The world of the Internet of Things (IoT), even though it is continuously morphing as a fresh paradigm at the intersection of technology and education, is still struggling with several difficulties that prevent its absorption into the e-learning platforms of higher education institutions (HEIs). The breadth of Internet of Things implementation in developing countries, particularly Jordan, Malaysia, Iran, Saudi Arabia, Iraq, and Bangladesh, remains behind, even though industrialized nations have made significant advancements in their utilization of IoT, with the United Kingdom, the United States of America, China, and Japan acting as prominent examples. In the realm of research that focuses on the progression of the IOT integration into the e-learning systems of economically challenged countries, there is a substantial disparity. In particular, the focus of this research is on Jordan to shed light on the primary variables that are either facilitating or hindering the adoption of the IoT within the e-learning sector of Jordan's HEIs. A comprehensive analysis of previous research has been undertaken as a first stage in this investigation. The goal of this analysis is to identify important factors that are involved in the process of IOT adoption. Following that, we used an inferential technique, collecting data from 306 respondents who were enrolled in Jordanian higher education institutions. During our investigation, we discovered that the rate of the IOT integration was significantly influenced by factors such as accessibility, usability, technical assistance, and individual capabilities. In addition, our findings suggest that factors such as attitude, behavior, financial preparedness, dependability, and training have a substantial impact on the adoption of the IOT. On the other hand, the study seemed to indicate that characteristics such as class capacity, awareness, system resources, and course design had a minor influence on the adoption rates inside HEIs. In conclusion, this study provides tangible suggestions to strengthen the integration of the IoT inside Jordanian HEIs. These recommendations provide significant insights that can be used by policy architects, government entities, and higher education institutions to overcome the challenges that relate to the deployment of IoT in the higher learning sector.

This paper presents an experimental programme designed to investigate the failure mode and ultimate capacity of pultruded glass fibre reinforced polymer (GFRP) cellular profiles subject to transverse loading. Presented in this study are the results of the characterisation of twenty six GFRP 152 × 76 × 6.4 mm I stub beams, 300 mm long. The beam specimens were categorised as plain for the control tests and those with circular or rectangular openings, centrally positioned. The specimens were subject to different loading configurations, noted as End Bearing with solid ground (EB), Interior Two Flange (ITF), Interior Bearing with solid base (IB) and End Two Flange (ETF) Results indicate a reduction in load-carrying capacity of the specimens with the opening when compared to the control specimens. The reduction was up to 20% for the specimens with circular openings and up to 25% for specimens with rectangular openings. The study revealed that loading configuration IB and ITF exhibit larger nonlinear behaviour and deformability than loading configurations EB and ETF. Various research has been conducted on its mechanical properties, connections, pultrusion techniques and web crippling behaviour of thin-walled GFRP section. Limited research can be found in the literature on the behaviour of pultruded GFRP beams with large perforation, subject to transverse static loadings.

In the present research work using a backward cup extrusion (BCE) die profile, different lubricating conditions on aluminum alloy AA6063 have been experimentally and numerically investigated to predict the extrusion load. It was obvious that due to an increase in applications of the extrusion process, many researchers have worked on the extrusion process using different methods to achieve their aims. This experiment was conducted with three different lubricants namely: Castor oil, Palm Oil and tropical coconut oil; as well as without lubricants. Different lubricating conditions were employed of varying strain rates ranges from 1.5×10-3s-1, 2.0×10-3s-1, 2.5×10-3s-1, and 3.0×10-3s-1; Numerical analysis and simulation for dry and lubricated conditions during extrusion load were also performed using DEFORM 3D software. The results show that prediction extrusion load increases with increasing strain rates. The maximum extrusion load was found to be higher for extrusion without lubricants. In all cases of strain rate, palm oil showed a lower extrusion load compared to the other lubricants. Castor oil indicated the highest extrusion load when the experiment is carried out using lubrication. There was a consistent agreement between the result gotten from the experiment and simulation result of the extrusion load-strike curve.

From the past decades, increasing attention has been paid to the quality level of technological and mechanical properties achieved by the Additive Manufacturing (AM); these two elements have achieved a good performance, and it is possible to compare this with the results achieved by traditional technology. Therefore, the AM maturity is high enough to let industries adopt this technology in a more general production framework as the mechanical manufacturing industrial one is. Since the technological and mechanical properties are also beneficial for the materials produced with AM, the primary objective of this paper is to focus more on managerial facets, such as the cost control of a production environment, where these new technologies are present. This paper aims to analyse the existing literature about the cost models developed specifically for AM from an operations management point of view and discusses the strengths and weaknesses of all models.

