Mr. Sumit Hassan Eshan | Smart Antenna | Young Researcher Award

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Mr. Sumit Hassan Eshan | Smart Antenna | Young Researcher Award

Lead Technical Engineer | Contessa Solutions and Consultants Ltd | Bangladesh

Mr. Sumit Hassan Eshan is a Bangladeshi researcher and engineering professional whose scholarly contributions span smart antennas, nanomaterial-based biomedical sensing, wireless power transfer, terahertz communication, and advanced materials for next-generation wireless systems. His research integrates materials science with electromagnetic design, focusing on nano-engineered antennas using graphene, carbon nanotubes, and transition-metal dichalcogenides for medical diagnostics, on-body sensing, and 6G terahertz applications. Eshan has authored 12 peer-reviewed publications, including four journal papers and eight conference papers across respected SCI and Scopus-indexed venues. One of his works was highlighted on the front cover of a Q1 journal, showcasing the novelty of his contributions to nanomaterial-enabled antennas for cancer detection. His citation record demonstrates his growing academic influence, with 76 citations, an h-index of 6, and an i10-index of 4 on Google Scholar, and 54 citations with an h-index of 5 on Scopus. Eshan’s research covers interdisciplinary domains such as wireless power amplification, nanomaterial spin-coating techniques, biomedical on-body antenna systems, and efficient THz structures for future communication technologies. His continuous engagement as a peer reviewer for prominent engineering journals further reflects his expertise in antenna design, wireless communication, applied electromagnetics, and emerging materials. With a strong foundation in experimental and simulation-based design using CST Studio Suite, Eshan aims to advance innovative antenna technologies that bridge healthcare diagnostics and next-generation wireless systems. His scholarly record positions him as a promising early-career researcher contributing impactful solutions at the intersection of engineering, materials science, and biomedical sensing.

Profiles

Scopus | ORCID | Google Scholar | LinkedIn | ResearchGate

Featured Publications

Hasan, R. R., Jasmine, J., Saleque, A. M., Eshan, S. H., Tusher, R. T. H., Zabin, S., Nowshin, N., Rahman, M. A., & Tsang, Y. H. (2023). Spin coated multi-walled carbon nanotube patch antenna for breast cancer detection. Advanced Materials Technologies, 8(20), 1–13. (Q1, cited)

Anowar, T. I., Hasan, R. R., Eshan, S. H., & Foysal, M. (2025). Enhanced wireless power transfer system using integrated RF amplification. Results in Engineering. (Q1, cited)

Hasan, R. R., Saha, S., Eshan, S. H., Basak, R., Ivan, M. N. A. S., Saleque, A. M., Tusher, R. T. H., Zabin, S., Rahman, M. A., & Tsang, Y. H. (2024). A compact spin-coated graphene UWB antenna for breast tumor detection. Advanced Engineering Materials. (Q1, cited)

Roy, A., Bhuiyan, M. R., Islam, M. A., Saha, P., Eshan, S. H., Hasan, R. R., & Basak, R. (2024). Tungsten disulfide based wearable antenna in terahertz band for sixth generation applications. Telecommunication Computing Electronics and Control, 22(2), 545–555. (Scopus, cited)

Lia, L., Zishan, M. S. R., Eshan, S. H., & Hasan, R. R. (2024). Graphene based terahertz patch antenna for breast tumor detection. Telecommunication Computing Electronics and Control, 22(5), 1073–1082. (Scopus, cited)

Prof. Na Guo | Computational Chemistry | Research Excellence Award

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Prof. Na Guo | Computational Chemistry | Research Excellence Award

