Vasso Papadimitriou | Engineering | Best Researcher Award

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Ms. Vasso Papadimitriou | Engineering | Best Researcher Award

Aristotle University of Thessaloniki | Greece

Ms. Vasso Papadimitriou is an accomplished researcher and academic affiliated with the Aristotle University of Thessaloniki and the Region of Central Macedonia, Greece. Her research primarily focuses on construction project management, cost estimation models, and the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly Artificial Neural Networks (ANNs), in the field of building renovation and project planning. She has contributed significantly to the development of predictive and hybrid models that enhance accuracy in early-stage and final cost estimation for construction and renovation projects. Dr. Papadimitriou’s innovative work combines ANN methodologies, including Radial Basis Function (RBF) and Multilayer Perceptron (MLP) networks, with Multi-Criteria Decision-Making (MCDM) approaches such as the TOPSIS Methodology to create efficient, data-driven tools for project assessment and optimization. Her research also aligns with Sustainable Development Goals (SDG 9 and SDG 17), focusing on promoting innovation, infrastructure, and partnerships for sustainable growth. She has published in international peer-reviewed journals indexed in Scopus, Web of Science (SCI-Expanded, ESCI), and other scientific databases. According to Scopus, she has 6 publications, 3 citations, and an h-index of 1. On Google Scholar, she holds 14 total citations, an h-index of 3, and an i10-index of 1, while ResearchGate records 6 publications, 11 citations, and an h-index of 2. Her interdisciplinary approach bridges civil engineering, computer science, and digital construction, contributing to advancements in cost modeling and sustainable infrastructure management. Through her publications and research collaborations, Dr. Papadimitriou continues to make impactful contributions to the field of engineering innovation and AI-driven construction technology. Her outstanding achievements and innovative contributions to predictive modeling and sustainable construction management make her a deserving nominee for the Best Researcher Award.

Publication Profile

Scopus | Google Scholar

Featured Publications

Papadimitriou, V. E., & Aretoulis, G. N. (2024). A final cost estimating model for building renovation projects. Buildings, 14(4), 1072.

Papadimitriou, V. E., Aretoulis, G. N., & Papathanasiou, J. (2024). Radial Basis Function (RBF) and Multilayer Perceptron (MLP) comparative analysis on building renovation cost estimation: The case of Greece. Algorithms, 17(9), 390.

Papadimitriou, V., & Aretoulis, G. (2023). Neural network models as a cost prediction tool to prevent building construction projects from a failure—A literature review. Proceedings of the Erasmus+ PROSPER Project International Scientific Conference, 1–10.

Papadimitriou, V. E., & Aretoulis, G. N. (2025). An innovative approach regarding efficient and expedited early building renovation cost estimation utilizing ANNs and the TOPSIS methodology. Algorithms, 18(11), 696.

Kritikos, P., Papadimitriou, V., & Aretoulis, G. N. (2021). Required project designers’ attributes as perceived by male and female engineers. International Journal of Decision Support System Technology (IJDSST), 13(4), 1–15.

 

 

Qiusong Liang | Engineering | Best Researcher Award

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Ms. Qiusong Liang | Engineering | Best Researcher Award

Northeast Forestry University | China

Ms. Qiusong Liang is a promising mechanical engineering researcher whose work focuses on advanced simulation, optimization, and design of electro-hydraulic and electromechanical systems. Her research emphasizes multi-objective optimization, structural dynamics, and fluid–structure interaction analysis to enhance the performance and reliability of servo and direct-drive valve mechanisms. She skillfully integrates computational tools such as ANSYS, SolidWorks, Maxwell, and AMESim for high-precision modeling and simulation, contributing significantly to innovations in flow control mechanisms, torque motor optimization, and cavitation noise reduction in hydraulic systems. Her recent studies explore the dynamic characteristics of torque motors and the coupling effects between electromagnetic and fluid systems, leading to improved high-response servo valve technologies for industrial and military applications. Ms. Liang’s research excellence and innovative approach have been recognized through publications in internationally indexed journals and notable contributions to engineering design projects. She maintains an active research profile with Scopus- and Google Scholar–indexed publications, accumulating documented citations and a growing h-index that reflect her rising academic influence in the field of mechanical system optimization and applied simulation engineering. Her commitment to applied research, precision design, and interdisciplinary collaboration has earned her recognition as a recipient of the Best Researcher Award, highlighting her as one of the emerging leaders in smart mechanical systems and sustainable automation technologies.

