Article Per Year (5 Year)
p-Index From 2021 - 2026
2.668
P-Index
This Author published in this journals
All Journal Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials Journal of Social Entrepreneurship and Creative Technology Journal of Multidisciplinary Sustainability Asean Journal of Paddisengeng Technology Journal of World Future Medicine, Health and Nursing Pengabdian: Jurnal Abdimas
Chen Mei
Zhejiang University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 18 Documents
Search

 1   2 
PERSONALIZED NANOMEDICINE APPROACHES ENABLED BY BIOINFORMATICS AND MACHINE LEARNING Zhang Li; Chen Mei; Wang Jing
Journal of Biomedical and Techno Nanomaterials Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v3i1.3559

Abstract

Personalized nanomedicine has emerged as a promising approach to tailor treatments to individual patients, enhancing therapeutic efficacy while minimizing side effects. The integration of bioinformatics and machine learning (ML) has the potential to revolutionize this field by enabling more precise and efficient drug delivery systems, biomarker identification, and therapeutic strategies. However, the full potential of these technologies in personalized nanomedicine remains underexplored. This study aims to explore how bioinformatics and machine learning can enable personalized nanomedicine approaches, particularly in the areas of drug delivery optimization, patient-specific treatment planning, and biomarker discovery. The research investigates the application of these technologies in identifying individualized treatment strategies and improving patient outcomes. A systematic review of the current literature on bioinformatics, machine learning, and personalized nanomedicine was conducted. Case studies and experimental research using these technologies were analyzed to identify trends, applications, and challenges. Machine learning models were applied to bioinformatics datasets to predict drug responses and optimize nanomedicine formulations. The study found that bioinformatics and ML significantly enhance the accuracy of drug efficacy predictions, biomarker identification, and the design of personalized nanomedicine treatments. Furthermore, these technologies have improved patient-specific therapy optimization in clinical trials. The combination of bioinformatics and machine learning holds great promise for advancing personalized nanomedicine, offering tailored therapeutic solutions that improve patient outcomes and treatment efficiency.
University-Industry Partnership in Encouraging Innovation in the Field of Creative Technology Chen Mei; Zhang Li; Zhou Hui
Journal of Social Entrepreneurship and Creative Technology Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v1i4.1730

Abstract

The increasing importance of innovation in the field of creative technology has prompted the need for stronger collaboration between universities and industries. These partnerships have become essential in bridging the gap between academic research and real-world applications, particularly in creative industries such as digital media, design, and technology. However, despite the potential benefits, the dynamics and effectiveness of university-industry partnerships in fostering innovation have not been fully explored. This research aims to examine how these partnerships encourage innovation within the creative technology sector, specifically focusing on the challenges, strategies, and outcomes of such collaborations. The study employs a mixed-methods approach, combining qualitative case studies of successful university-industry partnerships with quantitative surveys from stakeholders involved in creative technology ventures. Data were collected from universities, tech companies, and innovation hubs to assess the extent to which partnerships contribute to technological advancements, skill development, and economic growth in the creative technology sector. The findings reveal that university-industry partnerships play a crucial role in fostering innovation by providing access to cutting-edge research, resources, and a skilled workforce. These collaborations also help overcome challenges such as funding, market access, and technological expertise. However, the study also identifies barriers such as lack of communication, differences in organizational culture, and mismatched goals between academic institutions and industries. In conclusion, university-industry partnerships significantly contribute to innovation in creative technology but require a more structured approach to enhance their impact. Improving communication and aligning objectives are key to ensuring sustainable and effective collaboration.
Public Health Policy in the Age of Climate Change: Developing Sustainable Healthcare Systems for Future Generations Trisnawati Trisnawati; Chen Mei; Yang Xiang
Journal of Multidisciplinary Sustainability Asean Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v3i2.3493

