Increase of nutritional security in Sub-Saharan Africa through the production of dried products from underutilized crops

Currently, an estimated 20% of the population in Sub-Saharan Africa is food insecure with the incidence of hunger and malnutrition still rising. This trend is amplified by the socio-economic consequences of the COVID-19 pandemic. In contrast, more than a third of the harvestable perishable produce is lost due to a lack of preservation or failure to utilize preservation as is the case for underutilized crops (UCs). Moreover, some of the preservation techniques utilized are poor, leading to the deterioration of food quality, especially the micronutrients. In this study, we thus exemplarily investigated the impact of different drying settings on the quality of two highly nutritious UCs, namely cocoyam and orange-flesh sweet potato (OFSP) (40, 60, and 80 °C for cocoyam and 40, 50, 60, and 70 °C for OFSP) to deduce the optimum quality retention and further develop a theoretical design of processing units and processing guidelines for decentralized food processing. Drying cocoyam at 80 °C and OFSP at 60 °C, respectively resulted in a relatively shorter drying time (135 and 210 min), a lower total color difference (2.29 and 11.49-13.92), greater retentions for total phenolics (0.43 mg GAE/100 gDM and 155.0-186.5 mg GAE/100 gDM), total flavonoid (128 mg catechin/100 gDM and 79.5-81.7 mg catechin/100 gDM) and total antioxidant activity (80.85% RSA and 322.58-334.67 mg AAE/100 gDM), respectively for cocoyam and OFSP. The β-carotene, ascorbic acid and vitamin A activity per 100 gDM of the OFSP flours ranged between 6.91- 9.53 mg, 25.90 − 35.72 mg, and 0.53 − 0.73 mg RAE, respectively. © 2022 The Author(s). Published with license by Taylor and Francis Group, LLC.

Secure 6G Communication in Smart City Using Blockchain

The next-generation cellular network will aim to overcome the existing Fifth Generation (5G) networks' shortcomings. At the moment, academics and business are concentrating their efforts on the Sixth Generation (6G) network. This 6G technology is expected to be the next great game-changer in the telecommunications sector. Due to the outbreak of COVID-19, the entire globe has turned to virtual meetings and live video interactions in various fields as healthcare, business, and education. We explore the most recent viewpoints and future technology trends that are most likely to drive 6G in this paper. The incorporation of blockchain in 6G, will allow the network to efficiently monitor and manage resource consumption and sharing. We explore the potential of blockchain for sharing in 6G utilizing a variety of application scenarios in the smart city. To strengthen security and privacy in 6G networks, we introduce potential difficulties and solutions with various 6G technologies. In addition, we examine the security and privacy issues that may arise as a result of the current 6G standards and prospective 6G uses. Overall, our study aims to give insightful direction for future 6G security and privacy research. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Factors Associated with Food Insecurity Among Health Sciences Students During the COVID-19 Pandemic

This web-based survey explored factors associated with food insecurity (FI) among health sciences students during the COVID-19 pandemic. FI was assessed using the USDA 6-item tool. Multivariable logistic regression was used for data analyses. Of the 816 respondents, 74.7% were female and 22.1% were food insecure. An annual income of <$25,000, housing instability, use of a food pantry over the past 12 months, and receiving financial support from family were independently associated with increased odds of being food insecure even after adjusting for other covariates. Further research exploring FI screening and interventions among health sciences university students is needed.

Hierarchical energy efficient secure routing protocol for optimal route selection in wireless body area networks

Growth in technology has witnessed the comfort of an individual in domestic and professional life. Although, such existence was not able to meet the medical emergencies during the pandemic COVID-19 and during other health monitoring scenarios. This demand is due to the untouched Quality of Service network parameters like throughput, reliability, security etc. Hence, remote health monitoring systems for the patients who have undergone a medical surgery, bed ridden patients, autism affected subjects etc is in need that considers postural change and then forward to the caretaker in hospitals through wireless body area networks (WBAN). Security in these data are very important as it deals with the life of a subject. In this work, a Hierarchical Energy Efficient Secure Routing protocol (HEESR) is proposed that categorizes the deployed body nodes in to direct node and relay node based on the threshold vale. Unlike other conventional protocols the cluster head selection is based on the energy levels and the traffic priority data like critical and non-critical data, followed by an optimal route to forward the acquired data is identified and the data is compressed using Huffman encoding technique and encrypted using asymmetric cryptographic algorithm for secure data transmission. This protocol mainly appends security and routing efficiency in a hierarchical pattern through data prioritization and out performs the other conventional routing protocols by yielding a better energy consumption of 6%, throughput 92% and security of 93%, which has balanced the packet drop rate considerably and deliver the data within the stipulated time period. © 2022 The Authors