Publication Profile

Orcid

Featured Publications

  • Zhang, J., Liang, Q., Sun, J., Yan, B., Hu, Z., & Sun, W. (2025, October 29). Multi-objective optimization of torque motor structural parameters in direct-drive valves based on genetic algorithm. Actuators, 14(11), 527.

Mehran Pourgholi | Engineering | Best Researcher Award

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Assist. Prof. Dr. Mehran Pourgholi | Engineering | Best Researcher Award

Islamic Azad University | Iran

Assist. Prof. Dr. Mehran Pourgholi, an accomplished researcher in Civil and Structural Engineering, has made notable contributions to the fields of system identification, structural health monitoring, inverse problems, and stochastic subspace methods. His research focuses on enhancing the reliability of modal analysis and vibration-based damage detection in large-scale structures such as dams and steel buildings. Dr. Pourgholi integrates advanced computational approaches including entropy-based model selection, optimization algorithms, and error analysis frameworks to improve accuracy in structural system modeling. His collaborative work with experts from the University of Tabriz and Islamic Azad University has produced high-impact studies published in leading international journals such as the Journal of Vibration and Control, Mechanical Systems and Signal Processing, and Engineering Reports. According to Google Scholar, Dr. Pourgholi has 77 citations (68 since 2020), an h-index of 4, and an i10-index of 2, while Scopus records 51 citations across 46 documents with an h-index of 3. His influential publications on stochastic subspace identification and modal analysis have advanced understanding of dynamic behavior in civil structures. Recognized for his scholarly excellence, he has been honored with the Best Researcher Award for his significant impact in the field of structural system identification and vibration analysis.

Publication Profile

Scopus | Orcid | Google Scholar 

Featured Publications

Tarinejad, R., & Pourgholi, M. (2018). Modal identification of arch dams using balanced stochastic subspace identification. Journal of Vibration and Control, 24(10), 2030–2044.

Pourgholi, M., Mohammadzadeh Gilarlue, M. M., Vahdaini, T., & Azarbonyad, M. (2023). Influence of Hankel matrix dimension on system identification of structures using stochastic subspace algorithms. Mechanical Systems and Signal Processing, 186, 109893.

Pourgholi, M., Tarinejad, R., Khabir, M. E., & Mohammadzadeh Gilarlue, M. M. (2023). System identification of Karun IV Dam using balanced stochastic subspace algorithm considering the uncertainty of results. Journal of Vibration and Control, 29(23–24), 5342–5356.

Tarinejad, R., Pourgholi, M., & Yaghmaei-Sabegh, S. (2016). Signal processing of dynamic tests results using subspace identification based on orthogonal decomposition technique (SI-ORT). Modares Mechanical Engineering, 15(10), 104–116.

Pourgholi, M., Ghannadi, M., & Gavgani, S. S. (2024). Modal analysis of earthquake records for dams using stochastic subspace based on error analysis. Engineering Reports, 6(8), e12822

 

Mélanie Di Mario | Sustainable Materials | Best Researcher Award

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Ms. Mélanie Di Mario | Sustainable Materials | Best Researcher Award

Senior Scientist at Sciensano, Belgium

Ms. Mélanie Di Mario is a highly skilled scientist specializing in the field of analytical toxicology and biomedical sciences. Currently, she serves as a Scientist at Sciensano in Brussels, Belgium, where she oversees the Food Contact Materials National Reference Laboratory and represents Belgium at the European Reference Laboratory for food contact materials. Mélanie excels in developing and validating analytical methods for detecting and quantifying substances in food matrices and food contact materials using advanced techniques such as GC-MS/MS, LC-MS/MS, LC-UV, LC-FLUO, GC-FID, and LC-HRMS.

