Cuimin Sun | Agricultural and Biological Sciences | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Cuimin Sun
Guangxi University, China

Cuimin Sun
Affiliation Guangxi University
Country China
Scopus ID 57155480000
Documents 55
Citations 382
h-index 11
Subject Area Agricultural and Biological Sciences
Event International Forensic Scientist Awards
ORCID 0000-0003-4174-1094

Cuimin Sun is a researcher affiliated with Guangxi University whose scholarly record spans agricultural and biological sciences, computational prediction, plant nutrient assessment, microfluidics, and micro-extrusion technologies. The available bibliometric profile records 55 documents, 382 citations, and an h-index of 11, providing a quantitative basis for assessing research visibility and continuity. [1]

Abstract

Cuimin Sun’s research profile demonstrates interdisciplinary activity connecting agricultural intelligence, plant nutrient analysis, microfluidic systems, and advanced manufacturing. Recent publications include machine-learning approaches for maize and sugarcane nutrient assessment and engineering studies involving deformable droplets and concentrated silver paste. [2] [3] [4] These works indicate an applied research orientation supported by computational modeling, image-based analysis, and experimental validation.

Keywords

Agricultural sciences; biological sciences; machine learning; plant nutrient prediction; maize; sugarcane; microfluidics; micro-extrusion; image analysis; experimental validation.

Introduction

Research in agricultural and biological sciences increasingly integrates sensing, artificial intelligence, computational modeling, and experimental technologies. Sun’s recent publication record reflects this convergence. The reported studies address both agricultural decision-support problems and engineering methods that can contribute to precision-oriented scientific applications. The combination of quantitative modeling and experimentally grounded investigation provides a multidisciplinary basis for evaluating the researcher’s scholarly contributions.

Research Profile

Sun’s research profile includes artificial intelligence for crop nutrient assessment, particularly through image-based and attention-based neural networks. The SCBI-EfficientNetV2 study addresses regression prediction of nitrogen content in maize leaves, while WT-ResNet investigates nondestructive estimation of nitrogen, phosphorus, and potassium in sugarcane leaves. [3] [4] This work is complemented by studies in microfluidic separation and extrusion behavior, illustrating broader technical engagement with applied modeling and experimental systems.

Research Contributions

  • Development of lightweight deep-learning approaches for agricultural nutrient prediction.
  • Investigation of nondestructive plant nutrient assessment using leaf imagery.
  • Research into centrifugal-field microfluidic separation of deformable droplets.
  • Semi-theoretical modeling and experimental validation of extrusion swell behavior in concentrated silver paste.

Publications

Selected recent publications include Semi-Theoretical Modeling and Experimental Validation of the Extrusion Swell Ratio of Highly Concentrated Silver Paste in Micro-Extrusion, published in Micromachines in July 2026. [5] The February 2026 Agronomy article, SCBI-EfficientNetV2, reports a lightweight attention-based approach for maize nitrogen prediction. [3] Other recent studies address sugarcane nutrient prediction and deformable droplet separation in microfluidic systems. [4] [6]

Research Impact

The bibliometric record of 55 documents, 382 citations, and an h-index of 11 indicates an established level of scholarly dissemination and citation activity. [1] The thematic diversity of recent publications further suggests an ability to apply computational and experimental methods across agricultural and engineering contexts. Such breadth is relevant to research environments where interdisciplinary methods are increasingly used to address complex measurement and process-control problems.

Award Suitability

For the Best Researcher Award, Sun’s profile can be evaluated through publication activity, citation indicators, methodological diversity, and relevance to applied scientific research. The combination of agricultural machine learning, nondestructive crop assessment, and experimental engineering research provides evidence of sustained scholarly engagement. The documented metrics and selected publications offer measurable criteria for consideration within a research recognition framework.

Conclusion

Cuimin Sun presents a multidisciplinary research profile associated with Guangxi University and the Agricultural and Biological Sciences subject area. The available record combines measurable scholarly impact with research addressing crop nutrient prediction, artificial intelligence, microfluidics, and advanced manufacturing. These characteristics provide a substantive academic basis for consideration for the Best Researcher Award.

References

  1. Elsevier. (n.d.). Scopus author details: Cuimin Sun, Author ID 57155480000. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57155480000
  2. Sun, C., et al. (2026). SCBI-EfficientNetV2: A Lightweight Attention-Based Network for Regression Prediction of Nitrogen Content in Maize Leaves. Agronomy, 16(5), 544.
    https://doi.org/10.3390/agronomy16050544
  3. Sun, C., et al. (2025). WT-ResNet: A Non-Destructive Method for Determining the Nitrogen, Phosphorus, and Potassium Content of Sugarcane Leaves Based on Leaf Image. Agriculture, 15(16), 1752.
    https://doi.org/10.3390/agriculture15161752
  4. Sun, C., et al. (2025). An Open-Type Crossflow Microfluidic Chip for Deformable Droplet Separation Driven by a Centrifugal Field. Micromachines, 16(7), 774.
    https://doi.org/10.3390/mi16070774
  5. Sun, C., et al. (2026). Semi-Theoretical Modeling and Experimental Validation of the Extrusion Swell Ratio of Highly Concentrated Silver Paste in Micro-Extrusion. Micromachines, 17(7), 855.
    https://doi.org/10.3390/mi17070855
  6. MDPI. (n.d.). Micromachines and Agriculture publication records. Multidisciplinary Digital Publishing Institute.

Mukesh Sharma | Physics and Astronomy | Forensic Scientist of the Year Award

Published on by Forensic Scientist Awards

Forensic Scientist of the Year Award

Mukesh Sharma
State Forensic Science Laboratery, India

Mukesh Sharma
Affiliation State Forensic Science Laboratery
Country India
Google Scholar ID XgS1vCwAAAAJ
Documents 64
Citations 567
h-index 10
Subject Area Physics and Astronomy
Event International Forensic Scientist Awards

Mukesh Sharma is a researcher whose documented scholarly work spans forensic science, toxicological investigation, injury interpretation, and experimental studies involving radiation and momentum-density measurements. His research record combines forensic applications with scientific approaches relevant to evidence interpretation and analytical investigation. The available scholarly profile records 64 documents, 567 citations, and an h-index of 10, providing a quantitative basis for assessing his research visibility. [1]

Abstract

This academic recognition profile examines the research record of Mukesh Sharma in relation to the Forensic Scientist of the Year Award. His documented publications include work addressing forensic interpretation of injuries, toxicological plants with potential forensic relevance, and experimental physics investigations. The available citation profile reports 64 documents, 567 citations, and an h-index of 10. These indicators, together with the subject-area classification of Physics and Astronomy, provide evidence of sustained scholarly activity across interdisciplinary research contexts. [1]

Keywords

Forensic science; forensic toxicology; injury interpretation; forensic investigation; toxicological plants; radiation physics; momentum density; scientific research; scholarly impact.

