The novel coronavirus (COVID-19) pandemic has caused a considerable and long-lasting social and economic impact on the world. Along with other potential challenges across different domains, it has brought numerous cybersecurity challenges that must be tackled timely to protect victims and critical infrastructure. Social engineering–based cyber-attacks/threats are one of the major methods for creating turmoil, especially by targeting critical infrastructure, such as hospitals and healthcare services. Social engineering–based cyber-attacks are based on the use of psychological and systematic techniques to manipulate the target. The objective of this research study is to explore the state-of-the-art and state-of-the-practice social engineering–based techniques, attack methods, and platforms used for conducting such cybersecurity attacks and threats. We undertake a systematically directed Multivocal Literature Review (MLR) related to the recent upsurge in social engineering–based cyber-attacks/threats since the emergence of the COVID-19 pandemic. A total of 52 primary studies were selected from both formal and grey literature based on the established quality assessment criteria. As an outcome of this research study; we discovered that the major social engineering–based techniques used during the COVID-19 pandemic are phishing, scamming, spamming, smishing, and vishing, in combination with the most used socio-technical method: fake emails, websites, and mobile apps used as weapon platforms for conducting successful cyber-attacks. Three types of malicious software were frequently used for system and resource exploitation are; ransomware, trojans, and bots. We also emphasized the economic impact of cyber-attacks performed on different organizations and critical infrastructure in which hospitals and healthcare were on the top targeted infrastructures during the COVID-19 pandemic. Lastly, we identified the open challenges, general recommendations, and prospective solutions for future work from the researcher and practitioner communities by using the latest technology, such as artificial intelligence, blockchain, and big data analytics.

Currently, cybersecurity plays an essential role in computing and information technology due to its direct effect on organizations’ critical assets and information. Cybersecurity is applied using integrity, availability, and confidentiality to protect organizational assets and information from various malicious attacks and vulnerabilities. The COVID-19 pandemic has generated different cybersecurity issues and challenges for businesses as employees have become accustomed to working from home. Firms are speeding up their digital transformation, making cybersecurity the current main concern. For software and hardware systems protection, organizations tend to spend an excessive amount of money procuring intrusion detection systems, antivirus software, antispyware software, and encryption mechanisms. However, these solutions are not enough, and organizations continue to suffer security risks due to the escalating list of security vulnerabilities during the COVID-19 pandemic. There is a thriving need to provide a cybersecurity awareness and training framework for remote working employees. The main objective of this research is to propose a CAT framework for cybersecurity awareness and training that will help organizations to evaluate and measure their employees’ capability in the cybersecurity domain. The proposed CAT framework will assist different organizations in effectively and efficiently managing security-related issues and challenges to protect their assets and critical information. The developed CAT framework consists of three key levels and twenty-five core practices. Case studies are conducted to evaluate the usefulness of the CAT framework in cybersecurity-based organizational settings in a real-world environment. The case studies’ results showed that the proposed CAT framework can identify employees’ capability levels and help train them to effectively overcome the cybersecurity issues and challenges faced by the organizations.

Bone remodeling is a tightly coupled process between bone forming osteoblasts (OBs) and bone resorbing osteoclasts (OCs) to maintain bone architecture and systemic mineral homeostasis throughout life. However, the mechanisms responsible for the coupling between OCs and OBs have not been fully elucidated. Herein, we first validate that secreted extracellular vesicles by osteoclasts (OC-EVs) promote osteogenic differentiation of mesenchymal stem cells (MSCs) and further demonstrate the efficacy of osteoclasts and their secreted EVs in treating tibial bone defects. Furthermore, we show that OC-EVs contain several osteogenesis-promoting proteins as cargo. By employing proteomic and functional analysis, we reveal that mature osteoclasts secrete thrombin cleaved phosphoprotein 1 (SPP1) through extracellular vesicles which triggers MSCs osteogenic differentiation into OBs by activating Transforming Growth Factor β1 (TGFβ1) and Smad family member 3 (SMAD3) signaling. In conclusion, our findings prove an important role of SPP1, present as cargo in OC-derived EVs, in signaling to MSCs and driving their differentiation into OBs. This biological mechanism implies a paradigm shift regarding the role of osteoclasts and their signaling toward the treatment of skeletal disorders which require bone formation.

Huge amounts of data is generated with the emergence of new sensors technologies. Human Activity Recognition (HAR) data sets are generated from cameras, such as video or still images, capturing human behaviour through sensors like gyroscope, Bluetooth, sound sensors, and accelerometers. These generated data sources are collected by the researchers and formed into open data sets. However, these data sets often show issues during data set construction, sharing, searching, that could produce further challenges for reuse of the data by others. The main objective of this research is to explore the current issues and challenges faced by researchers in the HAR domain. A detail literature review was conducted to extract information from the published literature. Similarly, a questionnaire survey was sent to selected researchers having expertise in the HAR domain and who work with open data sets. The main issues and challenges were identified and classified into a hierarchical structure. This research will help HAR researchers to be aware of the current issues and challenges in the field of HAR open data sets. It will help to promote important attributes applicable to many open data sets, such as privacy, anonymity, platform maintenance, data sets descriptions, metadata, environmental conditions, resources, and training, while constructing and sharing new data sets.

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.