7712 research outputs found
Sort by
التّحليل اللّغويّ للتّنبّؤات المستقبليّة: دور اللّغة في تشكيل التّوجّهات المجتمعيّة والاقتصاديّة
No full textDefense in Depth: A Multilayered Approach
No full textDefense in Depth (DiD) is one of the most basic and fundamental cybersecurity strategies that uses layers of protection in order to protect systems and information from different and new kinds of threats. DiD as an approach involves linking several security controls and processes at the network, application and data, and physical levels in an effort to counteract an attack and minimize chances of a single vulnerability point. In this chapter, the authors analyze DiD as a concept and investigate whether it can effectively prevent or at least timely recognize and counter advanced cyber threats. Here, we identify important defense layers, how they work together, and issues related to DiD in a dynamic context. Further, the threats and innovations in DiD are assessed to inform the adaptation of the cybersecurity strategies within organizations in a constantly evolving threat environment
Interpreting Intelligence: Organizational Meaning-Making Processes In Ai-Enabled Leadership Development
No full textPurposeThis study aims to examine how organizations construct shared meanings around artificial intelligence (AI) in leadership development through a symbolic interactionist lens, addressing the underexplored social and interpretive dimensions that influence implementation success.Design/methodology/approachThe research uses an interpretive review of interdisciplinary literature spanning organizational behavior, leadership studies and information systems. Drawing on pragmatist thought from Blumer and Mead to contemporary scholars, the analysis identifies key mechanisms of technological meaning-making in organizational contexts.FindingsFour mechanisms facilitate shared technological understanding: negotiated social construction of AI meaning, personalized symbolic interaction with AI systems, creation of new symbolic environments through virtual/augmented reality (e.g. immersive leadership simulations where avatars interact in crisis scenarios) and development of AI literacy through collective interpretation. Successful AI integration depends on organizations\u27 ability to facilitate collective meaning-making processes rather than technological sophistication alone.Practical implicationsOrganizations implementing AI-enabled leadership advancement should focus on creating psychological safety zones through regular AI exploration sessions, facilitating cross-hierarchical dialogue via structured reflection protocols and establishing ethical frameworks through participatory processes. The study provides an implementation framework addressing both technical and social dimensions.Originality/valueThis study contributes a novel theoretical framework integrating symbolic interactionism with contemporary pragmatist thought, revealing the fundamentally social nature of technological implementation and providing context-sensitive approaches for diverse organizational settings
Examining teachers\u27 perceived competence in implementing culturally responsive teaching practices in inclusive classrooms in the United Arab Emirates
No full textThe United Arab Emirates (UAE) is a diverse, multicultural context which has introduced ambitious policies and strategic plans to accelerate inclusive education. The extent to which culturally responsive teaching is reflected in inclusive classrooms across the country has not been captured. This study used a cross-sectional survey methodology employing the culturally responsive teaching self-efficacy scale as a survey instrument to examine teachers\u27 perceived competence in implementing culturally responsive teaching practices with students with disabilities in inclusive classrooms. All mainstream schools in the UAE were approached to participate and data were collected from 999 teachers from public and private schools across the seven Emirates of the UAE. Data were subjected to confirmatory factor analysis, mean scores, multivariate analysis and multiple regression. The results indicate that teachers perceive competence in implementing culturally responsive teaching practices in inclusive classrooms. Slightly higher competence was reported in implementing practices that build relationships with culturally and linguistically diverse (CLD) students and their families than those that require knowledge and skills in working with CLD students or knowledge of the impact of cultural and linguistic diversity on learning. Demographic variables contributed additional insight with recent professional development in inclusive and/or special education emerging as a significant predictor of perceived competence in implementing culturally responsive teaching practices in inclusive classrooms. Implications for practice and further research are considered
Properties of a Class of Analytic Functions Associated With Exponentially Convex Functions
No full textStudies in univalent function theory comprising the exponential of differential characterizations are rarely considered. The prominent study in this direction is the study of so-called α-exponentially convex functions. Here we study a class of analytic functions which satisfy an analytic characterization influenced by the definition of the multiplicative derivative and α-exponentially convex functions. Integral representation and coefficient inequalities of the defined function class are the main results of the paper
Applications of AI in Sustainable Medical Practices: A Case Study Approach in Indonesia
