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

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 849
h-index 16
Subject Area Biotechnology
Event International Forensic Scientist Awards
ORCID 0000-0002-8549-4016

Ho Soo Lim is a South Korean researcher whose published work includes analytical methods, food-safety assessment, method validation, and measurement of chemical constituents in food matrices. Publicly indexed publications associate Lim with the Korea Ministry of Food and Drug Safety and its food-safety research activities. [1] [2]

Abstract

Ho Soo Lim’s documented research activity is centered on analytical science and food-safety research, with publications addressing analytical method development, method validation, measurement uncertainty, exposure assessment, and determination of food-related chemical constituents. Studies involving Lim have applied techniques including chromatography and mass spectrometry to characterize substances in food products and support reliable analytical measurements. [1] [2] [3]

Keywords

Ho Soo Lim, biotechnology, analytical science, food safety, analytical chemistry, method validation, measurement uncertainty, chromatography, mass spectrometry, food analysis, chemical contaminants, exposure assessment, forensic analytical science.

Introduction

Analytical measurement is an important component of food-safety and regulatory science because reliable identification and quantification of chemical constituents can support surveillance, standards development, exposure assessment, and regulatory decision-making. Lim’s published work reflects this research environment through studies of food additives, naturally occurring substances, and analytical procedures used for their determination. [1] [4]

Research Profile

The supplied bibliometric profile records 40 documents, 849 citations, and an h-index of 16 for Scopus Author ID 55462874300. These values describe the supplied research-profile snapshot and may change as databases update their records.Publicly available publications identify Lim with the Ministry of Food and Drug Safety in South Korea. Research affiliations in indexed articles include the Food Additives and Packaging Division and, in a later publication, the New Hazardous Substances Division of the National Institute of Food and Drug Safety Evaluation. [1] [7]

Research Contributions

One documented contribution concerns the comparison of four analytical approaches for determining sulfites in foods marketed in South Korea. The study evaluated optimized Monier-Williams, modified Rankine, high-performance liquid chromatography, and ion-exchange chromatography approaches, illustrating the application of comparative analytical methodology to food-safety testing. [2]

Publications

Selected publications associated with Ho Soo Lim include the following works. The selection is intended to illustrate documented research themes rather than represent a complete bibliography.

  • Lim, H. S., et al. (2016). Validation and measurement uncertainty evaluation of the GC-MS method for the direct determination of hexamethylenetetramine from foods. Food Science and Biotechnology, 25, 1305–1311.
  • Lim, H.-S., et al. (2014). Comparison of four different methods for the determination of sulfites in foods marketed in South Korea. Food Additives & Contaminants: Part A, 31(2), 187–196.
  • Lim, H. S., et al. (2015). Assessment of caffeine intake in the Korean population. Food Additives & Contaminants: Part A, 32(11), 1786–1798.
  • Kim, D.-B., et al. (2018). Sorbic, benzoic and propionic acids in fishery products: a survey of the South Korean market. Food Additives & Contaminants: Part A, 35(6), 1071–1077.
  • Lim, H. S., et al. (2024). Ultrafast PCR assay for identification of three king crabs—Chionoecetes japonicus, Chionoecetes opilio, and Paralithodes camtschaticus. Food Science and Biotechnology, 34(4), 1055–1062.

Research Impact

The supplied bibliometric indicators of 849 citations and an h-index of 16 provide quantitative measures of the research record as represented in the supplied Scopus profile. Within the published literature, Lim’s work has addressed analytical procedures and food-safety questions that are relevant to chemical measurement and regulatory monitoring. [1] [2]

Award Suitability

For the Innovative Research Award under the International Forensic Scientist Awards, the documented research record provides several areas that can be considered in an academic recognition review. These include analytical method development, validation of measurement procedures, chemical determination in complex food matrices, and collaborative application of analytical technologies. [1] [2]

Conclusion

Ho Soo Lim’s documented scholarly record reflects sustained involvement in analytical and food-safety research associated with the Korea Ministry of Food and Drug Safety. Published studies demonstrate work involving analytical method comparison, method validation, measurement uncertainty, chemical determination, and exposure assessment. [1] [2] [4]

