Maria de Lurdes Dinis | Engineering | Best Researcher Award – 1999

Posted on by Phenomenological Research Awards

Prof. Maria de Lurdes Dinis | Engineering | Best Researcher Award 

Full Professor at University of Porto | Portugal 

Prof. Maria de Lurdes Dinis is a highly accomplished academic at the University of Porto, widely recognized for her pioneering contributions in the field of Engineering. With a strong educational background culminating in a Ph.D. in Civil Engineering from the University of Porto, her doctoral research centered on advanced computational modeling, structural optimization, and sustainable design, laying the foundation for her long-standing research excellence. Over the course of her career, she has gained extensive professional experience, leading and collaborating on national and international research projects, with a focus on sustainable infrastructure, energy-efficient solutions, and computational approaches to structural mechanics. Her research interests span computational engineering, applied mechanics, sustainable construction, and innovative materials, where she consistently integrates theory with real-world engineering applications. Prof. Maria de Lurdes Dinis has demonstrated a broad set of research skills, including advanced simulation techniques, multidisciplinary project management, mentoring of Ph.D. students, and the ability to build cross-institutional collaborations. She has published 63 scholarly documents indexed in Scopus, which collectively have received 827 citations across 686 documents, reflecting her strong academic influence, with an h-index of 13 showcasing the impact of her research contributions. Her work appears in reputed international journals and IEEE/Scopus-indexed conferences, and she has actively participated in collaborative European consortia advancing engineering solutions. In recognition of her achievements, she has received awards and honors for both her scholarly excellence and her commitment to advancing engineering education, while also contributing to professional associations and volunteer platforms supporting student engagement and knowledge dissemination. In conclusion, Prof. Maria de Lurdes Dinis stands out as a leading researcher whose expertise, impactful publications, international collaborations, and dedication to academic leadership make her highly deserving of recognition. Her future research potential lies in further advancing sustainable engineering, expanding global collaborations, and continuing to shape the next generation of engineers through mentorship and innovation.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

  • Dinis, M. L., & Camotim, D. (2014). A numerical investigation of the post-buckling behavior of cold-formed steel columns. Thin-Walled Structures, 83(1), 121–133.

  • Dinis, M. L., Silvestre, N., & Camotim, D. (2012). Local-global interaction in cold-formed steel lipped channel columns: Numerical investigation. Journal of Constructional Steel Research, 68(1), 1–13.

  • Dinis, M. L., Silvestre, N., & Camotim, D. (2011). FEM-based analysis of cold-formed steel columns with distortional buckling. Thin-Walled Structures, 49(5), 614–631.

  • Dinis, M. L., & Camotim, D. (2009). Post-buckling behavior and strength of thin-walled lipped channel columns experiencing local–distortional interaction. International Journal of Structural Stability and Dynamics, 9(4), 691–714.

  • Dinis, M. L., Silvestre, N., & Camotim, D. (2008). On the mechanics of local-distortional interaction in cold-formed steel lipped channel columns. Thin-Walled Structures, 46(4), 401–420.

  • Dinis, M. L., Silvestre, N., & Camotim, D. (2007). Numerical investigation of the local–global interaction in lipped channel columns. Computers & Structures, 85(19–20), 1461–1474.

  • Dinis, M. L., Camotim, D., & Silvestre, N. (2006). FEM-based analysis of cold-formed steel members: Local–distortional interaction. Computers & Structures, 84(17–18), 1208–1227.

Abhijeet Das | Engineering | Cutting Edge Scientific Achievement Award

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Dr. Abhijeet Das | Engineering | Cutting Edge Scientific Achievement Award 

Research Consultant at C.V. Raman Global University | India

Dr. Abhijeet Das is a distinguished researcher in Civil and Water Resources Engineering, known for his impactful contributions to hydrology, water quality, and environmental sustainability. With a career spanning academic teaching, consultancy, and international collaborations, he has demonstrated excellence in advancing methods of water quality assessment, climate change analysis, and sustainable water management practices. His interdisciplinary approach integrates hydrological modeling, GIS, and machine learning, creating solutions for both local and global water challenges. Dr. Abhijeet Das has authored books, research articles, and innovative patents, reflecting his vision of applying science and technology to address pressing environmental and societal needs.

