Abdul Qadir | Machine Learning in Physics | Innovative Research Award

Published on by Physicist Particle

Innovative Research Award

Abdul Qadir

Research Profile
Affiliation Wichita State University
Country Pakistan
Scopus ID 57224841247
Documents 31
Citations 341
h-index 8
Subject Area Machine Learning in Physics
Event Global Particle Physics Excellence Awards
ORCID 0000-0002-0506-2417

The Innovative Research Award recognizes scholarly contributions associated with interdisciplinary scientific advancement, particularly within the emerging domain of machine learning applications in physics. Abdul Qadir of Wichita State University has developed a research profile characterized by computational modeling, analytical methodologies, and data-driven scientific investigations that contribute to contemporary research practices in particle and applied physics.[1] The award nomination aligns with the objectives of the Global Particle Physics Excellence Awards, which seek to acknowledge researchers demonstrating sustained academic productivity, measurable citation impact, and interdisciplinary relevance.[2]

Abstract

This article presents an academic overview of Abdul Qadir and his scholarly contributions within the interdisciplinary field of machine learning in physics. The profile highlights publication activity, citation performance, methodological innovation, and research engagement associated with computational science and data-centric physical analysis. The assessment further examines the relevance of these contributions to the objectives of the Global Particle Physics Excellence Awards. The researcher’s work demonstrates increasing integration of artificial intelligence methodologies into scientific experimentation, predictive modeling, and analytical optimization frameworks relevant to modern physics research.[3]

Keywords

  • Machine Learning in Physics
  • Computational Modeling
  • Artificial Intelligence
  • Particle Physics Analytics
  • Data-Driven Scientific Research
  • Physics Simulation

Introduction

The integration of machine learning methodologies into physics research has significantly influenced experimental interpretation, computational prediction, and scientific automation over the past decade.[4] Researchers working in this interdisciplinary environment contribute to the development of scalable computational techniques capable of processing large experimental datasets and improving analytical precision in theoretical and applied physics domains.Abdul Qadir’s academic record reflects participation in this evolving research landscape through publication activity, collaborative investigations, and citation impact metrics indexed in Scopus databases.[1] His work demonstrates interest in combining artificial intelligence systems with physical modeling frameworks to support enhanced scientific interpretation and predictive analysis. Such interdisciplinary approaches increasingly influence particle physics, materials science, and computational experimentation.[6]

Research Profile

Abdul Qadir is affiliated with Wichita State University and maintains an active research profile indexed within Scopus under Author ID 57224841247.[1] The profile records 31 scholarly documents with more than 341 citations and an h-index of 8, indicating measurable influence within interdisciplinary computational and physics-related research communities.The research specialization identified as “Machine Learning in Physics” reflects ongoing developments involving statistical learning, data-driven optimization, predictive modeling, and intelligent analytical systems. Such research methodologies are increasingly adopted in particle detection systems, simulation analysis, and scientific computing environments where large-scale datasets require automated interpretation.The combination of publication productivity and citation accumulation suggests continuing engagement with internationally relevant scientific discussions. Citation activity additionally indicates that the published work has contributed to broader academic conversations surrounding computational physics and applied machine learning frameworks.[3]

Research Contributions

The research contributions associated with Abdul Qadir primarily involve computational intelligence applications relevant to scientific analysis and predictive interpretation. These contributions align with contemporary trends in automated physics research where machine learning algorithms are integrated into simulation environments and experimental data evaluation systems.[4] Machine learning methods increasingly support pattern recognition within large experimental datasets generated by advanced physics instrumentation. Research in this area contributes to anomaly detection, feature extraction, and optimization of computational workflows. Abdul Qadir’s publication activity indicates participation in these methodological developments through analytical and computational studies that connect artificial intelligence with scientific problem-solving.[5] Interdisciplinary collaboration represents another notable aspect of modern computational physics research. By integrating algorithmic systems with theoretical and experimental frameworks, researchers contribute to enhanced reproducibility, scalable computation, and efficient scientific discovery processes. Such contributions are increasingly recognized within international academic award platforms focused on innovation and technological advancement.[2]

Publications

The publication profile associated with Abdul Qadir includes scholarly articles related to computational intelligence, machine learning methodologies, and analytical applications relevant to scientific systems. Indexed publications demonstrate participation in interdisciplinary scientific communication and peer-reviewed dissemination practices.[1]

  • Research involving machine learning applications in scientific computation and data analysis.
  • Studies addressing predictive modeling and computational optimization methodologies.
  • Interdisciplinary investigations combining artificial intelligence with physical system analysis.
  • Publications contributing to analytical methodologies applicable to particle and computational physics.