References

    1. Lim, Ho-Soo, et al. (2014). Comparison of four different methods for the determination of sulfites in foods marketed in South Korea. Food Additives & Contaminants: Part A, 31(2), 187–196.
      https://pubmed.ncbi.nlm.nih.gov/24428687/
    2. Kim, Jae-Min, et al. (2016). Method validation and measurement uncertainty for the simultaneous determination of synthetic phenolic antioxidants in edible oils commonly consumed in Korea. Food Chemistry, 213, 19–25.
      https://pubmed.ncbi.nlm.nih.gov/27451150/
    3. Kim, Jae-Min, et al. (2018). Identifying method validation and measurement uncertainty of brominated vegetable oil in soft drinks and carbonated waters commonly consumed in South Korea. Food Chemistry, 246, 323–327. DOI: 10.1016/j.foodchem.2017.11.047.
      https://pubmed.ncbi.nlm.nih.gov/29291856/
    4. Lee, Youngsun, et al. (2017). Simultaneous determination of sodium saccharin, aspartame, acesulfame-K and sucralose in food consumed in Korea using high-performance liquid chromatography and evaporative light-scattering detection. Food Additives & Contaminants: Part A, 34(5), 666–677.
      https://pubmed.ncbi.nlm.nih.gov/28277180/
    5. International Forensic Scientist Awards
      https://www.mfds.go.kr/english/index.do

 

Joonghyun Shim | Biochemistry, Genetics and Molecular Biology | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Joonghyun Shim
Seoul Women’s University, South Korea

Joonghyun Shim
Affiliation Seoul Women’s University
Country South Korea
Scopus ID 57030605400
Documents 34
Citations 636
h-index 14
Subject Area Biochemistry, Genetics and Molecular Biology
Event International Forensic Scientist Awards
ORCID 0000-0003-1025-2722

Joonghyun Shim is a researcher affiliated with Seoul Women’s University whose documented scholarly profile is situated within biochemistry, genetics and molecular biology. The research record includes investigations into cellular responses, natural-product-derived compounds and molecular mechanisms associated with skin biology and aging. Bibliographic information identifies 34 documents, 636 citations and an h-index of 14. [1]

Abstract

Joonghyun Shim’s research profile encompasses molecular and cellular investigations relevant to skin biology, pigmentation, photoaging and biochemical signaling. The documented publications examine mechanisms through which bioactive compounds and inflammatory or oxidative pathways influence dermal fibroblasts and melanogenesis. Recent work on Phycodris fimbriata extract evaluates UVA-associated photoaging through antioxidant enzymes and the ERK1/2 pathway, while earlier studies investigated cycloheterophyllin, gyrophoric acid and prostaglandin E2-related signaling. [2] [3] [4] [5]

Keywords

  • Biochemistry
  • Molecular Biology
  • Dermal Fibroblasts
  • Photoaging
  • Melanin Synthesis

Introduction

Research addressing molecular mechanisms of skin aging and pigmentation integrates biochemistry, cellular biology and natural-product research. Shim’s listed publications contribute to this area by examining cellular signaling, antioxidant responses and biochemical pathways in human dermal fibroblasts and melanogenesis models. The work provides a research context connecting molecular mechanisms with experimentally investigated bioactive substances. [3] [4]

Research Profile

The research profile is characterized by experimental investigation of molecular pathways associated with cellular aging, pigmentation and responses to environmental stress. The 2026 publication on UVA-induced photoaging examines antioxidant enzymes and ERK1/2 signaling in dermal fibroblasts, representing a recent extension of this research direction. [2]

Research Contributions

The documented studies address complementary mechanisms: inhibition of melanin synthesis, cellular responses to UVA exposure, anti-aging activity of gyrophoric acid, and prostaglandin E2 signaling through the E-prostanoid 1 receptor. [3] [4] [5] Collectively, these publications demonstrate a consistent interest in molecular mechanisms that can be investigated through biochemical and cellular approaches.

