Surakasi Raviteja | Research Excellence Award | Mechanical Engineering

Research Excellence Award

Surakasi Raviteja
Vignan’s Institute of Information Technology, India

Surakasi Raviteja
Affiliation Vignan’s Institute of Information Technology
Country India
Scopus ID 57222898698
Documents 89
Citations 1,263
h-index 20
Subject Area Mechanical Engineering
Event International Research Awards on Network Science & Graph Analytics
ORCID 0000-0002-0786-0105

Surakasi Raviteja is a researcher affiliated with Vignan’s Institute of Information Technology in India, with a stated subject-area focus in Mechanical Engineering. The researcher profile supplied for this recognition article records 89 documents, 1,263 citations, and an h-index of 20. These bibliometric indicators provide a quantitative description of the indexed research record and should be interpreted in the context of the relevant discipline, publication period, database coverage, and citation practices. [1]

Abstract

This academic recognition profile presents the research record of Surakasi Raviteja of Vignan’s Institute of Information Technology, India, in the field of Mechanical Engineering. Based on the profile information provided for this article, the researcher has 89 indexed documents, 1,263 citations, and an h-index of 20. The profile is considered in relation to research productivity, scholarly visibility, documented research contributions, and potential relevance to the Research Excellence Award associated with the International Research Awards on Network Science & Graph Analytics. Bibliometric information is presented as a descriptive measure rather than as a standalone assessment of research quality. [1]

Keywords

  • Research Excellence Award
  • Surakasi Raviteja
  • Mechanical Engineering
  • Research Publications
  • Bibliometric Research
  • Research Impact
  • Scholarly Collaboration
  • International Research Awards

Introduction

Research recognition commonly considers multiple dimensions of scholarly activity, including publication output, citation visibility, research contributions, collaboration, and sustained engagement with a scientific discipline. Bibliometric databases such as Scopus provide structured information that can be used to describe an author’s indexed publication and citation record. Such indicators are informative but do not independently establish the quality, originality, or societal value of individual research contributions. [1]

Within this context, the present profile describes Surakasi Raviteja as a Mechanical Engineering researcher affiliated with Vignan’s Institute of Information Technology. The supplied profile identifies 89 documents, 1,263 citations, and an h-index of 20. These figures are included as profile-level indicators and may change as databases are updated, publications are indexed, or citation records are revised. [1]

Research Profile

Surakasi Raviteja is associated with Vignan’s Institute of Information Technology and is identified in the supplied research information with the subject area of Mechanical Engineering. The profile includes a Scopus Author ID of 57222898698 and an ORCID identifier of 0000-0002-0786-0105, providing persistent identifiers through which scholarly records can be distinguished and connected across research information systems. [1] [2]

The supplied bibliometric record contains 89 documents, 1,263 citations, and an h-index of 20. These indicators describe different aspects of an indexed research record: documents represent indexed scholarly outputs, citations represent recorded references to those outputs, and the h-index combines publication and citation information according to a defined threshold. Because bibliometric measures vary by database, discipline, publication year, and indexing coverage, they are most appropriately considered alongside qualitative evidence. [1]

Research Contributions

The documented profile places the researcher’s primary subject area within Mechanical Engineering. On the basis of the information supplied for this article, the research record can be described through its publication activity, citation visibility, and continued representation in scholarly indexing systems. A complete assessment of individual contributions would require examination of the underlying publications, research methods, datasets, collaborations, and documented outcomes.

  • Development and dissemination of scholarly research within the stated Mechanical Engineering subject area.
  • Contribution to the indexed academic literature through a documented publication record.
  • Participation in a research ecosystem reflected by citation and scholarly indexing activity.
  • Maintenance of persistent researcher identifiers through Scopus and ORCID profiles.

The points above summarize the profile information available for this recognition page and should not be interpreted as claims about specific research findings that have not been independently documented in the supplied material.