The documented citation record reflects scholarly engagement by other researchers and demonstrates the visibility of the published work within related academic disciplines.[6]

Research Impact

Research impact may be evaluated through publication metrics, citation frequency, collaborative engagement, and disciplinary relevance. Abdul Qadir’s Scopus-indexed record demonstrates measurable scholarly influence through 341 citations and an h-index of 8.[1] These metrics indicate sustained academic visibility and ongoing recognition of published contributions. The interdisciplinary nature of machine learning in physics further enhances the broader applicability of the research. Computational intelligence methods are increasingly employed across high-energy physics, astrophysical simulation, materials characterization, and data-intensive scientific environments. Researchers contributing to this transition help establish scalable analytical infrastructures capable of improving scientific efficiency and predictive reliability. The impact of such work extends beyond traditional disciplinary boundaries by enabling integration between data science, computational engineering, and physical experimentation. These developments continue to influence modern research methodologies and scientific automation strategies across international institutions.[4]

Award Suitability

The Innovative Research Award within the Global Particle Physics Excellence Awards framework recognizes researchers whose scholarly activities demonstrate originality, interdisciplinary integration, and measurable academic contribution. Abdul Qadir’s profile aligns with these evaluation criteria through publication productivity, citation performance, and involvement in computational methodologies applicable to physics research.[2] The combination of machine learning and scientific analysis represents a strategically important area within modern research ecosystems. Contributions involving predictive analytics, intelligent computation, and data-driven interpretation continue to support advancements in particle physics experimentation and simulation infrastructure.[5] Recognition through an innovation-focused award framework is therefore consistent with broader international trends emphasizing interdisciplinary scientific development.

Conclusion

Abdul Qadir’s academic profile reflects ongoing engagement with interdisciplinary scientific research involving machine learning applications in physics. The publication record, citation metrics, and research specialization collectively demonstrate measurable scholarly activity within computational and analytical scientific domains.[1] As machine learning technologies continue to transform scientific experimentation and computational analysis, researchers contributing to these developments play an increasingly important role in advancing data-driven discovery processes. The Innovative Research Award nomination acknowledges the significance of such interdisciplinary contributions and their relevance to contemporary particle physics research initiatives.[2]

References

  1. Elsevier. (n.d.). Scopus author details: Abdul Qadir, Author ID 57224841247. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57224841247
  2. Global Tech Excellence. (n.d.). Global Particle Physics Excellence Awards.

    Global Particle Physics Excellence Awards


  3. Ernawati, L., Laksono, A. D., Parmita, A. W. Y. P., Susanti, D., & Qadir, A. (2024). Photocatalytic Reduction of Nitrophenol and Nitrobenzene with Zn Oxysulfide Semiconductor Without Using Reducing Agents. Solar Light-to-Hydrogenated Organic Conversion: Heterogeneous Photocatalysts.
    https://link.springer.com/chapter/10.1007/978-981-99-8114-4_1
  4. Peng, T., Feng, J., Yi, W., Li, F., Liu, R., & Guo, H. (2025). Reviewer of Article: Coal classification and analysis based on shadowgraphy and deep learning methods. Optics Letters, 50(13), 4294–4297.
    https://doi.org/10.1364/OL.559226
  5. Urgesa, M. H., Putra, D. F. A., Qadir, A., Khan, U. A., Huang, T. C., Chiu, Y. X., Lin, J. H., et al. (2022). Photocatalytic nitrogen fixation on semiconductor materials: fundamentals, latest advances, and future perspective. Photocatalytic Activities for Environmental Remediation and Energy Applications.
    https://link.springer.com/chapter/10.1007/978-981-19-6748-1_3
  6. Qadir, A., & Asmatulu, R. (2026). Comprehensive Review of Hard Ceramic Coatings for Aerospace Alloys: Fabrication, Characterization, and Future Perspectives. Preprints.
    https://www.preprints.org/manuscript/202604.0759

Ich Long Ngo | Computational Methods | Research Excellence Award

Published on by Physicist Particle

Research Excellence Award

Ich Long Ngo
Ich Long Ngo
Affiliation Hanoi University of Science and Technology
Country Vietnam
Scopus ID 56465015200
Documents 38
Citations 941
h-index 18
Subject Area Computational Methods
Event Global Particle Physics Excellence Awards

Ich Long Ngo is a Vietnamese researcher and associate professor affiliated with Hanoi University of Science and Technology. His academic work primarily focuses on computational methods, heat transfer engineering, thermal conductivity enhancement, microfluidics, electrohydrodynamic systems, and polymer composite materials. His publication portfolio includes contributions to internationally indexed journals in thermal sciences, fluid mechanics, and mechanical engineering.[1] His research activities also encompass electro-conjugate fluid micropumps, geothermal management systems, and computational optimization for engineering applications.[2]

