Xiaoqian Liu | Social Sciences | Best Researcher Award

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Ms. Xiaoqian Liu | Social Sciences | Best Researcher Award

Xinjiang University | China

Dr. Xiaoqian Liu is a dedicated researcher at Xinjiang University specializing in sustainable urban transportation systems, traffic optimization, and smart mobility solutions. Her academic contributions focus on integrating operational efficiency with passenger-oriented design to enhance the functionality of public transport and parking systems. She has authored two publications indexed in Scopus, with an h-index of 1 and 2 citations. Her study, Integrated Optimization of Feeder Bus Timetables and Vehicle Scheduling Considering Passenger Heterogeneity: Implications for Sustainable Urban Transit and Land Use Efficiency (Applied Sciences), explores optimizing feeder bus operations through advanced scheduling models that account for passenger diversity and land-use interaction. she co-authored Analysis of Preparation Mechanism of Electrospun Nanofiber Yarns in the Wool Textile Journal, showcasing her interdisciplinary approach bridging material science and industrial process optimization. Dr. Liu’s research highlights data-driven modeling, multi-objective optimization, and sustainable infrastructure development within the broader scope of social and environmental systems. Through her publications and collaborative efforts, she contributes to advancing smart city transport planning, efficient bus-network scheduling, and environmentally responsible mobility frameworks, reflecting a commitment to innovation and applied research excellence in the social sciences and engineering interface.

Profile : Scopus

Featured Publications

Liu, X. (2025). Integrated optimization of feeder bus timetables and vehicle scheduling considering passenger heterogeneity: Implications for sustainable urban transit and land use efficiency. Applied Sciences, 15(19), 10715.

Emmanuel Antwi | Engineering and Technology | Best Researcher Award

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Mr. Emmanuel Antwi | Engineering and Technology | Best Researcher Award

Missouri University of Science and Technology | United States

Dr. Emmanuel Atta Antwi is a Ph.D. candidate in Mining Engineering at the Missouri University of Science and Technology , having earned his BSc in Mining Engineering from the University of Mines and Technology in Tarkwa, Ghana, and a Certificate in Data Science & Machine Learning from the The AI Institute . With a strong foundation in mine planning, ventilation and production analysis, he has held roles as a Research Assistant, Strategic Mine Planning Engineer Intern and Mining Engineer Co-op in both surface and underground mining environments. His scholarly work includes peer-reviewed publications on topics such as underground mine communications under extreme dust conditions and stochastic modelling of electromagnetic wave propagation in mines. One recent article is titled “Stochastic Modeling of Electromagnetic Wave Propagation Through Extreme Dust Conditions in Underground Mines Using Vector Parabolic Approach” . His research interests include emergency self-escape systems, mine layout safety design, ventilation optimisation and production performance analytics. Recognised for mentoring new researchers and collaborating across disciplines, he aims to advance mining safety and cost-effective productivity through data-driven engineering solutions and cross-functional team leadership.

Profile : Orcid

Featured Publications

Antwi, E. A., Shafique, S., & Nima, Z. A. (2025). Stochastic modeling of electromagnetic wave propagation through extreme dust conditions in underground mines using vector parabolic approach. Information, 16(10), 891.

Olga Tarasova | Genetics and Genomics | Best Researcher Award

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Dr. Olga Tarasova | Genetics and Genomics | Best Researcher Award

Institute of Biomedical Chemistry, Russian Academy of Medical Sciences (RAMS) | Russia

Dr Olga A. Tarasova is a bioinformatician and computational chemist whose research bridges cheminformatics, machine-learning and virus–host interaction modelling. She gained her M.S. in Medical Cybernetics and PhD in Bioinformatics from the Institute of Biomedical Chemistry (Moscow, Russia). Since then, she has progressed through roles from junior researcher to senior researcher now leading advanced computational modelling studies of antiviral compounds and virus–host interplay (e.g., at the Laboratory of Structure-Based Drug Design and recently heading the Laboratory of Big Data Analysis in Digital Pharmacology at IBMC). Her research interests include (Q)SAR/QSPR modelling, fragment-based drug design, molecular docking, text/data mining and machine-learning for prediction of metabolic, toxicity and viral-resistance profiles. Among her honours: the First-Degree Prize for Best Investigation at the Young Scientists Forum of the Russian Academy of Sciences for her work on HIV–host interactions. Dr Tarasova combines rigorous methodological development with applied antiviral and host-interaction modelling, making her a strong contributor to computational drug-discovery and virology informatics.

Profile : Orcid

Featured Publications

Pozdniakova, N., Generalov, E., Shevelev, A., & Tarasova, O. (2025). RNA therapeutics: Delivery problems and solutions A review. Pharmaceutics, 17(10), 1305.

Ivanov, S. M., Sukhachev, V. S., Tarasova, O. A., Lagunin, A. A., & Poroikov, V. V. (2025). Analysis of genomic and transcriptomic data revealed key genes and processes in the development of major depressive disorder. International Journal of Molecular Sciences, 26(19), 9557.

Shevelev, A., Pozdniakova, N., Generalov, E., & Tarasova, O. (2025). siRNA therapeutics for the treatment of hereditary diseases and other conditions: A review. International Journal of Molecular Sciences, 26(17), 8651.

Tarasova, O., Petrou, A., Ivanov, S. M., Geronikaki, A., & Poroikov, V. (2024). Viral factors in modulation of host immune response: A route to novel antiviral agents and new therapeutic approaches. International Journal of Molecular Sciences, 25(17), 9408.

Stolbova, E. A., Stolbov, L. A., Filimonov, D. A., Poroikov, V. V., & Tarasova, O. A. (2024). Quantitative prediction of human immunodeficiency virus drug resistance. Viruses, 16(7), 1132.