Publications

The supplied profile reports 89 documents associated with the Scopus Author ID 57222898698. The number represents the indexed document count reported for the profile at the time of the supplied information. Individual publication titles, journals, publication years, co-authors, and DOI identifiers have not been provided in the source information for this article; therefore, no specific publication-level claims are made here. [1]

For publication-level verification, readers should consult the researcher’s indexed author record and persistent ORCID profile. These sources can provide a more detailed basis for examining publication metadata and researcher attribution. [1] [2]

Research Impact

The supplied bibliometric indicators provide a quantitative view of the researcher’s indexed scholarly visibility. A citation count of 1,263 and an h-index of 20 indicate that the profile has accumulated measurable citation activity within the database record supplied for this article. Such metrics can assist in describing research visibility, while their interpretation should account for disciplinary citation norms, publication age, database coverage, self-citation patterns, collaboration structures, and differences between research fields. [1]

Research impact can also extend beyond citation counts through methodological contributions, industrial or technological applications, educational value, collaboration, knowledge transfer, and other outcomes. Evidence for those dimensions should be evaluated from the underlying publications and documented research activities rather than inferred solely from bibliometric indicators.

Award Suitability

The Research Excellence Award profile recognizes the research record presented for Surakasi Raviteja within the context of the International Research Awards on Network Science & Graph Analytics. The supplied record identifies an established publication and citation profile in Mechanical Engineering, supported by 89 documents, 1,263 citations, and an h-index of 20. [1]

For an academic recognition process, the available profile indicators may be considered together with the quality and originality of publications, relevance of research contributions, scholarly collaborations, documented applications, consistency of research activity, and supporting evidence. The bibliometric information should therefore be treated as one component of a broader scholarly evaluation rather than as a substitute for peer assessment.

The award context and associated recognition information can be reviewed through the official event website. [4]

Conclusion

Surakasi Raviteja, affiliated with Vignan’s Institute of Information Technology, is presented in this academic recognition profile as a Mechanical Engineering researcher with a documented Scopus record comprising 89 documents, 1,263 citations, and an h-index of 20. The profile also includes Scopus Author ID 57222898698 and ORCID 0000-0002-0786-0105. [1] [2]

These indicators provide a concise description of the supplied scholarly record. A comprehensive academic assessment should additionally consider publication-level evidence, research originality, methodological rigor, collaboration, practical contributions, and broader research outcomes. The information presented here is intended as a structured academic recognition profile based on the supplied researcher data.

References

  1. Elsevier. (n.d.). Scopus author details: Surakasi Raviteja, Author ID 57222898698.
    Scopus.https://www.scopus.com/authid/detail.uri?authorId=57222898698
  2. ORCID. (n.d.). ORCID record: Surakasi Raviteja. ORCID
    .https://orcid.org/0000-0002-0786-0105
  3. Google Scholar. (n.d.). Google Scholar profile associated with Surakasi Raviteja
    .
    https://scholar.google.com/citations?user=W4ty4rQAAAAJ&hl=en&oi=sra
  4. International Research Awards on Network Science & Graph Analytics. (n.d.). Official award website.
    https://networkscience-conferences.researchw.com/

Ahmed Mohammed | Engineering | Best Researcher Award

Prof. Ahmed Mohammed | Engineering | Best Researcher Award

Engineering at university of mosul, Iraq

Prof. Ahmed Younis Mohammed is a Full Professor at the Department of Dams and Water Resources Engineering, College of Engineering, University of Mosul, Iraq. With over two decades of academic and research experience in hydraulic and water resources engineering, he has made significant contributions to the field through teaching, research, and scholarly reviews. He is an active member of international scientific societies like IAHS and IAHR and has served as a peer reviewer for reputed journals published by Elsevier and Taylor’s University.

Professional Profile:

Scopus

Orcid

Education Background

  • Master of Science (M.Sc.) in Hydraulics, Department of Water Resources Engineering, University of Mosul, Iraq (2000–2002)

  • Bachelor of Science (B.Sc. Eng.) in Irrigation and Drainage Engineering, University of Mosul, Iraq (1992–1996)

Professional Development

Prof. Mohammed has been associated with the University of Mosul since 2003, progressively advancing through academic ranks—from Assistant Lecturer to Full Professor in 2024. He has taught a broad spectrum of undergraduate courses, including Engineering Mechanics, Hydraulics, Fluid Mechanics I & II, Irrigation Principles, MATLAB Programming, and Design of Hydraulic Structures. His academic career has been rooted in the Department of Dams and Water Resources Engineering, where he has consistently contributed to student development and engineering research.

