The rapid advancement of artificial intelligence (AI) technologies has significantly transformed digital communication while simultaneously enabling sophisticated cyber threats, particularly AI-powered scams and deepfake-based deception. Deepfake technologies, which involve the generation of highly realistic synthetic audio-visual content, are increasingly exploited for impersonation, fraud, and misinformation, thereby posing serious risks to digital trust and cybersecurity. In Nigeria, the widespread adoption of digital platforms among tertiary institution students has heightened their exposure to such threats. This study examined the roles of cybersecurity awareness, digital literacy, and media literacy in shaping students’ preparedness to detect AI-powered scams and deepfakes in tertiary institutions in Enugu State, Nigeria. A descriptive survey design was adopted, involving 469 students selected through a multistage sampling technique from universities, polytechnics, and colleges of education. Data were collected using a structured Google Forms questionnaire and analysed using mean, standard deviation, and independent samples t-test at a 0.05 level of significance. The findings revealed that students possessed cybersecurity awareness, digital literacy, and media literacy to a great extent (Grand Mean = 3.34), and demonstrated preparedness against AI-powered scams and deepfakes to a great extent (Grand Mean = 3.21). However, their ability to detect manipulated media remained relatively weak. No significant difference was found between male and female students in both awareness and preparedness. The study concludes that while students demonstrate reasonable awareness, targeted educational interventions are required to improve their ability to detect sophisticated AI-driven threats. It recommends the integration of deepfake awareness and AI fraud detection strategies into tertiary institution curricula.

The overall objective of this study was to assess the hygienic quality of food sold on the streets of Kisangani and to identify alternative solutions to combat the pathogenic bacteria contaminating these foods. Bacteriological analyses of the samples (isolation and enumeration) revealed high bacterial loads of FMAT, Enterobacteria, and Salmonella, indicating significant food contamination. Thus, the average bacterial counts ranged from 13,209.09 to 648,272.42 CFU/g for FMAT; 30 to 809.09 CFU/g for Enterobacteria; and 0 to 1,663.64 CFU/g for Salmonella. Furthermore, Staphylococci were not detected at any of the sites. High levels of FMAT, Enterobacteria, and Salmonella were observed in the municipalities of Makiso, Kisangani, and Kabondo, respectively.

The purpose of study was to find out the effect of varied neuro mucular training on muscular endurance of school athletes. To achieve this purpose of the study, forty five school boys athletes from St.Marys school Nagerkiol, were randomly selected as subjects. The age of the subjects ranged between 12 and 13 years. They were divided into three equal groups. The experimental group-1, underwent jump rope training the experimental group-2 underwent ladder training and group 3 served as control group and did not do any specific training. The muscular endurance was selected as criterion variable and the measurement was recorded in counts. The selected two treatments were performed 3 days in a week for the period of twelve weeks, as per the stipulated training program. The collected pre and post data was critically analysed with apt statistical tool of one-way analysis of co-variance, for observed the significant adjusted post-test mean difference of three groups. The Scheffe’s post hoc test was used to find out pair-wise comparisons between groups with. To test the hypothesis 0.05 level of significant was fixed in this study.

The purpose of study was to find out the effect of concurrent complex training and aerobic training on anaerobic power of college handball players. To achieve this purpose of the study, forty five collegiate men handball players from affiliated colleges of Alagappa University, Karaikudi, were randomly selected as subjects. The age of the subjects ranged between 18 and 25 years. They were divided into three equal groups. The experimental group-1 (n=15, COM.T (a) AT, underwent complex training after aerobic training, the experimental group-2 (n=15, COM.T (b) AT) underwent complex training before aerobic training, and group 3 served as control group (n=10, CG) did not undergo any specific training. The anaerobic power was selected as criterion variable and the measurement was recorded in watts. The selected two treatments were performed 3 days in a week for the period of twelve weeks, as per the stipulated training program. The collected pre and post data was critically analysed with apt statistical tool of one-way analysis of co-variance, for observed the significant adjusted post-test mean difference of three groups. The Scheffe’s post hoc test was used to find out pair-wise comparisons between groups with. To test the hypothesis 0.05 level of significant was fixed in this study.

With the acceleration of the “Going Global” strategy in Hebei Province, this study aimed to examine the foreign-related communication competence of non-English major undergraduates and its alignment with local enterprise demands. A total of 308 undergraduates from a local application-oriented university in Hebei Province were investigated using questionnaires and semi-structured interviews, with data analyzed via SPSS 26.0. Results showed that participants’ overall competence was moderately low, with workplace English reading and writing as the core shortcoming, and structural mismatch with job requirements in equipment manufacturing, infrastructure and new energy industries. Self-rated English proficiency, gender and CET performance significantly affected the competence, while exam-oriented learning contributed little to intercultural literacy. Students had strong improvement motivation but cognitive misperception of job demands and heavy reliance on translation tools. This study provides empirical evidence for optimizing college English teaching to cultivate talents for Hebei’s internationalized enterprises.

The increasing volume of Automatic Identification System (AIS) data generated by maritime vessels poses significant challenges in data storage, transmission, and real-time processing, particularly in bandwidth-constrained environments. Traditional trajectory compression methods often fail to preserve safety-critical information, which is essential for collision avoidance and maritime situational awareness. This study proposes a Risk-Aware Deep Learning method that integrates sequence-to-sequence Long Short-Term Memory (LSTM) models with attention mechanisms and a domain-informed risk assessment framework to compress AIS trajectories efficiently. By assigning dynamic risk scores based on proximity to other vessels, traffic density, navigational hazards, and vessel manoeuvres, the model prioritises the preservation of high-risk trajectory segments. Experimental results demonstrate that the proposed method outperforms traditional geometric, spatiotemporal, and autoencoder-based approaches in terms of compression ratio, reconstruction fidelity, and safety feature retention. With a risk preservation score of 95% and a compression ratio of 7.5, this model provides an effective solution for maritime data management and supports real-time monitoring, predictive analytics, and autonomous navigation. Future work will explore real-time deployment, federated learning, and the integration of multi-modal maritime data sources.

The increasing integration of artificial intelligence (AI) into organizational environments has created new opportunities to improve information technology (IT) management processes. AI tools support managerial decision-making, automate routine and complex operational tasks, and enhance system monitoring, diagnosis, and performance optimization, enabling organizations to manage large volumes of data more efficiently and respond to operational needs more quickly and accurately. However, integrating AI into existing IT management systems presents several technical and managerial challenges, including system complexity, legacy infrastructure limitations, data governance requirements, security risks, compliance obligations, and the need for effective managerial oversight. Without a structured implementation approach, AI adoption may introduce operational risks and reduce transparency in IT processes. This paper proposes a conceptual framework for embedding AI tools into IT management systems by addressing both technical architecture and management requirements. The framework identifies key components such as data integration layers, AI analytics modules, management control interfaces, and governance mechanisms. It also highlights how predictive analytics and intelligent automation can enhance operational efficiency, risk management, and strategic planning while maintaining transparency and accountability. The study provides a structured approach to help organizations design AI-enabled IT management systems aligned with organizational objectives and effective managerial control.