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NANOFABRICATION STRATEGIES FOR ARTIFICIAL CELLS, TISSUES, AND ORGANS Silva Fitri; Miku Fujita; Daiki Nishida
Journal of Biomedical and Techno Nanomaterials Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v3i2.4073

Nanofabrication techniques have emerged as pivotal tools in the creation of artificial cells, tissues, and organs, which hold the potential to revolutionize regenerative medicine and organ transplantation. The ability to precisely engineer materials at the nanoscale allows for the replication of biological structures, enabling the development of functional tissue replacements and therapeutic devices. Traditional methods in tissue engineering often face challenges in mimicking the complexity of natural tissues and organs, leading to suboptimal functionality and biocompatibility. This study investigates various nanofabrication strategies used in the development of artificial cells, tissues, and organs, with an emphasis on their applications in biomedical fields. The main objective of this research is to assess the effectiveness of different nanofabrication approaches, such as 3D printing, self-assembly, and nanolithography, in replicating the architecture and functionality of human tissues. In vitro and in vivo models are employed to evaluate the biocompatibility, structural integrity, and functional performance of fabricated constructs. The results indicate that nanofabricated systems show significant promise in replicating the mechanical, biochemical, and cellular properties of natural tissues. In conclusion, nanofabrication offers an innovative approach to the creation of functional artificial tissues and organs, which could significantly impact the future of medical treatments, particularly in tissue regeneration and transplantation.

MACHINE LEARNING ALGORITHMS FOR REAL-TIME DETECTION AND PREDICTION OF SEISMIC ACTIVITIES TO ENHANCE DISASTER RISK MITIGATION STRATEGIES Nofirman Nofirman; Daiki Nishida; Giovanni Rossi
Research of Scientia Naturalis Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i2.4170

Earthquakes pose significant threats to human safety, critical infrastructure, and socioeconomic stability because their occurrence is highly complex and difficult to predict accurately in real time. Although conventional seismic monitoring systems have improved earthquake detection, they remain limited by computational constraints, delayed event recognition, and inadequate identification of nonlinear seismic patterns. This study evaluated the effectiveness of machine learning algorithms for real-time seismic detection and prediction and their contribution to disaster risk mitigation. A mixed-methods sequential explanatory design was employed using approximately 1.8 million seismic waveform segments representing 48,000 earthquake events collected from 320 monitoring stations across eight tectonically active regions. Quantitative analyses included comparative evaluation of supervised, ensemble, and deep learning algorithms using multivariate statistics, structural equation modeling, hierarchical regression, mediation, and moderation analyses, while qualitative evidence was examined through thematic analysis. Findings showed that deep learning and hybrid ensemble models consistently achieved higher prediction accuracy, computational efficiency, early warning reliability, and lower false alarm rates than conventional approaches. Improved prediction accuracy strengthened disaster response readiness, while dense sensor networks and institutional coordination enhanced operational effectiveness, supporting resilient earthquake risk mitigation and evidence-based emergency decision-making.

HARMONIZING RELIGIOUS AND INDIGENOUS VALUES AMONG THE BANUA COMMUNITY IN BERAU Emelia Nurwati R; Khamam Khosiin; Khairiyah Khairiyah; Daiki Nishida
Journal of Noesantara Islamic Studies Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnis.v3i3.3224

This study examines the harmonization of religious and indigenous values within the Banua community in Berau Regency, East Kalimantan. The research aims to explore how Islamic teachings are interpreted, practiced, and transmitted through adat (customary traditions) in everyday social life. Employing a qualitative approach grounded in interpretative phenomenological and hermeneutic perspectives, data were collected through in-depth interviews, participant observation, and document analysis involving customary leaders, religious figures, ritual practitioners, and community members. The findings reveal that religious values and adat function as complementary systems rather than competing frameworks. Islamic moral principles are embedded within customary norms, rituals, and communal practices, enabling religion to remain culturally resonant and socially meaningful. Cultural rituals such as Puncak Rasul and Terbangan (Hadrah) play a crucial role as media for value integration and intergenerational transmission. The novelty of this study lies in its empirical demonstration of harmonization as a lived, dialogical process within an underrepresented indigenous community in Kalimantan. The study implies that culturally grounded religious practices can strengthen social cohesion, cultural resilience, and moral continuity in plural societies.

