Bittu Ghosh | Civil Engineering | Research Excellence Award

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Dr. Bittu Ghosh | Civil Engineering | Research Excellence Award

Research Scholar | Nit Durgapur | India

Dr. Bittu Ghosh is a civil engineering researcher specializing in construction engineering and management with a strong focus on infrastructure digitalisation. His research centers on Building Information Modeling applications for claims, quality, safety, productivity enhancement, and dispute management in transportation and infrastructure projects. He has contributed extensively through peer-reviewed journal articles, patents, book chapters, and conference papers addressing BIM frameworks, artificial intelligence applications, document management systems, and sustainable construction practices. Professionally, he has experience in infrastructure project execution and management roles, combining academic research insights with practical construction industry implementation and technology-driven decision support.

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🟦 Citations   🟥 i10-index   🟩 h-index


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Featured Publications

3D Printing Technology and Future of Construction: A Review

– International Conference on Creative and Innovative Solutions in Civil Engineering

Digitalized Document Management System for Construction Claims and Disputes Using BIM

– Journal of Legal Affairs and Dispute Resolution in Engineering

Katayoun Kargar | Urban Drainage Systems | Research Excellence Award

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Dr. Katayoun Kargar | Urban Drainage Systems | Research Excellence Award

Toronto Metropolitan University | Canada

Dr. Katayoun Kargar is a civil engineering researcher whose work advances the understanding, modelling, and management of sewer blockages within urban drainage networks, particularly those caused by non-flushable consumer products. Her research integrates hydraulic modelling, data analytics, and infrastructure asset management to address performance deterioration in sewer systems and provide actionable strategies for municipalities. She has contributed original methodologies for analyzing blockage mechanisms using open datasets, quantified the hydraulic impacts of wipe-induced obstructions, and developed simulation frameworks capable of capturing flow alterations and risk conditions in sewer networks. Her work with SWMM includes creating and validating procedures that allow utilities to incorporate blockage dynamics into routine modelling, enabling more accurate capacity evaluations, maintenance prioritization, and system reliability assessments. By translating laboratory insights into practical planning tools, she supports municipalities in enhancing inspection regimes, optimizing rehabilitation strategies, and strengthening long-term infrastructure resilience. Her scholarly contributions include publications indexed in major scientific databases and presentations at leading professional forums, where her work has been recognized through notable awards for research excellence and technical communication. Complementing her academic achievements, she has professional experience in asset management consulting, applying modelling outputs and analytical approaches to assist municipalities in developing asset management plans, capital planning frameworks, and infrastructure lifecycle strategies. Her combined research and practical expertise contribute to advancing municipal wastewater infrastructure management and promoting data-informed decision-making in the engineering of sustainable urban drainage systems.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Shamshirband, S., Hashemi, S., Salimi, H., Samadianfard, S., Asadi, E., … (2020). Predicting standardized streamflow index for hydrological drought using machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 339–350.

Samadianfard, S., Hashemi, S., Kargar, K., Izadyar, M., Mostafaeipour, A., … (2020). Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Reports, 6, 1147–1159.

Kargar, K., Samadianfard, S., Parsa, J., Nabipour, N., Shamshirband, S., … (2020). Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms. Engineering Applications of Computational Fluid Mechanics, 14(1), 311–322.

Kargar, K., Safari, M. J. S., Mohammadi, M., Samadianfard, S. (2019). Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques. Water Science and Technology, 79(12), 2318–2327.

Safari, M. J. S., Mohammadi, B., Kargar, K. (2020). Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit. Journal of Cleaner Production, 276, 124267.

 

Deme Hirko | Civil | Best Researcher Award

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Mr. Deme Hirko | Civil | Best Researcher Award

