Mobile Edge Computing (MEC) mitigates cloud computing systems’ latency and limited responsiveness by offloading computationally intensive tasks from user devices to nearby Edge Servers (ESs). However, achieving efficient offloading under dynamic mobility, fluctuating link quality, and constrained resources remains a significant challenge. To address this, we propose MSQ, a lightweight and adaptive three-dimensional decision offloading model that jointly incorporates Mobility, Sociality, and QoS awareness. MSQ employs Kalman filtering for mobility prediction, Rényi entropy to quantify social affinity among mobile users, and Affinity Propagation (AP) clustering to reduce redundant ES candidates while balancing computational load. Comprehensive experiments across small and medium-scale MEC networks demonstrate that MSQ reduces average task delay by up to 78%, energy consumption by 66%, and load imbalance by 64% compared with a random offloading strategy while having decision latency below one millisecond. Moreover, MSQ lowers the 95th-99th percentile tail delays by 35-45%, ensuring smoother and more reliable user experience in real- time applications. These results confirm that MSQ offers a scalable, low-latency, and energy-efficient offloading decision suitable for dynamic and intelligent edge systems.

Information and Communication Technology (ICT) has become central to teaching and learning in higher education, yet effective integration depends on users’ ICT competence and institutional implementation strategies. This study examines the role of ICT literacy in supporting teaching and learning and evaluates whether cultural perceptions significantly influence ICT adoption among undergraduate students at Kaduna State University, Nigeria. A quantitative survey design was employed, with data collected from 150 students and academic staff and analysed using descriptive statistics and the Mann–Whitney U test. The findings suggest that ICT literacy is perceived as positively supporting access to learning resources, communication, and classroom engagement. However, statistical tests did not reveal significant differences between respondent groups, indicating broadly similar perceptions among students and staff. While respondents acknowledged cultural considerations, these factors did not exert a substantial influence on ICT acceptance or use within the institutional context. The results suggest that ICT literacy and institutional support structures play a more immediate role in shaping technology adoption than general cultural perceptions. From a management engineering perspective, the study highlights the importance of ICT infrastructure planning, structured digital skills training, and policy-driven ICT integration in higher education. The findings provide practical guidance for university administrators and education managers seeking to improve the effectiveness and sustainability of ICT-enabled teaching and learning systems. 

The design of an invisible cloak has attracted attention owing to its potential use in espionage and military applications. Advances in computer vision and image processing have enabled the creation of invisible cloaks. This study presents the design and detection of an invisible cloak using a cost-effective monocular camera. The proposed algorithm uses the OpenCV library in Python to create and detect the cloak by analyzing individual pixels in video frames to identify areas with minimal or no change in pixel values. The approach relies on pixel-level analysis using Gaussian curves for detection. Experimental validation of self-created and publicly available datasets demonstrates the effectiveness of the method. Although the algorithm performs well under static environmental conditions, challenges remain in dynamic settings, which will be addressed in future work to improve robustness. This study contributes to the development of practical and affordable invisibility cloak technology and reliable detection methods to mitigate potential misuse.

Due to lifestyle changes and daily behavioural routines of people living across the globe, cardiovascular diseases (CVD) are increasing in the modern world. In the treatment process, the prediction level of CVD is significantly required. Incorporating machine learning algorithms into CVD prediction can provide advantages such as reduced time consumption in the diagnostic process and improved decision-making. Hence, this research aims to implement a novel Lion-based Federated Learning for Disease Prediction (LbFLDP) technique to predict CVD. The novel approach includes three local hospital models and one centralized global model. The local models are trained using CVD dataset obtained from the kaggle website. After the training phase, the local models are used to predict CVD. These prediction features are then updated in the global model from the local models to enhance the prediction features in the global model. The global model is then initiated for predicting CVD. At this time, the performance of the suggested technique is evaluated in terms of accuracy, F-score, Precision, recall, and error rate. The proposed approach has 98.41 recall, 99.6% accuracy, 98.57 F-score, 98.57 precision, and 0.4% error rate.

With increasing developments in artificial intelligence and the need for more personalized digital experiences, user trust and engagement have become relevant factors to be considered for the success of e-commerce recommender systems. This study presents a bibliometric analysis of research trends from 2003 to 2023 by exploring the evolution of trust and engagement in this domain. Using data from the Scopus database, we investigated publication trends, influential works, key contributors, and emerging research themes. Our results reveal a surge in research output between 2020 and 2023, which shows an increasing scholarly appreciation of trust as a critical determinant of user engagement of recommender systems. The leading role of China in global contributions emphasized its reliance on social commerce models, where recommendations are powered by a community-based trust mechanism to drive user engagement. While foundational topics such as collaborative filtering and machine learning remain central, emerging themes (explainability, blockchain integration, and adaptive AI) highlight a shift toward more user-centric and secure systems. These reinforce trust through transparency and security while boosting engagement through active personalization. Thematic evolution from algorithmic development to AI-driven innovations shows how transparency, personalization, and security serve as vital trust-building influencers that drive user engagement in recommender systems. Also, regional disparities in research output, especially in Africa and South America reveal considerable gaps in understanding culturally specific trust factors and engagement patterns. This indicates the need for collaborative studies to develop inclusive recommender systems tailored to local context to bridge these gaps. These findings reflect that trust and engagement are not simply complementary features, but fundamental pillars that are influencing the future of e-commerce recommender systems. As AI advances toward explainable, secure, and adaptive designs, this research calls for urgent globally inclusive frameworks that address both technological sophistication and cultural diversity to ensure that recommender systems emerge as equitable tools for global e-commerce.