A privacy-preserving blockchain-based tracing model for virus-infected people in cloud

The outbreak of COVID-19 has exposed the privacy of positive patients to the public, which will lead to violations of users' rights and even threaten their lives. A privacy-preserving scheme involving virus-infected positive patients is proposed by us. The traditional ciphertext policy attribute-based encryption (CP-ABE) has the features of enhanced plaintext security and fine-grained access control. However, the encryption process requires the high computational performance of the device, which puts a high strain on resource-limited devices. After semi-honest users successfully decrypt the data, they will get the real private data, which will cause serious privacy leakage problems. Traditional cloud-based data management architectures are extremely vulnerable in the face of various cyberattacks. To address the above challenges, a verifiable ABE scheme based on blockchain and local differential privacy is proposed, using LDP to perturb the original data locally to a certain extent to resist collusion attacks, outsourcing encryption and decryption to corresponding service providers to reduce the pressure on mobile terminals, and deploying smart contracts in combination with blockchain for fair execution by all parties to solve the problem of returning wrong search results in a semi-honest cloud server. Detailed security proofs are performed through the defined security goals, which shows that the proposed scheme is indeed privacy-protective. The experimental results show that the scheme is optimized in terms of data accuracy, computational overhead, storage performance, and fairness. In terms of efficiency, it greatly reduces the local load, enhances personal privacy protection, and has high practicality as well as reliability. As far as we know, it is the first case of applying the combination of LDP technology and blockchain to a tracing system, which not only mitigates poisoning attacks on user data, but also improves the accuracy of the data, thus making it easier to identify infected contacts and making a useful contribution to health prevention and control efforts. © 2022 Elsevier Ltd

Crossing the Same River Multiple Times: Cautiously setting aside pandemic practices, pharma workers appear ready for flux, change, evolution, and expanding molecular diversity

The article presents results of an employment survey on trends and the state of the biotechnology and pharmaceutical industry in 2022. Findings reveal COVID-19 measures that are important to daily operations, stagnation of wage growth and decline in overall salary satisfaction, job security changes and drop in job satisfaction, and business concerns including corporate performance and industry growth.

Ransomware Identification Through Sandbox Environment

Latin America suffered more than 41 billion attempted cyberattacks in 2020, as the COVID-19 pandemic generated remote working, setting conditions for cybercriminals to exploit vulnerabilities in corporate computer networks. The general objective of this research was to implement sandbox technology to protect against ransomware attacks in a local network of a financial institution. The implementation of Sandbox technology was developed with opensource software. To this end, a server with sandbox technology was implemented and configured to manage all operations performed by customers. A test lab was implemented with five machines in a virtualized environment. Five types of ransomware were collected and downloaded from the tutorialjinni page, executed in the test lab and analyzed by Cuckoo Sandbox, the latter reported that of the five ransomware injected, 100% were detected and successfully isolated, using on average 0.89 Gb of ram memory and with an average time of 123.6 s, which demonstrated that Cuckoo Sandbox is effective and optimal in utilizing hardware resources, thus contributing to the perimeter security of the computer network. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter

Many researchers have studied non-expert users' perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users' tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results indicate the multi-faceted nature of non-expert users' perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives. © 2022

Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks

After the COVID-19 pandemic, cyberattacks are increasing as non-face-to-face environments such as telecommuting and telemedicine proliferate. Cyberattackers exploit vulnerabilities in remote systems and endpoint devices in major enterprises and infrastructures. To counter these attacks, fast detection and response are essential because advanced persistent threat (APT) attacks intelligently infiltrate endpoint devices for long periods and spread to large-scale environments. However, because conventional security systems are signature-based, fast detection of APT attacks is challenging, and it is difficult to respond flexibly to the environment. In this study, we propose an APT fast detection and response technique using open-source tools that improves the efficiency of existing endpoint information protection systems and swiftly detects the APT attack process. Performance test results based on realistic scenarios using the open-source APT attack library and MITER ATT&CK indicated that fast detection was possible with higher accuracy for the early stages of APT attacks in scenarios where endpoint attack detectors are interworking environments. © 2022 The Authors