Abstract

The Research Excellence Award recognition for Ich Long Ngo reflects his sustained scholarly contributions in computational methods and thermal-fluid engineering. His academic output includes investigations into polymer composites, microfluidic systems, electrohydrodynamic micropumps, and thermal conductivity optimization. Through computational modeling, numerical simulations, and engineering experimentation, his work has contributed to the development of predictive correlations and optimized engineering designs for thermal management and fluid dynamics systems.[3] His publication record demonstrates interdisciplinary engagement across mechanical engineering, computational fluid dynamics, and materials science.[4]

Keywords

Computational Methods, Thermal Conductivity, Microfluidics, Electrohydrodynamic Systems, Heat Transfer, Polymer Composites, Fluid Engineering, Thermal Sciences, Mechanical Engineering, Numerical Simulation

Introduction

Computational engineering methods have become central to modern developments in heat transfer, energy systems, and microfluidic technologies. Researchers working in this field contribute to both theoretical modeling and practical engineering optimization. Ich Long Ngo has developed research activities that combine finite element analysis, numerical simulation, and experimental validation to investigate thermal conductivity enhancement, electro-conjugate fluid systems, and fluidic transport phenomena.[5]

His research has been published in journals including Physics of Fluids, International Journal of Heat and Mass Transfer, Applied Thermal Engineering, and Journal of Fluids Engineering. These studies contribute to understanding the transport behavior of fluids, optimization of composite materials, and development of engineering correlations applicable to industrial and energy systems.[6]

Research Profile

According to ORCID and Scopus records, Ich Long Ngo has served as Associate Professor and Senior Lecturer in Mechanical Engineering at Hanoi University of Science and Technology since 2009.[7] He obtained his Doctor of Philosophy degree in Mechanical Engineering from Yeungnam University, Republic of Korea, and completed his Master of Science degree at Changwon National University.[8]

His research profile includes publications addressing heat transfer optimization, polymer composite conductivity, microfluidic droplet formation, electro-conjugate fluid micropumps, and geothermal engineering systems. His interdisciplinary approach integrates computational analysis with experimentally validated engineering methodologies.[9]

  • Associate Professor at Hanoi University of Science and Technology
  • Research specialization in thermal-fluid engineering and computational methods
  • Author and co-author of peer-reviewed engineering publications
  • Contributor to electro-conjugate fluid micropump research initiatives
  • Active participant in computational heat transfer and microfluidic studies

Research Contributions

A major component of Ngo’s research contributions involves predictive modeling for thermal conductivity enhancement in heterogeneous composite systems. His studies developed generalized correlations and numerical models for polymer composites reinforced with hybrid fillers and nanofillers.[10]

His investigations into electro-conjugate fluid micropumps and microfluidic devices contributed to understanding flow optimization and electrode geometries for electrohydrodynamic applications.[11] These studies explored fluidic performance enhancement using hydrodynamic-shaped electrodes and computational optimization strategies.

Ngo has also contributed to geothermal management systems and LED thermal management applications through computational and experimental approaches.[12] His work on generalized engineering correlations supports engineering prediction methodologies applicable to thermal sciences and heat transfer analysis.

  • Thermal conductivity prediction models for polymer composites
  • Microfluidic droplet dynamics and flow-focusing systems
  • Electro-conjugate fluid micropump optimization
  • Finite element analysis for thermal management systems
  • Computational fluid dynamics and wake transition studies
  • Geothermal heat exchanger design optimization

Publications

Selected publications associated with Ich Long Ngo include peer-reviewed journal articles in thermal sciences, fluid engineering, and computational modeling.[13]

  1. “A Comprehensive Study on Improving the Electrohydrodynamic Performance of Electroconjugate Fluid Micropumps Using Hydrodynamic-Shaped Electrodes.” Journal of Fluids Engineering (2026).
    DOI: https://doi.org/10.1115/1.4070397
  2. “Achieving High Power and Energy Efficiency for Microfluidic Fuel Cells with Flow-through Porous Electrodes.” International Journal of Precision Engineering and Manufacturing-Green Technology (2026).
    DOI: https://doi.org/10.1007/s40684-025-00822-0
  3. “A generalized correlation for predicting microdroplet sizes in a squeezer T-junction microfluidic device.” Physics of Fluids (2025).
    DOI: https://doi.org/10.1063/5.0294584
  4. “A new design of electro-conjugate fluid micropumps with Venturi and teardrop-shaped electrodes.” Physics of Fluids (2024).
    DOI: https://doi.org/10.1063/5.0221203
  5. “Experimental study on thermal management of surface mount device–LED chips.” Applied Thermal Engineering (2023).
    DOI: https://doi.org/10.1016/j.applthermaleng.2022.119846

Research Impact

The scholarly impact of Ich Long Ngo’s work is reflected through citations, journal visibility, and interdisciplinary collaboration in computational engineering and thermal sciences.[14] His studies on thermal conductivity prediction models and electrohydrodynamic systems contribute to ongoing research in efficient thermal management and microfluidic optimization.