📝professional profile

Scopus Profile

🎓Educational Details:

Since March 1, 2021, I have been pursuing my PhD at the University of Liège in collaboration with Sciensano in Liège, Belgium. My research is focused on advancing the field of biomedical sciences, leveraging the extensive resources and expertise available at both institutions. For more information about the University of Liège, you can visit their website. From September 1, 2012, to September 15, 2017, I completed my Bachelor and Master degrees in Biomedical Sciences with a specialty in Biological Analyses at the University of Liège. This comprehensive program provided me with a strong foundation in biomedical sciences and specialized skills in biological analyses, preparing me for my current research and professional roles.

👨‍🏫Professional Experience:

From February 5, 2019, to the present, I have been working as a Scientist at Sciensano in Brussels, Belgium. In this role, I am responsible for the Food Contact Materials National Reference Laboratory and serve as the Belgian representative at the European Reference Laboratory for food contact materials. I manage the practical organization of daily laboratory activities in accordance with ISO 17025 and oversee routine analysis of food additives and food contact materials. My work includes the development and validation of analytical methods for detecting and quantifying various substances in food matrices and food contact materials using advanced techniques such as GC-MS/MS, LC-MS/MS, LC-UV, LC-FLUO, GC-FID, and LC-HRMS. Additionally, I participate in proficiency tests, write scientific reports, papers, and procedures, supervise junior scientists, analysts, and interns, and organize workshops for Belgian official control laboratories. As the lead scientist, I also spearhead research projects related to food contact materials and food. Between January 2, 2019, and April 15, 2019, I worked as an Assistant Project Manager at Eurofins Amatsi Analytics in Toulouse, France. My responsibilities included checking raw data, results, and reports of clinical trials, writing and reviewing protocols, analytical reports, and standard operating procedures (SOPs), and participating in the GMP environment within the laboratory. Prior to that, from February 19, 2018, to January 2, 2019, I served as a Bioanalyst at Amatsigroup in Toulouse, France. My duties involved performing routine analyses on biological matrices for clinical trials, developing and validating analytical methods on biological matrices, identifying and resolving technical issues, and calibrating HPLC-MS/MS instruments.

Skills:

Ms. Mélanie Di Mario possesses a diverse set of digital skills essential for modern scientific research and collaboration. Her expertise includes information and data literacy, which enables her to effectively gather, analyze, and interpret complex data sets. She excels in communication and collaboration, facilitating seamless interaction with colleagues, stakeholders, and the scientific community through various digital platforms. Mélanie is also proficient in digital content creation, allowing her to produce high-quality scientific reports, presentations, and publications. Additionally, she prioritizes safety in all digital endeavors, ensuring secure data handling and adherence to best practices. Her problem-solving skills further enhance her ability to address and overcome technical and analytical challenges in her work.

📚Publications to Noted

Cannabinoids and Therapeutics | Cannabinoïdes et Thérapeutique

Authors: Fabresse, N., Becam, J., Carrara, L., Senechal, H., Salle, S.

Journal: Toxicologie Analytique et Clinique

Year: 2019

Volume: 31

Issue: 3

Pages: 153–172

Citations: 7

Fatal Nefopam (Acupan®) Overdose | Overdose Fatale de Nefopam (Acupan®)

Authors: Di Mario, M., Luisi, C., Martrille, L., Tournebize, J., Roman, E.

Journal: Toxicologie Analytique et Clinique

Year: 2018

Volume: 30

Issue: 4

Pages: 239–245

Citations: 1