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All Journal International Journal of Educatio Elementaria and Psychologia Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials
Ming Pong
Chiang Mai University
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Education Environmental Science Law, Crime, Criminology & Criminal Justice Physics Other

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 1 
THE EFFECTIVENESS OF GROUP COUNSELING IN ENHANCING SELF-CONFIDENCE AMONG ELEMENTARY SCHOOL STUDENTS Fauzi Aldina; Ming Pong; Aom Thai; Bernardus Agus Rukiyanto
International Journal of Educatio Elementaria and Psychologia Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijeep.v2i2.1884

Abstract

Self-confidence is a critical component of children’s personal and academic development, enabling them to engage actively in learning and social interactions. Elementary school students with low self-confidence often face challenges in expressing themselves, participating in group activities, and achieving academic success. This study investigates the effectiveness of group counseling in enhancing self-confidence among elementary school students. The research employed a quasi-experimental design with a pre-test and post-test control group. The sample consisted of 60 students identified as having low self-confidence, divided equally into an experimental group that received group counseling sessions and a control group following standard classroom activities. Data were collected through self-confidence questionnaires, behavioral observation sheets, and teacher interviews. The findings revealed a significant increase in self-confidence scores in the experimental group compared to the control group. Students in the experimental group demonstrated greater willingness to participate in discussions, share ideas, and take initiative in various school activities. These results confirm that group counseling effectively fosters a supportive environment where students can build self-assurance through peer interaction and guided facilitation. The study concludes that group counseling is a valuable tool for addressing self-confidence issues and recommends its broader implementation in elementary schools.
The Impact of Growth Mindset Interventions on Student Achievement Pong Krit; Ming Pong; Vicheka Rith; Suyitno Suyitno
Research Psychologie, Orientation et Conseil Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v2i3.2375

Abstract

A student’s underlying beliefs about intelligence whether it is a fixed trait or can be developed (a “mindset”) is a powerful predictor of academic resilience and achievement. Fostering a growth mindset, the belief that intelligence is malleable, has been identified as a critical target for educational interventions aimed at improving student success. This study aimed to quantitatively evaluate the impact of a targeted, school-based growth mindset intervention on the academic achievement and perseverance of middle school students in a challenging subject. A quasi-experimental, pre-test/post-test study was conducted with 250 8th-grade students. The intervention group (n=125) participated in six workshops focused on neuroplasticity and growth mindset principles. The control group (n=125) received standard study skills training. Academic achievement was measured by mathematics grades and standardized test scores. The intervention group demonstrated a statistically significant improvement in their mathematics grades (p < .01) and reported higher levels of academic perseverance compared to the control group. The control group showed no significant change in either measure over the same period. Targeted, low-cost growth mindset interventions are an effective strategy for improving student academic achievement. Fostering the belief that intellectual abilities can be developed through effort is a powerful pedagogical tool for enhancing student success and resilience.  
THE RELATIONSHIP BETWEEN SELF-COMPASSION AND PSYCHOLOGICAL FLOURISHING IN YOUNG ADULTS Aiman Fariq; Faisal Razak; Ming Pong
Research Psychologie, Orientation et Conseil Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v3i2.3738

Abstract

Psychological flourishing refers to the optimal functioning of individuals, characterized by high levels of well-being, personal growth, and life satisfaction. Self-compassion, the ability to treat oneself with kindness during times of failure or difficulty, has been increasingly recognized as a critical factor in promoting mental health and well-being. This study aims to examine the relationship between self-compassion and psychological flourishing in young adults. Specifically, it investigates how different aspects of self-compassion, such as self-kindness and mindfulness, contribute to overall well-being and personal growth in this demographic. A quantitative correlational research design was employed, using surveys to collect data from 300 young adults aged 18-30. The Self-Compassion Scale (SCS) was used to measure self-compassion, and the Psychological Flourishing Scale (PFS) was used to assess flourishing. Pearson’s correlation and regression analyses were conducted to determine the relationship between self-compassion and flourishing. The findings revealed a significant positive correlation between self-compassion and psychological flourishing. Higher levels of self-compassion were associated with greater well-being, personal growth, and life satisfaction among young adults. This study concludes that self-compassion is a significant predictor of psychological flourishing in young adults.  
Engineering Hybrid Quantum Systems: Strong Coupling Between Nitrogen-Vacancy Centers and a Superconducting Resonator Herri Trisna Frianto; Ming Pong; Rit Som
Journal of Tecnologia Quantica Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i3.2602

Abstract

Hybrid quantum systems that integrate solid-state qubits with superconducting circuits have emerged as a promising architecture for scalable quantum information processing. Achieving strong coherent coupling between distinct quantum subsystems, such as spin ensembles and microwave resonators, remains a critical challenge in realizing hybrid quantum technologies. This study aims to engineer and characterize a hybrid platform that couples nitrogen-vacancy (NV) centers in diamond with a superconducting coplanar waveguide resonator. A combination of cryogenic microwave spectroscopy and time-domain measurements was employed to evaluate coupling strength, coherence times, and collective spin photon interactions at millikelvin temperatures. The experimental results demonstrated a vacuum Rabi splitting of 22 MHz, confirming the realization of a strong coupling regime between the NV spin ensemble and the superconducting resonator. The coherence lifetime of the NV centers remained above 100 ?s under optimized magnetic field alignment, ensuring stable quantum-state transfer. The findings reveal that hybrid systems combining spin-based and superconducting components can serve as viable interfaces for quantum memory and quantum communication nodes. The study concludes that engineering such strong spin–photon coupling represents a foundational step toward the development of coherent, scalable hybrid quantum networks.
Heisenberg-Limited Metrology: Utilizing Entangled States for Ultra-Precise Gravitational Wave Detection Nurul Huda; Ming Pong; Pedro Silva
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.v3i1.3579