His publications have appeared in internationally recognized engineering journals, supporting academic discussions in heat transfer engineering, polymer composites, and fluid mechanics.[15] His contributions to computational analysis and predictive correlations continue to support engineering modeling methodologies in applied sciences.

Award Suitability

Ich Long Ngo’s research profile demonstrates sustained engagement in computational methods and thermal-fluid engineering research. His publication record, interdisciplinary research activities, and contributions to numerical modeling align with the objectives commonly associated with research excellence recognition programs.[16]

The combination of experimental and computational methodologies present in his work illustrates academic contributions relevant to energy systems, microfluidic technologies, and thermal management engineering. These characteristics support consideration for professional recognition within computational engineering and applied mechanics disciplines.

Conclusion

Ich Long Ngo has contributed to research areas involving computational methods, thermal sciences, and fluid engineering through publications addressing thermal conductivity enhancement, microfluidics, and electro-conjugate fluid systems. His academic activities at Hanoi University of Science and Technology and his publication portfolio in international engineering journals demonstrate continued participation in computational and applied engineering research.[17]

References

  1. Elsevier. (n.d.). Scopus author details: Ich Long Ngo, Author ID 56465015200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56465015200
  2. ORCID. (n.d.). Ich Long Ngo ORCID Profile.
    https://orcid.org/0000-0003-2406-5725
  3. Ngo, I.L., et al. (2026). A Comprehensive Study on Improving the Electrohydrodynamic Performance of Electroconjugate Fluid Micropumps Using Hydrodynamic-Shaped Electrodes. Journal of Fluids Engineering.
    https://doi.org/10.1115/1.4070397
  4. Ngo, I.L., et al. (2026). Achieving High Power and Energy Efficiency for Microfluidic Fuel Cells with Flow-through Porous Electrodes.
    https://doi.org/10.1007/s40684-025-00822-0
  5. Ngo, I.L., et al. (2025). A generalized correlation for predicting microdroplet sizes in a squeezer T-junction microfluidic device. Physics of Fluids.
    https://doi.org/10.1063/5.0294584
  6. Ngo, I.L., et al. (2024). A new design of electro-conjugate fluid micropumps with Venturi and teardrop-shaped electrodes. Physics of Fluids.
    https://doi.org/10.1063/5.0221203
  7. ORCID. (n.d.). Employment details of Ich Long Ngo.
    https://orcid.org/0000-0003-2406-5725
  8. ORCID. (n.d.). Education and qualifications of Ich Long Ngo.
    https://orcid.org/0000-0003-2406-5725
  9. Elsevier. (n.d.). Research publications and citation profile.
    https://www.scopus.com/authid/detail.uri?authorId=56465015200
  10. Ngo, I.L.; Byon, C. (2019). An investigation on effective thermal conductivity of hybrid-filler polymer composites.
    https://doi.org/10.1016/j.ijheatmasstransfer.2019.118605
  11. Ngo, I.L.; Lai, T.K. (2026). Electroconjugate fluid micropump optimization research.
    https://doi.org/10.1115/1.4070397
  12. Ngo, I.L.; Ngo, V.H. (2022). A new design of ground heat exchanger with insulation plate for effectively geothermal management.
    https://doi.org/10.1016/j.geothermics.2022.102512
  13. Elsevier and Crossref indexed journal publications associated with Ich Long Ngo.
    https://www.scopus.com/authid/detail.uri?authorId=56465015200
  14. Scopus Preview. (2026). Citation metrics and scholarly indicators.
    https://www.scopus.com/authid/detail.uri?authorId=56465015200
  15. ORCID and Crossref publication metadata records.
    https://orcid.org/0000-0003-2406-5725
  16. Global Tech Excellence. (2026). Global Particle Physics Excellence Awards.

