Purpose: The cardiovascular system is a vital system responsible for the distribution of oxygen and nutrients throughout the body. The complexity of interactions between the heart and blood vessels often presents challenges in monitoring and analyzing health conditions. The research proposes the development of a Human Digital Twin (HDT) for the cardiovascular system through application of two different modelling approaches geometric modeling and physic-based modeling. Through this model physical conditions can be represented and real time data integrated to offer insights into the dynamics of the cardiovascular system.
Methods: This model development is based on two major components: a geometric modeling and a physic-based modeling. The geometric model is done in 3D to show the structure of the heart in detail, while the physical-based model is tabulated with different measurable physical parameters in the cardiovascular system, such as blood pressure and flow rate. This information is integrated into the Five Dimension Digital Twin model, including physical, virtual, data, connection, and service dimensions for the accurate simulation of cardiovascular conditions.
Result: Results confirm that the Five-Dimensional Digital Twin (DT) could give further development to how the dynamics of the cardiovascular system behave, possibly in real-time updates on conditions and a supply of data that is far more detailed in view of analyzing risk and further representation of specific cardiovascular disorders while providing personalized medical support.
Novelty: The Five-Dimensional Human Digital Twin Model (HDTM) developed in this research introduces novel innovations in the monitoring and simulation of the cardiovascular system through the application of geometric and physic-based modeling techniques. This approach offers a higher level of detail, compared to previous models, and added value for the advancement of health technology by integrating real time data into the simulations. This model serves not only as an advanced analytical tool but also as a reference for further research on DT technology in the medical field.

Purpose: Cybercrime is an unlawful act that utilizes computer technology and the development of the internet. Cybercrime can occur on all electronic devices, including Android smartphones. Forensic handling, particularly mobile forensics, has become crucial in addressing drug trafficking cases conducted through Instagram. As the primary device for accessing Instagram, smartphones store digital data that can serve as evidence in investigations. This research aims to produce a more accurate comparison of results in analyzing Instagram Messenger data containing content related to drug trafficking.
Methods: The digital evidence data used in this research included five types of data: text chat, account, image, audio, and image view once. The forensic tools for obtaining digital evidence were MOBILedit, Belkasoft, Mobile Forensic SPF, and Magnet Axiom. The method proposed in this research followed the NIST framework, which consists of four stages: collection, examination, analysis, and reporting. This research followed the NIST framework because it is widely recognized in the field of digital forensics and provides a comprehensive guideline for handling digital evidence.
Result: Research results showed that Magnet Axiom had the best performance in digital forensic analysis, with a success rate of 74.1%. MOBILedit Forensic had a success rate of 62.5%, indicating lower performance. Mobile Forensic SPF had a success rate of 44.6%. In comparison, Belkasoft had the lowest success rate of 23.2%, showing that this software could be more effective in detecting and analyzing digital data than the others.
Novelty: In this study, the analysis process was conducted using four digital forensic tools, each showing variations in terms of efficiency and effectiveness. Each tool has advantages and disadvantages regarding speed, accuracy, and ability to extract and manage data.