Introduction

Forensic science integrates scientific methods with the investigation and interpretation of evidence. Research within the field frequently crosses disciplinary boundaries, particularly where analytical physics, toxicology, medicine, and evidentiary interpretation intersect. Sharma’s publication record reflects this interdisciplinary character, with studies addressing both applied forensic questions and experimentally oriented scientific problems. Such a profile is relevant to recognition frameworks that consider research contribution, scholarly continuity, and measurable academic impact.

Research Profile

Sharma’s documented research interests include forensic toxicology, interpretation of bodily injuries, and scientific analysis using physical measurement techniques. One review examines Indian toxicological plants as potential botanical weapons, demonstrating an intersection between toxicology, environmental sources, and forensic assessment. [3] Other publications investigate momentum-density measurements in tantalum and the performance of a 137Cs gamma-ray Compton spectrometer, reflecting a methodological foundation in experimental physics. [2] [4]

Research Contributions

The available publications indicate several identifiable areas of contribution:

  • Forensic interpretation of injuries and wounds, supporting structured assessment of physical evidence. [5]
  • Forensic toxicological review of plant-derived hazards and their potential evidentiary significance. [3]
  • Experimental investigations of momentum densities and radiation-based measurement systems relevant to applied physical research. [2] [4]

Publications

Selected publications associated with the supplied scholarly record include Forensic Interpretation of Injuries / Wounds Found on the Human Body, published in the Journal of Punjab Academy of Forensic Medicine and Toxicology; Forensic Study of Indian Toxicological Plants as Botanical Weapon (BW): A Review; Anisotropy in the Momentum Density of Tantalum; and Performance of 137Cs Gamma-Ray Compton Spectrometer for the Study of Momentum Densities. The record also includes a forensic medicine and toxicology textbook listing associated with Avichal Publishing Company. [2] [3] [4] [5] [6]

Research Impact

The supplied scholarly metrics indicate measurable research visibility, with 567 citations across 64 documented works and an h-index of 10. [1] Individual publications in the supplied record have also accumulated substantial citation activity, including the reported 245 citations for the forensic medicine and toxicology textbook entry and 72 citations for the study of anisotropy in tantalum. These figures should be interpreted as bibliometric indicators rather than as standalone measures of scientific quality.

Award Suitability

The available evidence supports consideration of Sharma for a forensic science research recognition based on the combination of documented scholarly output, citation activity, and research spanning forensic and scientific disciplines. His work on injury interpretation and toxicological evidence has direct relevance to forensic practice, while his physics-oriented publications demonstrate broader scientific engagement. The profile therefore presents a substantive research basis for consideration within the International Forensic Scientist Awards, subject to the award’s formal evaluation criteria and verification procedures.

Conclusion

Mukesh Sharma’s available research record demonstrates sustained scholarly activity at the intersection of forensic science, toxicology, injury interpretation, and experimental physics. With 64 documented publications, 567 citations, and an h-index of 10, the supplied bibliometric profile provides a measurable foundation for academic recognition. [1] His selected publications further illustrate the breadth of topics addressed across forensic and scientific research.

References

  1. Elsevier. (n.d.). Scopus author details: Mukesh Sharma, Author ID XgS1vCwAAAAJ. Scopus.
    https://scholar.google.com/citations?hl=en&user=XgS1vCwAAAAJ
  2. Ahuja, B. L., Sharma, M., & Mathur, S. (2006). Anisotropy in the momentum density of tantalum. Nuclear Instruments and Methods in Physics Research Section B.
  3. Sharma, M. (2011). Forensic study of Indian toxicological plants as botanical weapon (BW): A review. Journal of Environmental & Analytical Toxicology, 1(1), 1–5.
  4. Ahuja, B. L., & Sharma, M. (2005). Performance of 137Cs gamma-ray Compton spectrometer for the study of momentum densities. Pramana, 65(1), 137–145.
  5. Sharma, M. (2011). Forensic interpretation of injuries / wounds found on the human body. Journal of Punjab Academy of Forensic Medicine and Toxicology, 11(2), 105–109.
  6. Aggrawal, A. (2014). Textbook of Forensic Medicine and Toxicology. Avichal Publishing Company.

Michel Audiffren | Cognitive Science | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Michel Audiffren
Université de Poitiers, France

Michel Audiffren is a researcher affiliated with the University of Poitiers, France, whose scholarly work addresses cognitive science, mental fatigue, executive functioning, cognitive workload, and psychophysiological mechanisms associated with demanding cognitive activity.

Researcher Profile
Affiliation University of Poitiers
Country France
Scopus ID 57202571614
Documents 82
Citations 3,332
h-index 31
Subject Area Cognitive Science
Event International Forensic Scientist Awards
ORCID 0000-0002-2488-8089

Abstract

Michel Audiffren’s research profile reflects sustained investigation of cognitive performance, mental fatigue, executive control, and the psychophysiological processes underlying demanding cognitive tasks. His recent publications examine methods for inducing mental fatigue, differences between endurance athletes and nonathletes, effort engagement, electroencephalography, heart-rate variability, and prefrontal activation. [1] [2]

Keywords

Cognitive Science; Mental Fatigue; Executive Functions; Cognitive Workload; Psychophysiology; Prefrontal Cortex; EEG; Heart-Rate Variability; Endurance Athletes; Cognitive Performance.

Introduction

Research into mental fatigue and cognitive control is relevant to understanding how prolonged or demanding mental activity affects performance. Audiffren’s recent work contributes to this area through experimental paradigms and physiological measurements designed to characterize cognitive load and effort engagement. Studies using dual 2-back tasks and near-infrared spectroscopy illustrate complementary approaches to examining fatigue and executive processing. [1] [4]

Research Profile

The documented profile comprises 82 Scopus-indexed documents, 3,332 citations, and an h-index of 31. These indicators provide a quantitative overview of scholarly visibility while his publication themes provide a qualitative view of his research direction. The work spans experimental cognitive science and psychophysiological assessment, with particular attention to fatigue, effort, executive tasks, and neural activation.

