Adedoyin Adeyemi | Machine Learning | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Adedoyin Adeyemi
Federal University of Technology, Akure, Nigeria

Adedoyin Adeyemi
Affiliation Federal University of Technology, Akure
Country Nigeria
Scopus ID 59368133300
Documents 3
Citations 31
h-index 2
Subject Area Machine Learning
Event Computer Scientists Awards
ORCID 0009-0007-4701-1259

Adedoyin Adeyemi is a researcher affiliated with the Federal University of Technology, Akure, Nigeria, whose documented research activity includes work related to environmental modelling, hydrological analysis, land-use change and computational approaches relevant to machine learning. Available bibliographic information records three documents, 31 citations and an h-index of 2. These indicators provide a quantitative overview of the researcher’s indexed scholarly output and citation activity.

Abstract

The research profile of Adedoyin Adeyemi includes scholarly contributions addressing environmental and hydrological systems, with particular relevance to computational modelling of land-use change and flood-related processes. Recent collaborative research examines urbanization-driven land-cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models, as well as hydrological responses and flood hazards under projected climate and land-use change. These studies demonstrate an interdisciplinary connection between environmental science, spatial modelling, hydrology and computational analysis. [1] [2]

Keywords

  • Machine Learning
  • Hydrological Modelling
  • Land-Use Change
  • Flood Hazard Mapping
  • Ogun River Basin

Introduction

Research on flood risk increasingly combines hydrological modelling with spatial representations of land-use and environmental change. The Ogun River Basin provides an important setting for examining how urbanization and changing land cover can influence hydrological behaviour. Adeyemi’s collaborative publications contribute to this research area through computational modelling and assessment of projected environmental conditions. The work connects empirical geographical information with models capable of representing terrain, land-cover transitions and hydrological response. [1] [2]

Research Profile

Bibliographic records identify Adeyemi with three indexed documents and 31 citations, with a reported h-index of 2. The available publication evidence places his research within multidisciplinary environmental and computational investigations. Such work involves collaboration across hydrology, environmental modelling, geography and data-driven analytical methods.

Research Contributions

A notable contribution is the study of urbanization-driven land-cover change and floodplain transformation in the Ogun River Basin using Height Above Nearest Drainage (HAND) and Cellular Automata–Markov (CA-Markov) models. The approach provides a framework for examining relationships between terrain, projected land-cover transitions and floodplain characteristics. [1]

A second contribution concerns hydrological response and flood hazard mapping under projected climate and land-use change. Published in Water Science & Technology in 2026, the study examines how changing environmental conditions may influence hydrological behaviour and flood hazards in the basin. [2]

Publications

  1. “Urbanization driven land cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models.
  2. “Hydrological response and flood hazard mapping of the Ogun River Basin under projected climate and land-use change.” Water Science & Technology, 2026-05-15.

Research Impact

The documented citation record indicates that Adeyemi’s indexed publications have received scholarly attention. The research also has practical relevance to understanding flood hazards, land-use transformation and environmental planning. By combining spatial and hydrological modelling approaches, the reported studies contribute evidence that can support further research into basin-scale flood assessment and climate-sensitive land-use planning. [2]

Award Suitability

For an Innovative Research Award assessment, the documented profile presents relevant evidence of interdisciplinary research involving computational models, environmental change and flood-risk analysis. The use of HAND and CA-Markov approaches, together with hydrological hazard assessment under projected scenarios, provides a substantive basis for considering the research within an innovation-oriented academic recognition framework. Award decisions, however, should be based on the complete nomination record and independently verified evidence.

Conclusion

Adedoyin Adeyemi’s documented research profile reflects participation in multidisciplinary studies of hydrology, land-use change and flood hazards. His publications concerning the Ogun River Basin demonstrate the application of computational and spatial modelling to contemporary environmental questions. The available bibliographic indicators and publication record provide a concise scholarly basis for evaluating his research contributions in the context of the Innovative Research Award.

References

  1. Springer Nature. (2026). Urbanization driven land cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models.
    https://doi.org/10.1007/s44288-026-00529-y
  2. IWA Publishing. (2026). Hydrological response and flood hazard mapping of the Ogun River Basin under projected climate and land-use change. Water Science & Technology.
    https://doi.org/10.2166/wst.2026.274
  3. Elsevier. (n.d.). Scopus author details: Adedoyin Adeyemi, Author ID 59368133300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59368133300
  4. ORCID. (n.d.). Adedoyin Adeyemi, ORCID iD 0009-0007-4701-1259.
    https://orcid.org/0009-0007-4701-1259
  5. Computer Scientists Awards. (n.d.). Award and nomination information.
    https://computerscientists.net/

Saravanan S | Engineering | Excellence in Research Award

Published on by Computer Scientists Awards

Excellence in Research Award

Saravanan S
K.K Wagh Institute Of Engineering Education and Research, Nashik, India

Saravanan S
Affiliation K.K Wagh Institute Of Engineering Education and Research, Nashik
Country India
Scopus ID 57217569884
Documents 11
Citations 91
h-index 5
Subject Area Engineering
Event Computer Scientists Awards
ORCID 0000-0002-4671-1727

Saravanan S is an engineering researcher affiliated with K.K Wagh Institute Of Engineering Education and Research, Nashik. The documented research profile comprises 11 Scopus-indexed documents, 91 citations, and an h-index of 5. The publication record is particularly associated with power electronics, electric-vehicle charging systems, power-quality improvement, converter technologies, and intelligent control methods. [1]

Abstract

This academic recognition profile presents the research record of Saravanan S in engineering, with emphasis on power electronics and electric-vehicle energy systems. His documented publications address converter evaluation, wireless charging, fuzzy logic control, active power filtering, and power-quality enhancement. These topics represent practical engineering challenges involving efficient energy conversion, intelligent control, and improved electrical performance. [2]

Keywords

  • Power Electronics
  • Electric Vehicles
  • Power Quality
  • DC–DC Converters
  • Intelligent Control

Introduction

Modern electrical engineering increasingly requires power-conversion systems that combine efficiency, controllability, and power-quality performance. Saravanan S’s publication record reflects this research direction through studies of SEPIC converters, wireless EV charging, fuzzy logic control, and active power filters. [3]

