Optimizing Key Distribution in Peer to Peer Network Using B-Trees

Peer to peer network architecture introduces many desired features including self-scalability that led to achieving higher efficiency rate than the traditional server-client architecture. This was contributed to the highly distributed architecture of peer to peer network. Meanwhile, the lack of a centralized control unit in peer to peer network introduces some challenge. One of these challenges is key distribution and management in such an architecture. This research will explore the possibility of developing a novel scheme for distributing and managing keys in peer to peer network architecture efficiently

Green Product and Process Innovation, Corporate Environmental Ethics and Competitive Advantages among Manufacturing Firms in the Kingdom of Saudi Arabia

The prime objective of the study is to examine the impact of green product innovation, green process innovation, and corporate environmental ethics on the competitive advantages of Saudi manufacturing firms. In addition to that, the study has also planned to examine the mediating role of corporate environmental ethics and moderating role of corporate environmental management in the relationship between green product innovation, green process innovation, and competitive advantages of Saudi manufacturing. This study used a quantitative approach to research with a cross-sectional method for the collection of data. This study used purposive sampling for the collection of data from the production managers of the manufacturing industry of KSA. The participation of the production managers in current was on a volunteer base. A structured questionnaire was used to gather the data from the respondents. The scale item for all constructs was adapted from the previous studies and measured on a 5-point Likert scale. “Partial Least Squares” (PLS) method of analysis was employed for the analysis of the research model using the Smart-PLS (Ringle et al., 2020). The present study found out that if a firm is consistent about investing more in CEE, the KSA manufacturing industry would witness improvement in their GRpdI and competitive advantage. Thus, these findings can be utilized by the manufacturing industries in KSA. By observing the environmentalism approach of consumers and strict rules at the international level, the firms shouldn't avoid their environmental duties. This study intends to fulfill the purpose of contributing to managers of the manufacturing industry of KSA, their researchers and policy formulators which may further contribute to their respective areas

Applications of Fractional Supersaturated Process and Factor Analysis in the Systemic Risks of Financial Fraud

Fraud is defined as an act committed to deceive people and undermine their trust in financial institutions. The effects of fraud in government financial institutions, the private sector, and at the societal level show that it deeply feeds the economy. Objective. This study aims to shed light on the effects of financial fraud, measure its impact, and determine means to control it. Methods and Materials. This study considered a survey and a questionnaire was used to collect primary data. The total number of participants included in the questionnaire was 77. Two methods were used to search for the real effects of financial fraud, measure its impact, and determine the means to control it: factor and regression analyses. The data were analyzed using SPSS version 25.0. Results. The factor analysis results reveal the factors that contributed to the effects of financial fraud from A1 to A15, excluding factors A12 and A13. The regression analysis revealed the elements that contributed to the effects of financial fraud on A2, A5, and A14. Conclusion. Based on the findings of the two methodologies and the similarity in the outcomes of the causal variables, the fundamental and authentic aspects that lead to the effects of financial fraud were identified as A2, A5, and A14

Antioxidant effect of Arabian coffee (Coffea arabica L) blended with cloves or cardamom in high-fat diet-fed C57BL/6J mice

Purpose: To investigate the antioxidant activity of Coffea Arabica L in high-fat diet (HFD)-fed C57BL/6J mice.Methods: A decoction of Arabian coffee blended with or without cardamom or cloves was orally administered to HFD-fed C57BL/6J mice for a period of 60 days. At the end of the treatment, blood and tissue samples were taken to evaluate various parameters, including lipid peroxidation markers and antioxidants such as paraoxonase (PON1), superoxide dismutase (SOD), glutathione peroxidase (GPx), vitamin C, vitamin E, reduced glutathione (GSH) and catalase (CAT).Results: The activities of antioxidant enzymes (PON1, SOD, CAT, and GPx) and non-enzymatic antioxidants (GSH, vitamins C and E) were significantly elevated (p < 0.05) in mice administered Arabian coffee plus cardamom compared with those administered HFD alone or coffee alone. The levels of thiobarbituric reactive substances and oxidized LDL antibody (oxLDL-Ab) decreased significantly (p < 0.05) in mice administered Arabian coffee plus cardamom compared with those administered Arabian coffee plus cloves.Conclusion: The results indicate that Arabian coffee blended with cardamom or cloves exhibited enhanced free radical scavenging and antioxidant properties compared with those that received Arabic coffee alone. However, coffee with cardamom had more pronounced effects than coffee with cloves.Keywords: High-fat diet, Diabetes, Antioxidant, Arabian coffee, Cardamom, Clove

