مطالب

 Objective: This study examined the effectiveness of cognitive behaviour therapy (CBT) in reducing bullying behaviours among adolescents in Taraba State, Nigeria.Methods: This study adopted a quasi-experimental design. The population of this study was 3,222 SS1 students of the 2022/2023 Academic Session and a sample of 24 adolescents were drawn as subjects of the study using purposive sampling. The researchers used a questionnaire titled Adolescents’ Behavioural Problem Questionnaire (ABPQ) for data collection. The data collected were analyzed using paired samples and independent samples t-test to analyze the hypotheses.Results: The findings revealed that CBT was effective in reducing bullying behaviours among adolescents. The findings also revealed that there was gender difference in the effectiveness of CBT in reducing bullying behaviours among adolescents.Conclusion: The researchers concluded that CBT is effective in ameliorating bullying behaviours among adolescent students.

Early cancer detection is crucial for improving patient survival rates, as timely intervention greatly enhances treatment efficacy. One promising method for early detection is identifying cancerous cells through the detection of protein-level modifications, which serve as early indicators of malignancy. These protein modifications often result from complex biochemical processes that occurs before visible cellular abnormalities, making them critical targets for diagnostic technologies. In recent years, wireless biomedical sensors have advanced significantly, enabling precisely detecting these protein-level changes. These sensors have the potential to detect cancer at its earliest stages by monitoring the subtle alterations in protein structures and functions that distinguish healthy cells from cancerous ones. As the costs of genetic analysis continue to decrease, the development of Medical Internet of Things (MIoT) devices has become increasingly feasible. These devices are designed to perform real-time analyses of biological specimens—such as blood and urine—by detecting protein-level changes indicative of cancer. In this paper, a new machine learning method based on Extreme Randomized Trees (ERT) is developed to increase the speed of classification of cancerous cells based on single-cell Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). The proposed method enhances the classification speed of the limited and noisy ATAC-seq data as it requires less computation to determine the best splits at each node of the decision trees. This method can significantly improve near real-time cancer risk assessment using samples collected by MIoT. Our proposed method achieves classification accuracy comparable to state of the art single-cell ATAC-seq data analysis techniques while reducing processing time by 259%, challenged by various low-data scenarios. This approach presents an efficient solution for rapid cancer monitoring within the MIoT framework.