Professor | Guang’an Institute of Technology | China

Prof. Guo Na is a computational and physical chemist whose research bridges quantum chemistry, materials science, and nanomaterials for next-generation catalysis and energy technologies. Her work focuses on unraveling atomic-scale mechanisms that govern chemical reactivity, electrocatalytic conversion, and low-dimensional material behavior, with strong emphasis on first-principles modeling, computational simulations, and structure–property relationships in two-dimensional systems. She has made influential contributions to the design of atomically precise catalysts, including graphene-based platforms, single-atom catalysts, metal–carbon monolayers, and photo-responsive nanomaterials. Her studies have advanced understanding of electrochemical hydrogenation, CO2/CO reduction, nitrogen reduction, and molecular reactivity at metal–surface interfaces, providing pathways for sustainable chemical transformations. Across her career, she has collaborated extensively on multidisciplinary investigations published in leading journals such as Nature, Nature Communications, JACS, Angewandte Chemie, and Advanced Materials. Her research has also shed light on the physics of low-dimensional materials, optoelectronic modulation, and nanoscale device behavior, supported by advanced computational modeling of charge transfer, catalytic activation, and surface dynamics. Her publication record is reflected in Scopus with 107 citations from 107 documents and an h-index of 2, alongside additional visibility on Google Scholar, highlighting both the breadth and collaborative nature of her work. Overall, her research portfolio integrates theoretical rigor with practical applications in catalysis, clean energy, electronic materials, and molecular engineering, contributing significantly to material innovation and sustainable chemistry.

Publication Profile

Scopus

Featured Publications

Liu, X., Hu, C., Guo, N., Li, X., & Liu, B. (2025). Ruthenium-catalyzed electrochemical ketone hydrogenation to secondary alcohols under ambient conditions. Angewandte Chemie International Edition.

Tang, S., Guo, N., Chen, C., Yao, B., Liu, X., Ma, C., Liu, Q., Ren, S., He, C., & Liu, B. (2025). Electrochemical alkyne semi-hydrogenation via proton-coupled electron transfer on Cu(111) surface. Angewandte Chemie.

Yang, H., Guo, N., Zhang, C., Wang, L., et al. (2025). Scalable H2O2 production via O2 reduction using immobilized vanadyl phthalocyanine. Angewandte Chemie International Edition.

Su, J., Guo, N., Lu, J., Zhang, C., et al. (2024). Intelligent synthesis of magnetic nanographenes via chemist-intuited atomic robotic probe. Nature Synthesis.

Lyv, P., Guo, N., Lu, J., Zhang, C., et al. (2024). Air-stable wafer-scale ferromagnetic metallo-carbon nitride monolayer. Journal of the American Chemical Society.

 

Mr. Yixiang Xu | Computational fluid mechanics | Research Excellence Award

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Mr. Yixiang Xu | Computational fluid mechanics | Research Excellence Award

Lecturer | School of Mechanical Engineering, Suzhou University of Science and Technology | China

Mr. Xu Yixiang is a computational fluid dynamics researcher specializing in multiphase flow simulation, interfacial dynamics, and coupled numerical algorithms. His work centers on advancing the ISPH-FVM coupling framework, a hybrid method that integrates the Lagrangian strengths of incompressible smoothed particle hydrodynamics with the efficiency and stability of Eulerian finite volume solvers. Through this unified approach, he has developed improved surface-tension discretization schemes, enhanced mapping techniques, and robust interface-tracking models capable of handling large density ratios and complex topological evolutions. His contributions significantly advance the simulation accuracy of bubble rising, coalescence, droplet deformation, free-surface interaction, and thermo-magnetohydrodynamic phenomena. Xu’s research outputs demonstrate strong recognition in the field, reflected in his Scopus citation metrics of 78 citations, 9 indexed documents, and an h-index of 5, alongside additional citations recorded in Google Scholar. Supported by national research funding, his studies provide computational tools that deepen understanding of fluid behavior in engineering processes such as heat transfer, magnetic-field-driven flows, and advanced multiphase systems. His collaborative works with leading laboratories further reinforce the scientific impact of his ISPH-FVM advancements, which have been adopted to model complex flow behaviors in viscous liquids, conductive fluids, and ferrofluids. Xu’s continued innovations contribute to bridging meshless and grid-based computational paradigms, offering scalable and accurate methodologies for challenging fluid-mechanics applications.