Research Contributions

  • Validation of a dual 2-back paradigm for experimentally inducing acute mental fatigue. [1]
  • Investigation of resistance to mental fatigue among endurance athletes compared with nonathletes. [2]
  • Assessment of effort engagement using electroencephalography and heart-rate variability. [3]
  • Analysis of prefrontal cortex activation across executive tasks and cognitive-load conditions. [4]

Publications

Selected recent works include studies published in Acta Psychologica, Research Quarterly for Exercise and Sport, and Brain Sciences, together with a preprint addressing ego-depletion and physiological measures. [1] [2] [3] [4]

Research Impact

The combination of 82 documents, 3,332 citations, and an h-index of 31 indicates an established scholarly record. His research also demonstrates methodological breadth by integrating behavioral paradigms with EEG, heart-rate variability, and near-infrared spectroscopy, supporting multidisciplinary examination of cognitive performance and fatigue.

Award Suitability

The documented research record is relevant to a Best Researcher Award focused on sustained scholarly contribution in cognitive science. The combination of measurable bibliometric impact, experimentally oriented research, and recent publications addressing mental fatigue and executive functioning provides a substantive basis for consideration within the International Forensic Scientist Awards.

Conclusion

Michel Audiffren’s profile presents a coherent body of research centered on cognitive fatigue, executive functioning, effort, and psychophysiological mechanisms. His bibliometric record and recent experimental publications support recognition of sustained research activity in cognitive science.

References

  1. Audiffren, M. et al. (2026). Validation of a dual 2-back task as an effective method for inducing acute mental fatigue. Acta Psychologica.
    https://doi.org/10.1016/j.actpsy.2026.107901
  2. Audiffren, M. et al. (2025). Endurance Athletes Are More Resistant to Mental Fatigue Than Nonathletes. Research Quarterly for Exercise and Sport.
    https://doi.org/10.1080/02701367.2025.2501972
  3. Audiffren, M. et al. (2023). A Replication of the Ego-Depletion Effect: Control of Effort Engagement during the Depleting Task with Electroencephalography and Heart-Rate Variability. Preprint.
    https://doi.org/10.22541/au.168601847.79348701/v1
  4. Audiffren, M. et al. (2022). Load-Dependent Prefrontal Cortex Activation Assessed by Continuous-Wave Near-Infrared Spectroscopy during Two Executive Tasks with Three Cognitive Loads in Young Adults. Brain Sciences, 12(11), 1462.
    https://doi.org/10.3390/brainsci12111462
  5. Elsevier. (n.d.). Scopus author details: Michel Audiffren, Author ID 57202571614. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57202571614
  6. ORCID. (n.d.). Michel Audiffren, ORCID 0000-0002-2488-8089.
    https://orcid.org/0000-0002-2488-8089
  7. International Forensic Scientist Awards. (n.d.). Official Award Website.
    forensicscientist.org

Shivani Tiwari | Biology and Life Sciences | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Shivani Tiwari
Sir Padampat Singhania University, India

Shivani Tiwari
Affiliation Sir Padampat Singhania University
Country India
Google Scholar ID BeWAA2MAAAAJ
Documents 14
Citations 30
h-index 4
Subject Area Biology and Life Sciences
Event International Forensic Scientist Awards

Shivani Tiwari is a researcher working in Biology and Life Sciences, with scholarly contributions spanning plant science, microbial biotechnology, molecular biology, agricultural science, and biologically mediated nanotechnology. Her recorded research output includes studies addressing phytochemicals, microbial disease control, plant stress-related gene families, and agricultural soil characteristics. The available bibliometric profile reports 14 documents, 30 citations, and an h-index of 4.

Abstract

Shivani Tiwari’s research profile reflects interdisciplinary engagement with biological and agricultural questions. Her documented work includes investigation of phytochemicals in Asparagus racemosus, biosynthesis of silver nanoparticles using Bacillus species, genome-wide characterization of glutathione S-transferase genes in quinoa, and assessment of soil properties in mango-growing orchards. These studies demonstrate the application of biochemical, microbiological, molecular, and agricultural approaches to research questions of biological relevance [1][2].

Keywords

Plant science; phytochemicals; microbial biotechnology; silver nanoparticles; quinoa; glutathione S-transferase; agricultural physics; soil science; molecular biology; Biology and Life Sciences.

Introduction

Research in contemporary life sciences increasingly connects molecular mechanisms with agricultural, environmental, and applied biological systems. Tiwari’s publication record reflects this interdisciplinary orientation, linking plant-derived compounds and microbial processes with molecular characterization and agricultural assessment. Her work therefore provides a research profile that crosses several complementary areas within biological science [3].

Research Profile

The research profile is characterized by a combination of experimental and computationally informed biological investigation. Topics represented in the available publications include phytochemical exploration of Shatavari, microbial synthesis of silver nanoparticles for disease-control applications, gene-family identification in quinoa, and characterization of orchard soil properties. Together, these areas indicate an emphasis on biological resources, plant systems, microbial applications, and agricultural sustainability.

Research Contributions

A notable contribution is the investigation of silver nanoparticle biosynthesis using Bacillus species, combining microbiological approaches with nanobiotechnology for potential microbial disease-control applications [2]. Another study examines the glutathione S-transferase gene family in quinoa, providing a molecular perspective on an agriculturally important crop [3]. Work on Shatavari phytochemicals further contributes to the documentation of plant-derived bioactive compounds [1].

Publications

  • An Insight of Phytochemicals of Shatavari (Asparagus racemosus), 2023.
  • Biosynthesis of silver nanoparticles using Bacillus sp. for Microbial Disease Control: An In-vitro and In-silico Approach, 2016.
  • Genome-wide identification and characterization of glutathione S-transferase gene family in quinoa (Chenopodium quinoa Willd.), 2023.
  • Appraisal of soil properties in the mango growing orchards of Malihabad, India, 2016.

Research Impact

The available scholarly profile records 14 documents, 30 citations, and an h-index of 4. These indicators provide a quantitative snapshot of documented research visibility and should be interpreted in relation to publication age, disciplinary citation practices, and the researcher’s broader scholarly activities. The publication topics also demonstrate relevance across plant biology, biotechnology, molecular research, and agricultural science [4].

Award Suitability

The Best Researcher Award category is supported by a documented body of multidisciplinary research and measurable scholarly output. Tiwari’s work addresses distinct biological problems through complementary approaches, including phytochemical investigation, microbial biotechnology, molecular genomics, and agricultural assessment. On the evidence available, the profile demonstrates sustained participation in research activities relevant to Biology and Life Sciences.

