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.

External Links

• ORCID Profile
• Scopus Author Profile
• DOI Link
• Award Website

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

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–N_x 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.

External Links

• ORCID Profile:
• Scopus Author Profile:
•  DOI 
• Award Website:

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–N_x 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