Profile

Scopus 

Featured Publications 

Xu, Y., Yang, G., et al. (2023). A three-dimensional ISPH-FVM coupling method for simulation of bubble rising in viscous stagnant liquid. Ocean Engineering, 278, 114497. (Citations: 18)

Xu, Y., Yang, G., et al. (2023). Improvement of surface tension discrete model in the ISPH-FVM coupling method. International Journal of Multiphase Flow, 160, 104347. (Citations: 14)

Xu, Y., Yang, G., et al. (2021). A coupled SPH–FVM method for simulating incompressible interfacial flows with large density difference. Engineering Analysis with Boundary Elements, 128, 227–243. (Citations: 22)

Xu, Y. (2026). Numerical investigation of bubble-induced heat transfer under external electric field based on ISPH-FVM coupling method. International Journal of Heat and Fluid Flow, 117, 110132. (Citations: 6)

Xu, Y., Yang, G., et al. (2024). Comparison of surface tension models for the simulation of two-phase flow in an ISPH-FVM coupling method. European Journal of Mechanics – B/Fluids, 105, 57–96. (Citations: 10)

Prof. Xuejuan Chen | Differential Equations | Research Excellence Award

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Prof. Xuejuan Chen | Differential Equations | Research Excellence Award

Associate Professor | Jimei University | China

Prof. Xuejuan Chen is a distinguished computational mathematician whose research focuses on high-order numerical algorithms, fractional differential equations, nonlocal models, and advanced simulation methods for complex physical systems. Her work bridges theoretical numerical analysis with real-world applications, particularly in anomalous diffusion, optimal control, groundwater pollution modeling, and fractional dynamical systems. She has made significant contributions to the development of accelerated spectral deferred correction methods, high-precision finite difference schemes, distributed-order fractional models, and efficient Crank–Nicolson–based algorithms for nonsmooth data. Prof. Chen’s research stands out for its emphasis on accuracy, stability, and computational efficiency, offering scalable techniques for solving challenging nonlocal and fractional PDEs. With a strong publication record in leading journals such as Computers & Mathematics with Applications, Computer Methods in Applied Mechanics and Engineering, and Numerical Mathematics: Theory, Methods and Applications, she continues to advance the frontier of computational mathematics. Her scholarly influence is reflected in Scopus metrics, including 197 citations, 14 documents, and an h-index of 6, supported by contributions cited across fractional calculus, scientific computing, and applied mathematics. She also maintains a strong research presence on Google Scholar, where her citation count and impact continue to grow. Prof. Chen’s work not only advances numerical theory but also provides practical, high-accuracy computational tools for scientists and engineers working on nonlocal and fractional modeling problems in physics, engineering, and environmental science.

Publication Profile

Scopus | ORCID

Featured Publications

Chen, A., Chen, X., Yan, Y., & Guo, W. (2026). A corrected Crank–Nicolson scheme for the time fractional parabolic integro-differential equation with nonsmooth data. Mathematics and Computers in Simulation, 242, 279–296.

Wang, J., Chen, X., & Chen, J. (2025). A high-precision numerical method based on spectral deferred correction for solving the time-fractional Allen–Cahn equation. Computers & Mathematics with Applications, 180, 1–27.

Yang, Z., Chen, X., Chen, Y., & Wang, J. (2024). Accurate numerical simulations for fractional diffusion equations using spectral deferred correction methods. Computers & Mathematics with Applications, 153, 123–129.

Chen, X., Mao, Z., & Karniadakis, G. E. (2022). Efficient and accurate numerical methods using the accelerated spectral deferred correction for solving fractional differential equations. Numerical Mathematics: Theory, Methods and Applications, 15(4), 876–902.

Chen, X., Zeng, F., & Karniadakis, G. E. (2017). A tunable finite difference method for fractional differential equations with non-smooth solutions. Computer Methods in Applied Mechanics and Engineering, 318, 193–214.

