Cardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. Implemented on the Orange data mining platform, the ANN was trained using backpropagation and validated through 10-fold cross-validation. Dimensionality reduction via principal component analysis (PCA) enhanced computational efficiency, while Shapley additive explanations (SHAP) were used to interpret model outputs. Despite achieving 83.4% accuracy and high specificity, the model exhibited poor sensitivity to disease cases, identifying only 76 of 2233 positive samples, with a Matthews correlation coefficient (MCC) of 0.058. Comparative benchmarks showed that random forest and support vector machines significantly outperformed the ANN in terms of discrimination (AUC up to 91.6%). SHAP analysis revealed serum creatinine, diabetes, and hemoglobin levels to be the dominant predictors. To address the current study’s limitations, future work will explore LIME, Grad-CAM, and ensemble techniques like XGBoost to improve interpretability and balance. This research emphasizes the importance of explainability, data representativeness, and robust evaluation in the development of clinically reliable AI tools for heart disease detection.

Purpose: This paper investigates the potential of remote work platforms and digital entrepreneurship for the employment inclusion of People with Disabilities (PwD) in Oman. PwD face challenges in the labor market due to social stigma and a lack of awareness about remote work options. This study aims to assess the general digital and digital marketing skills of PwD in Oman, identify the gap between their skills and the requirements of virtual platforms for remote work, and recommend online training programs to bridge the gap. Method: The study will use a mixed-methods approach with questionnaires and real-life scenarios to assess skills and competencies. This study will employ a mixed-methods approach, combining quantitative (questionnaire) and qualitative (real-life scenarios) data collection methods. Results and conclusion: This study will use structural equation modelling, Linear probability and multiple response regression models to validate and identify predictors of skill levels and potential gaps between self-assessment and performance. This guides in the development of appropriate online training which improves the employment prospects of PwDs. Research Implication: PwDs can apply knowledge, understanding, and practical abilities gained through competency development initiatives in the business work process. With freedom and autonomy, they can make judgments and decisions that will improve their psychological state of mind and confidence. This also ensures access to equal opportunity, independence, and protection against discrimination. Originality/Value: The study contributes to Oman Vision 2040 and aligns with national priorities of promoting economic development and empowering vulnerable groups like PwD. This research aims to empower PwD in Oman through skills development and access to remote work opportunities, contributing to their social and economic inclusion.

As industrial activities increasingly impact the environment, the need for resource conservation and long-term energy security becomes paramount, positioning sustainable electricity production as a critical focus area. In this regard, the application of Machine Learning, a branch of Artificial Intelligence, has revealed considerable promise in optimizing various dimensions of energy generation, especially within renewable sectors such as wind energy. This research presents a case study that showcases the implementation of Machine Learning techniques in forecasting the failures and establishing predictive maintenance plans for wind energy systems. In this study, we employed Logistic Regression, Decision Tree, Bagging, Random Forest, Gradient Boosting, Adaboost, and XGBoost to analyze more than 40,000 rows of data from 40 different system parts. At the end of the analysis, Tuned XGBoost model has turned out to be the best model having the highest Recall value that correctly predicts the Failures. Through the application of diverse methodologies, we demonstrate the effectiveness of these techniques in anticipating potential failures and prioritizing maintenance or replacement of parts, which in turn facilitates substantial resource savings in terms of labor, costs, and time, thereby advancing sustainability and strategic planning.

The main objective is to measure the difference in the perceptions of bank marketing managers and their customers regarding the likeability, readability and responsiveness of social media content

This study investigates the perceptions of thirteen bank marketing managers and selected bank consumers regarding Instagram posts, utilizing Rosenberg and Hovland's components of attitude (affect, cognition, behavior) and Lavidge and Steiner's Hierarchy of Effects model. Instruments were developed to measure and compare perceptions, using non-parametric Mann-Whitney U tests. The findings reveal differences in perceptions between managers and consumers regarding emotional appeal and the likelihood of reading, liking or commenting on these posts. Furthermore, differences in perception were noted for video content and commenting behavior, Instagram Reels for likes, and positive messages for reading and commenting. These findings provide valuable insight to bank marketing managers in Kuwait for creating effective Instagram posts that resonate with their customers and enhance engagement and interaction.

Trust is a cornerstone of human social life that enables cooperation, communication, and psychological health across several relational, institutional, and cultural systems. This chapter provides an overview of the definition of trust, combining empirical and theoretical perspectives from different fields such as psychology, philosophy, sociology, economics, and neuroscience. The chapter begins by placing trust in an evolutionary framework and then examines the various definitions, noting the lack of consensus on key characteristics such as vulnerability, positive expectations, and risk. The developmental origins of trust are explored using leading theoretical classifications, including rational choice, personality-based, institutional, and affective models. The chapter further examines the relationship between trust and distrust, arguing that the two are functionally distinct psychological states that can simultaneously exist within the same relationship as opposed to representing opposite ends of a single continuum. The roles of emotion in influencing trust judgements are explored through both cognitive and affective pathways, using McAllister’s dual-trust framework and neuroimaging evidence that trust and distrust engage separate neural systems. The asymmetry between how trust is built and broken is examined, with particular attention to the influence of negative information on trust judgements and the relative fragility of trust once established. Trust is also examined across several disciplines and relational levels, from individual to interpersonal and broader societal levels. The chapter concludes with a discussion on the implications for relational repair and institutional design, advocating for contextual trust calibration, rather than maximization. This is essential for transparent governance in an increasingly digital world.