In this digital age, fraudulent practices are among the most challenging that organizations must be aware of due to the increasing use of online transactions. This also applies to the banking sector whose business has become more complex with the recent developments in information and communication technology, which has changed the nature of bank fraud requiring advanced prevention measures. From this perspective, this paper aims to determine how cybersecurity affects fraud prevention for Jordanian commercial banks. A five-dimensional NIST cybersecurity framework was used. The research data was collected from 173 information technology managers in commercial banks listed on the Amman Stock Exchange. Structural equation modeling (SEM) was applied to investigate research hypotheses. The results of the research demonstrated the significant impact of cybersecurity in fraud prevention, especially detect function which had the largest impact among the dimensions of cybersecurity. Therefore, a set of recommendations were formulated for policymakers in Jordanian commercial banks, the most important of which is the adoption of multi-factor authentication (MFA) approaches for customer accounts, employee access, and biometric systems that add an additional layer of protection and make access to sensitive information to unauthorized individuals more difficult.

Recent technological advancements are endless and have had a profound influence on everyone in every part of life throughout the preceding decades. Artificial intelligence is one such invention that has the potential to change the world. Now, artificial intelligence is being used in almost all commercial operations. Hence, this research attempted to investigate the impact of artificial intelligence dimensions, including natural language processing, machine learning, expert systems, and computer vision on the financial decisions of pharmaceutical companies in Jordan. A cross-sectional approach was used through a comprehensive survey to collect research data from 148 accountants and financial managers in pharmaceutical companies listed on the Amman Stock Exchange with a response rate of 81.3%. The research hypotheses were examined using structural equation modeling of the collected quantitative data. The results indicated that the dimensions of artificial intelligence positively impact financial decisions. Accordingly, companies should spend on building strong artificial intelligence infrastructure and skills. Access to modern artificial intelligence technology, data analysis tools and cloud computing resources are also essential to rationalizing financial decision-making. Besides, Jordan's pharmaceutical sector can overcome these limitations and realize the full potential of artificial intelligence in financial decision-making by solving data privacy issues, encouraging ethical AI re-search, investing in artificial intelligence expertise, and enhancing collaboration.

This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’ search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable time.

Diabetes Mellitus (DM) is a frequent condition in which the body's sugar levels are abnormally high for an extended length of time. It is a major cause of death with high mortality rates and the second leading cause of total years lived with disability worldwide. Its seriousness comes from its long-term complications, including nephropathy, retinopathy, and neuropathy leading to kidney failure, poor vision and blindness, and peripheral sensory loss, respectively. Such conditions are life-threatening and affect patients’ quality of life. Therefore, this paper aims to identify the most relevant features in the diagnosis of DM and identify the best classifier that can efficiently diagnose DM based on a set of relevant features. To achieve this, four different feature selection methods have been utilized. Moreover, twelve different classifiers that belong to six learning strategies have been evaluated using two datasets and several evaluation metrics such as Accuracy, Precision, Recall, F1-measure, and ROC area. The obtained results revealed that the correlation attribute evaluation method would be the best choice to handle the task of feature selection and ranking for the considered datasets, especially when considering the Accuracy metric. Furthermore, MultiClassClassifier would be the best classifier to handle Diabetes datasets, especially when considering True Positive, precision, and Recall metrics.

Mobile e-learning within the education domain is now an integral part in education technology. However, there has been little discussion about the benefits and the challenges of the use of M-learning in the higher education system in KSA. Interviews were the primary instruments used for data collection in identifying the enabling and deterring the quality attributes of mobile e-learning use in the context in Saudi Arabia higher education. The research results revealed that learning content/information quality, system quality, service quality and instructional quality were the primary antecedents of Blackboard as a Learning Management System (LMS) in general and within the mobile devices, and this situation can enhance user’s attitude towards the use of E-learning. Data were analyzed and results were discussed. Recommendations were made based on the findings.

This paper examines the potential determinants associated with a firm’s decision to use a corporate website. The probability of web-based corporate reporting adoption was measured by using a dichotomous variable, where one is given if the firm has a website and zero otherwise. Based on a sample of 1217 US listed firms, it was found that 950 firms have a website and 267 do not. Those firms with a website are larger, more profitable and have a larger board size with more female directors when compared with firms without websites. In addition, the results of the regression analysis revealed that firm size, profitability, leverage, board size and the percentage of female directors in the boardroom have a significant positive impact on the probability of a firm having a website. However, firm age has a significant negative impact on the probability of web-based corporate reporting adoption. A weakness in the previous literature has been the neglect of firms without an online presence, which implies a potential selection bias. Consequently, this research contributes to the international accounting literature by expanding our understanding in relation to the probability of firms adopting web-based corporate reporting and the economic consequences of them doing so through reducing asymmetric information, which acts as an incentive to encourage investment.