Publications

  • Phycodris Fimbriata Extract Attenuates UVA-Induced Photoaging in Dermal Fibroblasts by Modulating Antioxidant Enzymes and ERK1/2 Pathway. Current Issues in Molecular Biology, 2026. [2]
  • Inhibitory Effects of Cycloheterophyllin on Melanin Synthesis. Molecules, 2021. [3]
  • Anti-Aging Effects of Gyrophoric Acid on UVA-Irradiated Normal Human Dermal Fibroblasts. Natural Product Communications, 2020. [4]
  • Prostaglandin E2 Induces Skin Aging via E-Prostanoid 1 in Normal Human Dermal Fibroblasts. International Journal of Molecular Sciences, 2019. [5]

Research Impact

The available bibliometric record reports 34 documents, 636 citations and an h-index of 14, providing quantitative indicators of the visibility of the research output. [1] These indicators should be interpreted alongside publication quality, methodological contribution, reproducibility and relevance to the field rather than as standalone measures of research significance.

Award Suitability

The documented publication record aligns with an Innovative Research Award profile through its focus on experimentally investigated molecular mechanisms and bioactive compounds. The research spans pigmentation, oxidative stress, photoaging and signaling pathways, offering a coherent subject-area connection to biochemistry and molecular biology. Award consideration can be informed by the documented publications, research metrics and independent assessment of originality and contribution.

Conclusion

Joonghyun Shim’s academic profile combines measurable bibliometric activity with a focused body of molecular and cellular research. The cited studies provide evidence of sustained investigation into skin-related biochemical mechanisms, while the reported Scopus indicators offer a quantitative overview of research visibility. [1]

References

  1. Elsevier. (n.d.). Scopus author details: Joonghyun Shim, Author ID 57030605400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57030605400
  2. Shim, J. (2026). Phycodris Fimbriata Extract Attenuates UVA-Induced Photoaging in Dermal Fibroblasts by Modulating Antioxidant Enzymes and ERK1/2 Pathway. Current Issues in Molecular Biology.
    https://doi.org/10.3390/cimb48100971
  3. Shim, J. (2021). Inhibitory Effects of Cycloheterophyllin on Melanin Synthesis. Molecules.
    https://doi.org/10.3390/molecules26092526
  4. Shim, J. (2020). Anti-Aging Effects of Gyrophoric Acid on UVA-Irradiated Normal Human Dermal Fibroblasts. Natural Product Communications.
    https://doi.org/10.1177/1934578X20919545
  5. Shim, J. (2019). Prostaglandin E2 Induces Skin Aging via E-Prostanoid 1 in Normal Human Dermal Fibroblasts. International Journal of Molecular Sciences.
    https://doi.org/10.3390/ijms20225555

Okopi Johnson Odumu | Earth and Planetary Sciences | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Okopi Johnson Odumu
University of Jos, Nigeria

Okopi Johnson Odumu
Affiliation University of Jos
Country Nigeria
Scopus ID 60151792300
Documents 1
Citations 1
h-index 1
Subject Area Earth and Planetary Sciences
Event International Forensic Scientist Awards
ORCID 0009-0003-4894-9653

Okopi Johnson Odumu is a researcher affiliated with the University of Jos, Nigeria, whose documented research activity falls within Earth and Planetary Sciences. His listed scholarly record includes research addressing radiometric and geochemical indicators associated with anthills and their potential application to mineral exploration in the Bashar area of the Upper Benue Trough, North Central Nigeria. The research was published in Discover Minerals on 16 September 2026. [1]

Abstract

The documented research focuses on the use of radiometric and geochemical indicators in anthills as guides for mineral exploration within the Bashar sector of the Upper Benue Trough in North Central Nigeria. The study connects surface materials associated with anthills to geological investigation, presenting an approach relevant to mineral exploration and Earth science research. The publication appeared in Discover Minerals in September 2026. [1]

Keywords

  • Mineral exploration
  • Radiometric indicators
  • Geochemical indicators
  • Anthills
  • Upper Benue Trough