Profile:

Orcid 

Education:

Dr. Abhijeet Das holds a Ph.D. in Water Resources Engineering from C.V. Raman Global University, where he specialized in hydrological modeling, water quality management, and GIS-based analysis. He earned his M.Tech in Water Resources Engineering from Biju Patnaik University of Technology, following his B.Tech in Civil Engineering from the same institution. His education built a strong foundation in watershed hydrology, climate change impact assessment, and environmental sustainability. Dr. Abhijeet Das’s academic achievements were marked by high distinctions, and he consistently pursued excellence in research-oriented projects. His progression from undergraduate to doctoral studies reflects a clear dedication to solving water resource challenges.

Experience:

Dr. Abhijeet Das has accumulated valuable experience as a researcher, consultant, and educator in civil and water resources engineering. He has taught undergraduate and postgraduate students at premier engineering institutions, nurturing the next generation of engineers and researchers. His consultancy roles allowed him to lead projects involving hydrological assessments, water resource management, and GIS-based solutions for river basin studies. Beyond national engagements, he has collaborated with international universities, working on projects related to wastewater management, climate impact analysis, and geoinformatics. Dr. Abhijeet Das’s combined academic and professional experience reflects his ability to bridge research, teaching, and practical applications.

Research Interests:

Dr. Abhijeet Das’s research interests encompass watershed hydrology, water resources engineering, and hydrological extremes such as droughts and floods. He focuses on climate change impact assessment and its influence on water security, emphasizing sustainable solutions. His expertise extends to the Food-Energy-Water nexus, applying machine learning, GIS, and remote sensing to optimize water management strategies. Dr. Abhijeet Das also contributes to simulation-optimization modeling, neural networks, and fuzzy logic applications for water quality control. His work highlights environmental impact assessment and sustainable management approaches, ensuring that his research directly supports global goals of resilience, sustainability, and resource conservation.

Awards and Honors:

Dr. Abhijeet Das has been honored with multiple awards for his innovative research and outstanding academic contributions. His papers on water quality assessment and GIS-based modeling have received recognition at prestigious national and international conferences. He has won best paper awards for advancing novel methodologies in multivariate statistical analysis and decision-making approaches for water quality management. Additionally, his poster presentations have earned accolades for their clarity, innovation, and societal relevance. Dr. Abhijeet Das’s recognition extends to his role as a valued reviewer and editor for renowned international journals, reflecting his leadership and credibility within the scientific and engineering community.

Publications:

Title: An optimization-based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance
Year of Publication: 2025

Title: A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India
Citation: 2
Year of Publication: 2025

Title: Evaluation and prediction of surface water quality status for drinking purposes using an integrated water quality indices, GIS approaches, and machine learning techniques
Citation: 1
Year of Publication: 2025

Title: Bioplastics: a sustainable alternative or a hidden microplastic threat
Year of Publication: 2025

Title: Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning analysis
Year of Publication: 2025

Title: Geographical Information System–driven intelligent surface water quality assessment for enhanced drinking and irrigation purposes in Brahmani River, Odisha (India)
Citation: 6
Year of Publication: 2025

Title: Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India
Year of Publication: 2025

Conclusion:

Dr. Abhijeet Das has established himself as a dedicated scholar and innovator in water resources and environmental engineering. His research advances the understanding of hydrological systems, water quality monitoring, and climate change adaptation strategies. By integrating modern tools like GIS, machine learning, and multivariate statistical methods, he has proposed solutions with practical implications for sustainable development. Recognized with awards and widely published, Dr. Abhijeet Das represents a new generation of engineers combining academic rigor with societal impact. His body of work exemplifies excellence in research, making him a highly deserving candidate for recognition through this award nomination.