No full textArtificial Intelligence (AI) is transforming Indonesia\u27s healthcare sector by optimizing resources, improving patient care, and reducing environmental impacts, making it a key driver of sustainable medical practices. This study explores AI applications in Indonesia\u27s healthcare sustainability through qualitative case studies, highlighting AI\u27s role in predictive analytics, precision medicine, telemedicine, and resource management. Findings suggest that AI enhances efficiency while addressing Indonesia\u27s unique healthcare challenges, such as resource limitations, accessibility issues, and environmental concerns. The paper concludes with recommendations for integrating AI-driven sustainability into Indonesia\u27s healthcare system, emphasizing policy support, infrastructure development, and AI literacy among healthcare professionals
Climate Data Imputation and Quality Improvement Using Satellite Data
No full textCombating climate change has emerged as a global concern recently, and meteorological data remain an important measure for analyzing and predicting climate trends. However, ground weather stations and sensors can be impacted by faults due to accidents and unreliability, often resulting in, for example, missing data and lowering the overall quality of the data. This paper explores the impact of using satellite data as an input feature for machine learning algorithms. In particular, temperature, pressure, wind speed, and global horizontal radiation data are imputed using various machine learning algorithms to overcome potential data quality issues resulting from the ground stations. The results from two experiments highlight that the performance of the algorithms significantly increases by using satellite data as input features. For instance, the incorporation of satellite data improved the R2 values for temperature prediction using Random Forest and XGBoost to 0.86 and 0.84, respectively, demonstrating a notable enhancement compared to models without satellite data. The paper discusses several implications of these findings and outlines future research directions to further enhance the predictive accuracy of meteorological data imputation using satellite inputs
DDoS attack detection in Edge-IIoT digital twin environment using deep learning approach
No full textThe industrial Internet of Things (IIoT) and digital twins are redefining how digital models and physical systems interact. IIoT connects physical intelligence, and digital twins virtually represent their physical counterparts. With the rapid growth of Edge-IIoT, it is crucial to create security and privacy regulations to prevent vulnerabilities and threats (i.e., distributed denial of service (DDoS)). DDoS attacks use botnets to overload the target system with requests. In this study, we introduce a novel approach for detecting DDoS attacks in an Edge-IIoT digital twin-based generated dataset. The proposed approach is designed to retain already learned knowledge and easily adapt to new models in a continuous manner without retraining the deep learning model. The target dataset is publicly available and contains 157,600 samples. The proposed models M1, M2, and M3 obtained precision scores of 0.94, 0.93, and 0.93; recall scores of 0.91, 0.97, and 0.99; F1-scores of 0.93, 0.95, and 0.96; and accuracy scores of 0.93, 0.95, and 0.96, respectively. The results demonstrated that transferring previous model knowledge to the next model consistently outperformed baseline approaches
Catching them young! A comparative study of teacher retention among beginning teachers in Egypt and the United Arab Emirates
Get PDFIt is widely reported that teachers in their first 5 years of entering the profession are highly likely to leave their teaching positions. There have been many discussions on how to keep beginning teachers in the profession, mainly in Western countries. However, no such discourse exists on teacher retention in non-Western contexts, such as Saudi Arabia. This study aimed to extend Western literature to the Middle Eastern context by investigating the retention of beginning teachers in Egypt and the United Arab Emirates. Four hundred and sixty-six novice teachers were evaluated using the Teacher Retention Scale, which was developed on the basis of the four-capital retention model. The data were subjected to inferential statistics, such as structural equation modelling and multivariate analysis of variance. The results provide evidence that supports the four-capital model as an effective measure of teacher retention. Moreover, structural and psychological capitals were identified as significant predictors of human capital. The study suggests implementing targeted training programmes that would enable new teachers in Egypt and the United Arab Emirates to transition smoothly into the profession
Towards deep learning enabled cybersecurity risk assessment for microservice architectures
No full textThe widespread adoption of microservice architectures has given rise to a new set of software security challenges. These challenges stem from the unique features inherent in microservices. It is important to systematically assess and address these software security issues through effective security risk assessments. However, existing risk assessment approaches, such as expert-based manual assessment, prove inefficient in accurately evaluating the security risks of microservices. Furthermore, the absence of security vulnerability metrics hampers the evaluation of these risks. To address these issues, we propose CyberWise Predictor, a framework designed for predicting and assessing security risks associated with microservice architectures. Our framework employs transformers, which are deep learning-based natural language processing models, to analyze descriptions of vulnerabilities for predicting vulnerability metrics to assess security risks. Our experimental evaluation shows the effectiveness of CyberWise Predictor, achieving an average accuracy of 92% in automatically predicting vulnerability metrics for their risk assessment