Prof. Younghun Kwon | Quantum Computer | Research Excellence Award

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Prof. Younghun Kwon | Quantum Computer | Research Excellence Award

Professor | Hanyang University | South Korea

Prof. Younghun Kwon is a distinguished quantum physicist whose research has significantly advanced the foundations and applications of quantum information science, quantum computation, and artificial intelligence–driven quantum technologies. As the head of the Mathematical Science Lab at Hanyang University, he has made pioneering contributions to quantum state discrimination, quantum error correction, sequential state discrimination, quantum communication, quantum correlations, coherence theory, and the hardware implementation of superconducting quantum computing systems. His work spans both fundamental theoretical insights and practical architectures that form the building blocks of future quantum computers. Notably, he has proposed new hardware structures for superconducting quantum processors, introduced innovative quantum error-correcting strategies for biased-noise systems, and demonstrated groundbreaking results revealing how classical prior probabilities can induce nonlocal quantum effects. His achievements also include foundational solutions to multi-qubit and multi-party state discrimination problems, which have remained open for decades. His research integrates advanced mathematical modeling, experimental implementation frameworks, and AI-augmented quantum processing methods. With an extensive publication record covering high-impact journals, international conferences, and patented technologies, his work continues to influence quantum information theory worldwide. According to Scopus, his research output includes 23 documents with 119 citations and an h-index of 6, while Google Scholar reflects a broader research impact with significantly higher citation counts across quantum information science and hybrid quantum systems. His scholarly trajectory demonstrates sustained leadership in merging quantum mechanics, computation, and intelligent systems to accelerate the realization of practical large-scale quantum technologies.

Profile

Scopus | ORCID

Featured Publications

Ha, D., & Kwon, Y. (2023). Complete analysis to minimum-error discrimination of four mixed qubit states with arbitrary prior probabilities. Quantum Information Processing, 22, 67. (Citations: 3)

Kim, Y., Kang, J., & Kwon, Y. (2023). Design of quantum error correcting code for biased error on heavy-hexagon structure. Quantum Information Processing, 22, 230. (Citations: 5)

Namkung, M., & Kwon, Y. (2020). Understanding various types of unambiguous discrimination in view of coherence distribution. Entropy, 22, 1422. (Citations: 5)

Namkung, M., & Kwon, Y. (2019). Almost minimum-error discrimination of N-ary weak coherent states by Jaynes-Cummings Hamiltonian dynamics. Scientific Reports, 9, 19664. (Citations: 13)

Ha, D., & Kwon, Y. (2013). Complete analysis for three-qubit mixed-state discrimination. Physical Review A, 87, 062302. (Citations: 52)

Mr. Angelos Athanasiadis | Hardware Acceleration | Research Excellence Award

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Mr. Angelos Athanasiadis | Hardware Acceleration | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Mr. Angelos Athanasiadis is a researcher in Electrical and Computer Engineering whose work centers on high-performance FPGA architectures, hardware acceleration of Convolutional Neural Networks, and advanced emulation methodologies for heterogeneous computing systems. His research focuses on enabling full-precision, non-quantized deep learning inference on reconfigurable hardware, addressing challenges in energy efficiency, throughput optimization, and deployment in accuracy-critical environments such as aerial monitoring, autonomous systems, and embedded intelligence. He has contributed to the development of parameterizable high-level synthesis (HLS) IP libraries and FPGA-optimized computational kernels, including a fully customizable matrix multiplication framework that supports architectural exploration, resource scalability, and integration with modern AMD FPGA toolchains. Beyond acceleration frameworks, he has designed FUSION, an innovative open-source emulation platform that synchronizes QEMU and OMNeT++ using HLA/CERTI to achieve deterministic, timing-accurate, multi-node experimentation with sub-microsecond synchronization and complete observability of system-level interactions. His work expands the boundaries of distributed embedded system prototyping by combining CPUs, GPUs, and FPGAs into unified hybrid simulation environments. He has participated in collaborative research projects and contributed to publications in embedded systems, power/timing modeling, and FPGA computing. His citation record reflects an emerging academic profile, with metrics documented through Google Scholar and Scopus, including citation counts, h-index values, and related research indicators. Supporting documents, citations, and publication evidence can be verified through his academic profiles as required. His research continues to advance the intersection of hardware design, machine learning acceleration, and distributed system emulation, contributing tools and methods that strengthen reproducibility, scalability, and efficiency in modern computing research.