Abstract

Background. Public health systems worldwide are increasingly challenged by the accelerating impacts of climate change, including extreme weather events, shifting disease patterns, and growing health inequities. These challenges expose structural vulnerabilities in existing healthcare systems and highlight the urgent need for policy frameworks that integrate sustainability, resilience, and long-term population health considerations. Purpose. This study aims to examine how public health policy can be reoriented to support the development of sustainable healthcare systems capable of protecting future generations in the context of climate change. Method. The research employed a qualitative–analytical approach based on a systematic review of international policy documents, peer-reviewed journal articles, and reports from global health and environmental organizations published between 2015 and 2024. The analysis focused on policy strategies linking climate adaptation, health system strengthening, and sustainability principles. Results. The findings indicate that effective public health policies increasingly emphasize cross-sector collaboration, climate-resilient health infrastructure, low-carbon healthcare delivery, and preventive, community-based interventions. Countries that integrate environmental considerations into health governance demonstrate greater capacity to manage climate-related health risks and reduce long-term system costs. Conclusion. The study concludes that sustainable healthcare systems require transformative public health policies that move beyond short-term crisis responses toward integrated, forward-looking strategies. Embedding climate resilience and sustainability into public health policy is essential for safeguarding health equity and ensuring the viability of healthcare systems for future generations.
Smart Curriculum Mapping: A Blockchain Approach to Transparent and Customizable Educational Pathways Chen Mei; Sofia Lim; Ananya Rao
Journal of Paddisengeng Technology Vol. 1 No. 3 (2025)
Publisher : PT. Sinergi Bersahaja Sejahtera

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65224/jopate.v1i3.199

Abstract

Background. Traditional curriculum mapping often faces challenges of transparency, flexibility, and personalization. Existing digital systems tend to be centralized, limiting stakeholder trust and adaptability in designing individualized learning trajectories. Blockchain technology offers an innovative solution to ensure transparency, immutability, and decentralized control over educational data, enabling both institutions and learners to co-create customizable educational pathways. Purpose. This study aimed to investigate the potential of blockchain-based smart curriculum mapping in fostering transparent governance of curricula and supporting adaptive learning designs. Specifically, it examined how blockchain can integrate institutional requirements with learner-driven customization while ensuring accountability and security. Method. Using a mixed-method design, the research engaged 210 university students and 45 lecturers across three higher education institutions. Data were collected through surveys, interviews, and prototype testing of a blockchain-enabled curriculum mapping platform. The findings were analyzed using statistical methods and thematic coding to evaluate user perceptions, system usability, and pedagogical impact. Results. The findings indicate that blockchain-based curriculum mapping enhances trust among stakeholders by ensuring transparent records of course progress and requirements. Students reported increased autonomy in designing personalized pathways, while lecturers emphasized the benefits of immutable documentation for accreditation and evaluation. However, challenges such as technical literacy and system scalability were also identified. Conclusion. This study highlights the transformative role of blockchain in curriculum management. By integrating transparency, security, and learner-centered customization, smart curriculum mapping offers a scalable model for future educational governance. The findings contribute to both educational technology innovation and institutional policy-making, offering pathways toward more accountable and personalized higher education systems.
Preventive Lifestyle Interventions for Non-Communicable Diseases: Community-Based Innovations Miftahul Jannah; Chen Mei; Asriana Abdullah; Yang Xiang
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i5.2799

Abstract

Non communicable diseases (NCDs) remain a leading global health challenge, disproportionately affecting low- and middle-income countries and placing substantial burdens on healthcare systems and communities. Increasing evidence shows that preventive lifestyle interventions embedded in community settings can mitigate behavioral risk factors, yet empirical analyses of innovative community-based models remain limited. This study aims to examine the effectiveness of community-driven preventive lifestyle interventions in reducing modifiable NCD risk indicators and strengthening local health resilience. A mixed-methods design was employed, integrating quantitative assessment of biometric and behavioral indicators with qualitative exploration of community participation, program acceptability, and perceived benefits. Findings demonstrate that structured community interventions such as peer-led education, culturally adapted physical activity programs, and neighborhood health monitoring resulted in significant improvements in dietary behavior, physical activity levels, and blood pressure profiles across participating groups. The study concludes that community-based innovations play a critical role in promoting sustainable lifestyle modification and offer scalable models for NCD prevention, particularly in resource-constrained settings.
ARTIFICIAL INTELLIGENCE IN EARLY DISEASE DETECTION: REVOLUTIONIZING DIAGNOSTIC PRACTICES IN MEDICINE Safiullah Aziz; Shazia Akhtar; Chen Mei
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.3535