Conclusion

Shivani Tiwari presents a multidisciplinary research profile within Biology and Life Sciences, with publications addressing plant-derived compounds, microbial nanobiotechnology, crop genomics, and agricultural soil assessment. Her documented research output and citation indicators provide a suitable scholarly basis for consideration under the Best Researcher Award category.

References

  1. Pandey, V., Shri, M., Dubey, S., Saema, S., & Tiwari, S. (2023). An Insight of Phytochemicals of Shatavari (Asparagus racemosus). In Plants for Immunity and Conservation Strategies, 169–205.
  2. Tiwari, S., Gade, J., Chourasia, A., Aruna, J., et al. (2016). Biosynthesis of silver nanoparticles using Bacillus sp. for Microbial Disease Control: An In-vitro and In-silico Approach.
  3. Tiwari, S., Vaish, S., Singh, N., Basantani, M., Bhargava, A. (2023). Genome-wide identification and characterization of glutathione S-transferase gene family in quinoa (Chenopodium quinoa Willd.). 3 Biotech, 13(7), 230.
  4. Adak, T., Kumar, K., Singha, A., Pandey, G., Singh, V.K., Tiwari, S., & Vaish, S. (2016). Appraisal of soil properties in the mango growing orchards of Malihabad, India. Journal of Agricultural Physics, 16(1&2), 9–21.
  5. Author profile and publication metrics. Google Scholar author record for Shivani Tiwari.
    https://scholar.google.com/citations?user=BeWAA2MAAAAJ&hl=en

Paul Wolf | Business, Management and Accounting | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Paul Wolf
Technische Universität Berlin, Germany

Paul Wolf
Affiliation Technische Universität Berlin
Country Germany
Google Scholar ID 6JuOLlEAAAAJ
Documents 1
Subject Area Business, Management and Accounting
Event International Forensic Scientist Awards
ORCID 0000-0002-2616-2214

Paul Wolf is affiliated with Technische Universität Berlin and works within the subject area of Business, Management and Accounting. His documented research record includes work addressing digital platforms, online content performance and the relationship between platform signals and publication timing. His research profile is considered in relation to the Innovative Research Award presented through the International Forensic Scientist Awards.

Abstract

Paul Wolf’s documented publication, Faster After a Weak Video: Platform Signals and Upload Timing on YouTube, examines how platform signals and upload timing can influence subsequent content performance. Published in Information on 17 September 2026, the article provides a focused contribution to the study of digital platforms and online communication. [1]

Keywords

  • Digital platforms
  • YouTube
  • Platform signals
  • Upload timing
  • Content performance

Introduction

Research on digital platforms increasingly considers how algorithmic environments shape the visibility and timing of online content. Wolf’s publication contributes to this discussion by examining the relationship between a weak-performing video, platform signals and the timing of subsequent uploads. The work is situated within contemporary research on information systems, digital media and platform-mediated behavior. [1]

Research Profile

The available record identifies Wolf with Technische Universität Berlin and the Business, Management and Accounting subject area. His documented scholarly activity includes investigation of digital content dynamics, providing an analytical perspective on how creators and platforms interact through measurable signals. The available record lists one documented research document; citation and h-index values are not provided in the supplied data.

Research Contributions

The principal documented contribution concerns upload timing following weak content performance. By connecting platform signals with temporal publishing decisions, the research addresses a practical and scholarly question concerning online video ecosystems. Such work can support further investigation into platform analytics, audience response and strategic content scheduling. [1]

Publications

The documented publication is Faster After a Weak Video: Platform Signals and Upload Timing on YouTube, published in the journal Information on 17 September 2026. [2]

Research Impact

Because the supplied profile contains one documented publication and does not provide citation or h-index values, quantitative impact should be interpreted conservatively. The publication nevertheless provides a defined research contribution to the analysis of online video platforms and offers a basis for continued study of timing, recommendation signals and digital content performance. [1]

Award Suitability

The documented research aligns with the Innovative Research Award through its focus on a contemporary platform-mediated problem and its analytical treatment of upload timing and performance signals. The work demonstrates topical relevance to digital information research while providing a focused contribution suitable for consideration within an innovation-oriented academic recognition framework.

Conclusion

Paul Wolf’s documented research profile reflects an emerging contribution to the study of digital platforms and online content behavior. His publication on YouTube upload timing and platform signals provides a specific scholarly basis for consideration for the Innovative Research Award, while additional bibliometric evidence would be required for a broader quantitative assessment.

References

  1. Wolf, Paul. (2026). Faster After a Weak Video: Platform Signals and Upload Timing on YouTube. Information, published 17 September 2026.
    https://doi.org/10.3390/info17090907
  2. MDPI. (2026). Information, 17(9), 907. Article DOI: 10.3390/info17090907.
  3. International Standard Serial Number International Centre. (n.d.). ISSN 2078-2489.
  4. ORCID. (n.d.). Paul Wolf — ORCID record 0000-0002-2616-2214.
    https://orcid.org/0000-0002-2616-2214
  5. Google Scholar. (n.d.). Paul Wolf — author profile.
    https://scholar.google.com/citations?user=6JuOLlEAAAAJ&hl=en

Dr. Dr. Qayyum Shah | Chemical Engineering | Best Scholar Award

Published on by Forensic Scientist Awards

Best Scholar Award

Dr. Dr. Qayyum Shah
U.E.T (University of Engineering & Technology), Pakistan

Dr. Dr. Qayyum Shah
Affiliation U.E.T (University of Engineering & Technology)
Country Pakistan
Scopus ID 57192100360
Documents 31
Citations 507
h-index 12
Subject Area Chemical Engineering
Event International Forensic Scientist Awards
ORCID 0000-0002-7507-2370

Dr. Dr. Qayyum Shah is a researcher in Chemical Engineering whose documented scholarly work addresses computational fluid dynamics, nanofluids, magnetized flows, viscoelastic liquids, heat transfer, bioconvection, entropy generation, and related transport phenomena. His Scopus profile records 31 documents, 507 citations, and an h-index of 12, providing a bibliometric basis for considering his research contribution within the stated subject area. [1]

Abstract

Dr. Dr. Qayyum Shah’s research profile reflects an emphasis on mathematical and computational analysis of complex fluid systems relevant to engineering transport processes. His published studies examine magnetic effects, non-Newtonian and viscoelastic fluids, hybrid nanoparticles, bioconvection, chemical reactions, entropy generation, and heat-transfer behavior. Four documented publications from 2020 and 2021 illustrate this research direction across Crystals, Scientific Reports, and Mathematical Problems in Engineering. [2] [3] [4] [5]

Keywords

Chemical engineering; nanofluids; magnetohydrodynamics; heat transfer; non-Newtonian fluids; viscoelastic fluids; bioconvection; entropy generation; hybrid nanoparticles; computational optimization.