Publication Profile

ORCID | Google Scholar

Featured Publications 

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, 102625. (Citations: 1)

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). Energy-efficient FPGA framework for non-quantized convolutional neural networks. arXiv:2510.13362. (Citations: 1)

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal-Works in Progress in Embedded Computing, 10(2). (Citations: 2)

Katselas, L., Jiao, H., Athanasiadis, A., Papameletis, C., Hatzopoulos, A., & colleagues. (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs. Proceedings of the International Symposium on Power and Timing Modeling, Optimization. (Citations: 2)

Katselas, L., Athanasiadis, A., Hatzopoulos, A., Jiao, H., Papameletis, C., & colleagues. (2017). Embedded toggle generator to control the switching activity. Conference publication. (Citations: 2)

Mr. Sajjad Naseri | AI in Designing | Research Excellence Award

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Mr. Sajjad Naseri | AI in Designing | Research Excellence Award

Ball State University | United States

Sajjad Naseri is an emerging interdisciplinary researcher working at the intersection of sustainable architecture, urban design, and green building performance, with a strong emphasis on integrating artificial intelligence into the built environment. His work spans data-driven evaluation of LEED-certified buildings, hospitality sustainability outcomes, climate-responsive design performance, and AI-enabled planning applications. He has contributed to impactful research exploring how certification levels influence user satisfaction, how climatic variables shape sustainability metrics, and how emerging digital tools can support more resilient, livable, and equitable urban settings. His cross-continental academic and research background—covering environmental design, architectural processes, and urban planning—strengthens his ability to synthesize global perspectives with applied analytical methods. Naseri has co-authored multiple journal articles, conference papers, and collaborative planning documents, demonstrating a strong commitment to evidence-based, practice-oriented research. His scholarly presence continues to expand, supported by measurable impact indices that reflect sustained and growing citations across platforms. His Google Scholar profile reports 60 citations, an h-index of 5, and an i10-index of 3, while Scopus citation metrics (when indexed) complement his publication visibility in architecture, sustainability, and built-environment research. With contributions published in recognized journals and conferences—including Buildings, Advances in Civil Engineering, Current Opinion, and the American Planning Association—his research helps bridge academic theory with real-world environmental planning and performance assessment. Naseri’s current work continues to advance sustainability-focused design and intelligent urban systems, positioning him as a promising scholar in green building analytics, AI-driven spatial design, and climate-responsive architectural strategies.

Publication Profile

Google Scholar

Featured Publications

  • Talebian, S., Golkarieh, A., Eshraghi, S., Naseri, M., & Naseri, S. (2025). Artificial Intelligence Impacts on Architecture and Smart Built Environments: A Comprehensive Review. Advances in Civil Engineering, 2(1). Citations: 15. https://www.aceesjr.com/article_212817.html

  • Ghorashi, S. M., Ezzatfar, M., Hatami, R., Bagheri, A., Naseri, S., & Najafabadi, R. N. (2024). The role of subcultures in creating new social issues: Qualitative analysis. Current Opinion, 4(3), 679–696. Citations: 14. http://currentopinion.be/index.php/co/article/view/315

  • Naseri, S., Eshraghi, S., & Talebian, S. (2024). Innovative sustainable architecture: A lesson learned from amphibious house in the UK. Current Opinion, 4(4), 766–777. Citations: 10. http://currentopinion.be/index.php/co/article/view/318

  • Naseri, S. (2024). AI in Architecture and Urban Design and Planning: Case studies on three AI applications. GSC Advanced Research and Reviews. Citations: 8. https://gsconlinepress.com/journals/gscarr/

  • Najafabadi, R. N., Avar, S., Karimi, M., Anbari, M., & Naseri, S. (2024). Ecological restoration of historical monuments with a focus on the restoration of Chogha Zanbil. World Journal of Advanced Research and Reviews, 23(02), 240–250. Citations: 8. https://wjarr.com/

Assoc. Prof. Dr. Essa M. Saied | Computer-Aided Drug Design | Research Excellence Award

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Assoc. Prof. Dr. Essa M. Saied | Computer-Aided Drug Design | Research Excellence Award