Abstract

The integration of Artificial Intelligence (AI) in medicine has the potential to revolutionize early disease detection, improving diagnostic practices and patient outcomes. Early detection of diseases such as cancer, cardiovascular conditions, and neurological disorders significantly enhances treatment efficacy and survival rates. However, traditional diagnostic methods often suffer from limitations such as diagnostic errors, delayed results, and subjectivity. AI technologies, particularly machine learning (ML) and deep learning (DL), have demonstrated the ability to analyze large datasets, recognize patterns, and predict outcomes with greater accuracy and speed than conventional methods. This study aims to explore the impact of AI on early disease detection, focusing on its applications in diagnostic medicine. The research employs a systematic review of AI-based diagnostic tools and their clinical performance across various diseases. Data from peer-reviewed journals and clinical trials are analyzed to assess the accuracy, efficiency, and clinical implementation of AI technologies. The findings reveal that AI has the potential to significantly improve diagnostic accuracy, reduce diagnostic errors, and expedite disease detection, particularly in resource-limited settings. However, challenges remain regarding data privacy, algorithm transparency, and integration into clinical practice. In conclusion, AI stands poised to transform early disease detection, but careful consideration of ethical and technical challenges is essential for its widespread adoption.
The Synergy of Social Capital and Vocational Training: Evaluating the Sustainability of Community-Based Home Industries in Emerging Economies Aries Purwanto; Muh Irfan Mukhlishin; Suparno Suparno; Chen Mei
Pengabdian: Jurnal Abdimas Vol. 4 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/abdimas.v4i3.3794

Abstract

Background. Community-based home industries play a vital role in the economic development of emerging economies, offering a source of livelihood and preserving local cultures. The synergy between social capital and vocational training is increasingly recognized as crucial for the sustainability of these industries. Social capital, encompassing networks, relationships, and trust, alongside vocational training, which enhances skills and productivity, can significantly impact the long-term success of home-based businesses. Purpose. This study aims to evaluate how the interaction between social capital and vocational training influences the sustainability of community-based home industries in emerging economies. Method. A mixed-methods approach was employed, including surveys to measure business performance and qualitative interviews to understand the role of social networks and training in business success. Results. The results reveal that firms with strong social networks and access to vocational training show higher levels of productivity, market access, and longevity. These businesses experienced up to a 30% increase in productivity and a 25% improvement in sustainability compared to those without access to both factors. Conclusion. The findings emphasize that integrating social capital with vocational training enhances the resilience of small businesses in low-resource environments, providing valuable insights for policymakers seeking to support sustainable entrepreneurship.
ADVANCED SENSOR FUSION: SIGNAL PROCESSING ARCHITECTURES FOR AUTONOMOUS ENGINEERING PLATFORMS Justam Justam; Liu Yang; Chen Mei
Journal of Moeslim Research Technik Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i4.4323

Abstract

The reliability of autonomous engineering platforms depends fundamentally on the seamless integration of high-bandwidth, multi-modal sensory data to perceive dynamic environments. This research addresses the persistent challenge of computational latency and perception failure in traditional sensor fusion architectures when operating under adverse conditions. The study aims to evaluate a novel hybrid signal processing architecture that optimizes the balance between edge-level feature extraction and centralized semantic synthesis. Utilizing a “Hardware-in-the-Loop” methodology, the proposed framework was tested on an embedded GPU testbed using synchronized LiDAR, RADAR, and camera datasets across 500 diverse navigational scenarios. Results demonstrate that the hybrid architecture achieves a 56% reduction in processing latency, maintaining a mean response time of 12.4 milliseconds without compromising positional accuracy, which remained stable at 0.11 meters RMSE. Furthermore, the implementation of an entropy-driven weighting mechanism allowed the system to maintain 99.2% anomaly detection accuracy during simulated sensor failures. This research concludes that decentralized feature processing is essential for the operational continuity of energy-constrained autonomous systems. The findings provide a scalable blueprint for developing resilient, low-power perception modules, asserting that hardware-aware signal processing is a prerequisite for achieving Level 5 autonomy in complex, real-world engineering applications.
Co-Authors Ananya Rao Anna Schneider Aries Purwanto Asriana Abdullah Ethan Tan Justam Justam Liu Yang Loso Judijanto Maria Clara Reyes Miftahul Jannah Muh Irfan Mukhlishin Musmulyadin Musmulyadin Nike Triwahyuningsih Nina Anis Nong Chai Ryan Teo Safiullah Aziz Shazia Akhtar Sofia Lim Sun Wei Suparno Suparno Trisnawati Trisnawati Wang Jing Wang Jun Yang Xiang Zhang Li Zhou Hui