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Forest Restoration and Rehabilitation: A Comparative Analysis of Techniques Pong Krit; Napat Chai; Ton Kiat
Journal of Selvicoltura Asean Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v1i3.1661

Forest ecosystems are essential for biodiversity, climate regulation, and human well-being. However, deforestation and degradation threaten these vital resources, necessitating effective restoration and rehabilitation techniques. Understanding the strengths and weaknesses of various methods is crucial for improving restoration outcomes. This study aims to conduct a comparative analysis of different forest restoration and rehabilitation techniques. The objectives include evaluating their ecological effectiveness, cost-efficiency, and suitability for diverse ecological contexts. A systematic literature review was conducted, analyzing peer-reviewed articles, case studies, and reports related to various restoration techniques. Key techniques examined included natural regeneration, reforestation, afforestation, and assisted natural regeneration. Data were synthesized to highlight the comparative advantages and challenges of each method. Findings indicate that natural regeneration often yields the highest ecological success, particularly in undisturbed areas. Reforestation and afforestation techniques showed varying success rates based on species selection and site conditions. Assisted natural regeneration emerged as a cost-effective approach, promoting biodiversity while minimizing intervention. This analysis concludes that no single technique is universally applicable. Effective forest restoration requires tailored approaches that consider local ecological conditions and socio-economic factors. Policymakers and practitioners should prioritize collaborative strategies that integrate multiple techniques to enhance restoration success and ecological resilience.

A Neurocognitive Approach to Early Reading Intervention for Elementary School Children with Dyslexia Yulian Purnama; Pong Krit; Ton Kiat
Research Psychologie, Orientation et Conseil Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v2i4.2525

Early reading difficulties, particularly dyslexia, pose significant challenges for elementary school children, affecting academic achievement and long-term literacy development. Neurocognitive research suggests that deficits in phonological processing, working memory, and rapid automatized naming are core contributors to reading impairments. Understanding these underlying cognitive mechanisms is crucial for designing effective early reading interventions that target both skill acquisition and brain-based processing. This study aims to investigate the efficacy of a neurocognitive-based early reading intervention for children with dyslexia, focusing on improvements in reading fluency, decoding accuracy, and phonological awareness. A quasi-experimental design was employed with 60 elementary school participants diagnosed with dyslexia, divided into intervention and control groups. Standardized neurocognitive assessments and reading tests were administered pre- and post-intervention. Results indicated that children receiving the neurocognitive intervention demonstrated significant gains in decoding accuracy, reading fluency, and phonological awareness compared to the control group. The study concludes that interventions informed by neurocognitive principles can effectively enhance reading outcomes for children with dyslexia, providing both practical and theoretical insights into tailored literacy instruction.

Unsupervised Classification of Topological Phase Transitions in Many-Body Quantum Systems Using Variational Quantum Eigensolvers Safiullah Aziz; Amir Raza; Ton Kiat
Journal of Tecnologia Quantica Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i5.3197

The study of topological phase transitions in many-body quantum systems has gained significant attention due to its implications for quantum computing and condensed matter physics. Traditional methods of classifying topological phases often rely on computationally expensive techniques or labeled data, which can be impractical for large systems. This research aims to develop a novel, scalable approach for unsupervised classification of topological phase transitions using Variational Quantum Eigensolvers (VQEs) in conjunction with unsupervised machine learning algorithms. The objective is to efficiently classify quantum phases without requiring pre-labeled data, offering a more efficient solution for studying large, interacting quantum systems. The methodology involves simulating quantum systems, including a 1D spin chain and a 2D topological insulator, and optimizing their ground states using VQEs. Key quantum features, such as energy spectra and correlation functions, are extracted and fed into clustering algorithms to identify different topological phases. The performance of the unsupervised learning algorithm is evaluated through clustering purity and accuracy metrics. The results demonstrate that the proposed method successfully identifies trivial and non-trivial phases with high accuracy (95% for the 1D spin chain and 92% for the 2D topological insulator).