Introduction

Advanced fluid modelling provides mathematical tools for studying transport, thermal, and reactive processes in engineering systems. Within this field, Shah’s documented publications investigate flow configurations involving stretching surfaces, rotating systems, magnetic influences, nanoparticles, and complex rheological properties. These topics connect fluid mechanics with thermal engineering and computational modelling. [2] [4]

Research Profile

The research profile is characterized by analytical and computational treatment of coupled fluid-flow and heat-transfer problems. The reported studies consider thixotropic nanofluid behavior, Oldroyd-B viscoelastic fluids, hybrid nanoparticle suspensions, chemical reactions, and slip conditions. Such models are commonly used to examine how physical parameters influence velocity, temperature, entropy generation, and related engineering quantities. [2] [3]

Research Contributions

  • Analysis of magnetic dipole effects in thixotropic nanofluid flow over curved stretched surfaces. [2]
  • Computational optimization of bioconvection thin Oldroyd-B nanofluid deposition and entropy generation. [3]
  • Assessment of rotating magnetized hybrid-nanoparticle mixtures with chemical reactions. [4]
  • Analytical treatment of UCM viscoelastic liquid with slip and heat-flux conditions using a Galerkin approach. [5]

Publications

A framework for the magnetic dipole effect on the thixotropic nanofluid flow past a continuous curved stretched surface was published in Crystals in 2021. [2] Other documented studies include Computational optimization for the deposition of bioconvection thin Oldroyd-B nanofluid with entropy generation in Scientific Reports, and Rotating flow assessment of magnetized mixture fluid suspended with hybrid nanoparticles and chemical reactions of species. [3] [4]

Research Impact

The available Scopus metrics provide a quantitative indication of the visibility of the research record, with 31 indexed documents, 507 citations, and an h-index of 12. [1] The publication record also demonstrates activity across established peer-reviewed journals and includes DOI-identified research outputs, allowing individual studies to be independently located and evaluated.

Award Suitability

The documented research record aligns with the academic scope of a Best Scholar Award through its concentration on Chemical Engineering and mathematically intensive studies of advanced fluid systems. The combination of indexed publications, citation activity, and a defined body of work in nanofluid and transport modelling provides relevant evidence for an academic recognition assessment. Final award decisions remain subject to the applicable evaluation criteria of the International Forensic Scientist Awards.

Conclusion

Dr. Dr. Qayyum Shah’s documented scholarly profile centers on Chemical Engineering research involving nanofluids, complex fluids, magnetic effects, heat transfer, and computational modelling. The available bibliometric information and selected publications establish a research record that can be examined through both quantitative indicators and publication-level evidence.

References

  1. Elsevier. (n.d.). Scopus author details: Dr. Dr. Qayyum Shah, Author ID 57192100360. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57192100360
  2. Shah, Q. (2021). A framework for the magnetic dipole effect on the thixotropic nanofluid flow past a continuous curved stretched surface. Crystals, 11(6), 645.
    DOI: https://doi.org/10.3390/cryst11060645
  3. Shah, Q. (2021). Computational optimization for the deposition of bioconvection thin Oldroyd-B nanofluid with entropy generation. Scientific Reports.
    DOI: https://doi.org/10.1038/s41598-021-91041-5
  4. Shah, Q. (2021). Rotating flow assessment of magnetized mixture fluid suspended with hybrid nanoparticles and chemical reactions of species. Scientific Reports.
    DOI: https://doi.org/10.1038/s41598-021-90519-6
  5. Shah, Q. (2020). Analytical Solution of UCM Viscoelastic Liquid with Slip Condition and Heat Flux over Stretching Sheet: The Galerkin Approach. Mathematical Problems in Engineering.
    DOI: https://doi.org/10.1155/2020/7563693
  6. ORCID. (n.d.). Dr. Dr. Qayyum Shah, ORCID record 0000-0002-7507-2370.
    https://orcid.org/0000-0002-7507-2370
  7. International Forensic Scientist Awards. (n.d.). Official Award Website.
    forensicscientist.org

Dinh-Thai Kim | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Dinh-Thai Kim
Vietnam National University, Vietnam

Dinh-Thai Kim
Affiliation Vietnam National University
Country Vietnam
Scopus ID 57265279400
Documents 49
Citations 194
h-index 8
Subject Area Computer Science and Artificial Intelligence
Event International Forensic Scientist Awards
ORCID 0000-0002-9060-4769

Dinh-Thai Kim is a researcher affiliated with Vietnam National University whose documented research profile is situated within computer science and artificial intelligence. The available bibliometric record lists 49 documents, 194 citations, and an h-index of 8. [1] Recent publications demonstrate work spanning computer vision, deep learning, federated learning, speech emotion recognition, agricultural imaging, and Vietnamese sign-language classification.

Abstract

Dinh-Thai Kim’s research profile reflects applied artificial intelligence research addressing visual recognition, natural-language and speech-related intelligence, and machine-learning applications. Recent work includes lightweight object detection for cashew kernel assessment, growth-stage detection in lychee orchards, privacy-preserving speech emotion recognition, and graph-based Vietnamese sign-language classification. [2]–[5]

Keywords

Artificial intelligence; computer vision; deep learning; object detection; federated learning; speech emotion recognition; sign-language classification; agricultural AI.

Introduction

The research record associated with Kim illustrates the application of contemporary machine-learning methods to practical recognition problems. The publications supplied for this profile indicate an emphasis on deployable computer-vision systems and specialized intelligent models, including applications involving agricultural products, orchard monitoring, human speech, and gesture-based language recognition.

Research Profile

Kim’s documented research spans several complementary areas of artificial intelligence. The work on YOLOv8s-based lychee detection applies object-detection techniques under natural orchard conditions, while CSTAR-Det addresses lightweight vision-based assessment of cashew kernels. [2] [3] Other research extends machine learning toward Vietnamese speech emotion recognition and sign-language classification. [4] [5]

Research Contributions

  • Development of lightweight vision-based approaches for agricultural quality assessment.
  • Application of object detection to crop growth-stage recognition under natural conditions.
  • Investigation of prototype-based federated learning for privacy-preserving speech emotion recognition.
  • Use of graph convolutional networks and skeletal points for Vietnamese sign-language classification.