Research Focus

His core research interests include:

  • Hydraulics and Hydraulic Structures

  • Open Channel Flow and Energy Dissipation

  • Hydraulic Modeling and MATLAB Applications

  • Design and Analysis of Weirs, Gates, and Dams

  • Water Resources Engineering and River Dynamics

His M.Sc. thesis focused on the hydraulic performance of vertical and inclined gates on weirs, contributing valuable insights into flow regulation and structural optimization.

Author Metrics:

Prof. Mohammed has contributed to academic knowledge through multiple publications and scholarly reviews. His expertise is recognized through reviewer certifications from prestigious journals such as:

  • Journal of Flow Measurement and Instrumentation (Elsevier, Impact Factor: 1.203)

  • Journal of Engineering Science & Technology (JESTEC) (Taylor’s University, SJR: 0.19)

  • Scientia Iranica (Elsevier, Impact Factor: 0.679)

Awards and Honors:

  • Certificate of Reviewing – Journal of Flow Measurement and Instrumentation, Elsevier

  • Certificate of Reviewing – JESTEC, Taylor’s University

  • Certificate of Reviewing – Scientia Iranica, Elsevier

Publication Top Notes

📝 1. Machine learning-based modeling of discharge coefficients in labyrinth sluice gates

Journal: Flow Measurement and Instrumentation
Date: March 2025
DOI: 10.1016/j.flowmeasinst.2025.102823
Authors: Thaer Hashem, Ahmed Y. Mohammed, Ali Sharifi
Summary:
This paper presents advanced machine learning models to predict discharge coefficients in labyrinth sluice gates. Various algorithms are evaluated, providing a powerful tool for hydraulic design and optimization. The results show that ML techniques can outperform traditional empirical methods in accuracy and reliability.

📝 2. Flow Characteristics in Vertical Shaft Spillway with Varied Inlet Shapes and Submergence States

Journal: Tikrit Journal of Engineering Sciences
Date: November 24, 2024
DOI: 10.25130/tjes.31.4.4
Authors: Intisar Azher Hadi, Ahmed Younis Mohammed
Summary:
This study investigates the influence of different inlet geometries and submergence levels on the hydraulic behavior of vertical shaft spillways. Using both physical modeling and analytical methods, the authors identify optimal configurations for energy dissipation and flow stability.

📝 3. Unlocking Precision in Hydraulic Engineering: Machine Learning Insights into Labyrinth Sluice Gate Discharge Coefficients

Journal: Journal of Hydroinformatics
Date: November 2024
DOI: 10.2166/hydro.2024.310
Authors: Thaer Hashem, Iman Kattoof Harith, Noor Hassan Alrubaye, Ahmed Y. Mohammed, Mohammed L. Hussien
Summary:
The paper delves into the use of machine learning to enhance accuracy in predicting discharge coefficients for labyrinth sluice gates. It integrates multiple ML models and compares their performance against hydraulic experiment data, pushing the boundaries of smart engineering systems in water structures.

📝 4. Hydraulic Characteristics of Labyrinth Sluice Gate

Journal: Flow Measurement and Instrumentation
Date: April 2024
DOI: 10.1016/j.flowmeasinst.2024.102556
Authors: Thaer Hashem, Ahmed Y. Mohammed, Thair J. Alfatlawi
Summary:
This paper analyzes the hydraulic performance of labyrinth-shaped sluice gates under various flow conditions. The findings offer valuable insights for engineers designing water conveyance systems, focusing on maximizing flow efficiency and minimizing energy loss.

📝 5. Estimating Critical Depth and Discharge over Sloping Rough End Depth Using Machine Learning

Journal: Journal of Hydroinformatics
Date: March 2024
DOI: 10.2166/hydro.2024.242
Authors: Ahmed Y. Mohammed, Parveen Sihag
Summary:
This study employs ML algorithms to estimate critical flow parameters like depth and discharge over rough, sloped surfaces. It demonstrates the capability of ML in modeling complex open-channel hydraulics where traditional approaches may fall short.

Conclusion

Prof. Ahmed Younis Mohammed exemplifies academic excellence, research innovation, and professional service. His pioneering integration of machine learning in hydraulic engineering, extensive publication record, and consistent contributions to engineering education make him highly deserving of the Best Researcher Award in Engineering.

He stands out as a researcher who not only contributes to fundamental knowledge but also applies it to real-world problems in water infrastructure—making him a transformative force in 21st-century civil and environmental engineering.