Humboldt University of Berlin | Germany

Dr. Essa M. Saied is a distinguished bioorganic and biochemistry researcher whose work bridges synthetic medicinal chemistry, lipid biology, and small-molecule drug discovery. His research focuses on designing and synthesizing bioactive molecules, stereoselective synthetic methodologies, heterocyclic compounds, and lipid-based probes to investigate fundamental biological processes. He has made landmark contributions to understanding sphingolipid metabolism, ceramidase activity, lipid transfer mechanisms, and the structure–function relationships of bioactive lipids. His expertise spans synthetic chemistry, combinatorial library design, high-throughput screening, molecular modelling, virtual screening, enzymatic assay development, and structure–activity relationship optimization. Dr. Saied’s research has advanced knowledge of lipid-associated diseases, anticancer agents, antimicrobial strategies, and molecular mechanisms underlying metabolic and neurological disorders. He has authored around 70 peer-reviewed publications with strong global impact, reflected in a Scopus h-index of 31 with over 2,229 citations and a Google Scholar h-index of 35 with more than 2,872 citations. His body of work is widely referenced across the fields of lipidomics, medicinal chemistry, structural biology, and biochemical pharmacology. Dr. Saied’s publications include pioneering structural studies of adiponectin receptors, mechanistic analysis of ceramide-related pathways, discovery of small-molecule enzyme inhibitors, and the development of innovative analytical and spectroscopic tools to characterize lipid isomers. His research output demonstrates a consistent record of high-impact contributions, shaping modern understanding of lipid chemistry and its role in human disease.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Vasiliauskaite-Brooks, I., Sounier, R., Rochaix, P., Bellot, G., Fortier, M., Hoh, F., Bechara, C., Saied, E. M., Arenz, C., Leyrat, C., De Colibus, L., & Granier, S. (2017). Structural basis for the ceramidase activity of adiponectin receptors. Nature, 544, 120–123. Citations: 230.

Vasiliauskaité-Brooks, I., Healey, R. D., Rochaix, P., Saint-Paul, J., Sounier, R., Grison, C., Waltrich-Augusto, T., Fortier, M., Hoh, F., Saied, E. M., Arenz, C., Leyrat, C., & Granier, S. (2018). Structure of a human intramembrane ceramidase explains enzymatic dysfunction found in leukodystrophy. Nature Communications, 9, 5437. Citations: 51.

Lone, M. A., Huelsmeier, A. J., Saied, E. M., Karsai, G., Arenz, C., von Eckardstein, A., & Hornemann, T. (2020). Subunit composition of the mammalian serine-palmitoyltransferase defines the spectrum of straight and methyl-branched long-chain bases. Proceedings of the National Academy of Sciences, 117, 15591–15598. Citations: 85.

Saied, E. M., El-Maradny, Y. A., Osman, A. A., Darwish, A. M. G., Abo Nahas, H. H., & El-Seedi, H. R. (2021). A comprehensive review about the molecular structure of SARS-CoV-2: Insights into natural products against COVID-19. Pharmaceutics, 13, 1759. Citations: 66.

Kirschbaum, C., Saied, E. M., Greis, K., Mucha, E., Gewinner, S., Schöllkopf, W., Meijer, G. J. M., von Helden, G., Poad, B. L. J., Blanksby, S. J., Arenz, C., & Pagel, K. (2020). Resolving sphingolipid isomers using cryogenic infrared spectroscopy. Angewandte Chemie, 132, 13740. Citations: 51.

Dr. Gu Shan | Power Systems | Women Researcher Award

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Dr. Gu Shan | Power Systems | Women Researcher Award

Associate Professor | Zhejiang University of Water Resources and Electric Power | China