Kuwaiti EFL Students’ Perceptions of the Effectiveness of the Remedial English Course 099 at the College of Technological Studies

The study aims to evaluate the English remedial course 099 taught in the College of Technological Studies (PAAET) as part of the English program which disseminates English Language Skills to EFL students studying at this college. This study is expected to provide sufficient information to policymakers and educators involved with this program at all levels, with the intention to help them evaluate this course and make useful decisions to improve English Language Teaching in order to combat the deficiency in the English language suffered by college students in Kuwait. A number of 155 students participated in a questionnaire of 15 statements divided into four areas: reading, grammar, writing, and speaking skills. The findings of the study showed that most EFL students benefited from the English course 099, and their language skills were improved. However, there were some drawbacks and weaknesses of the program in terms of learners’ assessments and follow up. The significance of the study arises from the fact that it would enable decision-makers and course evaluators to pinpoint the strengths and weaknesses of the course and hence find ways to improve it

Pharmacological Potential of Hippophae rhamnoides L. Nano-Emulsion for Management of Polycystic Ovarian Syndrome in Animals’ Model: In Vitro and In Vivo Studies

The most common female endocrinopathy, polycystic ovarian syndrome (PCOS), generally affects women of childbearing age. Hippophae rhamnoides L. has been traditionally used to improve menstrual cyclicity. Gas chromatography by flame ionization detection analysis showed that it contained various phytoconstituents such as omega-3 fatty acid, phytosterols, palmitic acid, oleic acid, and linoleic acid. H. rhamnoides L. (HR) nano-emulsion was also formulated. HR and its encapsulated nano-emulsion (HRNE) were evaluated for the treatment of PCOS. Thirty-five healthy female adult albino rats were acquired and divided into seven groups (n = 5). Letrozole (1 mg/kg) was used for 5 weeks to induce the disease. To confirm disease (PCOS) induction, the animals were weighed weekly and their vaginal smears were analyzed daily under a microscope. After PCOS induction, animals were treated with metformin, HR, and HRNE with two different doses (0.5/kg and 1 g/kg, p.o.) for 5 weeks. At the end of the treatment, animals were euthanized, and blood was collected for hormonal assessment, lipid profiling, and liver functioning test assessment. Both the ovaries were preserved for histopathology and liver for the purpose of assessment of antioxidant potential. The results revealed that HR and HRNE at both doses improved the hormonal imbalance; follicle-stimulating hormone, estrogen, and progesterone levels are increased, while luteinizing hormone surge and testosterone level are controlled. Insulin sensitivity is improved. Ovarian histopathology showed that normal ovarian echotexture is restored with corpus luteum and mature and developing follicles. HR and HRNE also improved the lipid profile and decreased lipid peroxidation (MDA) with improved antioxidant markers (SOD, CAT, and GSH). Results were statistically analyzed by one-way analysis of variance and were considered significant only if p < 0.05. In conclusion, it can be postulated that H. rhamnoides L. proved effective in the management of PCOS and its nano-emulsion effects were statistically more significant, which might be due to better bioavailability

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online social networks is an important task to fight this issue. Clickbait posts use phrases that are mainly posted to attract a user’s attention in order to click onto a specific fake link/website. That means clickbait headlines utilize misleading titles, which could carry hidden important information from the target website. It is very difficult to recognize these clickbait headlines manually. Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose. However, the obtained performance (accuracy) only reached 87% and still needs to be improved. In addition, most of the existing studies were conducted on English headlines and contents. Few studies focused specifically on detecting clickbait headlines in Arabic. Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language. The proposed approach includes three main phases: data collection, data preparation, and machine learning model training and testing phases. The collected dataset included 54,893 Arabic news items from Twitter (after preprocessing). Among these news items, 23,981 were clickbait news (43.69%) and 30,912 were legitimate news (56.31%). This dataset was pre-processed and then the most important features were selected using the ANOVA F-test. Several machine learning (ML) methods were then applied with hyperparameter tuning methods to ensure finding the optimal settings. Finally, the ML models were evaluated, and the overall performance is reported in this paper. The experimental results show that the Support Vector Machine (SVM) with the top 10% of ANOVA F-test features (user-based features (UFs) and content-based features (CFs)) obtained the best performance and achieved 92.16% of detection accuracy