Publications

CSTAR-Det: A lightweight vision-based framework for conveyor-based cashew kernel quality assessment. Array, listed publication date December 2026. [2]

YOLO-Lychee: A YOLOv8s-Based Detector for Lychee Growth-Stage Detection under Natural Orchard Conditions. EAI Endorsed Transactions on AI and Robotics, 27 July 2026. [3]

EmoFedProto: Privacy-Preserving Vietnamese Speech Emotion Recognition via Prototype-Based Federated Learning. EAI Endorsed Transactions on AI and Robotics, 23 April 2026. [4]

Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification. Iranian Journal of Electrical and Electronic Engineering, March 2026. [5]

Research Impact

The supplied Scopus metrics record 194 citations across 49 documents, with an h-index of 8. [1] These indicators provide a quantitative view of scholarly visibility, while the publication record shows application-oriented research across multiple AI domains. Citation indicators should be interpreted in relation to publication age, field practices, and database coverage.

Award Suitability

The documented record provides evidence relevant to recognition in a research category focused on computer science and artificial intelligence. The combination of bibliometric activity and recent research involving computer vision, federated learning, and deep-learning classification offers a factual basis for consideration under the Best Researcher Award category. [1]–[5]

Conclusion

Dinh-Thai Kim’s documented research profile combines measurable scholarly activity with recent applications of artificial intelligence to agricultural vision, speech processing, privacy-preserving learning, and sign-language recognition. The available record supports a multidisciplinary description within computer science and artificial intelligence and provides the principal bibliographic and impact indicators presented on this page.

References

  1. Elsevier. (n.d.). Scopus author details: Dinh-Thai Kim, Author ID 57265279400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57265279400
  2. Kim, Dinh-Thai. (2026). CSTAR-Det: A lightweight vision-based framework for conveyor-based cashew kernel quality assessment. Array.
    https://doi.org/10.1016/j.array.2026.101282
  3. Kim, Dinh-Thai. (2026). YOLO-Lychee: A YOLOv8s-Based Detector for Lychee Growth-Stage Detection under Natural Orchard Conditions. EAI Endorsed Transactions on AI and Robotics.
    https://doi.org/10.4108/airo.12140
  4. Kim, Dinh-Thai. (2026). EmoFedProto: Privacy-Preserving Vietnamese Speech Emotion Recognition via Prototype-Based Federated Learning. EAI Endorsed Transactions on AI and Robotics.
    https://doi.org/10.4108/airo.11595
  5. Kim, Dinh-Thai. (2026). Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification. Iranian Journal of Electrical and Electronic Engineering.
    https://doi.org/10.22068/IJEEE.22.1.3717
  6. ORCID. (n.d.). Dinh-Thai Kim, ORCID record.
    https://orcid.org/0000-0002-9060-4769
  7. International Forensic Scientist Awards. (n.d.). Official award website.
    forensicscientist.org

Roberto De Moraes | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Roberto de Moraes
AECOM, United States

Roberto de Moraes
Affiliation AECOM
Country United States
Scopus ID 58072897300
Documents 2
Subject Area Engineering
Event International Forensic Scientist Awards

Roberto de Moraes is an engineering researcher affiliated with AECOM whose recent scholarly work addresses lunar construction-site screening and geotechnical decision-making. His 2026 publication, co-authored with Chrysothemis Paraskevopoulou, presents an evidence-to-decision framework for interpreting penetration-response observations while maintaining a distinction between measured evidence, mechanical interpretation, uncertainty, and asset-specific verification. The article appeared in Applied Sciences, Volume 16, Issue 19, Article 9648, with DOI 10.3390/app16199648. [1]

Abstract

The featured research develops a mechanics-informed framework for early lunar construction-site screening from penetration-response observations. The approach separates direct observations from conventional mechanical interpretation, preliminary construction ground zoning, uncertainty closure, and asset-specific verification. It recognizes that lunar penetration response may be affected by density, particle morphology, stratigraphy, disturbance, probe configuration, boundaries, and acquisition limitations. Rather than assigning an unsupported numerical historical-stress parameter, the framework uses an auditable evidence ledger and explicit decision gates. Its application to Apollo 16 evidence demonstrates how sparse legacy observations can inform investigation priorities while preserving uncertainty and avoiding unsupported design classifications. [1][2]

Keywords

Lunar regolith; penetration response; construction-site screening; geotechnical site investigation; evidence-to-decision framework; Apollo 16; mechanical-state hypothesis; lunar construction; engineering geology.

Introduction

Lunar construction requires engineering decisions from limited and instrument-dependent ground observations. The lunar surface differs substantially from terrestrial construction environments, making direct transfer of conventional Earth-based assumptions inappropriate without suitable calibration. NASA has identified geotechnical site investigation, topographic mapping, rock removal, grading, and ground-feature formation among relevant capabilities for lunar surface preparation. [3]

The study by de Moraes and Paraskevopoulou addresses this problem by establishing a structured process for interpreting penetration-response evidence. The framework is designed to identify where additional measurements are necessary and to distinguish preliminary screening information from project-specific design parameters. This approach is consistent with the broader scientific understanding that lunar surface conditions are shaped by impact processes, regolith evolution, geological setting, and surface interactions. [2][4]

Research Profile

Roberto de Moraes’s documented research contribution in the supplied publication centers on lunar geotechnical engineering and evidence-based construction-site screening. The work combines penetration observations with geological, stratigraphic, terrain, disturbance, and uncertainty considerations. Its methodological emphasis is on traceability: each interpretation is connected to a stated engineering decision, an uncertainty-closure action, or an independent verification requirement.

The publication also demonstrates an interdisciplinary connection between lunar science, geotechnical engineering, construction planning, and engineering decision analysis. The authors emphasize that penetration response alone should not automatically be converted into density, strength, stiffness, or construction-performance parameters. Instead, these properties require project-specific calibration and verification. [1]

Research Contributions

  • Development of an evidence-to-decision framework for preliminary lunar construction-site screening.
  • Separation of direct measurements, mechanical interpretation, construction zoning, uncertainty closure, and asset verification.
  • Use of Apollo 16 penetration-response evidence as a methodological demonstration rather than as unsupported numerical calibration.
  • Emphasis on compatibility checks, uncertainty documentation, alternative explanations, and independent verification before engineering adoption.