Digital Native Media in the Face of Journalistic Security

In the scenario marked by digitalization, digital media have found spaces that allow them to face labor problems and at the same time face the risks and vulnerability that threaten the security of journalists. Therefore, this research is established to know some issues related to security: economic limitations of access to public information, censorship, and self-censorship in the journalistic exercise in Ecuador. The observation was carried out within the framework of the pandemic unleashed by the presence of COVID-19 and its variants. For the execution of the research, the qualitative methodology was applied with semi-structured interviews to investigative journalists of consolidated digital native media. Among the conclusions are noted that the pandemic brought with it labor precariousness;however, digital media have managed to maintain themselves with external funds or own resources. For the journalistic exercise, public information becomes a severe concern because access has been restricted, and there is little transparency in its disclosure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

OPENING OPPORTUNITIES

The article discusses the significance of telework for workers with disabilities. Topics discussed include increased open-mindedness about granting part-time or full-time telework as a reasonable accommodation, reasonable accommodations under the Title I of the Americans with Disabilities Act (ADA) and work environment or hiring process to create equality for someone with a disability.

Random Forest in Whitelist-Based ATM Security

Accelerated by the COVID-19 pandemic, the trend of highly-sophisticated logical attacks on Automated Teller Machines (ATMs) is ever-increasing nowadays. Due to the nature of attacks, it is common to use zero-day protection for the devices. The most secure solutions available are using whitelist-based policies, which are extremely hard to configure. This article presents the concept of a semi-supervised decision support system based on the Random forest algorithm for generating a whitelist-based security policy using the ATM usage data. The obtained results confirm that the Random forest algorithm is effective in such scenarios and can be used to increase the security of the ATMs.

Privacy-preserving COVID-19 contact tracing solution based on blockchain

The COVID-19 pandemic has severely affected daily life and caused a great loss to the global economy. Due to the very urgent need for identifying close contacts of confirmed patients in the current situation, the development of automated contact tracing app for smart devices has attracted more attention all over the world. Compared with expensive manual tracing approach, automated contact tracing apps can offer fast and precise tracing service, however, over-pursing high efficiency would lead to the privacy-leaking issue for app users. By combing with the benign properties (e.g., anonymity, decentralization, and traceability) of blockchain, we propose an efficient privacy-preserving solution in automated tracing scenario. Our main technique is a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation. By means of aggregating multiple signatures from different contacts at the mutual commitment phase, we only need fewer zero-knowledge proofs to complete the task of identifying contacts. It inherently leads to the benefits of saving storage and consuming less time for running verification algorithm on blockchain. Furthermore, we perform an experimental comparison by timing the execution of signature verification with and without aggregate signature, respectively. It shows that our solution can actually preserve the full-fledged privacy protection property with a lower computational cost. © 2022

Changing Food Patterns during the Pandemic: Exploring the Role of Household Dynamics and Income Stabilization Strategies

COVID-19 still looms as the largest risk to the agriculture, energy, and health sectors, threatening sustainable global economic development. The literature shows that the COVID-19 pandemic can divert governments' attention away from climate change, renewable energy, and food security challenges that are necessary to address for sustainable economic growth. The COVID-19 pandemic has consistently influenced environmental behaviors, as it has primarily decreased income levels and disrupted food systems worldwide. This study examined the impacts of COVID-19 on food consumption patterns, food diversity, and income challenges and explored the factors affecting food consumption patterns during the pandemic. The data collected through an online survey from 1537 Chinese households were analyzed through a paired t-test, a mixed-design ANOVA, and a logistic regression analysis. The results revealed that the consumption of the majority of individual food commodities decreased during the COVID-19 pandemic. Among the individual food items, the consumption of pork witnessed the greatest decrease during the COVID-19 pandemic compared to the normal period. The decrease in food diversity was higher for the households whose income was affected compared to the households whose income was not affected during the COVID-19 pandemic. Furthermore, the consumption quantities of various food groups declined more for highly income-affected households than for medium and slightly affected households during the pandemic. Households that adopted a dissaving income-stabilizing strategy were 47% points more likely to maintain their food consumption patterns during the pandemic. Farmers were 17% points and 19% points less likely to suffer worsened food consumption compared to self-employed and wage workers, respectively, during the pandemic. Thus, self-production methods such as kitchen gardening can assist households to maintain and improve their consumption of food commodities during the COVID-19 pandemic. © 2022 by the authors.