Research Profile

The available bibliographic profile records 11 documents and 91 citations, with an h-index of 5. The research is situated within Engineering and demonstrates a publication focus on applied electrical and power-electronic systems. [1]

Research Contributions

  • Evaluation of a bridgeless DCM SEPIC converter using a sliding-mode control approach for power-factor correction. [4]
  • Investigation of wireless charging systems for electric vehicles using DC–DC conversion and fuzzy logic control. [5]
  • Application of optimal active power filtering to mitigate harmonics and improve EV charging-station power quality. [6]

Publications

  1. “Evaluation and Improvement of a Bridgeless DCM SEPIC Converter for Power Factor Correction Employing a SMC Controller.” MAPAN, 2026. DOI.
  2. “Evaluation and improvement of wireless charging system with DC–DC converter for EV employing the fuzzy logic controller.” Multiscale and Multidisciplinary Modeling, Experiments and Design, 2024. DOI.
  3. “Harmonic mitigation using optimal active power filter for the improvement of power quality for a electric vehicle changing station.” e-Prime – Advances in Electrical Engineering, Electronics and Energy, 2024. DOI.

Research Impact

The reported citation count of 91 and h-index of 5 provide quantitative indicators of scholarly visibility. The research themes also align with engineering priorities surrounding electrified transportation, efficient power conversion, charging infrastructure, and power-quality management. [1]

Award Suitability

The Excellence in Research Award profile is supported by a documented engineering publication record addressing contemporary power-electronic and electric-vehicle applications. The combination of indexed research output, citations, and focused technical contributions provides an objective basis for academic recognition within the stated subject area. [2]

Conclusion

Saravanan S’s documented research demonstrates sustained engagement with applied engineering problems involving power conversion, EV charging, intelligent control, and power quality. The available scholarly metrics and publications provide a concise evidence base for an Excellence in Research Award recognition profile.

References

  1. Elsevier. (n.d.). Scopus author details: Saravanan S, Author ID 57217569884. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57217569884
  2. ORCID. (n.d.). Saravanan S, ORCID 0000-0002-4671-1727.
    https://orcid.org/0000-0002-4671-1727
  3. Computer Scientists Awards. (n.d.). Computer Scientists Awards.
    https://computerscientists.net/
  4. Saravanan S. (2026). Evaluation and Improvement of a Bridgeless DCM SEPIC Converter for Power Factor Correction Employing a SMC Controller. MAPAN.
    https://doi.org/10.1007/s12647-026-00955-w
  5. Saravanan S. (2024). Evaluation and improvement of wireless charging system with DC–DC converter for EV employing the fuzzy logic controller. Multiscale and Multidisciplinary Modeling, Experiments and Design.
    https://doi.org/10.1007/s41939-024-00491-7
  6. Saravanan S. (2024). Harmonic mitigation using optimal active power filter for the improvement of power quality for a electric vehicle changing station. e-Prime – Advances in Electrical Engineering, Electronics and Energy.
    https://doi.org/10.1016/j.prime.2024.100527

Om Sahu | Computer Science & Artificial Intelligence | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Om Sahu
University Of Petroleum & Energy Studies (UPES) Dehradoon, India

Om Sahu
Affiliation University Of Petroleum & Energy Studies (UPES) Dehradoon
Country India
Scopus ID 57215754785
Documents 10
Citations 47
h-index 4
Subject Area Computer Science & Artificial Intelligence
Event Computer Scientists Awards
ORCID 0000-0003-0515-399X

Om Sahu is a researcher affiliated with the University Of Petroleum & Energy Studies (UPES) Dehradoon, India, whose documented research activity is associated with computer science and artificial intelligence. The available scholarly record identifies 10 documents, 47 citations, and an h-index of 4. These indicators provide a bibliometric snapshot of the researcher’s indexed publication activity and citation impact.

Abstract

Om Sahu’s research profile reflects activity in computer science and artificial intelligence, with published work applying deep learning to practical detection problems. His documented publications include research on multi-disease detection and smart-factory product defect detection. The available bibliometric information records 10 documents, 47 citations, and an h-index of 4. [1]

Keywords

Artificial intelligence; deep learning; computer science; machine learning; medical image analysis; disease detection; smart manufacturing; product defect detection; computer vision; intelligent systems.

Introduction

Deep learning is increasingly used for classification, recognition, and automated detection across scientific and industrial applications. Within this broader area, Sahu’s documented work addresses both healthcare-oriented detection and manufacturing-oriented quality inspection. His publications indicate an applied research orientation in which computational methods are used to address domain-specific recognition and decision-support problems. [2]

Research Profile

The researcher’s indexed profile is associated with the University Of Petroleum & Energy Studies (UPES) Dehradoon and the subject area of computer artificial intelligence. The reported Scopus identifier is 57215754785, while the ORCID identifier is 0000-0003-0515-399X. The bibliometric record supplied for this recognition page comprises 10 documents, 47 citations, and an h-index of 4. [1]

Research Contributions

Sahu’s recent publications demonstrate the application of deep learning to two distinct detection contexts. The first examines multi-disease detection, while the second focuses on identifying product defects in smart-factory environments. Together, these studies illustrate the use of computational intelligence and pattern-recognition techniques across healthcare and industrial settings. [2] [3]

Publications

  • Multi-Disease Detection Using Deep Learning. O.P. Sahu, Om Prakash, A. Singh, Arya, D. Prakash, Deo. ISED 2025 – 13th International Conference on Intelligent Systems and Embedded Design, Proceedings, 2025. IEEE Xplore. [2]
  • Smart Factory Product Defect Detection Using Deep Learning. Prakash, D.; Singh, A.; Bais, Y.D.S.; Majumdar, V.; Patel, E.; Sahu, O.P. Lecture Notes in Mechanical Engineering, conference paper, 2025. [3]

Research Impact

The reported 47 citations and h-index of 4 indicate measurable scholarly attention to the researcher’s indexed output. [1] The subject applications also demonstrate relevance beyond a single domain, connecting artificial intelligence methods with medical detection and smart manufacturing. The available evidence supports describing the profile as an emerging applied research contribution rather than making broader claims about field-wide influence.