Introduction

Mineral exploration commonly integrates geological, geochemical and geophysical evidence to identify areas that may warrant further investigation. Within this context, the study by Okopi Johnson Odumu examines anthill-associated materials as potential indicators for mineral exploration in a defined geological setting in Nigeria. The work is specifically associated with the Bashar area of the Upper Benue Trough, providing a regional focus for its Earth science contribution. [1]

Research Profile

Odumu’s documented scholarly profile is presently represented by one Scopus-indexed document, one citation and an h-index of 1. His research classification is within Earth and Planetary Sciences, while his identified publication addresses the intersection of geochemical and radiometric observations with mineral exploration. These records provide a concise bibliometric snapshot of the researcher’s indexed scholarly activity. [2]

Research Contributions

The principal contribution documented in the available publication is the investigation of anthills as guides for mineral exploration. By considering radiometric and geochemical indicators in relation to anthill materials, the research addresses a field-based pathway for interpreting geological information in the Bashar region. Its significance is therefore connected to methodological application within mineral exploration rather than to a broad claim of universal applicability. [1]

Publications

The identified publication is Radiometric and geochemical indicators in anthills as guides for mineral exploration in Bashar Upper Benue Trough North Central Nigeria, published in Discover Minerals on 16 September 2026. It is recorded as a journal article and is associated. [1]

Research Impact

The current indexed metrics document one Scopus citation and an h-index of 1. Because the publication is dated September 2026, these bibliometric indicators represent an early stage of indexed impact and should be interpreted in relation to publication age and future citation development. [2]

Award Suitability

For an Innovative Research Award, the documented work provides a clearly identifiable research theme involving radiometric and geochemical indicators, anthill materials and mineral exploration. Its relevance can be considered in relation to the award’s emphasis on research innovation, while the available bibliometric record provides supporting documentation of indexed scholarly activity. [1] [2]

Conclusion

Okopi Johnson Odumu’s documented research profile is centered on Earth and Planetary Sciences, with a published study examining radiometric and geochemical indicators in anthills for mineral exploration in the Upper Benue Trough. The publication establishes a specific research contribution, while the available Scopus record provides an initial bibliometric basis for documenting its scholarly presence.

References

  1. Odumu, O. J. (2026). Radiometric and geochemical indicators in anthills as guides for mineral exploration in Bashar Upper Benue Trough North Central Nigeria. Discover Minerals. DOI: 10.1007/s44346-026-00020-z.
    https://doi.org/10.1007/s44346-026-00020-z
  2. Elsevier. (n.d.). Scopus author details: Okopi Johnson Odumu, Author ID 60151792300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60151792300
  3. ORCID. (n.d.). Okopi Johnson Odumu, ORCID iD 0009-0003-4894-9653.
    https://orcid.org/0009-0003-4894-9653
  4. Springer Nature. (2026). Discover Minerals. Publication record associated with DOI 10.1007/s44346-026-00020-z.
  5. International Forensic Scientist Awards. (n.d.). Official Award Website.
    forensicscientist.org

Rajesh Kumar Samala | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Rajesh Kumar Samala
Visvesvaraya College of Engineering and Technology, India

Rajesh Kumar Samala
Affiliation Visvesvaraya College of Engineering and Technology
Country India
Scopus ID 57204032308
Documents 20
Citations 109
h-index 5
Subject Area Engineering
Event International Forensic Scientist Awards
ORCID 0000-0002-0324-4436

Rajesh Kumar Samala is an engineering researcher whose indexed scholarly work spans nanotechnology, additive manufacturing, machine learning, Internet of Things applications, and technology-assisted analysis. His Scopus record comprises 20 documents, 109 citations, and an h-index of 5. [1] The research profile demonstrates an interdisciplinary orientation in which computational methods and engineering systems are applied to contemporary technical and applied research questions.

Abstract

Rajesh Kumar Samala’s research record reflects work across engineering technologies with particular emphasis on computational intelligence, advanced manufacturing, nanotechnology, and connected systems. His recent publications address nanoparticle toxicology, deep-learning-based tool wear prediction, healthcare-related statistical assessment, and machine-learning-driven energy optimization in wireless sensor networks. [2] [3] These studies illustrate the application of quantitative and computational approaches to engineering and technology-oriented problems.