Dr. Shan Gu is a researcher in the fields of energy engineering, sustainable power systems, and environmental technology, with a strong focus on biomass utilization, air-pollutant mitigation, and life cycle assessment. Her work integrates engineering experimentation, process optimization, and environmental impact evaluation to advance the development of clean energy technologies. She has contributed significantly to the study of biomass pyrolysis, nanosilica extraction from agricultural waste, and the operational behavior of circulating fluidized bed gasifiers. Her research on biomass CFB gasification systems, including the coupling of gasifiers with industrial steam boilers, has generated important insights into practical challenges such as slagging, ash deposition, and system optimization. These contributions have provided evidence-based guidance for the scaling, operation, and environmental performance improvement of biomass-based energy systems. Dr. Gu has authored more than 30 research publications, including multiple SCI-indexed articles, with several featured in high-impact journals. Her scholarly work demonstrates strong visibility, with measurable academic influence across citation databases. According to Scopus, she has 14 indexed documents, 16 citations by 15 documents, and an h-index of 2. Her Google Scholar profile shows significantly higher engagement, with over 250 citations across her most influential works, including widely referenced studies on nanosilica production and biomass gasification, each exceeding 100 citations. Her publications continue to inform ongoing research in sustainable materials, renewable energy pathways, and the optimization of energy–environment systems, positioning her as an active contributor to advancing cleaner technologies and carbon-reduction strategies.

Publication Profile

Scopus

Featured Publications

Gu, S., Zhou, J., Luo, Z., Wang, Q., & Ni, M. (2013). A detailed study of the effects of pyrolysis temperature and feedstock particle size on the preparation of nanosilica from rice husk. Industrial Crops and Products. Citations: 121.

Gu, S., Zhou, J., Yu, C., & Shi, Z. (2015). A novel two-staged thermal synthesis method of generating nanosilica from rice husk via pre-pyrolysis combined with calcination. Industrial Crops and Products. Citations: 105.

Gu, S., Zhou, J., Luo, Z., & Shi, Z. (2015). Kinetic study on the preparation of silica from rice husk under various pretreatments. Journal of Thermal Analysis and Calorimetry. Citations: 25.

Gu, S., Zhou, J., Lin, B., & Luo, Z. (2015). Life cycle greenhouse gas impacts of biomass gasification-exhausted heat power generation technology in China. Journal of Biobased Materials and Bioenergy..

Li, R., Gu, S., Ye, Y., Li, Z., Zhou, L., & Xu, C. (2025). System optimization and primary electrical design of a 50 MW agrivoltaic power station: A case study in China.

Mr. Abir Das | Artificial Intelligence | Research Excellence Award

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Mr. Abir Das | Artificial Intelligence | Research Excellence Award

Siliguri Government Polytechnic College | India

Abir Das is an emerging AI/ML researcher whose work spans deep learning, computer vision, medical imaging, and explainable AI. With a strong foundation in developing end-to-end AI systems, his research focuses on Vision Transformers, self-supervised learning, noisy-label correction, and interpretable models for high-stakes applications such as healthcare, EEG signal analysis, and industrial fault diagnosis. He has contributed as the first author to multiple international journals, working extensively on hybrid deep learning models, CLIP-based zero-shot learning, EEG motor imagery classification, and sensor-driven diagnostic pipelines. His research integrates expertise in PyTorch, TensorFlow, and modern transformer architectures, emphasizing human-centered, reliable, and transparent AI solutions. He has actively explored the intersection of computer vision and embedded systems, enhancing drone autonomy, depth estimation, and real-time object detection, while also contributing to speech technologies through accent-conversion and multimodal learning. His scientific output includes publications in reputable venues such as Scientific Reports, MDPI Sensors, and Computers, Materials & Continua. His growing scholarly impact is reflected in Scopus metrics: 11 citations from 11 documents with an h-index of 1, and Google Scholar metrics: 12 citations, h-index 1, i10-index 1. His work continues to advance practical and theoretically grounded AI methodologies, blending deep learning innovations with real-world applications across biomedical imaging, EEG analysis, and industrial AI systems.

Publication Profile

Scopus | Google Scholar

Featured Publications

Das, A., Singh, S., Kim, J., Ahanger, T. A., & Pisa, A. A. (2025). Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models. Scientific Reports, 15(1), 27161.

Zereen, A. N., Das, A., & Uddin, J. (2024). Machine fault diagnosis using audio sensor data and explainable AI techniques: LIME and SHAP. Computers, Materials & Continua, 80(3).

Das, S. S. A. (2025). Few-shot and zero-shot learning for MRI brain tumor classification using CLIP and Vision Transformers. Sensors, 25(23), 7341.