Award Suitability

Based on the supplied publication and bibliometric information, Om Sahu presents a documented research profile relevant to the Best Researcher Award category at the Computer Scientists Awards. The combination of indexed publications, citations, and research activity in artificial intelligence provides objective material for consideration. Final award decisions should remain subject to the applicable evaluation criteria and review by the responsible award committee.

Conclusion

Om Sahu’s academic profile reflects research activity in computer artificial intelligence, particularly the application of deep learning to disease detection and smart-factory quality inspection. With 10 indexed documents, 47 citations, and an h-index of 4 in the supplied record, the profile provides a measurable basis for academic recognition. [1] His documented publications further indicate an applied orientation connecting artificial intelligence with practical research challenges.

References

  1. Elsevier. (n.d.). Scopus author details: Om Sahu, Author ID 57215754785. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57215754785
  2. Sahu, O.P.; Singh, A.; Prakash, D. et al. (2025). Multi-Disease Detection Using Deep Learning. ISED 2025 – 13th International Conference on Intelligent Systems and Embedded Design, Proceedings.
    https://ieeexplore.ieee.org/document/11405003
  3. Prakash, D.; Singh, A.; Bais, Y.D.S.; Majumdar, V.; Patel, E.; Sahu, O.P. (2025). Smart Factory Product Defect Detection Using Deep Learning. Lecture Notes in Mechanical Engineering. DOI: 10.1007/978-981-95-0063-5_34.
    https://doi.org/10.1007/978-981-95-0063-5_34
  4. ORCID. (n.d.). Om Sahu, ORCID iD 0000-0003-0515-399X.
    https://orcid.org/0000-0003-0515-399X
  5. IEEE. (2025). IEEE Xplore Digital Library: Multi-Disease Detection Using Deep Learning.
    https://ieeexplore.ieee.org/document/11405003
  6. Springer Nature. (2025). Lecture Notes in Mechanical Engineering: Smart Factory Product Defect Detection Using Deep Learning.
    https://link.springer.com/chapter/10.1007/978-981-95-0063-5_34

Lin Wang | Cybersecurity and Cryptography | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Lin Wang
China University of Petroleum (East China), China

Lin Wang
Affiliation China University of Petroleum (East China)
Country China
Scopus ID 57215074985
Documents 35
Citations 1,443
h-index 19
Subject Area Cybersecurity and Cryptography
Event Computer Scientists Awards
ORCID 0000-0001-5644-8865

Lin Wang is a researcher affiliated with China University of Petroleum (East China), China. His reported scholarly record includes 35 documents, 1,443 citations, and an h-index of 19. The research publications supplied for this profile address catalytic materials, single-atom catalysts, photocatalytic carbon dioxide conversion, oxygen evolution, and membrane-based ion transport. These topics demonstrate a research portfolio situated within advanced materials and chemical-energy technologies. The following article presents a structured academic overview based on the supplied bibliographic information and does not constitute an independent verification of authorship, metrics, or award eligibility.

Abstract

This academic profile examines the reported research activities of Lin Wang in the context of the Innovative Research Award. The supplied publications describe investigations into covalent triazine frameworks, ruthenium-based single-site catalysts, manganese-doped oxide nanoarrays, two-dimensional metal–organic framework membranes, and iridium single-atom catalysts. Collectively, these studies concern the design of functional materials for energy conversion and selective transport. The profile summarizes the available research themes, publication record, and reported bibliometric indicators while distinguishing documented information from award assessment.

Keywords

Innovative Research Award; Lin Wang; China University of Petroleum; photocatalytic CO2 reduction; single-atom catalysts; ruthenium catalysis; oxygen evolution; metal–organic frameworks; membrane separation; advanced materials.

Introduction

Contemporary materials research increasingly combines nanoscale structural engineering with catalytic and separation technologies. Research into carbon dioxide conversion, water oxidation, and selective ion transport seeks to improve efficiency, stability, and material functionality. The publications supplied for Lin Wang identify several studies within this broad scientific landscape. Their reported approaches include coordination-environment engineering, template-directed synthesis, and the construction of hybrid membrane architectures. [2]

Research Profile

The supplied profile records 35 documents, 1,443 citations, and an h-index of 19, associated with Scopus author identifier 57215074985. These figures are presented as reported values and may change as bibliographic databases are updated. The listed subject area is Cybersecurity and Cryptography, whereas the supplied publication titles primarily concern catalysis and advanced materials. Consequently, subject classification and publication authorship should be independently checked before formal institutional or award use.

Research Contributions

The supplied studies indicate several recurring research directions:

  • Single-site ruthenium coordination within covalent triazine frameworks for photocatalytic carbon dioxide reduction. [1]
  • Manganese-doped ruthenium–titanium oxide nanoarrays designed for acidic oxygen evolution. [2]
  • Two-dimensional metal–organic framework and graphene oxide hybrid membranes for selective ion transport. [3]
  • Coordination engineering of iridium single-atom catalysts for carbon dioxide hydrogenation to formic acid. [4]

Publications

The following publication titles and author lists are reproduced from the supplied information. Lin Wang appears among the listed authors of each work; the exact contribution and publication metadata require verification from the original records.

  1. Lu Wang, Lin Wang, Saifei Yuan, Liping Song, Hao Ren, Yuankang Xu, Manman He, Yuheng Zhang, Hang Wang, Yichao Huang, Tong Wei, Jiangwei Zhang, Yuichiro Himeda, and Zhuangjun Fan. Covalently-bonded single-site Ru-N2 knitted into covalent triazine frameworks for boosting photocatalytic CO2 reduction.
  2. Xinyuan Qin, Ruili Gao, Yuan Li, Junyu Zhang, Lin Wang, Yan Zhou, Lianming Zhao, Chuande Wu, and Yichao Huang. In-situ construction of Mn-doped RuTiOx nanoarray via template-directed replacement reaction for highly efficient and durable acidic oxygen evolution.
  3. Yu Cheng, Hang Wang, Longyu Wang, Zhong Wang, Zhibang Liu, Ziyi Zhang, Qiang Ma, Chao Zhang, Xiaoxue Zhang, Dongmin Bian, Lin Wang, and Chuan-De Wu. Interlayer-engineered 2D MOF-GO hybrid membranes with sub-nanometer channels for selective ion transport.
  4. Yuankang Xu, Lin Wang, Yuanying Liu, Linghao Liu, Yanzhuo Zhao, Tao Shu, Fanqiang Meng, Hang Wang, Tong Wei, Yichao Huang, and Zhuangjun Fan. Coordination engineering directs d-band center optimization for efficient CO2 hydrogenation to formic acid on Ir–Nx single-atom catalysts.