Abstract

Gravitational wave detection has reached unprecedented sensitivity through interferometric technologies, yet it remains fundamentally constrained by quantum noise, particularly the standard quantum limit (SQL). Advances in quantum metrology suggest that entangled states can surpass classical limits and approach the Heisenberg limit, offering a pathway to ultra-precise measurements. This study aims to investigate the potential of entangled quantum states to enhance sensitivity in gravitational wave detectors under realistic conditions. A theoretical–computational approach was employed, combining analytical modeling with large-scale numerical simulations of interferometric systems. Various quantum states, including NOON states, squeezed states, and hybrid entangled–squeezed configurations, were evaluated using quantum Fisher information and phase variance as performance metrics. The results indicate that entangled states achieve Heisenberg-limited scaling in ideal conditions, significantly outperforming classical and squeezed states. Hybrid states demonstrate superior robustness against loss and decoherence, maintaining enhanced sensitivity in non-ideal environments. These findings suggest that the integration of entangled states into interferometric detectors can substantially reduce quantum noise and improve detection capabilities. This study concludes that entanglement-based metrology offers a promising and practical pathway toward next-generation gravitational wave detection with ultra-high precision.
Implementation of Information Technology in Increasing the Efficiency and Effectiveness of MSME Business Processes Ming Pong; Teddy Oswari; Fatimah Fahurian; Triyugo Winarko
Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v2i1.1978

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in driving economic growth, particularly in emerging economies. However, MSMEs often face challenges related to limited resources, outdated business processes, and inefficient operational practices. The implementation of information technology (IT) has emerged as a potential solution to improve business processes, streamline operations, and enhance competitiveness. This study aims to examine the impact of information technology implementation on the efficiency and effectiveness of business processes in MSMEs. The research seeks to identify key factors that influence the adoption of IT, as well as assess how it contributes to improving operational efficiency, decision-making, and overall business performance in MSMEs. The findings revealed that MSMEs that integrated IT solutions experienced notable improvements in process efficiency, reduced operational costs, and enhanced decision-making. Over 70% of surveyed businesses reported better inventory management and faster response times to customer needs. Moreover, businesses leveraging IT tools showed a significant increase in productivity and profitability. The implementation of information technology significantly enhances the efficiency and effectiveness of MSME business processes.
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

Abstract

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.
REAL-TIME SENSING OF AIRBORNE POLLUTANTS USING IOT-INTEGRATED ELECTROCHEMICAL SENSORS Ardi Azhar Nampira; Ming Pong; Siri Lek
Research of Scientia Naturalis Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i5.2383

Abstract

Air pollution poses a significant threat to public health, demanding effective real-time monitoring solutions. Traditional monitoring systems are often costly and sparsely located, limiting their spatial-temporal resolution. This study aimed to develop and validate a low-cost, IoT-integrated electrochemical sensor system for the real-time detection of key airborne pollutants. We fabricated electrochemical sensors for nitrogen dioxide (NO?), sulfur dioxide (SO?), and volatile organic compounds (VOCs), which were then integrated with a microcontroller and a wireless communication module. The system was calibrated and validated against reference instruments in both laboratory and field conditions. The developed sensors exhibited high sensitivity, good selectivity, and rapid response times (<60s). Field data demonstrated a strong correlation (R² > 0.92) with co-located reference-grade analyzers, and the IoT platform successfully provided continuous data visualization via a cloud dashboard. This study confirms that IoT-integrated electrochemical sensors provide a scalable and cost-effective solution for building dense, real-time air quality monitoring networks, offering significant potential for urban environmental management.
AI POWERED DIGITAL HISTOPATHOLOGY: PREDICTING IMMUNOTHERAPY RESPONSE USING DEEP LEARNING Loso Judijanto; Som Chai; Ming Pong; Justam Justam; Ardi Azhar Nampira
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2379

Abstract

Immunotherapy has revolutionized cancer treatment, yet predicting which patients will respond remains a major clinical challenge. Current predictive biomarkers, such as PD-L1 expression, have limited accuracy and fail to capture the complex interplay of cells within the tumor microenvironment. Digital histopathology, the analysis of digitized tissue slides, combined with artificial intelligence (AI), offers a novel approach to identify complex morphological patterns that could serve as more robust predictive biomarkers. Objective: A deep learning model, specifically a convolutional neural network (CNN), was trained on a large, multi-center cohort of digitized tumor slides from patients with non-small cell lung cancer who had received ICI therapy. The model was trained to identify subtle morphological features and the spatial arrangement of tumor cells and tumor-infiltrating lymphocytes. The model’s predictive performance was rigorously validated on an independent, held-out test cohort, and its performance was compared to the predictive accuracy of PD-L1 staining. The AI-powered model successfully predicted immunotherapy response with a high degree of accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort.
Co-Authors Aiman Fariq Aom Thai Ardi Azhar Nampira Ardi Azhar Nampira Faisal Razak Fatimah Fahurian Fauzi Aldina Herri Trisna Frianto, Herri Trisna Justam Justam Loso Judijanto Nong Chai Nurul Huda Pedro Silva Pong Krit Rit Som Rukiyanto Rukiyanto, Rukiyanto Siri Lek Som Chai Suyitno Suyitno Teddy Oswari Ton Kiat Triyugo Winarko Vicheka Rith