Keywords

  • Engineering research
  • Nanoparticle toxicology
  • Deep learning
  • Additive manufacturing
  • Internet of Things

Introduction

Contemporary engineering research increasingly combines experimental investigation with machine learning, networked technologies, and data-driven modelling. Samala’s indexed publications reflect this broader development through studies addressing both emerging materials and computational engineering applications. The documented research covers publications from 2024 and 2025, indicating recent engagement with interdisciplinary engineering topics. [1]

Research Profile

The research profile is characterized by the integration of engineering analysis and computational techniques. Work on additive manufacturing applies deep learning to tool wear prediction, while another study examines crossbreed clustering for energy optimization in wireless sensor networks. [3] Research concerning nanoparticle properties and toxicological effects extends the profile toward materials-related engineering and technology assessment. [2]

Research Contributions

  • Investigates relationships between nanoparticle characteristics and toxicological effects.
  • Applies deep learning to tool wear prediction in additive manufacturing.
  • Explores machine learning and IoT methods for energy optimization.
  • Contributes to interdisciplinary engineering research involving data-driven methodologies.

Publications

Selected indexed publications include research on nanoparticle toxicology, additive manufacturing, healthcare analysis, and wireless sensor networks. The 2025 article on nanoparticle properties and toxicological effects was published in Proceedings on Engineering Sciences. [2] The 2025 study on deep learning and tool wear prediction appeared in Progress in Additive Manufacturing. [3]

Research Impact

The available Scopus metrics record 20 documents, 109 citations, and an h-index of 5. [1] These bibliometric indicators provide a quantitative view of indexed research visibility and should be interpreted in relation to publication age, field-specific citation practices, and database coverage.

Award Suitability

The documented research themes correspond to an Innovative Research Award context through their emphasis on emerging technologies, computational approaches, and interdisciplinary engineering applications. The publication record provides identifiable evidence of research activity in areas including nanotechnology, additive manufacturing, machine learning, and IoT-enabled systems. [2] [3]

Conclusion

Rajesh Kumar Samala’s indexed research profile presents a multidisciplinary engineering portfolio combining computational intelligence, advanced manufacturing, nanotechnology, and connected systems. The documented publications and Scopus metrics provide a concise evidence base for recognizing his continuing contribution to contemporary engineering research.

References

  1. Elsevier. (n.d.). Scopus author details: Rajesh Kumar Samala, Author ID 57204032308. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57204032308
  2. Samala, R. K., et al. (2025). Exploring the Interrelationship Between Nanoparticle Properties and Their Toxicological Effects. Proceedings on Engineering Sciences.
    https://doi.org/10.24874/PES07.03A.009
  3. Samala, R. K., et al. (2025). Enhancing Tool Wear Prediction with Deep Learning Models in Additive Manufacturing Processes. Progress in Additive Manufacturing.
    https://doi.org/10.1007/s40964-025-01372-2
  4. Samala, R. K., et al. (2024). Assessment of Primary Lung Cancer Survival Rates in Relation to the Number of Thoracoscopic Lobectomies Performed in Hospitals. Onkologia I Radioterapia.
  5. Samala, R. K., et al. (2024). Machine Learning and Internet of Things Driven Energy Optimization in Wireless Sensor Networks through Crossbreed Clustering. Journal of Intelligent Systems and Internet of Things.
    https://doi.org/10.54216/JISIoT.130204
  6. Elsevier. (n.d.). Scopus indexed publication records associated with Author ID 57204032308. Scopus.

