This study fills a significant need in the literature by exploring the efficacy of wearable technologies as helpful aids for special needs students in Saudi Arabia. This 12-month quantitative study used a purposive sample of 150 kids representing a range of disability classifications. This study examines the effects of wearable technology, such as smartwatches and augmented reality goggles, on students’ concentration and performance in the classroom. Wearable technology offers great promise, as descriptive statistics show that the experimental group had better involvement and academic achievement. The experimental and control groups vary significantly in terms of academic performance and engagement, as shown by independent samples t-tests. Wearable technology’s distinct benefits are further shown by regression analysis, which shows a favorable correlation with academic achievement after the intervention. According to the results, wearable tech has great promise for inclusive education in Saudi Arabia. Strategic integration, teacher professional development, ongoing research, better accessibility, and wearable gadget customization are some of the suggestions. Stakeholders may use these recommendations as a road map to build a welcoming and technologically sophisticated classroom. This study adds to the growing body of knowledge on assistive technology, especially in Saudi Arabia, and has important implications for academics, politicians, and educators.

The menace of road traffic accidents (RTAs) has become a major constraint to development in most developing countries because of driving behaviour. This study examines the effects of road users’ education programmes on driving behaviour toward RTA reduction in Nigeria. Data for the study were collected by random sampling of 287 respondents. The respondents comprising road safety officers and drivers were selected at six (6) zonal headquarters of the Federal Road Safety Commission. The questionnaire presented seventeen (17) statements in a 5-point Likert scale for the respondents to rank in order of importance as they have influenced driving behaviour. The data collected were analysed using exploratory factor analysis to identify the most significant effects of road user education on driving behaviour. The study found that road user education programmes have influenced driving behaviour by improving bad driving acts, maintaining good vehicle conditions, and obeying road communication signs. The finding implies that appropriate driving behaviour will reduce road traffic accidents.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

This research uses both quantitative and qualitative research methodologies to examine the complex factors affecting community resilience in various settings. In this case, the research explores how social cohesion, governance effectiveness, adaptability, community involvement, and the specified difficulties influence resilience results by using the five pillars of resilience as variables. Descriptive and inferential statistics are used to test hypotheses on the relationships between social cohesion, governance effectiveness, adaptive capacity, and community resilience variables. Qualitative data provides further insights into the quantitative results by providing broader views and experiences of the community. The study shows how social capital is important in increasing community capacity, stressing the importance of social relations and trust in developing community solutions to disasters. Another major factor that stands out is the governance factor that ensures that decisions are made, and actions taken in line with the community’s best interest in improving its ability to prepare for and respond to disasters. Adaptive capacity is seen as a key component of resilience and this paper emphasizes the importance of communities to come up with measures that can be adjusted to the changing circumstances. In summary, this study enriches theoretical understanding and offers practical applications of the processes that can enhance community resilience based on the principles of social inclusion, sound governance, and context-specific solutions.

Objective: As the scale and importance of official development assistance (ODA) continue to grow, the need to enhance the effectiveness of ODA policies has become more critical than ever before. In this context, it is essential to systematically classify recipient countries and establish tailored ODA policies based on these classifications. The objective of this study is to identify an appropriate methodology for categorizing developing countries using specific criteria, and to apply it to actual data, providing valuable insights for donor countries in formulating future ODA policies. Design/Methodology/Approach: The data used in this study are the basic statistics on the Sustainable Development Goals (SDGs) published annually in the SDGs Report. The analytical method employed is decision tree analysis. Results: The results indicate that the 167 countries analyzed were classified into 10 distinct nodes. The study further limited the scope to the five nodes representing the most disadvantaged developing countries and suggested future directions for aid policies for each of these nodes.

Purpose: The purpose of this paper is to explore the impact of Artificial Intelligence on the performance of Indian Banks in terms of financial metrics. The study focused specifically on the NIFTY Bank Index. The paper also advocates that a greater transparency in disclosing AI related information in a Bank’s annual report is required even if it is voluntary. Design/Methodology/Approach: The paper uses a mixed method approach where quantitative and qualitative analysis is combined. A dynamic panel data model is used to understand the impact of AI of Return on Equity (RoE) of 12 Indian Banks in the NIFTY Bank Index over a five-year period. In addition to that, Content analysis of annual reports of banks was conducted to examine AI related disclosure and transparency. Findings: The paper highlights that the integration of Artificial Intelligence (AI) significantly influences the financial performance of sample banks of India. Return on Equity the specific parameter positively influenced with adoption of AI. The profitability of banks is positively impacted by reduced errors and improved operational efficiency. The content analysis of annual reports of the banks indicates different approach for AI disclosure where some banks give detailed information and some are not transparent about AI initiatives. The findings suggest that a higher level of transparency could enhance confidence of all stakeholders. Theoretical Implications: The positive relation between adoption of AI and financial performance, specifically ROE, gives a foundation for academic research to explore the dynamics of emerging technology and financial systems. The study can be extended to explore the impact on other performance indicators in different sectors. Practical Implications: The findings of this study emphasize the importance of transparent AI related disclosures. A detailed reporting about integration of AI helps in enhanced stakeholders’ confidence in case of banking industry. The regulatory framework of banks may also consider making mandatory AI disclosure practices to ensure due accountability to maximize the benefits of AI in banking.