian social distance monitoring of 2D videos. © 2023 World Scientific Publishing Company.

Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirus-based pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. © 2022 IEEE.

Does gender really matter? Exploring determinants behind consumers' intention to use contactless fitness services during the COVID-19 pandemic: a focus on health and fitness apps

PurposeThis research created a theoretical framework based on theory of consumption values (TCV) and theory of perceived risk (TPR) to investigate the determinant factors behind consumers' intention to use health and fitness apps during the COVID-19-related lockdown. In addition, based on selectivity hypothesis theory (SHT), this study also explored how gender differences moderate the relationships between the determinants and consumers' behavioral intention.Design/methodology/approachA total of 613 respondents completed a self-reported online questionnaire. Structural equation modeling was conducted to test the role of potential determinants in influencing consumers' behavioral intention. Hierarchical multiple regression was performed to examine the moderating effect of gender.FindingsThe findings of this research revealed that physical appearance, general health, enjoyment, affiliation and condition have positive influences on consumers' behavioral intention, while privacy risk and security risk exert negative impact on consumers' behavioral intention. More importantly, the moderating results indicated that only affiliation, privacy risk and security risk have stronger influences on female, while other predictors showed the same effects on both genders.Practical implicationsFitness providers should embrace health and fitness apps as a new contactless tool to offer services during and after the COVID-19-related lockdown. Fitness providers and app developers need to focus more on the utility and quality of their health and fitness apps. In addition, they should add more gamification elements to health and fitness apps because these elements could increase consumers' hedonic experience especially during the lockdown. Third, the security systems in health and fitness apps should be continuously updated to decline privacy risk during and after the COVID-19-related lockdown. Lastly, when female consumers are targeted during the lockdown, fitness providers should make more efforts to imbue health and fitness apps with more social features and improve the level of security.Originality/valueAlthough the importance of contactless technologies has been highlighted ever since the beginning of the COVID-19 pandemic, there has been very little research on the usage of health and fitness apps during the lockdown based on TCV and TPR. Meanwhile, the moderating role of gender differences in this context remains underexplored. This research is one of the early attempts to fill in these gaps. The findings of this study will enhance the theoretical framework regarding the acceptance and use of health and fitness apps;it also challenges the generalizability of SHT in the context of the COVID-19-related lockdown. Moreover, several important implications for the health and fitness industry during and after the COVID-19 pandemic were suggested.