Research Impact

The reported citation count and h-index suggest measurable scholarly visibility. The supplied publication themes are relevant to energy conversion, catalytic reaction engineering, and membrane-based separations. However, the practical, industrial, or societal impact of these studies cannot be established solely from titles and bibliometric indicators. Such assessment would require analysis of experimental results, independent citations, technological adoption, and verified research contributions.

Award Suitability

The supplied research themes may be relevant to an Innovative Research Award because they concern the development and optimization of advanced functional materials. Evidence potentially relevant to an award assessment includes originality, methodological rigor, reproducibility, documented outcomes, and the significance of the research problem. Final suitability should be determined by the Computer Scientists Awards committee using verified publications, eligibility requirements, and an independent evaluation of the nominee’s contribution.

Conclusion

Lin Wang’s supplied profile combines reported bibliometric indicators with publications addressing catalytic materials, carbon dioxide conversion, oxygen evolution, and selective ion transport. The listed works provide a basis for documenting research activity in advanced materials and energy-related technologies. Verification of author identity, database metrics, institutional affiliation, and individual contributions remains necessary before the profile is used as a definitive academic or award record.

References

  1. Wang, L., Wang, L., et al. Covalently-bonded single-site Ru-N2 knitted into covalent triazine frameworks for boosting photocatalytic CO2 reduction. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S0926337322010384
  2. Qin, X., Gao, R., et al. In-situ construction of Mn-doped RuTiOx nanoarray via template-directed replacement reaction for highly efficient and durable acidic oxygen evolution. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S0021979726013111
  3. Cheng, Y., Wang, H., et al. Interlayer-engineered 2D MOF-GO hybrid membranes with sub-nanometer channels for selective ion transport. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S1005030226002100
  4. Xu, Y., Wang, L., et al. Coordination engineering directs d-band center optimization for efficient CO2 hydrogenation to formic acid on Ir–Nx single-atom catalysts. ScienceDirect.
    https://www.sciencedirect.com/science/article/pii/S2772834X26000576
  5. Elsevier. (n.d.). Scopus author details: Lin Wang, Author ID 57215074985. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57215074985
  6. ORCID. (n.d.). ORCID record: Lin Wang, ORCID iD 0000-0001-5644-8865. ORCID.
    https://orcid.org/0000-0001-5644-8865

Sara Amjad | Computer Science | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Sara Amjad
Aligarh Muslim University, India

Sara Amjad
Affiliation Aligarh Muslim University
Country India
Scopus ID 57222341800
Documents 2
Citations 483
h-index 2
Subject Area Computer Science
Event Computer Scientists Awards
Google Scholar ID MCnRe20AAAAJ&hl

Sara Amjad is a researcher associated with Aligarh Muslim University whose scholarly record includes work spanning cellular metabolism, molecular signaling, genetics, neuroscience, and computationally relevant biomedical research. Her documented publications include collaborative review research examining NAD+-dependent cellular processes and the genetics of glutamate signaling in autism spectrum disorder. These works provide evidence of interdisciplinary engagement across molecular biology, health sciences, and data-informed research.

Abstract

Sara Amjad’s research profile is represented by collaborative publications addressing biological signaling, metabolism, genetics, and neurodevelopment. Her work on NAD+ reviews its importance in energy metabolism, DNA repair, gene expression, cellular stress, aging, cancer, and neurodegeneration. [1] A second publication examines genetic alterations involving glutamate and its receptors in autism spectrum disorder, connecting molecular genetics with neuroscience and non-invasive imaging. [2]

Keywords

  • Computer Science
  • NAD+ metabolism
  • Cellular signaling
  • Genetics
  • Neuroscience
  • Autism spectrum disorder

Introduction

The supplied publication record indicates an interdisciplinary research orientation. In the NAD+ review, the authors describe NAD+ as a critical coenzyme involved in bioenergetics and cellular signaling, while discussing its relationship with aging, metabolic disorders, cancer, and neurodegeneration. [1] The autism spectrum disorder review evaluates genetic changes affecting glutamate and glutamate receptors and considers imaging approaches for investigating altered glutamatergic pathways. [2]

Research Profile

The provided Scopus information lists two documents, 483 citations, and an h-index of 2. These supplied metrics should be interpreted as a profile snapshot because citation databases can change over time. The publications also demonstrate collaboration across institutions and disciplines, including molecular medicine, genetics, neuroscience, imaging, and biomedical research.

Research Contributions

Amjad’s documented contribution includes participation in research that synthesizes evidence across complex biological systems. The NAD+ article identifies cellular energy metabolism, DNA damage repair, gene expression, and stress response as major areas influenced by NAD+ biology. [1] The genetics review addresses glutamatergic signaling and receptor biology in relation to autism spectrum disorder, emphasizing interactions among genetics, neuronal function, and imaging. [2]

Publications

  • Role of NAD+ in regulating cellular and metabolic signaling pathways, Molecular Metabolism, 2021, 49:101195.. [1]
  • Genetics of glutamate and its receptors in autism spectrum disorder, Molecular Psychiatry, 2022, 27:2380–2392. . [2]

Research Impact

The supplied citation count of 483 indicates that the documented publication set has received substantial scholarly attention relative to its size. The cited research addresses topics with broad biomedical relevance, including metabolism, cellular signaling, genetics, and neurodevelopment. The Nature publication also records citation and access metrics on its publisher page, illustrating continuing visibility of the research area. [2]

Award Suitability

Based on the supplied profile and publication evidence, Sara Amjad can be considered for an Innovative Research Award in recognition of interdisciplinary scholarly participation and research addressing contemporary questions in cellular metabolism, molecular genetics, neuroscience, and biomedical science. The assessment should remain subject to the award committee’s independent verification of affiliation, authorship, bibliographic records, and current research metrics.