Elijah Gbenga Osunkentan | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Elijah Gbenga Osunkentan
Federal University of ABC

Elijah Gbenga Osunkentan
Affiliation Federal University of ABC
Country Brazil
Scopus ID 60381839900
Documents 1
Subject Area Engineering
Event International Forensic Scientist Awards
Google Scholar ID 8mrS_zkAAAAJ

Elijah Gbenga Osunkentan is associated with engineering research focused on electrical power systems, microgrids, fault detection, transmission-line analysis, and intelligent computational methods. The supplied publication record indicates work involving statistical voltage-based islanding detection and machine-learning approaches for identifying electrical faults. These themes connect conventional power-system analysis with data-driven techniques for improving the monitoring and diagnosis of modern electrical networks. [1]

Abstract

The research profile of Elijah Gbenga Osunkentan centers on engineering applications in electrical power systems. Documented work includes passive islanding detection in microgrids, transmission-line fault detection using Support Vector Machines (SVM) and Artificial Neural Networks (ANN), and fault-location methods using Long Short-Term Memory (LSTM) neural networks. These studies represent complementary approaches to system monitoring, classification, and intelligent fault diagnosis. [1] [2] [3]

Keywords

Microgrids; islanding detection; power systems; fault detection; transmission lines; SVM; ANN; LSTM; intelligent fault location; electrical engineering.

Introduction

Modern electrical networks require dependable methods for detecting abnormal operating conditions and locating faults. Microgrids introduce additional operational considerations because distributed generation can alter system behavior during islanded and grid-connected states. Machine-learning techniques can complement established analytical approaches by supporting classification and pattern recognition. The supplied research record addresses these engineering challenges through statistical and intelligent computational methods. [1] [2]

Research Profile

Osunkentan’s documented research combines statistical signal interpretation with machine-learning models for electrical-system diagnosis. The reported areas include passive islanding detection, transmission-line fault classification, and intelligent fault-location techniques. This combination reflects an interdisciplinary engineering approach in which computational models are applied to practical power-system monitoring problems. [1] [2] [3]

Research Contributions

  • Development of a statistical voltage-based approach for passive islanding detection in microgrids. [1]
  • Comparative investigation of SVM and ANN techniques for transmission-line fault detection. [2]
  • Application of LSTM neural networks to intelligent transmission-line fault-location analysis. [3]

Publications

The supplied publication information identifies three relevant research outputs. The 2026 article, “An efficient and cost-effective statistical voltage-based method for passive islanding detection in microgrids,” appears in Discover Electronics 3(1), article 132. A 2025 conference contribution examines SVM and ANN for transmission-line fault detection, while another work addresses transmission-line fault location using LSTM neural networks. [1] [2] [3]

Research Impact

The available record documents research activity across journal, conference, and preprint-oriented channels. The stated Scopus record contains one document; citation and h-index values were not supplied and are therefore not inferred here. The research themes have practical relevance to power-system monitoring because islanding detection and fault diagnosis address operational reliability and system protection requirements. [1]

Award Suitability

The documented research aligns with an Innovative Research Award category through its focus on statistical analysis, machine-learning methods, microgrid operation, and intelligent fault diagnosis. The supplied record provides identifiable research outputs and methods that can be considered in an academic recognition assessment. Final award decisions remain subject to the applicable evaluation criteria and review process of the International Forensic Scientist Awards. [4]

Conclusion

Elijah Gbenga Osunkentan’s supplied research record demonstrates a focused engineering interest in electrical-system diagnostics, microgrid islanding detection, transmission-line fault identification, and neural-network-based fault location. The combination of statistical and intelligent methods provides a coherent basis for documenting his research profile within an innovation-oriented academic recognition context.

References

  1. Osunkentan, E. G. (2026). An efficient and cost-effective statistical voltage-based method for passive islanding detection in microgrids. Discover Electronics, 3(1), 132.
  2. Osunkentan, E. G., dos Santos, R. C., & Da Silva, A. M. (2025). Comparative Analysis of SVM and ANN for Fault Detection in Transmission Lines. 2025 16th IEEE International Conference on Industry Applications (INDUSCON).
  3. Silva, A. M. (n.d.). An Effective Intelligent Method for Fault Location in Transmission Lines Using LSTM Neural Networks. SSRN 5928668.
  4. International Forensic Scientist Awards. (n.d.). International Forensic Scientist Awards.
    forensicscientist.org
  5. Elsevier. (n.d.). Scopus author details: Elijah Gbenga Osunkentan, Author ID 60381839900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60381839900
  6. Google Scholar. (n.d.). Elijah Gbenga Osunkentan publication and citation record.
    Google Scholar Profile