The article presents the results of an empirical study on the attitudes of students at Ukrainian higher education institutions toward the role of artificial intelligence (AI), particularly ChatGPT, in the context of their future professional careers. The aim of this study is to determine whether students perceive ChatGPT (a generative AI tool) as a threat, an opportunity, or a multidimensional phenomenon that requires critical evaluation. The research methodology included the construction of two composite indices. These were the ChatGPT Opportunities Index and the ChatGPT Threats Index, both related to career development. The indices were based on responses from 354 students. All participants took part in the international "Global ChatGPT Student Survey". Data analysis employed descriptive statistics, analysis of variance (ANOVA), correlation analysis, clustering, and the χ² test. The results showed that the ChatGPT Opportunities Index was moderately higher than the ChatGPT Threats Index. This indicates a predominantly cautious optimism in students’ attitudes toward AI. At the same time, statistical analysis did not reveal any significant relationships between these indices and such variables as level of education, gender, or confidence in future employment. Cluster analysis identified three types of student attitudes: Realists, Reflective Optimists, and Disengaged. A synthesis of the results indicates that students show both interest in ChatGPT and a need for support from educational institutions in developing critical interaction skills with intelligent technologies. The study concludes that there is a need to integrate AI literacy into academic programs. It also highlights the importance of developing interdisciplinary training models and implementing educational interventions that foster adaptability and digital resilience among students.

Accurate and immediate incident identification is essential in the cybersecurity area, as it allows the timely detection of threats, along with countermeasures and mitigation, ensuring security for organizations and individuals. This reduces false positives and enables efforts to be concentrated on real risks. This paper presents a framework that integrates ontologies and Large Language Models (LLMs) to identify incidents from events within the context of security threats. Ontology rules are employed to infer probable incidents, resulting in an initial set of incidents for analysis. Furthermore, ontologies provide contextual information, which is combined with event data to formulate queries for LLMs. These interactions with LLMs produce a second set of probable incidents. The outputs from ontol-ogy-based inferences and LLM-driven responses are then compared, and the discrepancies are leveraged to refine ontology rules and adjust LLM responses. Experimental results, focusing on context generation and incident detection, demonstrate that the integration of ontologies and LLMs significantly enhances the accuracy of incident identification when compared to using only LLMs.

This paper investigates the digits of π within a probabilistic framework based on Markov chains, proposing this model as a rigorous tool to support the conjecture of π’s  uniformity. Unlike simple frequency analyses, the Markov approach captures the dynamic structure of transitions between digits, allowing us to compute empirical stationary distributions that reveal how local irregularities evolve toward global equilibrium. This ergodic behavior provides quantitative, model based evidence that the digits of π tend toward fairness in the long run. Beyond its mathematical significance, this convergence toward uniformity invites a broader conceptual interpretation.

In the context of increasing cyber threats, digital misinformation, and online ethical dilemmas, the role of teachers in promoting safe and responsible digital behavior has become more critical than ever. This study explores the effectiveness of the Cyber Safety and Security Literacy Program (CSLP) in enhancing cyber security competency and cyber socialization among prospective teachers. The CSLP was designed as a structured educational intervention aimed at equipping future educators with the knowledge, skills, and ethical orientation necessary to navigate cyberspace confidently and responsibly. A pre-experimental one-group pre-test and post-test design was adopted, involving 50 purposively selected B.Ed. students from various teacher education institutions. The two-month intervention was delivered through Google Classroom and Google Meet, ensuring flexibility and interactive participation. The CSLP comprised 12 carefully curated modules covering critical themes such as cyber threats, digital identity protection, cyber bullying prevention, cyber ethics, safe communication, and responsible social media use. To evaluate the program’s impact, data were collected using two standardized tools—the Cyber Security Competency Scale (CSC) and the Cyber Socialization Scale (CSS) both were developed through systematic procedures and supported by strong theoretical grounding and expert validation, providing evidence of their validity. Statistical analysis using paired-sample t-tests revealed significant improvements in participants' cyber security competency (t(49) = 30.55, p < .01, d = 4.32) and cyber socialization (t(49) = 17.75, p < .01, d = 2.51), indicating a large effect size in both domains. The findings affirm that the CSLP is an effective intervention for fostering digital responsibility, ethical awareness, and safe online behavior among future educators. The study emphasizes the urgent need to integrate comprehensive cyber security literacy programs within teacher education curricula, positioning teachers not only as informed digital citizens but also as proactive facilitators of cyber safety and ethical conduct in the learning environment. 

The recent information and technology developments have impacted data utilization and showed the importance of storing different data types for various purposes. The huge amount of data exchanged between systems through the web, networks, and data storage systems are prone to third-party attacks and demands an effective data security system irrespective of the application. Researchers and developers have secured data using different steganography and cryptography techniques. Steganography uses different mediums to hide sensitive data such as images, videos, text, and audio. This review study discussed the importance of recent trends in steganography and cryptography systems in data security. Various methods and techniques of steganography and their hybrid systems, along with cryptography, have produced efficient results for data security. These methods and techniques are thoroughly reviewed to understand the development of a secure system based on steganography. The image-based steganography systems are widely used in several studies rather than video and audio-based steganographic systems. This paper aims to review different techniques practiced in steganography secure systems and specifically focused on Advanced Encryption Systems, Elliptic Curve Cryptography, and other hybrid systems since they are primarily used among developers and researchers in data security. Overall, developing an efficient security system based on steganography should be resilient to different types of third-party attacks and consider data integrity and data confidentiality to prevent loss of information.