Conclusion

Sara Amjad’s documented research contributions demonstrate engagement with interdisciplinary biomedical questions and collaborative scientific scholarship. Her publications on NAD+ signaling and glutamate genetics provide a foundation for evaluating her research profile within an innovation-focused academic recognition framework.

References

  1. Amjad, S. et al. (2021). Role of NAD+ in regulating cellular and metabolic signaling pathways. Molecular Metabolism, 49, 101195.
    https://doi.org/10.1016/j.molmet.2021.101195
  2. Nisar, S. et al. (2022). Genetics of glutamate and its receptors in autism spectrum disorder. Molecular Psychiatry, 27, 2380–2392.
    https://doi.org/10.1038/s41380-022-01506-w
  3. PubMed. (2021). Role of NAD+ in regulating cellular and metabolic signaling pathways. PMID 33609766.
    https://pubmed.ncbi.nlm.nih.gov/33609766/
  4. PubMed. (2022). Genetics of glutamate and its receptors in autism spectrum disorder. PMID 35296811.
    https://pubmed.ncbi.nlm.nih.gov/35296811/
  5. Elsevier. (n.d.). Role of NAD+ in regulating cellular and metabolic signaling pathways. Molecular Metabolism.
    https://www.sciencedirect.com/science/article/pii/S2212877821000351
  6. Springer Nature. (2022). Genetics of glutamate and its receptors in autism spectrum disorder. Molecular Psychiatry.
    https://www.nature.com/articles/s41380-022-01506-w

Saifal Abbas  | Engineering | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Saifal Abbas
Chang’an University, China

Saifal Abbas
Affiliation Chang’an University
Country China
Scopus ID 60811683500
Documents 3
Subject Area Engineering
Event Computer Scientists Awards
ORCID 0009-0005-6121-5309

Saifal Abbas is an engineering researcher affiliated with Chang’an University, China. The available scholarly profile records three documents, with zero citations and an h-index of zero at the stated profile snapshot. His listed publications address pavement engineering, asphalt sustainability, pavement condition assessment, artificial intelligence, and computer-vision-based infrastructure monitoring. These themes provide an interdisciplinary basis for consideration under an Innovative Research Award.

Abstract

The research profile of Saifal Abbas is centered on engineering applications involving transportation infrastructure, pavement assessment, sustainable asphalt materials, and artificial-intelligence-assisted condition monitoring. His documented publications demonstrate a progression from neural-network-based pavement management to high-reclaimed-asphalt-pavement mixtures and lightweight computer-vision approaches for crack detection. [1] [2] [3]

Keywords

  • Pavement engineering
  • Artificial intelligence
  • Computer vision
  • Sustainable asphalt
  • Infrastructure monitoring

Introduction

Modern transportation engineering increasingly combines materials science, data-driven assessment, and automated infrastructure inspection. Abbas’s publication record reflects this convergence, particularly through research examining pavement condition, reclaimed asphalt mixtures, and machine-learning-supported crack detection. [1] [2]

Research Profile

The available profile identifies Engineering as the principal subject area. The three documented works cover artificial neural networks for pavement condition and maintenance management, sustainability and performance of high-RAP asphalt mixtures, and a lightweight YOLO26s-based approach for multi-class pavement crack detection. [1] [2] [3]

Research Contributions

The publication portfolio connects infrastructure management with computational methods and sustainable materials. The artificial-neural-network study addresses data-driven pavement condition assessment, while the high-RAP study examines performance, durability, sustainability, and emerging technologies. The 2026 Sensors article further applies lightweight object detection to automated pavement crack identification, with an emphasis on edge deployment. [1] [2] [3]

Publications

  1. Evaluating YOLO26s for Multi-Class Pavement Crack Detection: A Lightweight Approach for Sustainable Edge Deployment.
    Sensors, 12 August 2026.
  2. High-RAP Asphalt Mixtures (>40%): Mechanical Performance, Durability, Sustainability, and Emerging Technologies.
    Scientific Journal of Engineering Research, 5 December 2025.
  3. Evaluating Pavement Condition Index and Maintenance Management using Artificial Neural Networks.
    European Journal of Applied Science, Engineering and Technology, 1 March 2024.

Research Impact

The current bibliometric profile records three documents, zero citations, and an h-index of zero. These metrics should be interpreted in the context of the documented publication record and its recent chronology rather than as a standalone assessment of research quality. The research topics nevertheless address practical engineering challenges involving pavement maintenance, material sustainability, and automated inspection.

Award Suitability

For the Innovative Research Award category, the profile presents a relevant combination of engineering research and computational innovation. In particular, the integration of lightweight deep-learning-based pavement inspection with sustainable infrastructure objectives provides a coherent basis for academic recognition, subject to the award committee’s independent evaluation and eligibility criteria.

Conclusion

Saifal Abbas’s documented research portfolio demonstrates work at the intersection of pavement engineering, sustainable materials, artificial intelligence, and automated infrastructure assessment. The three listed publications establish a focused research trajectory with potential relevance to data-driven and sustainable transportation engineering.

References

  1. Elsevier. (n.d.). Scopus author details: Saifal Abbas, Author ID 60811683500. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60811683500
  2. MDPI. (2026). Evaluating YOLO26s for Multi-Class Pavement Crack Detection: A Lightweight Approach for Sustainable Edge Deployment. Sensors. DOI: https://doi.org/10.3390/s26165113
  3. Scientific Journal of Engineering Research. (2025). High-RAP Asphalt Mixtures (>40%): Mechanical Performance, Durability, Sustainability, and Emerging Technologies.
    DOI: https://doi.org/10.64539/sjer.v1i4.2025.321
  4. European Journal of Applied Science, Engineering and Technology. (2024). Evaluating Pavement Condition Index and Maintenance Management using Artificial Neural Networks.
    DOI: https://doi.org/10.59324/ejaset.2024.2(2).15
  5. ORCID. (n.d.). ORCID record: Saifal Abbas. ORCID.
    https://orcid.org/0009-0005-6121-5309
  6. Computer Scientists Awards. (n.d.). Computer Scientists Awards official website.
    https://computerscientists.net/

Anna-Maria Lazarova | Robotics and Automation | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Anna-Maria Lazarova
Technical University of Sofia, Bulgaria

