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All Journal Journal of Computer Science Advancements Journal of Tecnologia Quantica Research of Scientia Naturalis Scientechno: Journal of Science and Technology Journal Neosantara Hybrid Learning Rechtsnormen: Journal of Law
Rithy Vann
Royal University
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Physics

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 1 
Quantum Sensing of Weak Magnetic Fields using Diamond NV Centers in Biological Environments Rithy Vann; Amir Raza; Nomsa Zulu
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i1.3581

Abstract

Quantum sensing using nitrogen-vacancy (NV) centers in diamond has emerged as a powerful approach for detecting extremely weak magnetic fields with high spatial resolution and ambient operational conditions. Despite their proven sensitivity in controlled environments, the performance of NV-based sensors in biological systems remains challenged by decoherence, optical scattering, and environmental noise. This study aims to investigate the capability of diamond NV centers to detect weak magnetic fields in biologically relevant environments and to evaluate the factors influencing their performance. An experimental–computational approach was employed, combining optical detection of magnetic resonance (ODMR) measurements with simulations of spin dynamics under varying environmental conditions. Nanodiamond samples were tested across buffer solutions, cell culture media, and tissue-like environments. The results indicate that NV centers retain the ability to detect weak magnetic fields in biological settings, although sensitivity decreases due to reduced coherence time and optical contrast. Surface functionalization improves stability and partially mitigates environmental effects, enhancing overall sensor performance. These findings suggest that NV-based quantum sensors offer a promising platform for non-invasive biological magnetometry, provided that material engineering and noise mitigation strategies are optimized. This study concludes that integrating quantum sensing with biological systems is feasible and can advance applications in biomedical diagnostics and cellular imaging..
SYSTEMATIC REVIEW OF THE UTILIZATION OF ARTIFICIAL INTELLIGENCE IN FORENSIC DENTISTRY AS A ROLE MODEL FOR IMPLEMENTATION AT RSAL DR MINTOHARDJO Fredy Budhi Dharmawan; Yun Mukmin Akbar; Mohammad Ali Nugroho; Ahmad Faisol; Rithy Vann
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3615

Abstract

Forensic odontology plays a critical role in human identification, yet conventional methods remain time-consuming, subjective, and limited in handling large-scale data, particularly in disaster and military contexts. This study aims to systematically review the utilization of artificial intelligence in forensic odontology and to develop a contextual role model for implementation at RSAL dr Mintohardjo. A systematic review design was employed by analyzing peer-reviewed articles from major databases published between 2014 and 2025 using predefined inclusion criteria and thematic synthesis. Findings indicate that artificial intelligence, especially deep learning models, significantly improves accuracy, efficiency, and scalability in dental identification, age estimation, and bite mark analysis, with performance often exceeding ninety percent under controlled conditions. Results further reveal that successful implementation depends on data quality, interdisciplinary collaboration, and institutional readiness, while challenges include ethical concerns, data limitations, and lack of standardized protocols. The study concludes that artificial intelligence has strong potential to transform forensic odontology practices and can serve as a strategic role model for institutional adoption, provided that technological integration is aligned with infrastructure, human resources, and governance frameworks. Implications extend to policy development, capacity building, and future research directions emphasizing real-world validation and sustainable implementation strategies in complex healthcare environments globally
Nanostructured Catalysts for Efficient Energy Conversion: Recent Advances Rithy Vann; Ravi Dara; Vann Sok
Research of Scientia Naturalis Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i4.1573

Abstract

The global transition towards sustainable energy sources has driven significant research into developing advanced catalytic materials that can enable efficient energy conversion processes. Nanostructured catalysts, with their unique physiochemical properties, have emerged as promising candidates to address the challenges associated with energy conversion technologies, such as low conversion efficiencies and high production costs. Understanding the recent advancements in the field of nanostructured catalysts is crucial for accelerating the development of next-generation energy conversion systems. This review article aims to provide a comprehensive overview of the recent progress in the design, synthesis, and application of nanostructured catalysts for efficient energy conversion. The study investigates the underlying principles governing the enhanced catalytic performance of nanomaterials and examines their potential impact on diverse energy conversion processes, including fuel cells, water splitting, and photocatalytic systems. The research methodology involves an extensive literature review of peer-reviewed journal articles, conference proceedings, and patent documents published within the last five years. The analysis focuses on the latest developments in the synthesis and characterization of nanostructured catalysts, as well as their performance evaluation under realistic operating conditions. The review highlights the successful implementation of various nanostructured catalyst architectures, such as nanoparticles, nanotubes, nanosheets, and core-shell structures, in enhancing the catalytic activity, selectivity, and stability for energy conversion applications. Significant advancements in the rational design of catalysts through the control of composition, morphology, and surface properties are discussed, along with their impact on improving energy conversion efficiencies and reducing production costs. The study concludes that the continued development of nanostructured catalysts holds great promise for addressing the current challenges in energy conversion technologies. The insights gained from this review can guide future research directions and facilitate the translation of nanostructured catalyst innovations into practical, large-scale energy conversion systems.
DEEP LEARNING APPROACHES FOR PREDICTING DEFORESTATION PATTERNS AND BIODIVERSITY HOTSPOT LOSS IN SUMATRA Rithy Vann; Ming Kiri; Aaraf Sharma; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i1.2861

