Game analysis on general purpose technology cooperative R&D with fairness concern from the technology chain perspective

General purpose technologies (GPTs) are regarded as a major source of productivity advancement and economic growth. As a kind of platform technology, GPTs have strong knowledge spillovers, which causes a single subject to lack R&D motivation and adopt a wait-and-see strategy. Cooperation R&D is an effective mode choice for GPTs. For this, three models based on upstream-led, downstream-led and balanced power structures were constructed to study the cooperation R&D modes of GPTs and influencing factors from a technology chain perspective. This study aims to reveal the effects of fairness concerns and power structures on three models. This study also focuses on the roles of knowledge spillovers and government support. The results indicate that different power structures will lead to an unequal distribution of profits between firm U and firm D in the technology chain. The balanced power structure should be the preferred model. The profits of firms in the leading position are always higher than those of firms in the following position. In addition, fairness concerns negatively impact the performance of firms, which may improve the bargaining ability of firms in the following position, but this does not bring a sustainable benefit. Government support (e.g., knowledge and technology support and R&D subsidies) and knowledge spillovers are two key factors influencing the decisions and outcomes of the technology chain. When a firm's relative innovation contribution level is greater, its profits in the leading position are the highest, followed by those in the balanced power structure, and they are lowest in the following position. In contrast, profits under balanced power are the highest, and those in the following position are still the lowest. This study enables a theoretical understanding of how and why the R&D process of GPTs can be regarded as a technology chain. It also sheds light on the fact that the balance power structure model should be the preferred choice and that both fairness concerns and government support should be considered for improving the R&D efficiency of GPT cooperation R&D in practice

Intrusion Detection System Based on User Behavior Using Data Mining Techniques

Intrusion Detection System (IDS) in computer technology is a little bit different than physical intrusion detection system, which detects any physical changes in the protected premises. In computer technology, IDS is used to examine all traffics and activities either in computer unit or network. These IDS could be old technology systems, which we refer in this paper as traditional Intrusion Detection System (tIDS), or it could be an intelligent system based on AI, machine learning, data mining and other intelligent techniques. In tIDS, which based on errors detection, the system works according to its database. This database is usually predefined by security experts. IDS is used to classify suspicious behaviors as intrusion acts or regular activities. Experts update the database manually [1]. Thus, it is hard to keep track of every single update and hard to analyze an event as a suspicious act with acceptable efficiency and satisfaction. So the need for automated tools became immanent to support security experts. Such a support could be achieved using data mining techniques as one of the possible ways to automate the system. This may handle the problem with high degree of accuracy. This paper demonstrates the advantage of using data mining techniques in IDS. The system depends on users behaviors in order to extract features and then generate rules. The generated rules will be used as a pattern recognition tool. These rules enable the system to classify any irregular activity as an intrusion act.. In this research we hypothesize that, depending on time of a day and location of the activity in the database we could classify a suspicious behavior as an intrusion acts. The experimental results show high level of accuracy, efficiency, robustness where the system can handle errors, scalability where we can use the system in large number of users as well as reliability as the system shows 0% error rate of this techniqu

Impact of lockdown due to the COVID-19 pandemic on mental health among the Libyan population.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic may have a potentially serious effect on mental health and increase the risk of anxiety, depression, and post-traumatic stress disorders in people. In this study, we aimed to determine the prevalence of psychological illness and the impact of the COVID-19 pandemic on the Libyan population's mental health.MethodA cross-sectional survey, conducted in both online and paper modes and consisting of five sections, was completed in more than 30 cities and towns across Libya. The first section consisted of questions on basic demographic characteristics. The second section contained a survey related to the lockdown status, activities, related stress levels, and quarantine. The third section comprised the self-administered 9-item Patient Health Questionnaire (PHQ-9). The fourth section contained the 7-item Generalized Anxiety Disorder Scale (GAD-7), and the fifth section contained the Impact of Event Scale-Revised (IES-R).ResultOf the 31,557 respondents, 4,280 (13.6%) reported severe depressive symptoms, with a mean [standard deviation (SD)] PHQ-9 score of 8.32 (5.44); 1,767 (5.6%) reported severe anxiety symptoms, with a mean (SD) GAD-7 score of 6 (4.6); and 6,245 (19.8%) of the respondents reported post-traumatic stress disorder (PTSD), with a mean (SD) score of 15.3 (18.85). In multivariate analysis, young age, being female, unmarried, educated, or victims of domestic violence or abuse, work suspension during the pandemic, and having increased workload, financial issues, suicidal thoughts, or a family member with or hospitalized due to COVID-19 were significantly associated with a high likelihood of depressive and anxiety symptoms, as well as PTSD. Internal displacement due to civil war was also associated with PTSD.ConclusionTo our knowledge, this is the first study to analyze the psychological impacts of the COVID-19 pandemic and civil war in Libya. Further study on the development of strategies and interventions aimed at reducing the mental disease burden on the Libyan population is warranted

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population