Anna-Maria Lazarova
Affiliation Technical University of Sofia
Country Bulgaria
Scopus ID 59425275700
Documents 3
Subject Area Robotics and Automation
Event Computer Scientists Awards
ORCID 0009-0009-3104-2901

Anna-Maria Lazarova is a researcher associated with the Technical University of Sofia whose documented scholarly work concerns robotics, automation, agricultural automation, and engineering systems. Her publications address the design of automated systems and specialized manufacturing solutions, providing evidence of interdisciplinary engagement between mechanical engineering, automation, electrical systems, and software development. [1]

Abstract

The research profile of Anna-Maria Lazarova reflects work in robotics and automation with particular attention to practical engineering systems. Her documented publications include research on modular mold design for injection molding of small nonmetal components and automated agricultural systems for sowing, watering, and chemical treatment. These studies demonstrate an applied orientation toward integrating mechanical design, automation, electrical engineering, and software components into functional systems. [1] [2]

Keywords

Robotics; Automation; Agricultural Automation; Injection Molding; Manufacturing Systems; Automated Sowing; Automated Watering; Chemical Treatment; Electrical Systems; Software Engineering.

Introduction

Automation research increasingly combines mechanical structures with sensing, control, electrical components, and software. Within this context, Lazarova’s published work illustrates an engineering approach directed toward the development and refinement of automated equipment. The reported research spans manufacturing applications and agricultural operations, indicating an interest in translating engineering concepts into practical systems. [2] [3]

Research Profile

The available bibliographic record lists three documents, with a Scopus author identifier of 59425275700. The subject classification associated with the profile is Robotics and Automation. The publication topics indicate multidisciplinary engineering activity involving automated machinery, manufacturing design, electrical subsystems, and software implementation. Citation and h-index values in the supplied profile are currently zero, which is consistent with a relatively limited indexed publication record and should not by itself be interpreted as a measure of research quality.

Research Contributions

One contribution concerns the design of a modular mold intended for injection molding of small-sized nonmetal parts. The study connects product-oriented manufacturing requirements with mold design and modular engineering principles. [1] A second research direction addresses an automated system for sowing, watering, and chemical treatment, demonstrating the application of automation to agricultural operations. [2] A related publication focuses specifically on the electrical and software components of such an automated system, highlighting the importance of integrated control architecture. [3]

  • Modular manufacturing design for small nonmetal components.
  • Automation of agricultural sowing, watering, and treatment processes.
  • Integration of electrical and software components within automated equipment.

Publications

  • Design of Modular Mold for Injection Molding of Small-Sized Nonmetal Parts
    Anna-Maria Lazarova, Stiliyan Nikolov, Reneta Dimitrova.
    Engineering Proceedings, MDPI.
  • Design of an Automated System for Sowing, Watering and Chemical Treatment
    Stiliyan Nikolov, Slav Dimitrov, Anna-Maria Lazarova.
    AIP Conference Proceedings, Volume 3063, Article 060009.
  • Development of Electrical and Software Part of an Automated System for Sowing, Watering and Chemical Treatment
    Slav Dimitrov, Anna-Maria Lazarova.
    AIP Conference Proceedings, Volume 3063, Article 060006.

Research Impact

The practical orientation of the reported research provides relevance to engineering applications where automation can improve repeatability, coordination, and system integration. The agricultural automation studies are particularly relevant to the development of equipment capable of coordinating multiple operational stages, while the manufacturing study addresses modular tooling for specialized production requirements. [1] [2]

Award Suitability

The documented research aligns with the scope of an Innovative Research Award because it addresses applied engineering problems through system design and technological integration. Evidence includes research on modular manufacturing tooling and the development of automated agricultural equipment incorporating electrical and software elements. [1] [3] This assessment is based on the supplied publication record and does not imply a comparative ranking against other researchers.

Conclusion

Anna-Maria Lazarova’s available scholarly record presents a focused engineering profile in robotics and automation. Her publications demonstrate involvement in manufacturing design and automated agricultural systems, including their electrical and software components. These contributions provide a documented basis for recognizing her engagement with applied automation research and interdisciplinary engineering development.

References

  1. Lazarova, Anna-Maria; Nikolov, Stiliyan; Dimitrova, Reneta. Design of Modular Mold for Injection Molding of Small-Sized Nonmetal Parts. MDPI, Engineering Proceedings.
    https://www.mdpi.com/2673-4591/154/1/36
  2. Nikolov, Stiliyan; Dimitrov, Slav; Lazarova, Anna-Maria. Design of an automated system for sowing, watering and chemical treatment. AIP Conference Proceedings, 3063, 060009.
    https://pubs.aip.org/aip/acp/article-abstract/3063/1/060009/3266757/
  3. Dimitrov, Slav; Lazarova, Anna-Maria. Development of electrical and software part of an automated system for sowing, watering and chemical treatment. AIP Conference Proceedings, 3063, 060006.
    https://pubs.aip.org/aip/acp/article-abstract/3063/1/060006/3266754/
  4. Elsevier. (n.d.). Scopus author details: Anna-Maria Lazarova, Author ID 59425275700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59425275700
  5. ORCID. (n.d.). Anna-Maria Lazarova, ORCID 0009-0009-3104-2901.
    https://orcid.org/0009-0009-3104-2901
  6. Computer Scientists Awards. (n.d.). Computer Scientists Awards.
    https://computerscientists.net/

Santiago Antúnez | Engineering | Breakthrough Research Award

Published on by Computer Scientists Awards

Breakthrough Research Award

Santiago Antúnez
Universidad Politécnica de Madrid, Spain

Santiago Antúnez
Affiliation Universidad Politécnica de Madrid
Country Spain
Scopus ID 59939203500
Documents 1
Citations 2
h-index 1
Subject Area Engineering
Event Computer Scientists Awards

Santiago Antúnez is an engineering researcher affiliated with Universidad Politécnica de Madrid and a contributing author to research on hybrid magnetic-levitation transportation. His documented research contribution concerns the development and assessment of Maglev-Derived Systems designed to improve railway performance while retaining compatibility with existing infrastructure. The associated 2025 study was published in Sustainability, volume 17, article 5056. [1]

Abstract

The research examines a hybrid Maglev-Derived System (MDS) intended to combine magnetic-levitation principles with existing railway infrastructure. The study considers propulsion through Linear Synchronous Motors, magnetic levitation and guidance, and the use of virtually coupled passenger pods. Its central objective is to investigate whether railway capacity and journey times can be improved while maintaining comparatively efficient energy use and limiting major infrastructure reconstruction. [1]

Keywords

Maglev; Maglev-Derived Systems; hybrid transportation; railway engineering; magnetic levitation; Linear Synchronous Motor; virtual coupling; sustainable transportation; railway interoperability.