The Impact of Climate Change on Forest Ecosystems: A Biomolecular Perspective Nong Chai; Ming Pong; Ton Kiat
Research of Scientia Naturalis Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i3.1572

Climate change has emerged as one of the most significant environmental challenges of our time, profoundly affecting forest ecosystems worldwide. Recent studies have revealed that alterations in temperature, precipitation patterns, and atmospheric CO2 concentrations are causing unprecedented changes at the molecular level within forest organisms. Understanding these biomolecular responses is crucial for predicting and managing forest ecosystem resilience in the face of climate change. This study aimed to investigate the molecular mechanisms underlying forest species' adaptation to climate change and identify key biomarkers associated with stress response and resilience. The research employed a comprehensive approach combining transcriptomics, proteomics, and metabolomics analyses of various forest species across different climatic zones. Samples were collected from 20 forest sites over a three-year period, analyzing molecular responses to temperature fluctuations, drought stress, and elevated CO2 levels. Results demonstrated significant alterations in gene expression patterns related to heat shock proteins, antioxidant enzymes, and stress-responsive transcription factors. Notable changes were observed in metabolic pathways involved in carbon fixation, water use efficiency, and secondary metabolite production. The study identified 15 novel molecular markers associated with climate resilience in forest species. Furthermore, findings revealed distinct biomolecular adaptation strategies among different species and ecological niches. This research concludes that understanding molecular responses to climate change is essential for developing effective forest conservation strategies and predicting ecosystem adaptability. The identified molecular markers can serve as valuable tools for monitoring forest health and implementing targeted conservation measures in the face of ongoing climate change.

MICROBIAL CONTRIBUTIONS TO SOIL HEALTH AND CROP YIELD IN ORGANIC FARMING SYSTEMS Yovita Yovita; Siri Lek; Ton Kiat
Research of Scientia Naturalis Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i1.2005

Soil health is a critical component of sustainable agriculture, particularly in organic farming systems. Microbial communities play a vital role in maintaining soil quality and enhancing crop productivity. Understanding these contributions is essential for optimizing organic farming practices. This study aims to investigate the specific roles of microbial communities in promoting soil health and improving crop yield in organic farming systems. By examining various microbial interactions and their effects on nutrient cycling, the research seeks to identify key factors influencing agricultural productivity. A field study was conducted on several organic farms, where soil samples were collected and analyzed for microbial diversity and activity. Crop yield data were obtained from participating farmers, and statistical analyses were performed to assess the relationships between microbial metrics and crop productivity. Findings indicate that higher microbial diversity and activity correlate positively with improved soil health indicators, such as organic matter content and nutrient availability. Additionally, crops grown in soils with robust microbial communities demonstrated significantly higher yields compared to those from less diverse microbial environments. This research underscores the importance of microbial contributions to soil health in organic farming systems.

Synthesis and Characterization of Magnetic Nanoparticles as Contrast Agents for Tumor Imaging Ton Kiat; Siri Lek; Aom Thai; Muntasir Muntasir
Journal of Biomedical and Techno Nanomaterials Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v1i3.1759