Biodiversity Hotspots and Conservation Priorities in Tropical Asia Haruto Takahashi; Miku Fujita; Daiki Nishida
Journal of Selvicoltura Asean Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v1i5.1667

Tropical Asia is home to a rich diversity of species and ecosystems, yet it faces significant threats from habitat loss, climate change, and human activities. Identifying biodiversity hotspots in this region is crucial for prioritizing conservation efforts and ensuring the protection of unique species and habitats. This research aims to evaluate biodiversity hotspots in tropical Asia and establish conservation priorities based on ecological significance and vulnerability. The study seeks to provide actionable recommendations for policymakers and conservationists to enhance biodiversity preservation in these critical areas. A spatial analysis was conducted using geographic information systems (GIS) to map biodiversity hotspots across tropical Asia. Data from various sources, including species distribution records and habitat assessments, were analyzed to identify regions with high biodiversity and significant conservation needs. Stakeholder interviews were also conducted to gather insights on local conservation challenges. The findings revealed several key biodiversity hotspots, including the Indo-Burma region and the Sundaland region, which are critically endangered due to deforestation and habitat fragmentation. The analysis indicated that targeted conservation efforts in these areas could significantly enhance species protection and ecosystem resilience. This study concludes that prioritizing conservation actions in identified biodiversity hotspots is essential for mitigating biodiversity loss in tropical Asia. Collaborative efforts among governments, NGOs, and local communities are vital to developing effective conservation strategies that address both ecological and socio-economic challenges.

Long-Lived Quantum Coherence in the Fenna-Matthews-Olson Complex: Implications for Energy Transfer Efficiency in Photosynthesis Chai Pao; Rit Som; Daiki Nishida
Journal of Tecnologia Quantica Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i6.3200

Quantum coherence has been shown to play a crucial role in optimizing energy transfer in photosynthetic systems, especially in the Fenna-Matthews-Olson (FMO) complex, which is responsible for efficiently capturing light energy in photosynthetic bacteria. While quantum coherence is often considered fragile and short-lived in biological systems, recent studies have indicated its potential for sustaining long-lived coherence, facilitating highly efficient energy transfer. This research investigates the implications of long-lived quantum coherence in the FMO complex for energy transfer efficiency, exploring how coherence persistence enhances the system’s performance. The objective of this study is to analyze the effects of long-lived quantum coherence on energy transfer efficiency in the FMO complex under varying environmental conditions, such as temperature and bath coupling. The results demonstrate that long-lived quantum coherence directly correlates with higher energy transfer efficiency, with temperature and environmental factors playing a significant role in maintaining coherence. The study shows that the FMO complex utilizes quantum coherence as an active resource to optimize energy conversion, achieving efficiencies well beyond classical expectations. In conclusion, this research underscores the importance of quantum coherence in biological energy transfer processes and offers insights into bio-inspired quantum systems for efficient energy harvesting.

THE QUBIT PARADOX: WHY MORE QUBITS ACTUALLY LOWER ERROR RATES? Miku Fujita; Ren Suzuki; Daiki Nishida
Journal of Computer Science Advancements Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i5.3329

Physical qubits intuitively introduces greater cumulative noise and control complexity. This “Qubit Paradox” presents a fundamental barrier to scalability, suggesting that larger systems might become inherently less stable. This research aims to rigorously validate the threshold theorem, defining the precise boundary where topological protection overcomes physical noise accumulation. We utilized high-fidelity Monte Carlo simulations of Rotated Surface Codes, scaling from distance d=3 to d=9, under realistic circuit-level noise models including leakage and crosstalk. Decoding was executed using the Minimum Weight Perfect Matching (MWPM) algorithm to analyze logical failure rates across 109 error correction cycles. Results identify a critical physical error threshold of approximately 0.57%. Below this value, logical error rates exhibited exponential suppression via power-law decay, reducing by seven orders of magnitude at distance-9. Conversely, systems operating above this threshold demonstrated error amplification with increased scale. We conclude that the paradox resolves only when individual gate fidelity surpasses the threshold, mandating that hardware optimization must precede quantitative scaling. These findings establish a validated roadmap for the transition from the NISQ era to fault-tolerant architecture.

SOFTWARE DEFINED NETWORKING ARCHITECTURE FOR ENTERPRISE COMPUTER NETWORKS PERFORMANCE OPTIMIZATION Miku Fujita; Daiki Nishida; Setiawan Setiawan
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.v4i2.3783

The increasing complexity and demands of modern enterprise networks have highlighted the limitations of traditional network management systems. Network performance issues such as latency, congestion, and inefficient resource allocation have become significant challenges for businesses that rely on high-speed, reliable communication. Software Defined Networking (SDN) architecture has emerged as a promising solution to these challenges, offering centralized control, flexibility, and real-time optimization. This study investigates the impact of SDN architecture on the performance optimization of enterprise computer networks. The research aims to assess how SDN can improve network throughput, reduce latency, and enhance overall network efficiency in large-scale enterprise environments. A mixed-methods approach was employed, using both quantitative performance metrics and qualitative feedback from network administrators and IT managers. The results show a significant improvement in key network performance indicators, with throughput increasing by 30%, latency decreasing by 25%, and packet loss reducing by 18% after the implementation of SDN. The study concludes that SDN is a highly effective approach for optimizing network performance, offering greater scalability, security, and efficiency in enterprise settings. SDN enables real-time, dynamic network management, making it a crucial technology for modernizing enterprise network infrastructure.