Introduction

Conventional high-speed maglev systems generally depend on dedicated infrastructure, creating substantial barriers where established railway networks already occupy valuable corridors. The reported research addresses this limitation by examining a hybrid configuration capable of operating on conventional railway infrastructure with appropriate adaptations. The work forms part of the MaDe4Rail research context supported through Europe’s Rail Joint Undertaking. [2]

Research Profile

The supplied bibliometric profile records one Scopus-indexed document, two citations, and an h-index of one. These figures represent a limited but identifiable publication record and should be interpreted as a current bibliometric snapshot rather than a comprehensive measure of research quality. The published article identifies Santiago Antunez among the research team and associates the work with Universidad Politécnica de Madrid. [3]

Research Contributions

The principal contribution associated with Antúnez is participation in research evaluating a hybrid MDS architecture. The proposed system combines magnetic levitation with railway interoperability, allowing MDS vehicles and conventional rolling stock to potentially share established corridors. The study evaluates virtual coupling of individual passenger pods and considers propulsion, levitation, guidance, signalling compatibility, travel performance and energy consumption. [1]

Publications

  • Felez, J.; Vaquero-Serrano, M.A.; Portillo, D.; Antunez, S.; et al. “A New Concept of Hybrid Maglev-Derived Systems for Faster and More Efficient Rail Services Compatible with Existing Infrastructure.” Sustainability, 17(11), 5056, 2025. [1]

Research Impact

The study contributes to sustainable transportation research by examining how maglev-derived technology might improve existing railway services without requiring an entirely separate railway corridor. The published simulations investigate reductions in journey time and energy consumption through operational optimisation and aerodynamic considerations. The article also reports an initial cost–benefit assessment indicating potential economic advantages for European railway applications. [1]

Award Suitability

For recognition under the Breakthrough Research Award category, the documented work provides a relevant engineering research basis through its focus on railway innovation, interoperability and sustainable transport systems. The contribution is particularly aligned with research themes involving advanced mobility infrastructure and the practical adaptation of emerging technologies to established transport networks. This assessment is based on the documented publication and the supplied researcher profile rather than on a broader claim of career-wide achievement. [1]

Conclusion

Santiago Antúnez’s documented contribution to hybrid Maglev-Derived Systems represents participation in research addressing the engineering challenge of integrating advanced magnetic-levitation concepts with existing railway infrastructure. The associated publication provides a substantive technical basis for considering faster, interoperable and potentially more efficient rail services. [1]

References

  1. Felez, J.; Vaquero-Serrano, M.A.; Portillo, D.; Antunez, S.; et al. (2025). A New Concept of Hybrid Maglev-Derived Systems for Faster and More Efficient Rail Services Compatible with Existing Infrastructure. Sustainability, 17(11), 5056.
    DOI: https://doi.org/10.3390/su17115056
  2. European Commission. Maglev-Derived Systems for Rail — MaDe4Rail Project Results. CORDIS.
  3. Polytechnic University of Madrid. Scientific record: A New Concept of Hybrid Maglev-Derived Systems for Faster and More Efficient Rail Services Compatible with Existing Infrastructure.
  4. Felez, J.; Vaquero-Serrano, M.A.; Portillo, D.; et al. (2025). A New Concept of Hybrid Maglev-Derived Systems for Faster and More Efficient Rail Services Compatible with Existing Infrastructure. Preprints.org, Version 1.
    DOI: https://doi.org/10.20944/preprints202504.1002.v1
  5. MDPI. Sustainability 2025, 17(11), 5056 — Article Version Notes.
  6. Archivo Digital UPM. A New Concept of Hybrid Maglev-Derived Systems for Faster and More Efficient Rail Services Compatible with Existing Infrastructure.
    https://oa.upm.es/95268/

Sanjai Arul  | Advanced Algorithms | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Sanjai Arul
PhD Researcher, Department of Communication, Yuan Ze University, Taoyuan District, Taiwan

Sanjai Arul
Affiliation Yuan Ze University
Country Taiwan
Scopus ID 60054483100
Documents 1
Subject Area Advanced Algorithms
Event Computer Scientists Awards
ORCID 0009-0007-8427-1180

Sanjai Arul is a PhD Researcher in the Department of Communication at Yuan Ze University, Taiwan, whose documented research work includes advanced algorithmic methods for wireless communication and integrated sensing. His current publication record includes a 2026 journal article addressing convex optimization and interference cancellation in OFDM integrated sensing and communication systems.[1]

Abstract

This article presents the academic profile of Sanjai Arul in relation to the Innovative Research Award. His documented research focuses on advanced algorithms and communication systems, with particular relevance to OFDM integrated sensing and communication. His 2026 publication proposes a convex optimization-based three-slot framework incorporating interference cancellation, demonstrating research engagement with contemporary wireless-system design.[1]

Keywords

Advanced Algorithms; Convex Optimization; OFDM; Integrated Sensing and Communication; Interference Cancellation; Wireless Communications.

Introduction

Integrated sensing and communication combines sensing and wireless communication functions within shared system architectures. Algorithmic optimization is important in such systems because transmission, sensing, interference, and resource-allocation requirements must be considered jointly. Arul’s documented 2026 work addresses this problem through a structured optimization framework.[1]

Research Profile

Arul is affiliated with Yuan Ze University in Taiwan as a PhD Researcher in the Department of Communication. The supplied Scopus record identifies one document, zero citations, and an h-index of zero. These bibliometric values describe the available indexed record and should be interpreted in the context of an emerging research profile.