Magnetic nanoparticles (MNPs) have emerged as promising materials for biomedical applications, particularly as contrast agents in tumor imaging. Early and accurate tumor detection is critical for improving treatment outcomes, yet current imaging techniques often lack sensitivity and specificity. This study aimed to synthesize and characterize magnetic nanoparticles for their potential as contrast agents in tumor imaging. The nanoparticles were synthesized using a co-precipitation method, followed by surface modification with organic compounds to enhance stability and targeting specificity. Characterization included transmission electron microscopy (TEM), X-ray diffraction (XRD), vibrating sample magnetometer (VSM), and dynamic light scattering (DLS). Cytotoxicity and targeting efficiency were evaluated in vitro using cultured human tumor cells. The results demonstrated that the synthesized nanoparticles had an average size of 25 ± 5 nm, superparamagnetic properties with a saturation magnetization of 55 emu/g, and high colloidal stability due to surface modifications. Fluorescence imaging revealed significant accumulation of the nanoparticles in tumor cells, while cytotoxicity tests showed cell viability above 85% at concentrations up to 100 ?g/mL. These findings indicate the nanoparticles are safe and effective for tumor imaging. This study highlights the importance of integrating synthesis, characterization, and biological evaluation to optimize nanoparticle design for biomedical applications. While the results are promising, further in vivo studies are needed to evaluate nanoparticle distribution, accumulation, and clearance in complex biological systems. The findings provide a foundation for future research and development of advanced contrast agents for tumor imaging

Creative Entrepreneurship Mindset: Comparative Study between Millennial Generation and Generation Z Ton Kiat; Siri Lek; Aom Thai
Journal of Social Entrepreneurship and Creative Technology Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v1i3.1725

The increasing prominence of creative entrepreneurship has been driven by shifts in consumer behavior, technological advancements, and changing work environments. Both the Millennial and Generation Z cohorts exhibit distinct entrepreneurial tendencies, shaped by different societal, technological, and economic contexts. However, there remains a gap in understanding how these generational differences impact the development of a creative entrepreneurship mindset. This study aims to compare the creative entrepreneurship mindset between Millennials and Generation Z, examining the factors that influence their entrepreneurial behaviors, decision-making, and approach to creative industries. A comparative research design was employed, utilizing both qualitative and quantitative methods. A total of 200 participants, 100 Millennials and 100 Generation Z individuals, were surveyed and interviewed. The survey focused on key entrepreneurial traits such as risk-taking, innovation, and adaptability, while the interviews provided deeper insights into their motivations, challenges, and perceptions regarding creative entrepreneurship. The findings indicate significant differences between the two generations in terms of risk tolerance, innovation, and digital fluency. Generation Z tends to exhibit higher levels of digital fluency and a preference for social media-driven entrepreneurship, while Millennials show more inclination towards traditional business models. Additionally, Generation Z shows a stronger preference for flexible, remote work environments, valuing autonomy and self-expression. The study concludes that while both generations demonstrate entrepreneurial potential, their approaches to creative entrepreneurship differ significantly. Understanding these differences can help educators, policymakers, and entrepreneurs develop tailored support programs to foster entrepreneurial growth in both generations.

REVITALIZING CULTURAL HERITAGE: AN AR-BASED DIGITAL-PRENEURSHIP START-UP FOR SUSTAINABLE TOURISM AND COMMUNITY EMPOWERMENT Rit Som; Ton Kiat; Giovanni Rossi
Journal of Social Entrepreneurship and Creative Technology Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v2i6.2989

Cultural heritage is a vital aspect of community identity and history, yet many regions face challenges in preserving and promoting their heritage in the face of modern economic pressures. Traditional tourism practices often lead to the commercialization and degradation of cultural sites, while communities struggle to benefit economically from their heritage. Augmented Reality (AR) technology offers a promising solution by providing immersive, interactive experiences that can both preserve and promote cultural heritage while supporting sustainable tourism. This study explores the implementation of an AR-based digital-preneurship start-up model designed to revitalize cultural heritage through tourism while empowering local communities. The research employs a mixed-methods approach, combining quantitative surveys and qualitative interviews with both local stakeholders and tourists. The findings reveal that the AR platform significantly enhanced both tourist engagement and local economic outcomes, increasing community participation in tourism-related activities and boosting income for local businesses. The study concludes that AR-based digital-preneurship offers a scalable, sustainable model for cultural heritage revitalization, providing communities with a new avenue for economic development and cultural preservation. This research contributes to the growing body of knowledge on the intersection of technology, entrepreneurship, and sustainable tourism.

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