Pricing extreme mortality risk in the wake of the COVID-19 pandemic

In pricing extreme mortality risk, it is commonly assumed that interest rate and mortality rate are independent. However, the COVID-19 pandemic calls this assumption into question. In this paper, we employ a bivariate affine jump-diffusion model to describe the joint dynamics of interest rate and excess mortality, allowing for both correlated diffusions and joint jumps. Utilizing the latest U.S. mortality and interest rate data, we find a significant negative correlation between interest rate and excess mortality, and a much higher jump intensity when the pandemic experience is considered. Moreover, we construct a risk-neutral pricing measure that accounts for both diffusion and jump risk premia, and we solve for the market prices of risk based on mortality bond prices. Our results show that the pandemic experience can drastically change investors' perception of the mortality risk market in the post-pandemic era. © 2022 Elsevier B.V.

Measures to Ensure the Sustainability of Information Systems in the COVID-19 Environment

National defense requires uninterrupted decision-making, even under direct or indirect impacts of non-traditional threats such as infectious diseases. Since all work utilizes information systems, it is very important to ensure the sustainability and availability of information systems. In particular, in terms of security management, defense work is being performed by dividing the network into a national defense network and a commercial Internet network. This study suggests a work execution plan for sustainability that takes into account the efficiency of work performed on the Internet and the effectiveness of security through effective defense information system operation. It is necessary to minimize the network contact points between the national defense network and the commercial Internet and to select high-priority tasks from various tasks and operate them efficiently. For this purpose, actual cases were investigated for an institution, "Organization A”, and characteristics were presented. Through the targeted tasks and operation plans presented in this paper to improve the effectiveness of defense tasks and ensure security, it will be possible to increase the sustainability and availability of task performance even under non-traditional threats such as infectious diseases. © 2022 by the authors.

A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.

Structural analysis of cotton stalk Puller and Shredder Machine

World scenario after pandemic COVID-19 has been drastically changing and researchers more focusing on, to minimize the post-pandemic effects on economy, energy sustainability and food security. Agriculture sector is playing pivotal role in world food security and energy sustain -ability. There is high need to optimize the mechanization technologies to increase the yield in limited energy inputs and operation time to fulfill the world growing food demand. This research is mainly focused on the design development and structural analysis aiding with Finite Element Analysis (FEA) approach for Cotton Stalk Puller and Shredder machine (CSPS) to cut the crop leftovers, soil conditioning (shredding the plant waste into soil) and sowing of next crop in single run by con-serving input resources. The experimental trials revealed that there is high pressure on cutting blades, chocking of shredder section and excessive pulling load on tractor hitches, which affected the machine's performance. To mitigate deficiencies and design optimization to improve the machine safety/reliability, the structure analysis carried out. Six core components of machine including baseplate, blade, gear system, root digger, pulley and shaft has investigated as per field conditions. The results revealed that the material of blade, root digger and teeth of gear system receiving the high stress under the operational conditions which results the edge wear and damage. The carbonization up to one-millimeter thickness can provide the extra strength to bear the exces-sive load on edge layers.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Effects of Covid-19 Lockdown: A Perception Survey

The purpose of the study is to examine the perceptions of people on the effect of lockdown and outbreak of COVID-19 in the National Capital Region (NCR) area of India. In this survey, the two cities of the NCR area, i.e., Delhi and Guru gram are selected for the collection of primary data. The primary data have been collected through the questionnaire. A total of 204 respondents have answered the pre-framed questionnaire. Five-point Likert Scales have been used to judge the degree of agreement with the statements. Descriptive statistics are used to analyze the data. The findings suggest that people pay more attention to personal hygiene during the pandemic time and they feel that working from home is a challenge in India due to bandwidth and technological issues. Job security is a big concern in front of people due to lockdown. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.