Research Contributions

  • Application of convex optimization to integrated sensing and communication.
  • Development of a three-slot OFDM framework with interference cancellation.
  • Research connecting algorithmic optimization with next-generation wireless communication systems.[1]

Publications

  1. Sanjai Arul. A Convex Optimization-Based Three-Slot Framework for OFDM Integrated Sensing and Communication with Interference Cancellation. Electronics, 2026.

Research Impact

The available bibliometric record currently reports no citations. Consequently, citation-based impact remains limited to date. The subject and methodological focus of the publication nevertheless place the research within an active area involving wireless communication, sensing, optimization, and interference management.[1]

Award Suitability

For the Innovative Research Award, the documented work provides a relevant basis for consideration because it applies an optimization-oriented approach to a contemporary communication-system problem. Award assessment should consider the originality, methodological rigor, technical significance, and broader contribution of the submitted research alongside independently verified academic records.

Conclusion

Sanjai Arul’s research profile reflects early-stage scholarly activity in advanced algorithms and communication engineering. His 2026 publication provides a specific contribution involving convex optimization, OFDM, integrated sensing and communication, and interference cancellation, establishing a documented foundation for recognition under an innovation-focused award category.[1]

References

  1. Arul, Sanjai. (2026). A Convex Optimization-Based Three-Slot Framework for OFDM Integrated Sensing and Communication with Interference Cancellation. Electronics, 15(16), 3610. DOI: 10.3390/electronics15163610.
    https://doi.org/10.3390/electronics15163610
  2. Elsevier. (n.d.). Scopus author details: Sanjai Arul, Author ID 60054483100. Scopus.
    https://www.scopus.com/pages/authors/60054483100
  3. ORCID. (n.d.). ORCID record: Sanjai Arul.
    https://orcid.org/0009-0007-8427-1180
  4. MDPI. (2026). Electronics, Volume 15, Issue 16. MDPI.
    https://doi.org/10.3390/electronics15163610
  5. Yuan Ze University. (n.d.). Academic and research information.

Bo Xiang Lee  | Agricultural and Biological Sciences | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Bo Xiang Lee
Agricultural and Biological Sciences, Taiwan

Bo Xiang Lee
Affiliation Agricultural and Biological Sciences
Country Taiwan
Documents 2
Subject Area Agricultural and Biological Sciences
Event Computer Scientists Awards
ORCID 0009-0007-1263-9943

Bo Xiang Lee is a researcher associated with the field of Agricultural and Biological Sciences whose documented research activity includes work on green manure crops, non-sulfur photosynthetic bacteria, wastewater sludge, and circular bioeconomy approaches. The available publication record comprises two documented research outputs, including a 2026 article in Agronomy and a 2026 preprint addressing the economic and environmental potential of forest-residue valorization. [1] [2]

Abstract

This academic recognition profile presents Bo Xiang Lee in relation to the Innovative Research Award. The documented research portfolio reflects an interdisciplinary orientation toward agricultural sustainability, biological resource management, waste valorization, and circular-economy applications. Recent work examines growth responses of green manure crops under biological and wastewater-sludge treatments and evaluates forest residues as resources for economic and net-zero strategies. [1] [2]

Keywords

Agricultural Sciences; Biological Sciences; Green Manure; Photosynthetic Bacteria; Wastewater Sludge; Circular Bioeconomy; Forest Residues; Sustainability; Resource Valorization; Net-Zero Strategies.

Introduction

Contemporary agricultural research increasingly connects biological production with resource recovery and environmental sustainability. Within this context, Lee’s documented outputs address two complementary challenges: improving the understanding of biological treatments for crop systems and identifying productive pathways for converting residual biomass into useful resources. These themes align with broader research priorities involving circular resource use and lower-impact production systems.

Research Profile

The available record identifies Agricultural and Biological Sciences as the principal subject area. Bibliographic information lists two documents, with zero recorded citations and an h-index of zero in the supplied profile data. These metrics represent the current documented record and should be interpreted in relation to publication timing, particularly because the listed outputs are recent.

Research Contributions

  • Investigation of green manure crop growth under treatments involving non-sulfur photosynthetic bacteria and wastewater sludge. [1]
  • Assessment of forest-residue valorization through economic viability and net-zero potential within a circular bioeconomy framework. [2]

Publications

  1. Lee, B. X. (2026). Growth Responses of Two Green Manure Crops to Treatments with Non-Sulfur Photosynthesis Bacteria and Wastewater Sludge. Agronomy, 16(16), 1534. Publication date: 11 August 2026.
  2. Lee, B. X. (2026). From waste to wisdom: Assessing economic viability and net-zero potential of valorization of forest residues in a circular bioeconomy. SSRN, Preprint.

Research Impact

The supplied bibliometric indicators currently show no citations and an h-index of zero. However, the publication dates indicate that the research is newly documented, making citation-based evaluation premature. The subject matter nevertheless provides a basis for future investigation into sustainable agriculture, biomass utilization, biological treatments, and circular production systems.

Award Suitability

The Innovative Research Award recognizes a research profile in which emerging scholarly work demonstrates a relevant and innovative direction. Lee’s documented publications provide evidence of research activity connecting agricultural biology with resource recovery and sustainability. The suitability of the recognition should be considered alongside the complete nomination record, independent review, and the award’s formal evaluation criteria.

Conclusion

Bo Xiang Lee’s current publication record reflects emerging research at the intersection of agricultural and biological sciences, sustainable crop management, waste utilization, and circular bioeconomy development. The two documented 2026 outputs establish a focused foundation for further scholarly work and future measurable research impact.

References

  1. Lee, B. X. (2026). Growth Responses of Two Green Manure Crops to Treatments with Non-Sulfur Photosynthesis Bacteria and Wastewater Sludge. Agronomy, 16(16), 1534.
    https://doi.org/10.3390/agronomy16161534
  2. Lee, B. X. (2026). From waste to wisdom: Assessing economic viability and net-zero potential of valorization of forest residues in a circular bioeconomy. SSRN preprint.
    https://doi.org/10.2139/ssrn.6366405
  3. MDPI. (2026). Agronomy.
  4. ORCID. (n.d.). ORCID record for Bo Xiang Lee.
    https://orcid.org/0009-0007-1263-9943
  5. Computer Scientists Awards. (n.d.). Official Award Website.
    https://computerscientists.net/