Lecturer | Stellenbosch University | South Africa

Mr. Deme Hirko is a Water Resources Engineer, Climate Researcher, and Lecturer with extensive experience in teaching, research, and project coordination in the fields of hydrology, irrigation, and water systems management. His work focuses on sustainable water resource strategies, integrating advanced hydrological models with machine learning to optimize water allocation and assess the impacts of climate change. As a PhD researcher at Stellenbosch University, he develops Python-based analytical tools and applies data-driven methods to enhance climate impact assessments and water management efficiency. He has taught and supervised numerous students in hydraulic engineering and water resource modelling, combining academic rigor with practical insights from his earlier experience in infrastructure project management. His research explores the intersection of climate science, data analytics, and high-performance computing, aiming to improve resilience in water-scarce regions. He has published several peer-reviewed papers on topics such as machine learning applications in hydrology, water allocation modelling, and irrigation performance evaluation. In addition to his academic work, he actively participates in international conferences and specialized training programs related to climate modelling, Earth observation, and water resource assessment. Committed to interdisciplinary collaboration, he strives to advance research that supports equitable and data-informed water management policies, particularly within the context of climate variability in Africa.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Hirko, D. B., Du Plessis, J. A., & Bosman, A. (2025). Using machine learning and satellite data to analyse climate change in the Upper Awash Sub-basin, Ethiopia. Physics and Chemistry of the Earth, Parts A/B/C.

Hirko, D. B., Du Plessis, J. A., & Bosman, A. (2025). Review of machine learning and WEAP models for water allocation under climate change. Earth Science Informatics.

Hirko, D. B., Gebul, M. A., Du Plessis, J. A., & Emama, W. O. (2023). Assessment of irrigation water allocation, Koftu, Ethiopia. Water Practice & Technology, 18(6).

Hirko, D. B., & Du Plessis, J. A. (2023, April 16). Assessing the effectiveness of Koftu’s small-scale irrigation schemes in enhancing water resource utilization in Ethiopia [Preprint]. ESSOAr.

Hirko, D. B. (2022, July 27). Irrigation land suitability assessment of Sibilu River catchment using geographic information system. Irrigation & Drainage Systems Engineering.

Lilian Huang | Engineering | Best Researcher Award

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Prof. Lilian Huang | Engineering | Best Researcher Award

Professor Harbin Engineering University China

Lilian Huang is a professor at the School of Information and Communication Engineering, Harbin Engineering University. With a strong background in navigation, guidance, and control, she has made significant contributions to fractional-order chaotic systems, nonlinear dynamics, and chaos-based encryption. Her research has been widely published in high-impact journals, and she holds multiple patents related to chaotic system control and encryption techniques.

Profile

Scopus

🎓 Education

Lilian Huang earned her Ph.D. (2005) and Master’s (2002) in Navigation, Guidance, and Control from Harbin Institute of Technology. She completed her Bachelor’s in Automation from Zhongyuan University of Technology in 1996. Her academic journey laid a strong foundation for her expertise in control systems and nonlinear dynamics.

đź’Ľ Experience

Currently serving as a professor at Harbin Engineering University since 2012, Lilian Huang has also worked as an associate professor (2005-2012) and postdoctoral researcher (2005-2008) at the same institution. She was a visiting scholar at the University of Delaware in 2016-2017. Before joining academia, she worked as an assistant engineer at Harbin Dong’an Engine Manufacturing Co., Ltd. (1996-2000), gaining industry experience.

🔬 Research Interests

Lilian Huang’s research focuses on fractional-order chaotic systems, nonlinear dynamics, and chaos control. She explores their applications in encryption, secure communication, and dynamic system analysis. Her work contributes to developing novel chaotic systems with enhanced security features for information processing.

🏆 Awards & Recognitions

She has led several prestigious research projects funded by the National Natural Science Foundation of China and Heilongjiang Province. Her innovations in chaos-based encryption and nonlinear control have been recognized through numerous patents and high-impact publications.

📚 Publications Top Notes: 

Multi-Image Encryption Algorithm Based on Novel Spatiotemporal Chaotic System and Fractal Geometry – IEEE Transactions on Circuits and Systems I, 2024. Read here

Design and Implementation of Grid-Wing Hidden Chaotic Attractors with Only Stable Equilibria – IEEE Transactions on Circuits and Systems I, 2023. Read here

Generating Multiwing Hidden Chaotic Attractors with Only Stable Node-Foci – IEEE Transactions on Industrial Electronics, 2024. Read here

A Construction Method of N-Dimensional Non-Degenerate Discrete Memristive Hyperchaotic Map – Chaos, Solitons & Fractals, 2022. Read here

A Novel 3D Non-Degenerate Hyperchaotic Map with Ultra-Wide Parameter Range and Coexisting Attractors – Nonlinear Dynamics, 2023. Read here

Exploring extreme multistability in cyclic symmetric conservative systems via two distinct methods

Image encryption based on 2D-SAHM chaotic system and a novel DNA operation rule

Locally Active Memristor-Based De-Jong map and Information Hiding