Publications

The principal publication associated with this recognition is An Evidence-to-Decision Framework for Lunar Construction-Site Screening from Penetration Response, authored by Roberto de Moraes and Chrysothemis Paraskevopoulou and published in Applied Sciences in 2026. The article is identified as Volume 16, Issue 19, Article 9648 and is available through an open-access publication record. [1]

Research Impact

The research provides a structured method for converting sparse lunar ground-response observations into investigation priorities without overstating the certainty of legacy evidence. Its potential engineering relevance lies in defining where additional soundings, samples, geophysical observations, controlled trials, or asset-level verification may be required. The broader significance is methodological: the framework establishes a transparent boundary between evidence-supported screening and the calibrated parameters required for final engineering design.

Award Suitability

The documented publication provides evidence of research activity in engineering and lunar geotechnical investigation. Its stated methodology addresses a specialized engineering problem through a structured framework linking field-response evidence, uncertainty, construction decisions, and verification. These characteristics provide a factual basis for considering the work within an Innovative Research Award context, subject to the award’s formal evaluation criteria and independent review.

Conclusion

Roberto de Moraes’s documented 2026 research presents an evidence-to-decision approach for lunar construction-site screening based on penetration response. The work emphasizes compatibility, uncertainty, alternative explanations, preliminary zoning, and independent verification rather than unsupported conversion of sparse measurements into design parameters. In this context, the publication represents a focused contribution to engineering research concerning future lunar construction and geotechnical site investigation.

References

  1. de Moraes, R.; Paraskevopoulou, C. An Evidence-to-Decision Framework for Lunar Construction-Site Screening from Penetration Response. Applied Sciences 2026, 16(19), 9648.
    https://doi.org/10.3390/app16199648
  2. Heiken, G.H.; Vaniman, D.T.; French, B.M., eds. Lunar Sourcebook: A User’s Guide to the Moon. Cambridge University Press, 1991.
  3. National Aeronautics and Space Administration. Fiscal Year 2024 STTR Phase I Solicitation: Lunar Surface Site Preparation. NASA, 2024.
  4. Lucey, P.; Korotev, R.L.; Gillis, J.J.; Taylor, L.A.; Lawrence, D.; Campbell, B.A.; et al. Understanding the Lunar Surface and Space-Moon Interactions. Reviews in Mineralogy and Geochemistry 2006, 60, 83–219.
  5. Elsevier. Scopus Author Details: Roberto de Moraes, Author ID 58072897300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58072897300
  6. MDPI. Applied Sciences, Volume 16, Issue 19, Article 9648. 2026.
    https://www.mdpi.com/2076-3417/16/19/9648

Oumayma Mabrouk | Renewable Energy Technologies | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Oumayma Mabrouk
University of Angers, France

Oumayma Mabrouk
Affiliation University of Angers
Country France
Scopus ID 60883647200
Documents 1
Citations 1
h-index 1
Subject Area Renewable Energy Technologies
Event International Forensic Scientist Awards

Oumayma Mabrouk is a researcher affiliated with the University of Angers in France whose documented research contribution concerns photovoltaic-system performance diagnosis, renewable-energy monitoring, and data-driven anomaly detection. Her recent article in Solar Energy presents a regime-aware framework for generating robust reference curves for photovoltaic power output under changing meteorological conditions. The publication identifies an approach based on unsupervised learning, statistical reference corridors, and constrained time-series alignment. [1]

Abstract

Mabrouk’s documented publication addresses the challenge of detecting abnormal photovoltaic-system behavior without relying on extensive labeled fault datasets. The study develops reference corridors using median and interquartile-range statistics after identifying representative meteorological regimes through k-means clustering. Limited Dynamic Time Warping is then used to compare daily photovoltaic power profiles with regime-specific reference curves. The reported validation uses receiver operating characteristic analysis and synthetic fault scenarios, with the published study reporting area-under-the-curve values above 0.80. [1]

Keywords

  • Photovoltaic systems
  • Renewable energy technologies
  • Anomaly detection
  • Unsupervised learning
  • Data-driven diagnosis

Introduction

Reliable monitoring is an important component of photovoltaic-system operation because environmental variability can make it difficult to distinguish normal changes in power production from genuine performance anomalies. The article by Mabrouk and co-authors approaches this problem by separating meteorological regime identification from photovoltaic power anomaly detection. This design seeks to reduce the possibility that changes in weather conditions are interpreted directly as equipment faults. [1]

Research Profile

The available publication record places Mabrouk’s research within renewable energy technologies, with a specific focus on photovoltaic performance monitoring and intelligent diagnostic methods. The 2026 Solar Energy article develops an unsupervised framework that uses environmental variables to identify weather regimes and then constructs statistical reference models for daily photovoltaic power behavior. The research combines machine-learning clustering, robust statistics, and time-series analysis in a single diagnostic workflow. [1]

Research Contributions

The principal contribution is a regime-aware methodology for establishing expected photovoltaic power behavior. Environmental measurements are used to distinguish representative meteorological conditions, while normalized daily power profiles are summarized through median curves and interquartile-range corridors. Limited Dynamic Time Warping provides constrained temporal alignment, and complementary indicators are used for anomaly classification. The study reports that this combination can identify deviations without requiring labeled fault examples. [1]

Publications

The principal documented publication is Robust reference curves generation for photovoltaic systems performance diagnosis using a data-driven approach, authored by Oumayma Mabrouk, Abdérafi Charki, Nizar Chatti, and Xavier Sidambarompoulé. It was published in Solar Energy, volume 317, article 114958, in 2026. The article is indexed in the supplied Scopus profile information and has a reported DOI of 10.1016/j.solener.2026.114958. [1] [2]

Research Impact

The supplied bibliometric record lists one document, one citation, and an h-index of 1. These indicators represent the currently supplied record rather than a comprehensive assessment of research influence. The publication itself addresses a practical problem in photovoltaic operations by combining interpretable statistical reference modeling with unsupervised anomaly detection. Its reported evaluation indicates that the proposed indicators can discriminate injected abnormal behavior from normal operational variation. [1]

Award Suitability

For the Best Researcher Award context, the documented evidence includes a peer-reviewed 2026 journal publication addressing renewable-energy system diagnosis and a methodological contribution combining k-means clustering, robust statistical modeling, and constrained time-series analysis. These documented elements provide a research basis for consideration within an award process focused on renewable energy technologies. Any final award determination remains subject to the applicable evaluation criteria and review procedure of the International Forensic Scientist Awards.