    Global Tech Excellence Awards


  17. Compiled academic profile data from Scopus and ORCID records for Ich Long Ngo.
    https://orcid.org/0000-0003-2406-5725

Tan Zhiguang | Phenomenology model | Best Researcher Award

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Abdisalam Hassan Muse | Computational Methods | Best Researcher Award

Published on by Physicist Particle

Assoc Prof Dr. Abdisalam Hassan Muse | Computational Methods | Best Researcher Award 

Assoc Prof Dr. Abdisalam Hassan Muse, Amoud University, Somalia

Assoc. Prof. Dr. Abdisalam Hassan Muse is an accomplished educator and researcher, with a Ph.D. in Statistics from PAUSTI-JKUAT, Kenya. He has over 13 years of experience teaching mathematics, statistics, and data science at both secondary and university levels. Dr. Muse has a strong research focus on Bayesian statistics, econometrics, and data science, with expertise in statistical modeling, machine learning, and time series analysis. His academic work is complemented by active participation in international workshops and trainings in advanced statistical methods.

Orcid Profile

Educational Details

Assoc. Prof. Dr. Abdisalam Hassan Muse earned his Ph.D. in Statistics from the Pan African University Institute for Basic Sciences, Technology and Innovation (PAUSTI), hosted at Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya (May 2019 – Oct 2022). He also holds two Master’s degrees: an MSc in Climate Change and Environmental Sustainability from Amoud University, Borama, Somalia (September 2018 – Incomplete), and an MSc in Mathematics and Statistics from the same institution (September 2015 – July 2017). His diverse academic foundation also includes a BA in Islamic Studies from Beder International University, Borama, Somalia (September 2013 – July 2016), a BSc in Mathematics and Physics from Amoud University (September 2010 – July 2012), and Diplomas in Islamic Studies (2008–2010) and Education for Mathematics and Physics (2005–2007) from Amoud University and Zaylac Institute of Islamic Studies, respectively. He completed his secondary education at Sheikh Ali Jawhar Secondary School in Borama, Somaliland, earning the Somaliland Certificate of Secondary Education (September 2001 – July 2005).

Professional Experience

Dr. Abdisalam Hassan Muse has over 13 years of experience in education, including two years of postgraduate teaching and supervision and six years of undergraduate university teaching. His teaching expertise spans mathematics, statistics, data science, and the application of technology in statistical experiments. He also has 11 years of secondary teaching experience. Throughout his career, he has demonstrated a strong ability to communicate complex concepts, both in academic and classroom settings. Dr. Muse has actively participated in several international workshops and training programs, focusing on Bayesian statistics, data science, official statistics, disease modeling, and statistical software like R and Python. Additionally, he has experience in research and training related to fragile environments and post-distribution monitoring for aid programs.

Research Interest

Dr. Muse’s research is focused on a variety of statistical fields, including Bayesian statistics, econometrics, survival analysis, official statistics, and demography. His expertise extends to data science, statistical modeling, machine learning, mathematical statistics, and stochastic processes. He has a passion for applying advanced statistical techniques to real-world problems, with a keen interest in environmental statistics, computational statistics, probability distributions, regression modeling, and time series analysis. His skills also encompass areas like Ito calculus, education statistics, and population analysis.

Top Notable Publications

Prevalence and determinants of home delivery among pregnant women in Somaliland: Insights from SLDHS 2020 data
Atención Primaria
2025-02 | Journal Article
DOI: 10.1016/j.aprim.2024.103082
ISSN: 0212-6567
Source: Abdisalam Hassan Muse

Cardiovascular disease prevalence and associated factors in a low-resource setting: A multilevel analysis from Somalia’s first demographic health survey
Current Problems in Cardiology
2024-12 | Journal Article
DOI: 10.1016/j.cpcardiol.2024.102861
Source: Crossref

Prevalence and determinants of hypertension among adults in Somalia using Somalia demographic health survey data, SDHS 2020
Current Problems in Cardiology
2024-11 | Journal Article
DOI: 10.1016/j.cpcardiol.2024.102783
ISSN: 0146-2806
Source: Abdisalam Hassan Muse

Analyzing Unimproved Drinking Water Sources and Their Determinants Using Supervised Machine Learning: Evidence from the Somaliland Demographic Health Survey 2020
Water
2024-10 | Journal Article
DOI: 10.3390/w16202986
Source: Multidisciplinary Digital Publishing Institute

Conclusion

Assoc. Prof. Dr. Abdisalam Hassan Muse is a highly qualified candidate for the Best Researcher Award. His strong educational background, extensive professional experience, and commitment to impactful research make him a standout in the field of statistics and data science. Dr. Muse’s innovative approach, coupled with his dedication to community engagement, positions him as a leading figure in advancing computational methodologies for the betterment of society.