Cybercrime poses a growing threat to individuals, businesses, and governments in the digital age. This research aims to conduct a comprehensive study of the legal frameworks developed by international organizations to combat cybercrime, providing a comparative analysis of their approaches and highlighting strengths, weaknesses, and areas for improvement. The study employs a qualitative research methodology, utilizing a doctrinal approach to examine primary and secondary legal sources for data analysis. The results reveal the ongoing efforts of the United Nations and other international bodies to establish a unified approach to combating cybercrime through conventions on Cybercrime. The research emphasizes the importance of harmonizing laws, fostering international cooperation, and adapting to evolving cyber threats while maintaining a balance between security and individual rights. Recommendations include strengthening legal frameworks, enhancing public-private partnerships, and investing in capacity building and technical assistance for developing countries. The study concludes by highlighting the critical importance of comprehensive and harmonized cybercrime legislation in the global fight against cybercrime and calls for continued efforts to address the challenges posed by this ever-evolving threat.

Purpose: The level of the environment is gradually declining, especially with regard to the serious problem of solid waste. Solid waste segregation-at-source is seen as the most essential approach to helping the natural environment minimize the amount of waste generated before being transferred to waste disposal sites and landfills in many rapidly growing towns and cities in developing countries. However, a number of previous environmental-based research have focused only on the general scope of recycling, sustainable development, and the purchase intention for sustainable food products. This situation has led to useful and relevant information on the research scope of households’ intention to segregate solid waste at source, which remains largely unanswered. The aim of this paper is, therefore, to provide a literature review to develop a novel theoretical framework in understanding the determinants of households’ intention to practise solid waste segregation-at-source. Theoretical framework: The study provides a detailed explanation of the application of the Theory of Reasoned Action, the Fietkau-Kessel Model, the Focus Theory of Normative Conduct, and the Value-Basis Theory to predict the relationship between attitude, subjective norms, environmental concerns, and environmental knowledge of households on intention to practise solid waste segregation-at-source. Design/methodology/approach: This research is descriptive in nature. Findings: A better understanding of the potential mediator and moderator is needed to contribute to the body of knowledge on the causal relationship between the studied variables. In conclusion, the researchers discuss how the framework can be used to address future research implications as more evidence emerges. Research, practical and social implications: The current study is expected to broaden previous research in order to improve general understanding of attitudes and subjective norms towards the specific research scope of solid waste segregation-at-source.

In the realm of contemporary business, Business Intelligence (BI) offers significant potential for informed decision-making, particularly among executives. However, despite its global popularity, BI adoption in Malaysia’s service sector remains relatively low, even in the face of extensive data generation. This study explores the factors influencing BI adoption in this sector, employing the Technology Acceptance Model (TAM) as its conceptual framework. Drawing on relevant BI literature, the study identifies key TAM factors that impact BI adoption. Using SEM modelling, it analyses quantitative data collected from 45 individuals in managerial roles within Malaysia’s service sector, particularly in the Klang Valley. The findings highlight the crucial role of Perceived Usefulness in influencing the Behavioral Intention to adopt BI, serving as a mediating factor between Computer Self-efficacy and BI adoption. In contrast, Perceived Ease of Use does not have a direct impact on BI adoption and does not mediate the relationship between Computer Self-efficacy and Behavioral Intention. These insights demonstrate the complex nature of BI adoption, emphasizing the importance of Perceived Usefulness in shaping Behavioral Intentions. The outcomes of the study aim to guide executives in Malaysia’s service sector, outlining key considerations for successful BI adoption.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Boron and tungsten carbides, B₄C and WC, are hard materials widely used in modern technologies. Further improvement of their performance characteristics involves the development of new B₄C and WC-based and/or related composites in a nanodispersed state. This article provides a review of available literature research on B-C-W systems, which would be useful in future studies in this direction.

Three-dimensional (3D) bioprinting is a promising technological approach for various applications in the biomedical field. Natural polymers, which comprise the majority of 3D printable “bioinks”, have played a crucial role in various 3D bioprinting technologies during the layered 3D manufacturing processes in the last decade. However, the polymers must be customized for printing and effector function needs in cancer, dental care, oral medicine and biosensors, cardiovascular disease, and muscle restoration. This review provides an overview of 3D bio-printed natural polymers—commonly employed in various medical fields—and their recent development.