CALL FOR PAPER - Vol 10 - Issue 1

This paper provides a comprehensive analysis of the potential use of 5G Core Standalone (5GC SA) for developing private industrial networks in the oil and gas industries, mining industry, logistics operations, and manufacturing industries of Saudi Arabia. It includes a valuation of the benefits associated with the use of standalone core functionalities, private radio access networks, edge computing capabilities, network slicing technologies, data sovereignty capabilities, and industrial security solutions in critical missions. To complete the task, the systematic review approach was utilized to collect insights about standards, industrial white papers, and peer-reviewed articles published in the 2020-2025 period. It was established that the value of a private 5GC SA is greater than just improved connectivity because it allows for creating a new digital operational layer for automation, predictive maintenance, remote control, industrial safety, robotics, digital twin adoption, and immediate industrial governance. Regarding the importance of the introduction of private 5GC SA in the industries of Saudi Arabia, its implementation will make great contributions to the realization of such strategic plans as the Kingdom's Vision 2030, NIDLP, mining, the future of energy, intelligent ports, and manufacturing localization. Finally, the paper presents a framework for the industrialization of 5G core as well as obstacles to its implementation in the Saudi Arabian industries, which include spectrum, OT integrations, cybersecurity, devices, cost structure, and skill-related issues.

Saudi Arabia’s Vision 2030 has turned industrial localization from a procurement preference to a national resilience agenda. Stainless steel pipe is a strategic product as it links energy, petrochemicals, desalination, food processing, mining, health infrastructure and giga-project utilities. This review evaluates the development of a resilient and self-sustaining stainless steel pipe supply chain in Saudi Arabia through analyzing demand drivers, localization barriers, capability enablers and governance requirements. Using a narrative review approach, we synthesize policy documents, industry reports and recent supply-chain literature published between 2020 and 2025. The analysis shows that self-sufficiency does not exclude the complete isolation from the global trade. But it should be understood as the ability to secure critical grades, technical services, specifications and qualified capacity in times of market shocks. Key barriers include alloy input dependency (Raw Material), high capital intensity, fragmented demand signals, skills shortages, testing and certification gaps, technology transfer risk and energy and environmental compliance costs. Key enablers include long-term offtake, local content incentives, supplier development, digital traceability, industrial clusters, technical standard alignment and circular scrap recovery. The paper suggests discrete localization steps from import assurance to integrated domestic capability. The results link industrial self-reliance with regional development, employment generation, export preparedness and low supply-chain vulnerability in a way that backs Vision 2030.

Multi-cloud adoption is shifting from an optional sourcing tactic to a strategic operating model for Saudi enterprises that need to scale digital services, protect regulated data and sustain business continuity across complex provider ecosystems. This paper proposes a governance framework for secure and scalable enterprise cloud adoption in Saudi Arabia, with a focus on critical workloads in government services, banking, energy, healthcare, logistics, retail, and industrial operations. The paper adopts a structured narrative review approach influenced by recent systematic review practice in cloud computing, social mobile analytics and cloud-based service environments. Literature and standards published between 2020 and 2025 were synthesised to identify domains of governance, barriers to adoption, security controls, mechanisms of resilience and enablers of implementation. The review argues that delivering multi-cloud value is more than just spreading workloads across multiple providers. Value is delivered when executive accountability, data classification, identity governance, policy-as-code, observability, financial control, incident response, sovereignty requirements and vendor exit planning are managed by a single control plane. Governance should also be aligned with the Saudi context in terms of national cybersecurity controls, cloud service provisioning rules, data localisation expectations, and Vision 2030 digital transformation priorities. Our proposed framework has six dimensions: Strategic alignment, regulatory compliance, secure architecture, operational resilience, FinOps enabled scalability and continuous assurance. The review is translated into an adoption roadmap with two graphical models and two synthesis tables. The study concludes that Saudi enterprises can reduce vendor dependency and accelerate innovation via multi-cloud but only if governance is continuous, evidence-based and embedded into engineering workflows.

This study determined on the school heads motivational strategies and their relationship to the teachers ‘teaching job satisfaction of the select Public schools of Bohol, Dumaguete and Bayawan Division during the school year 2023-2024 as basis for motivation-based leadership plan.There were seventy five (75) total respondents of this study headed by four (4) school principals for each school and seventy one (71) teaching staff of the four (4) selected public schools of Bohol, Dumaguete City and Bayawan City Division.The researcher employed descriptive design using the quantitative and qualitative approach in this research study. This frequency distribution research made use of quantitative and qualitative approach and interview method in qualitative approach. This study utilized a researcher-adapted and modified instruments. The questionnaire is divided into four (4) parts: (1) the respondents’ profile, The second one extent of motivational leadership strategies of school heads with four indicators as to positive characteristics and behaviors, behavior, teaching learning effectiveness. The third (3) instrument is the teachers’ satisfaction. There are 38 items in the questionnaire which are patterned from the two sets of factors—hygiene and motivators—as described by Herzberg in his theory (FGD) interview. As to the extent of motivational leadership strategies as demonstrated by school heads. Findings revealed that leadership rooted in trust, growth, and thoughtful role assignments created a ripple effect on teacher motivation and classroom success. On the issues and concerns the most pressing concern, limited teacher involvement, communication gaps, and insufficient personalization in leadership practices On the basis of the findings, a conclusion was drawn. On the test on significant relationship there is a strong and significant positive correlation between school heads’ motivational strategies particularly in terms of behavioral dimensions and positive characteristics and various facets of teaching-related job satisfaction. In the light of the findings, it is recommended that the output of the study would be implemented.

Pneumonia still constitutes one of the major causes of mortality across the globe, specifically targeting children, elderly people, and immunocompromised patients. Diagnosis and timely intervention can help reduce disease burden and patient morbidity. Traditional methods of diagnosing pneumonia using chest X-ray images depend largely on the involvement of radiologists, making the entire process slow and prone to observer variability, especially in limited health care facilities. This paper introduces an automated pneumonia diagnosis system based on deep learning using ResNet34 architecture. The model uses image pre-processing techniques, transfer learning, and data augmentation to boost feature extraction capabilities while reducing overfitting. The trained system classifies chest X-ray images into pneumonia and non-pneumonia classes using a user-friendly web application built on the Streamlit platform. The designed system provides a good computer-aided diagnosis system which may help medical professionals conduct initial screening for pneumonia, reduce diagnostic effort, and increase the accessibility of health care facilities in rural areas.

Heart disease is a major cause of mortality worldwide, making early and accurate prediction essential for effective clinical intervention. This paper presents a feature-optimized heart disease prediction framework using machine learning techniques to improve diagnostic performance. The proposed approach incorporates data preprocessing, feature optimization, and classification to identify the most relevant clinical attributes while reducing redundancy. Several machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost, are evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results demonstrate that feature optimization enhances prediction accuracy and model efficiency, with ensemble-based methods achieving superior performance. The proposed framework offers an effective decision-support tool for the early detection of heart disease and has the potential to assist healthcare professionals in improving diagnostic accuracy and patient care.