Applying Lean to the F-15 Maintenance Process for the Royal Saudi Air Force

The thesis was about applying lean to the F-15 maintenance process in Royal Saudi Air Force. The researcher collected the data by using several questionnaires. Researcher used external lean expert recommendation to avoid bias, the F-15 maintenance process improved theoretically

The Impact of Human Resources Management Practices on Job Satisfaction among Telecommunication Firms in Jordan

Job satisfaction has become the target of many organizations which sought to achieve it through various practices to motivate the employee and give the best in their jobs. The major purpose of this research is to examine the impact of human resource management HRM practices on job satisfaction in Jordanian telecommunication firms. The data used in this study were collected through a survey questionnaire was designed and adapted to fit the scope of the study and measuring the variables involving in this work to provide new valued insights from 223 samples from three different telecommunication firms operating in Jordan named Orange, Umniah and Zain. This sector has an essential contribution to this sector in the national economy and foreign investment. The results showed a positive impact of HRM practices on job satisfaction which consists of three constructs in this research name workplace environment; incentives and rewards; and job security. The findings were consistent with some studies and found a significant effect of the different activities of HRM on job satisfaction. The research also provides several helpful implications for practitioners and academicians to make new studies and filling the existed research gap by examining new variables handles contemporary issues and providing innovative business solutions. Keywords: human resource management; job satisfaction; telecommunication firms; Jordan. DOI: 10.7176/EJBM/12-17-02 Publication date:June 30th 202

The Effect of Strategic Planning on Effectiveness and Efficiency of Human Resources Management

The main purpose of this work is to examine the effect of strategic planning on Human Resources Management HRM practices. 200 survey questionnaires were sent to different employees and managers working in the hospitals sector in Jordan and only 169 questionnaires were received. Statistical Package of Social Sciences SPSS analytical software was used in this study to analysis the data and testing the hypotheses. The study examines the effect of strategic planning on HRM. The strategic planning analyzed as one construct while HRM practices have five constructs i.e. HRM planning; performance management; recruitment and selection; training and compensation. The results indicated that significant positive impact of strategic planning on HRM practices being analyzed in this study except for the performance management. Thus, the finding concluded that effective strategic planning will result in sufficient and effective HRM practices. Keywords: strategic planning, human resource management, hospitals industry, Jordan DOI: 10.7176/EJBM/12-17-03 Publication date:June 30th 202

A framework for cloud-based healthcare services to monitor noncommunicable diseases patient

Monitoring patients who have noncommunicable diseases is a big challenge. These illnesses require a continuous monitoring that leads to high cost for patients\u27 healthcare. Several solutions proposed reducing the impact of these diseases in terms of economic with respect to quality of services. One of the best solutions is mobile healthcare, where patients do not need to be hospitalized under supervision of caregivers. This paper presents a new hybrid framework based on mobile multimedia cloud that is scalable and efficient and provides cost-effective monitoring solution for noncommunicable disease patient. In order to validate the effectiveness of the framework, we also propose a novel evaluation model based on Analytical Hierarchy Process (AHP), which incorporates some criteria from multiple decision makers in the context of healthcare monitoring applications. Using the proposed evaluation model, we analyzed three possible frameworks (proposed hybrid framework, mobile, and multimedia frameworks) in terms of their applicability in the real healthcare environment

Predicting Certification in MOOCs based on Students’ Weekly Activities

Massive Open Online Courses (MOOCs) have been growing rapidly, offering low-cost knowledge for both learners and content providers. However, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). This can impact seriously the business model of MOOCs. Nevertheless, MOOC research on learners’ purchasing behaviour on MOOCs remains limited. Thus, the umbrella question that this work tackles is if learner’s data can predict their purchasing decision (certification). Our fine-grained analysis attempts to uncover the latent correlation between learner activities and their decision to purchase. We used a relatively large dataset of 5 courses of 23 runs obtained from the less studied MOOC platform of FutureLearn to: (1) statistically compare the activities of non-paying learners with course purchasers, (2) predict course certification using different classifiers, optimising for this naturally strongly imbalanced dataset. Our results show that learner activities are good predictors of course purchasibility; still, the main challenge was that of early prediction. Using only student number of step accesses, attempts, correct and wrong answers, our model achieve promising accuracies, ranging between 0.81 and 0.95 across the five courses. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenu

Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs

Since their ‘official’ emergence in 2012 (Gardner and Brooks 2018), massive open online courses (MOOCs) have been growing rapidly. They offer low-cost education for both students and content providers; however, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). The most recent literature on MOOCs focuses on identifying factors that contribute to student success, completion level and engagement. One of the MOOC platforms’ ultimate targets is to become self-sustaining, enabling partners to create revenues and offset operating costs. Nevertheless, analysing learners’ purchasing behaviour on MOOCs remains limited. Thus, this study aims to predict students purchasing behaviour and therefore a MOOCs revenue, based on the rich array of activity clickstream and demographic data from learners. Specifically, we compare how several machine learning algorithms, namely RandomForest, GradientBoosting, AdaBoost and XGBoost can predict course purchasability using a large-scale data collection of 23 runs spread over 5 courses delivered by The University of Warwick between 2013 and 2017 via FutureLearn. We further identify the common representative predictive attributes that influence a learner’s certificate purchasing decisions. Our proposed model achieved promising accuracies, between 0.82 and 0.91, using only the time spent on each step. We further reached higher accuracy of 0.83 to 0.95, adding learner demographics (e.g. gender, age group, level of education, and country) which showed a considerable impact on the model’s performance. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenue

Design of a Pedestrian Bridge Over Babcock Street

Objectives: • Design a safe and functional Pedestrian Bridge over Babcock Street. • Design a Pedestrian Safety Plan for W. University Blvd. • Make all designed environmentally and ecologically friendly

Structure-Based Virtual Screening of Antiviral Compounds Targeting the Norovirus RdRp Protein

Background: Human noroviruses (NV) are the primary etiological organisms causing acute gastroenteritis around the world, causing severe morbidity and imposing a significant economic burden. The RNA-dependent RNA polymerase (RdRp) is essential for viral replication and could be a promising target for anti-NV therapeutics. Despite the discovery of a few NV RdRp inhibitors, the majority of these pharmaceuticals have demonstrated limited efficacy in inhibiting viral replication in cellular models.Methods: In this study, computational screening of antiviral compounds was conducted targeting the NV RdRp protein. The assessment was based on binding poses and the key residues of RdRp involved in interactions with compounds.Results: The compounds namely, Ribavirin, BMS806, Dihydromyricetin, R7935788, and LY2784544 were found to bind the RdRp protein with high affinity. Notably, these compounds displayed significantly lower binding affinities compared to the positive control, PPNDS. In addition, these compounds exhibited many RdRp protein binding residues that were also present in the PPNDS.Conclusion: The results presented here suggest that these compounds have the potential to be used as inhibitors of NV RdRp in the development of antiviral medications. Nevertheless, due to the computational nature of this study, it is imperative to do experimental validation.Keywords: Noroviruses; RdRp; Virtual screening; Antiviral Compounds 

Deep learning: parameter optimization using proposed novel hybrid bees Bayesian convolutional neural network

Deep Learning (DL) is a type of machine learning used to model big data to extract complex relationship as it has the advantage of automatic feature extraction. This paper presents a review on DL showing all its network topologies along with their advantages, limitations, and applications. The most popular Deep Neural Network (DNN) is called a Convolutional Neural Network (CNN), the review found that the most important issue is designing better CNN topology, which needs to be addressed to improve CNN performance further. This paper addresses this problem by proposing a novel nature inspired hybrid algorithm that combines the Bees Algorithm (BA), which is known to mimic the behavior of honey bees, with Bayesian Optimization (BO) in order to increase the overall performance of CNN, which is referred to as BA-BO-CNN. Applying the hybrid algorithm on Cifar10DataDir benchmark image data yielded an increase in the validation accuracy from 80.72% to 82.22%, while applying it on digits datasets showed the same accuracy as the existing original CNN and BO-CNN, but with an improvement in the computational time by 3 min and 12 s reduction, and finally applying it on concrete cracks images produced almost similar results to existing algorithms