FACTORS INFLUENCING POLITICAL PARTICIPATION IN LEBANON: THE MEDIATING ROLE OF PERCEIVED CONGRUENCE

Abstract This study analyzes the factors that influence political participation. These factors include: political information efficacy, political interest, community engagement, political party affiliation and perceived congruence. Moreover, this study addresses the mediating role of perceived congruence on the relationship between political interest, community engagement and political participation. A quantitative survey method was used and structured questionnaire was administered to a convenience sample of 412 respondents. The findings of this study revealed that political interest, community engagement, and political party affiliation have a positive effect on political participation. In addition, the results indicated that perceived congruence has no direct or mediate effect on political participation. The current study enhances marketing literature to understand political behaviors under unusual political situations. In contrast, this research supports both political parties and governments for better understanding the factors that influence political participation which guide them to fulfill their political marketing objectives and gain citizens’ support

Predictors of Continued Breastfeeding at One Year among Women Attending Primary Healthcare Centers in Qatar: A Cross-Sectional Study

The number of babies in Qatar being exclusively breastfed is significantly lower than the global target set by the World Health Organization. The purpose of this study was to assess knowledge, attitude, and practice (KAP), selected barriers, and professional support as well as their association with continued breastfeeding at one year of age. A sample of Qatari and non-Qatari mothers ( = 195) who attended a well-baby clinic held at primary health care centers in Qatar completed a self-administered questionnaire. Descriptive analysis, the Pearson Chi-squared test, and logistic regression were performed. Around 42% of the mothers stopped breastfeeding when their child was aged between 0 and 11 months old. Mothers who had only one or female child stopped breastfeeding between the ages of 0 and 6 months ( = 0.025, 0.059). The more optimal the breastfeeding practices followed by the mothers, the older the age of the infant when they stopped breastfeeding ( = 0.001). The following factors were inversely associated with breastfeeding duration: the mother's perceptions that she "did not know how to breastfeed," or "wasn't making enough milk," and the need "to return to work/school", with = 0.022, 0.004, and 0.022, respectively. These findings present factors that should be considered when planning for health education and promotion programs to prolong breastfeeding duration in Qatar

HadithTrust: Trust Management Approach Inspired by Hadith Science for Peer-to-Peer Platforms

Peer-to-peer (P2P) platforms are gaining increasing popularity due to their scalability, robustness and self-organization. In P2P systems, peers interact directly with each other to share resources or exchange services without a central authority to manage the interaction. However, these features expose P2P platforms to malicious attacks that reduce the level of trust between peers and in extreme situations, may cause the entire system to shut down. Therefore, it is essential to employ a trust management system that establishes trust relationships among peers. Current P2P trust management systems use binary categorization to classify peers as trustworthy or not trustworthy. However, in the real world, trustworthiness is a vague concept; peers have different levels of trustworthiness that affect their overall trust value. Therefore, in this paper, we developed a novel trust management algorithm for P2P platforms based on Hadith science where Hadiths are systematically classified into multiple levels of trustworthiness, based on the quality of narrator and content. To benchmark our proposed system, HadithTrust, we used two state-of-art trust management systems, EigenTrust and InterTrust, with no-trust algorithm as a baseline scenario. Various experimental results demonstrated the superiority of HadithTrust considering eight performance measures

Can Brand Affinity Outperform Political Parties' Rejection When Nominating Celebrity Politicians in a Post-Rebellion Multi-Party Context?

In competitive political contexts, sustaining power is the ultimate goal for political parties. Nominating celebrity politicians can be a double-edged sword for parent brands in attracting votes and influencing voting intention. This study contributes to the moderating role of brand affinity towards celebrity politicians. It considers celebrities' cognitive perceived benefits and voting intention relationship in a multi-party parliamentarian election. A cross-sectional survey with a stratified proportional random sampling technique in fifteen Lebanese districts ensured a representative sample. One thousand two hundred sixty-nine responses were found eligible for analysis. Findings indicate that brand affinity positively moderates the negative relationship between perceived benefits and voting intention. This study offers a new understanding of celebrity politicians' implementation strategy and campaign management and considers the contribution of the affective intelligence theory. It provides implications, limitations, and promising directions for future research on celebrity politicians

Incremental Ant-Miner Classifier for Online Big Data Analytics

Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas

Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining

In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings

Crowd Evacuation in Hajj Stoning Area: Planning through Modeling and Simulation

Pilgrimage is one of the largest mass gatherings, where millions of Muslims gather annually from all over the world to perform Hajj. The stoning ritual during Hajj has been historically vulnerable to serious disasters that often cause severe impacts ranging from injuries to death tolls. In efforts to minimize the number and extent of the disasters, the stoning area has been expanded recently. However, no research has been carried out to study the evacuation effectiveness of the current exit placements in the area, which lies at the heart of effective minimization of the number and extent of the disasters. Therefore, this paper presents an in-depth study on emergency evacuation planning for the extended stoning area. It presents a simulation model of the expanded stoning area with the current exit placement. In addition, we suggested and examined four different exit placements considering evacuation scenarios in case of no hazard as well as two realistic hazard scenarios covering fire and bomb hazards. The simulation studied three stoning phases, beginning of stoning, during the peak hour of stoning, and ending of stoning at three scales of population sizes. The performance was measured in the light of evacuation time, percentage of evacuees, and percentage of crowd at each exit. The experimental results revealed that the current exits are not optimally positioned, and evacuation can be significantly improved through introducing a few more exits, or even through changing positions of the current ones

Patterns of folic acid use in pregnant Saudi women and prevalence of neural tube defects — Results from a nested case–control study

Background: Although the role of folic acid (FA) in preventing neural tube defects (NTDs) is well documented, its optimal intake in pregnant women is still low in many countries. Here, we prospectively studied the prevalence of NTDs in the newborns and the patterns of FA intake in pregnant Saudi mothers. Methods: This case–control study was nested within a 3-year project (July 2010 to June 2013) to study the patterns of birth defects in the offspring of Saudi women who received their antenatal care and delivered at Prince Sultan Military Medical City, Riyadh—Saudi Arabia. Enrolled mothers were divided into 4 groups: group 1 (FA taken before pregnancy and continued regularly after conception), group 2 (FA taken post-conception), group 3 (no FA intake), and group 4 (did not remember or were unsure of taking FA). Control mothers were randomly selected from those with normal first obstetrical ultrasound scan at 18–22 weeks of gestation. Results: The cohort included 30,531 mothers giving birth to 28,646 infants. We studied 1179 mothers of babies with birth defects (BDs) and 1262 control mothers. There were 237 (9.7%) mothers in-group 1; 2001 (82%) in-group 2; 154 (6.3%) in-group 3; and 49 (2%) in-group 4. There were 49 babies with NTDs, a prevalence of 1.7/1000 total births. Among the studied mothers 2274 (93%) took FA either full or partial course. Conclusion: The high prevalence of NTDs and the low optimal FA intake highlight the need for a strict implementation of staple food fortification and health education program for Saudi women