Hoang-Sy Nguyen’s research while affiliated with Thu Dau Mot University and other places

Recent advancements in three dimensional (3D) printing technologies have transformed both industrial practices and everyday applications. In the biomedical domain, 3D bioprinting at the cellular and tissue levels has emerged as a promising approach with significant potential. Although machine learning (ML) has been successfully applied in various aspects of conventional 3D printing, including process optimization, dimensional accuracy analysis, defect detection, and material property prediction, its adoption in the context of 3D bioprinting remains limited. This review examines the current ML techniques used in traditional 3D printing and explores their potential contributions to the development of bioprinting technologies. Notably, existing studies have demonstrated up to a 25% improvement in dimensional accuracy and a 30% reduction in printing time when ML is applied to scaffold optimization. We argue that the integration of ML could significantly influence the future development of 3D bioprinting, opening new avenues for innovation in biomedical engineering.

This paper delves into the cutting-edge applications of Machine Learning (ML) within modern Additive Manufacturing (AM), employing bibliometric analysis as its methodology. Formulated around three pivotal research questions, the study navigates through the current landscape of the research field. Utilizing data sourced from Web of Science, the paper conducts a comprehensive statistical and visual analysis to unveil underlying patterns within the existing literature. Each category of ML techniques is elucidated alongside its specific applications, providing researchers with a holistic overview of the research terrain and serving as a practical checklist for those seeking to address particular challenges. Culminating in a vision for the Smart Additive Manufacturing Factory (SAMF), the paper envisions seamless integration of reviewed ML techniques. Furthermore, it offers critical insights from a practical standpoint, thereby facilitating shaping future research directions in the field.

The digital economy has engendered Health Metaverse, an innovative technology with vast potential to transform healthcare through immersive experiences. The Health Metaverse serves as a convergence point for a multitude of technologies, including artificial intelligence (AI), virtual reality in heath, augmented reality in health, internet-connected medical devices, quantum computing, and more. This convergence opens up possibilities, for advancing quality healthcare. Therefore, reviewing recent influential literature is critical to understand current methods and envision future improvements. This study utilizes a hybrid bibliometric-structured methodology combining descriptive and bibliometric network analysis. To gather information we conducted searches on the Web of Science database and reviewed references. Our inclusion criteria focused on articles and reviews published between January 2012 and June 2023. We used keyword groups for our searches. Then performed bibliometric analysis followed by content analysis. Papers were reviewed, analyzed and categorized into focuses on multimodal medical information standards, medical/social data fusion, telemedicine, online health management, and medical AI. This bibliometric analysis of 34 thousand publications over 10 years proposes medical and health informatics in the Metaverse. Five future research direction clusters were identified. It delineates intelligent solutions bridging healthcare barriers. In conclusion, this review examines the Metaverse, in healthcare explores cutting edge technologies, applications, projects and highlights areas where adaptation may be needed. It identifies adaptation issues and suggests solutions warranting further research.

The integration of the Internet of Things (IoT) in healthcare has been a popular topic in recent years. This article provides a comprehensive review of the medical IoT for healthcare, emphasizing the state of the art, the enabling technologies to adopt virtuality and reality interaction, and human-centered communication for healthcare (the Metaverse, Extended Reality (XR), blockchain, Artificial Intelligence (AI), robotics). In particular, we assess the number of scientific articles and patents within the period 2015–2022. We then use the two-stage process following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and screening techniques. From that, the relations among the published papers can be visualized. This study examines the insights by evaluating the weights and connections of the nodes in the bibliometric networks. The reviewed papers showcase the rapid growth of IoT-related studies and intellectual property developments, reflecting the burgeoning interest and investment in this domain. As this paper delves into the network of interconnections between these works, it fosters a deeper understanding of the current state of IoT applications in healthcare and uncovers potential research gaps and areas for future exploration. This paper also provides a brief view of the role of IoT in healthcare research and application in combination with emerging technologies such as AI, blockchain, the IoT-enabled Metaverse, robotics, and cloud computing. The article can serve as a guideline and inspiration for both researchers and practitioners in the smart health service sector.

Digital transformation, modern technologies to enable autonomous production capabilities, and smart logistical systems are prerequisite strategies to gain competitive advantages in developing countries. Additive manufacturing (AM) or three-dimensional printing offering more significant advantages, can manufacture complex geometries of products with various materials. AM plays a main role in reducing the number of parts, helping reduce and eliminate assembly time and cost. However, the application is still limited, especially in developing countries like Vietnam. This research paper aims to identify and assess the challenges associated with applying additive manufacturing in Southeast Vietnam. This study is conducted in three phases. Step one identifies challenges by reviewing previous studies. Step two applies semi-structured interviews with experts to consolidate these challenges to build a multi-level hierarchical structure of challenges. The third stage is to use a multi-hierarchical process method to rank these issues in order to organize the necessary resources for a successful response. The results show that there are five main challenges and 25 sub-challenges in implementing additive printing technology. Using the Average AHP method, financial and strategic challenges are ranked first and second significant, respectively. This paper contributes by identification of Additive Manufacturing implementation challenges based on the literature review and semi-structured interview with the experts. It provides an assessment of the challenges to help the practitioners robust the addictive manufacturing implementation in the manufacturing industry in Vietnam with limited resources.

In recent years, close-range photogrammetric scanning systems have become increasingly popular due to their low-cost hardware, components, and user-friendly software. These systems utilize high-resolution cameras and advanced algorithms to produce accurate and precise 3D models, which are essential for analyzing, documenting, and preserving cultural heritage. While there are various methods for creating 3D models, minimizing cost and time is a primary concern. This paper focuses on photogrammetry as an option for surface reconstruction that might be used to make 3D representations of transparent objects. The objective of the research is to investigate the feasibility of creating appropriate 3D models for surface reconstruction and units of transparent objects through photogrammetry, utilizing open-source photogrammetry algorithms.

Simultaneous wireless information and power transfer (SWIPT) has been utilized widely in wireless sensor networks (WSNs) to design systems that can sustain themselves by harvesting energy from the surrounding areas. In this study, we investigated the performance of the so-called low-power energy harvesting (LPEH) WSN. Being different from other studies, we equipped each relay with a battery whose characteristics were described by an on/off (1/0) decision scheme as per the Markov property. In this context, an optimal loop interference relay selection (OPLIRS) was proposed and investigated. Moreover, the crucial role of the log-normal distribution method in characterizing the LPEH WSN’s constraints was proven and emphasized. The system performance was evaluated in terms of the overall ergodic outage probability (OP) both analytically and numerically with Monte Carlo simulation. Readers can refer to this paper for guidelines on defining the networks’ constraints, analytically derivating the problems, or use the presented results for possible comparison studies.

Additive Manufacturing (AM) has been proven to be a significant development for production as well as become an attractive alternative manufacturing process compared with Subtractive Manufacturing (SM). This can be explained by the massive advantages of AM including mass customisation, less material wastage and ability to create prototypes. However, in order to make use of the AM process, firms, developers and designers require a virtual sharing platform, which can be used to trade designs and make the whole industry better off. This desire requires ways to protect the models and designs from digital thieves. Moreover, internal transactions of designs inside a single firm also require a significant level of security. So, this paper offers different ways of protecting both physical models and virtual designs. The solutions in this paper can be used to ensure the integrity of the physical products and virtual designs during different kinds of transactions.