Conclusion

Oumayma Mabrouk’s documented research contribution centers on data-driven photovoltaic performance diagnosis. Her 2026 Solar Energy publication presents a structured approach for modeling expected photovoltaic behavior across meteorological regimes and detecting deviations without labeled fault data. The available bibliometric information records one document, one citation, and an h-index of 1, while the publication provides the principal evidence for assessing her current research profile.

References

  1. Mabrouk, O., Charki, A., Chatti, N., & Sidambarompoulé, X. (2026). Robust reference curves generation for photovoltaic systems performance diagnosis using a data-driven approach. Solar Energy, 317, 114958.
    https://doi.org/10.1016/j.solener.2026.114958
  2. Elsevier. (2026). Robust reference curves generation for photovoltaic systems performance diagnosis using a data-driven approach. Solar Energy, Volume 317, Article 114958.
  3. Scopus. (n.d.). Scopus author details: Oumayma Mabrouk, Author ID 60883647200. Elsevier.
    https://www.scopus.com/authid/detail.uri?authorId=60883647200
  4. HAL. (2026). Publication record for Robust reference curves generation for photovoltaic systems performance diagnosis using a data-driven approach. Université d’Angers research record.
    https://cv.hal.science/xavier-sidambarompoule
  5. Chatti, N. (2026). Selected publications and research outputs: Solar Energy, 2026. University of Angers / LARIS.
    https://perso-laris.univ-angers.fr/~nizar.chatti/

Ho Soo Lim | Biotechnology | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Ho Soo Lim
Korea Ministry of Food and Drug Safety, South Korea

Ho Soo Lim
Affiliation Korea Ministry of Food and Drug Safety
Country South Korea
Scopus ID 55462874300
Documents 40
Citations 852
h-index 16
Subject Area Biotechnology
Event International Forensic Scientist Awards
ORCID 0000-0001-7632-4309

Ho Soo Lim is a researcher affiliated with the Korea Ministry of Food and Drug Safety whose documented work focuses on biotechnology and analytical methods for food identification and authentication. His research record includes molecular assay development and chromatographic analysis applied to food safety, species identification, and regulatory analysis. The available bibliometric profile records 40 documents, 852 citations, and an h-index of 16.

Abstract

Ho Soo Lim’s research profile reflects a sustained focus on analytical biotechnology and food-related molecular identification. His recent publications address conventional, real-time, and ultrafast real-time PCR approaches for distinguishing commercially relevant species, while earlier work examined chromatographic determination of food-related chlorophyllin compounds. These studies demonstrate application-oriented methodological research connecting molecular biology with food analysis and regulatory needs. [1] [5]

Keywords

Biotechnology; food analysis; PCR; real-time PCR; ultrafast PCR; species identification; molecular authentication; food safety; chromatographic analysis.

Introduction

Accurate identification of biological materials is important in food analysis, authentication, traceability, and safety assessment. PCR-based methods can provide targeted species discrimination, while chromatographic and mass-spectrometric techniques support chemical characterization. Lim’s publications collectively address these analytical requirements through method development and comparative evaluation. [2] [3]

Research Profile

The research profile is centered on analytical biotechnology, particularly molecular assays designed for species-specific identification. Recent studies investigate PCR formats ranging from conventional amplification to real-time and ultrafast real-time workflows. The publication record also includes analytical chemistry, demonstrating methodological breadth across molecular and instrumental approaches.

Research Contributions

  • Development of PCR assays for accurate identification of Ibacus novemdentatus, supporting molecular discrimination of the smooth fan lobster. [1]
  • Development of a species-specific SYBR Green real-time PCR assay to differentiate Lupinus angustifolius and Lupinus albus. [2]
  • Comparative evaluation of conventional, real-time, and ultrafast real-time PCR assays for Euphausia pacifica identification. [3]
  • Application of ultrafast PCR to the identification of three king crab species. [4]

Publications

Selected publications include studies in Food Analytical Methods, Food Science and Biotechnology, and Food Chemistry. The 2026 work on Ibacus novemdentatus is listed for publication in November 2026, while the Lupinus study appeared in August 2026. The earlier publications extend the methodological record to crustacean identification and food additive analysis. [1] [2] [5]

Research Impact

The supplied bibliometric record reports 40 documents, 852 citations, and an h-index of 16. These indicators provide quantitative context for the visibility of the research output, while the publication topics show practical application to species authentication and food analytical methodology. Bibliometric indicators should be interpreted alongside publication quality, methodological contribution, and field-specific citation practices.

Award Suitability

For the International Forensic Scientist Awards, the documented research provides evidence of methodological work in biotechnology and analytical identification. The combination of species-specific PCR development, rapid assay formats, and food-analysis applications establishes a research profile relevant to analytical and identification-oriented scientific practice. [1] [3]

Conclusion

Ho Soo Lim’s documented research combines molecular assay development with food analytical science. His publications demonstrate work on conventional, real-time, and ultrafast PCR technologies alongside instrumental analytical methods. Together with the reported bibliometric indicators, this record provides a structured basis for recognizing research activity in biotechnology and analytical identification.

References

  1. Lim, Ho Soo, et al. (2026). Development of Conventional, Real-Time, and Ultrafast Real-Time PCR Assays for the Accurate Identification of Ibacus Novemdentatus (Smooth Fan Lobster). Food Analytical Methods.
    https://doi.org/10.1007/s12161-026-03265-8
  2. Lim, Ho Soo, et al. (2026). Development of species-specific SYBR green-based real-time PCR assay to differentiate between Lupinus angustifolius and Lupinus albus. Food Science and Biotechnology.
    https://doi.org/10.1007/s10068-026-02207-8
  3. Lim, Ho Soo, et al. (2026). Comparative evaluation of conventional, real-time, and ultrafast real-time PCR assays for accurate identification of Euphausia pacifica. Food Science and Biotechnology.
    https://doi.org/10.1007/s10068-025-02079-4
  4. Lim, Ho Soo, et al. (2025). Ultrafast PCR assay for identification of three king crabs—Chionoecetes japonicus, Chionoecetes opilio, and Paralithodes camtschaticus. Food Science and Biotechnology.
    https://doi.org/10.1007/s10068-024-01708-8
  5. Lim, Ho Soo, et al. (2019). Simultaneous determination of sodium iron chlorophyllin and sodium copper chlorophyllin in food using high-performance liquid chromatography and ultra-performance liquid chromatography–mass spectrometry. Food Chemistry.
    https://doi.org/10.1016/j.foodchem.2018.10.015
  6. Elsevier. (n.d.). Scopus author details: Ho Soo Lim, Author ID 55462874300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=55462874300