Middle East College (MEC) is a college based in Muscat, Oman. It is one of the leading higher education institution in the Sultanate of Oman, with over 5,100 students. MEC is affiliated to Coventry University, UK. It is located in the Knowledge Oasis Muscat.Middle East College has undergraduate and postgraduate programmes in the disciplines of engineering, business, and technology. MEC has partnerships with Microsoft IT Academy (ITA), Oracle Academy Initiative Program, Cisco Networking Academy Program, EC-Council Scholarship, SAP, Linux Professional Institute and Information Technology Authority, Oman.
This study explores how innovation, economic diversification, and digitalization are boosting Oman's efforts toward building a smart economy within the context of Oman's banking and financial regulatory sector, while considering the role of artificial intelligence and governmental support. Supported by the and the Resource-Based View and Innovation Diffusion Theory, this study views innovative and digital competences as key national resources that help governments and organizations to adapt to technological variation and reinforce economic pliability. By using a quantitative approach and convenient sampling, the data were collected through a closed-ended structured questionnaire from 296 individuals representing businesses across Oman and analyzed using SmartPLS 4.0. The results demonstrate that innovation, diversification, and digitalization have a positive and significant impact on governmental support, which eventually plays a mediating role in leading the implementation of a smart economy. Although artificial intelligence was expected to strengthen the effects of digitalization and innovation, the findings reveal that its moderating role is not yet significant, suggesting an early stage of AI diffusion within the banking sector. These results not only confirm Resource-Based View and Innovation Diffusion Theory in an emerging economy but also present practical understandings for business leaders and policymakers. Furthermore, these findings underscore the importance of institutional readiness and diffusion maturity in shaping the role of advanced technologies in smart economy development. This study also suggests that incorporating AI-driven innovation, digital capability development, and strong governance can support Oman to attain the Vision 2040 goals of endorsing diversification, inclusive economic growth, and sustainability in the digital era.
There is diminished transparency, fragmented information exchange, and lack of trust among geographically dispersed stakeholders, which increasingly challenge global supply chains. The classic centralized systems of supply chain management are not always capable of being able to offer real-time traceability and data integrity which is dependable and effective in contract enforcement. The proposed study is a blockchain-based smart contract design that is focused on ensuring increased transparency, traceability and trust in global supply chain management. The suggested framework will combine automated smart contracts, cryptographic provenance tracking, permissioned blockchain consensus, and a decentralized trust score evaluation mechanism to overcome some of the major operation and governance challenges. A simulated assessment with a multi-tier global supply chain setting of 15 blockchain nodes and 12,000 transactions was performed through experimentation. The findings show that the proposed system attained an average transaction delay of 210 ms, which is very low compared to centralized systems (520 ms), with throughput being raised to 120 transactions per minute. End-to-end traceability performance also improved significantly, with a reduction in trace-back time to 8 s compared with 95s this represents a 100% tampering detection rate. The consensus mechanism ensured that the ledger integrity failed only at a rate of less than 1.1%, even when more than 30% of nodes were faulty. Risk-wise, the trust evaluation algorithm dynamically enhanced reliable supplier scores up to 12%, which facilitated the selection of reliable partners. On the whole, the results prove that smart contracts based on blockchains can drastically enhance the efficiency of operations, data integrity, and confidence in global supply chains, with the platform capable of providing a resilient and scalable backbone for the future supply chain management model.
The rapid advancement of Advanced Driver Assistance Systems (ADAS) and Automated Vehicles (AVs) has increased global interest in understanding how automated driving logic influences traffic performance, safety, and environmental outputs. While most existing ADAS rely on kinetic thresholds such as headway or speed, empirical evidence indicates that drivers often adopt their behaviours (both positive and negative) when interacting with automated technologies. This study investigates the impact of different automated driving behavioural logics (Normal, Cautious, and Aggressive) on the traffic performance at an urban signalised intersection in the UAE through integrating behavioural adaptation theory with agent-based microsimulation using PTV VISSIM Software. A calibrated and validated simulation model based on real-word traffic data was developed to evaluate seven scenarios representing different levels of automated vehicle penetration and behavioural compositions. Automated driving logic was implemented using behavioural parameters derived from the CoEXit automated vehicle framework.The results indicate that the scenario with 100
Beams commonly fail in shear due to diagonal tension, shear compression, or web shear, typically caused by insufficient reinforcement, overload, or material weaknesses. One solution is the use of fiberreinforced polymer (FRP) wrapping, which enhances durability and strength. FRP, a composite of fibers and resin, exhibits superior mechanical properties such as impact resistance, stiffness, and corrosion resistance. This study investigates the effectiveness of Polypropylene Fiber Reinforced Polymer (PFRP) in strengthening reinforced concrete (RC) beams. Mechanical behaviour was assessed through compression, split tensile, and flexural tests. RC beams wrapped with PFRP were also analyzed in terms of shear strength using experimental and simulation methods. The results indicate higher strength in PFRP-wrapped beams compared to unwrapped ones.
Knowledge discovery helps mitigate the shortcomings of classical machine learning, especially those so-called imbalanced, high-dimensional, and noisy data challenges. Adaptive combination of multiple models, voting and other data fusion strategies, and the incorporation of other disparate information fusion methods characterize ensemble learning, which addresses the improvement of a predictive model’s accuracy, stability, and generalization. This paper provides a summary of the important approaches to ensemble learning and their real-world uses, emphasizing challenges and opportunities for future work. This paper also discusses how ensemble learning integrates with emergent areas such as deep learning and reinforcement learning. This paper also describes the most important machine learning methods for predicting heart disease, which include decision trees, support vector machines, artificial neural networks, Naïve Bayes, random forest, and K-nearest neighbors.