Mathematical Biology: Modeling the Dynamics of Ecosystems and Biodiversity Khoironi Fanana Akbar; Daiki Nishida; Joni Wilson Sitopu
Research of Scientia Naturalis Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i6.1586

Background: Mathematical biology plays a crucial role in understanding the dynamics of ecosystems and biodiversity. By employing mathematical models, researchers can analyze complex biological interactions and predict changes within ecosystems over time. This approach is vital for addressing environmental challenges and informing conservation strategies. Objective: This study aims to develop mathematical models that accurately represent the dynamics of ecosystems and the factors influencing biodiversity. The focus is on identifying key interactions between species and their environment, as well as the implications of these interactions for ecosystem stability. Methodology: A combination of differential equations and computational simulations was employed to model various ecological scenarios. Data from field studies and ecological surveys were utilized to parameterize the models, allowing for realistic representations of species interactions and environmental influences. Results: Findings indicate that specific species interactions, such as predation and competition, significantly affect biodiversity and ecosystem dynamics. The models revealed thresholds beyond which ecosystems could shift to alternative stable states, emphasizing the importance of maintaining biodiversity for ecosystem resilience. Conclusion: This research highlights the value of mathematical modeling in the study of ecosystems and biodiversity. By providing insights into the intricate relationships between species and their environment, the study contributes to a better understanding of ecological dynamics and informs effective conservation strategies.

A COMPUTATIONAL STUDY OF THE MOLECULAR DOCKING OF BIOACTIVE COMPOUNDS FROM INDONESIAN MEDICINAL PLANTS Neneng Windayani; Daiki Nishida; Sara Cont
Research of Scientia Naturalis Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i6.2506

The growing interest in natural products as a source of bioactive compounds has led to the exploration of medicinal plants for their therapeutic potentials. Indonesia, with its rich biodiversity, is home to numerous medicinal plants, many of which have yet to be fully explored for their pharmacological activity. This research investigates the molecular docking of bioactive compounds derived from Indonesian medicinal plants to assess their potential interactions with various therapeutic targets. The primary objective of this study was to evaluate the binding affinities and interactions of these compounds with proteins involved in diseases such as cancer and microbial infections. Using molecular docking simulations, a range of bioactive compounds were tested for their binding potential against selected targets. The findings revealed several promising compounds with high binding affinity and stability, indicating their potential as lead candidates for drug development. This computational study highlights the significant therapeutic potential of Indonesian medicinal plants and provides a foundation for further in vitro and in vivo evaluations. The results suggest that these natural products could contribute to the development of novel pharmacological agents, particularly in the fight against cancer and infections.

ADVANCED NANOCARRIERS FOR CONTROLLED DRUG AND GENE DELIVERY IN CHRONIC DISEASES Ren Suzuki; Daiki Nishida; Nila Trisna Yulianti
Journal of Biomedical and Techno Nanomaterials Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v3i2.3585

Chronic diseases such as cancer, cardiovascular diseases, and neurodegenerative disorders pose significant treatment challenges due to their complexity and resistance to conventional therapies. Nanocarriers, as advanced drug and gene delivery systems, offer a promising solution to address these challenges by providing controlled release, improved targeting, and enhanced therapeutic efficacy. The ability to design nanocarriers that are biocompatible, stable, and capable of precise targeting to diseased tissues holds potential for revolutionizing the treatment of chronic diseases. This study aims to explore the design, development, and evaluation of advanced nanocarriers for controlled drug and gene delivery in chronic diseases. The research focuses on evaluating the efficacy of various nanocarriers, including liposomes, dendrimers, and nanoparticles, in improving drug bioavailability, targeting precision, and therapeutic outcomes in chronic disease models. The research utilizes in vitro cell culture studies and in vivo animal models to assess the effectiveness of different nanocarriers. Characterization techniques, including dynamic light scattering (DLS), transmission electron microscopy (TEM), and drug release assays, are used to evaluate the properties and performance of the nanocarriers. The study demonstrates that advanced nanocarriers significantly improve drug delivery efficiency, reduce systemic toxicity, and enhance therapeutic outcomes in chronic disease models. Gene delivery using nanocarriers also shows promising results in terms of targeted therapy. Advanced nanocarriers are a promising tool for controlled drug and gene delivery, offering potential breakthroughs in the treatment of chronic diseases by improving precision and minimizing side effects.

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