This research strives to offer valuable insights into business intelligence and analytics utilization. Besides, its expansion encompasses essential success factors for business intelligence and strategic management in the hospitality industry. The study's sample comprises 387 hotel managers from the Jordanian hospitality industry. Data collected to support the research goal was examined using multiple regression analysis through SPSS, with the assistance of PROCESS Macro v3.5. The results of this study reveal that business intelligence and analytics can serve as a bridge to connect crucial success factors for business intelligence and strategic management. These findings have implications for existing literature and stakeholders. Hotels that have integrated business intelligence and analytics acknowledge the advantages of enhancing their strategic management capabilities and decision-making processes.

This study investigates the relationships between Supply Chain Collaboration, Risk Management Strategies, Supplier Development, Trust, and Supply Chain Resilience in the Jordanian retail sector. This research aims to analyze the direct impacts of these Supply chain management practices on resilience, as well as mediating effect Trust in between this relation. The study used quantitative data to collect the data from 291 managers and executives in the Jordanian retail sector. The findings suggest that Supply Chain Collaboration and Risk Management Strategies have positive significant effects on Supply Chain Resilience, while Supplier Development has no direct significant effect. The results show that each of the supply chain management practices has significant positive effects on Trust and, therefore, significantly contribute to increasing Supply Chain Resilience. In addition, Trust is identified to play a mediating role in the relationships between trust on both SCMPs and resilience. This paper extends the existing literature on supply chain resilience and provides empirical insights into significant factors that shape in enhancing resilience within the retail trade industry, notably focusing on Jordan. The implications for managers, practitioners and society suggest that collaborative relationships, effective risk management strategies as well trust building initiatives are vital to increase resilience among supply chains. This research is unique as it thoroughly investigates the collaboration between SCM practices, relational variables and resilience in an understudied context providing a new understanding of the interplay of different antecedents at play on resilient supply chains creation.

With the increase of construction activities in Ghana, there is an increasing demand in building materials leading to the shortage of the conventional materials. The informal sector is gradually seeing the introduction of quarry dust as a substitute of sand in block production. This paper focuses on a comparative analysis of the quality of sandcrete blocks and quarry dust cement blocks. Block samples were gathered from various suppliers around the Prampram and Dawhenya areas and through various laboratory tests were tested for their dimension tolerance, water absorption and compressive strengths. Aggregate samples were also taken from suppliers for sieve analyses. The study revealed that the quarry dust cement blocks contained relatively higher percentages of coarse grade particles compared to the sandcrete blocks. The total average water absorption of sandcrete blocks was found to be 3.90% while quarry dust showed an improved value of 3.28%. Sandcrete blocks were averagely found to be of a higher compressive strength of 4.31N/mm2, with quarry dust at 3.0N/mm2. The study suggested the likelihood of a lesser use of cement in the production of quarry dust cement blocks due to the similarities in colour between the quarry dust and cement, hence, negatively affecting its compressive strength.

In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. This study presents a sophisticated simulation for NIDS tailored to intelligent vehicle environments, utilizing the extensive capabilities of Scapy—a robust network manipulation tool—to generate a highly accurate dataset of network traffic reflective of real-world scenarios. We created a diverse dataset involving 100,000 network flows, covering a wide array of benign, malicious, and anomalous traffic patterns, to thoroughly evaluate the detection capabilities of our proposed system. This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. These metrics showcase the system's enhanced capability to precisely classify various network flows, emphasizing the importance of realistic synthetic data in boosting the training and accuracy of NIDS in intelligent vehicles. The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.

In this paper, experimental and numerical studies are performed to determine the maximum deflection and strength of cylindrical shells. The considered models are subjected to concentrated loads applied progressively until plastic failure of the structures. In order to determine the effect of the geometrical parameters, several models with different thicknesses, lengths, and radius are considered and analysed with various boundary conditions. The numerical analysis is carried out by the finite element method using ABAQUS code. The main objective of the present investigation is to determine the maximum strength that caused damage of the models. The results of the experimental and numerical analysis are recorded, discussed and commented on. It is observed that the thickness, length,and radius dimensions with boundary conditions have great effect on the strength load values that create damage.

In production/inventory control systems, the goal of the controller is to generate sophisticated decisions by controlling the order rate and inventory level. This paper aims at modeling a dynamic-stochastic production/inventory control system under two sources of variability (uncertainty) including uncertainties on demand rate and frustrating rate. The study deals with obtaining a robust optimal design of a Proportional-Integral-Derivative (PID) controller in in the stochastic control system. For this purpose, a new robust simulation-optimization method in the class of computational intelligence is proposed. To cope with the unknown distribution of uncertainty, the crossing weighted uncertainty scenarios are combined with the proposed method. Within this study, a new sequential robust efficient global optimization is proposed to make a trade-off between optimal and robustness terms in final optimization results. Finally, a numerical case with simulation experiments is conducted to demonstrate the advantages of the proposed policy in terms of optimal result, robustness, and computational cost.