Abstract

Deforestation in Sumatra, Indonesia, represents a critical environmental challenge that has led to the degradation of biodiversity hotspots and poses serious threats to both local ecosystems and global climate stability, driven largely by rapid forest conversion into agricultural land, illegal logging, and extensive land-use changes, making accurate prediction of deforestation patterns essential for effective conservation planning. This study applies deep learning approaches to predict deforestation patterns in Sumatra while simultaneously assessing their impacts on biodiversity hotspots, with the objective of developing a model capable of identifying areas at high risk of deforestation and estimating potential biodiversity losses. The research employs deep learning algorithms, specifically Convolutional Neural Networks and Recurrent Neural Networks, to analyze satellite imagery, historical deforestation data, land-use changes, and biodiversity hotspot maps, enabling the model to capture both spatial and temporal trends in deforestation dynamics. The results demonstrate that the proposed deep learning model achieves a high prediction accuracy of 92 percent in identifying deforestation hotspots and successfully highlights key biodiversity-rich areas that are highly vulnerable to rapid forest loss, with agricultural expansion and infrastructure development emerging as the dominant drivers of deforestation in these regions. Overall, the findings confirm that deep learning provides a powerful and reliable tool for predicting deforestation patterns and assessing biodiversity hotspot degradation, offering valuable evidence-based insights for policymakers and conservation practitioners to prioritize protection efforts and design targeted interventions aimed at mitigating further environmental damage in Sumatra.
COMMUNITY-BASED SOCIAL EDUCATION FOR SUSTAINABLE DEVELOPMENT – AN INDONESIAN PERSPECTIVE ON COLLABORATIVE LEARNING MODELS Rithy Vann; Vicheka Rith; Suyitno Suyitno
Journal Neosantara Hybrid Learning Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v3i1.2174

Abstract

Community-based social education plays a crucial role in fostering sustainable development, especially in countries with diverse social structures such as Indonesia. However, the implementation of collaborative learning models within this framework remains underexplored. This study aims to investigate the effectiveness of community-based social education in supporting sustainable development through the application of collaborative learning models from an Indonesian perspective. Employing a qualitative research method with a case study approach, data were collected through interviews, focus group discussions, and observations involving educators, community leaders, and learners in selected rural and urban communities. The findings reveal that collaborative learning models significantly enhance community engagement, improve critical thinking skills, and promote shared responsibility among participants. Furthermore, the integration of local wisdom and cultural values into learning processes strengthens the relevance and sustainability of educational programs. The study concludes that community-based social education, when supported by well-structured collaborative learning models, can serve as an effective strategy for achieving sustainable development goals. It emphasizes the need for policy support, capacity building, and continuous evaluation to ensure the scalability and impact of such educational initiatives.  
Harmonizing Customary Law (Hukum Adat) with State Law in Natural Resource Management: A Case Study of Forest Communities in Kalimantan Dara Vann; Vanna Sok; Rithy Vann; Arief Fahmi Lubis
Rechtsnormen: Journal of Law Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rjl.v3i5.2657

Abstract

Background. The tension between customary law (hukum adat) and state law in natural resource management remains a central issue in Indonesia’s environmental governance, particularly in forest areas inhabited by indigenous communities. Customary practices have historically governed access, ownership, and conservation of forest resources, yet their legitimacy often conflicts with state-imposed regulatory frameworks that prioritize national economic interests. Purpose. The research aims to analyze the harmonization of customary and state legal systems in forest management within Kalimantan, exploring the juridical and sociocultural mechanisms that facilitate or hinder coexistence.   Method. A qualitative socio-legal research design was employed, integrating field observations, interviews with community leaders, and document analysis of statutory and customary legal instruments. Results. The findings reveal that effective harmonization depends on legal recognition of adat rights, participatory governance, and adaptive legal pluralism that bridges traditional norms with modern regulatory structures. However, conflicts persist due to overlapping jurisdiction, bureaucratic rigidity, and extractive economic policies.   Conclusion. The study concludes that sustainable forest governance requires an inclusive legal framework that institutionalizes customary law as a complementary not subordinate component of environmental regulation.  
Co-Authors Aaraf Sharma Ahmad Faisol Amir Raza Arief Fahmi Lubis Dara Vann Fredy Budhi Dharmawan Ming Kiri Mohammad Ali Nugroho Nomsa Zulu Ravi Dara Rustiyana, Rustiyana Suyitno Suyitno Vann Sok Vanna Sok Vicheka Rith Yun Mukmin Akbar