Advancements in technology have had a profound impact on the way we learn, teach, and access knowledge. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a dynamic, engaging, and inclusive space for education. One of the promising advancements in the field of artificial intelligence technology is ChatGPT which offers personalized and effective learning experiences by providing students with customized feedback and explanation. The effect of ChatGPT must be compared with the effect of Google at the educational level since both present a source of information and explanation. Thus, this study aims at investigating the differences between these two learning sources to measure their effectiveness from different perspectives. The model proposed in this study was evaluated using the PLS-SEM approach, utilizing data collected from 153 university students in the UAE. The results of this evaluation indicate that the GPT (Generative Pre-trained Transformer) has a significant impact on user acceptance, mediated by information quality, system quality, perceived learning value, and perceived satisfaction. These factors play a crucial role in determining users' acceptance of the GPT. However, it is important to note that some aspects of the model were not supported, suggesting that they do not have a significant predictive effect on the use of ChatGPT. Nonetheless, the findings of this study contribute to the existing literature on AI and environmental sustainability, providing valuable insights for practitioners, policymakers, and AI product developers. These insights can help guide the development and implementation of AI technologies in a way that aligns with users' needs and preferences while considering the larger environmental context.

This paper reports on a new experimental study for the behaviour of reciprocally connected and supported Fibre Reinforced Polymer (FRP) hollow square profiles axially loaded under several boundary conditions. The study aims to determine the ultimate load of the assembly and failure mechanism of mutually connected units. For the tests, FRP reciprocal frames units (RF) of 100 × 100 × 6.4 mm thick square hollow sections were designed, fabricated and assembled using mechanical fasteners. A bespoke steel test rig allowed for varied support boundary conditions. The observed failure modes were dominated by web buckling, bearing failure around the bolted areas and localised failure. The 100 × 100 mm RF unit achieved the highest load capacity of 16.4 kN and frame stiffness of 1.7 kN/mm, under the pin-pin-roller support boundary conditions. This paper presents the experimental procedure, results and observations.

Finite Element Analysis was carried out to describe the effect of frictional boundary conditions and percentage reduction on deformation modelling (forward extrusion) of Aluminum AA6063 alloy. Curved die profiles of regular polygons (square, hexagonal, heptagonal, and octagonal) were designed using MATLAB R2009b and Autodesk Inventor 2013 to generate the coordinate and the solid CAD model of the die profile respectively form a circular billet. The numerical analysis was performed using DeformTM-3D commercial package with frictional boundary conditions of 0.38 and 0.75 representing the wet and dry condition and varying the percentage reduction of 50%, 70%, and 90%. The results of the temperature distribution, effective stress, effective strain, and strain rate were reported. As the percentage area reduction increases, the extrusion pressure also increases with an increasing frictional condition, and die length. Also, extrusion pressure decreases when the side of the polygon increases from square-shaped section follow by hexagonal shaped-section and least in octagonal shaped-section for both friction factors and percentage area reductions. For a given percentage reduction and cross-sectional area, there is no distinct difference between the predictive loads for the shaped-polygons. When the result of this analysis is compared with the experimental results from the literature, it is evident that DeformTM-3D is an effective tool for finite element analysis of non-isothermal deformation processes.

This study aims to shed light on the way customers determine their utilization of international payment services with blockchain applications. The primary data is collected from the 373 individuals who frequently use the international payment services in Vietnam. We apply the combination of TAM, UTAUT, and ISS models to test the determinants of the Intention to use International payment services at Commercial banks applying Blockchain. These determinants are Quality of System, Quality of Service, Quality of Information, Perceivable usefulness, Convenient usefulness, and Social influence. The research results show that (i) All of the above factors are positively associated with customers’ intention to use international payment services in Vietnamese banks which implement blockchain; (ii) The quality of information has the strongest positive impact on the customer's intended use. It refers that the international payment services in banks should be safe, accurate and timely; (iii) Differently from the hypothesis, the social influence indicator is positively related to the customer's intention to use the service, affirming the potential interests of customers on blockchain if applying successfully in banks; (iv) Although current customers have not paid much attention whether banks apply blockchain in international payment or not, they have taken concentration on fundamental risks, costs, and convenience of the services. This signal is consistent with the fact that only 2/46 commercial banks and 49 foreign bank branches in Vietnam provide international payment service with blockchain application nowadays.