Tchilabalo Bouyo | Microbiology | Research Impact Award

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Dr. Tchilabalo Bouyo | Microbiology | Research Impact Award

University of Lomé | Togo

Dr. Tchilabalo Bouyo is a Biological Engineer and early-career researcher specializing in molecular bacteriology, natural product chemistry, and antimicrobial resistance. With demonstrated experience in laboratory management and applied microbiological research, he has developed strong expertise in DNA extraction, PCR-based detection, and molecular characterization of pathogenic organisms. His analytical proficiency spans RStudio, SPSS, Excel, and GraphPad Prism, enabling robust statistical analysis data visualization and quality assurance in biomedical research. His background includes impactful roles at Centro Investigacion Biomedica and Infirmerie NDE Tokion Séminaire where he contributed to microbiological diagnostics antimicrobial resistance identification and laboratory quality compliance.Bouyo’s research output includes seven scientific documents consisting of journal articles experimental studies and preprints indexed in Scopus Clarivate and Crossref with 1 citation and an h-index of 1 reflecting a growing and internationally visible academic trajectory. A cornerstone of his scholarly contribution is the 2025 article Clarithromycin-resistant Helicobacter pylori in Africa: a systematic review and meta-analysis published in Antimicrobial Resistance  Infection Control. This influential work provides comprehensive evidence on antibiotic resistance trends across Africa contributing to global policy discussions and antimicrobial stewardship strategies.In parallel Bouyo has extensively investigated phytochemicals toxicity profiles and antimicrobial properties of medicinal plants widely used in traditional medicine in Togo and West Africa. His studies on species such as Pteleopsis suberosa Piliostigma thonningii Calotropis procera Anchomanes difformis and Tetrapleura tetraptera highlight the therapeutic potential of indigenous flora and support the scientific validation of ethnopharmacological practices. These works have appeared in journals such as Scientific Reports Journal of Applied Biotechnology and Chemical Science International Journal.Collaborating with multidisciplinary teams across Africa Asia and South America Bouyo contributes to global research networks in natural product drug discovery antimicrobial resistance surveillance and evidence-based traditional medicine. His work advances public health by bridging laboratory science cultural knowledge systems and modern biomedical innovation reinforcing his commitment to impactful and socially relevant global health research.

Profiles:  Scopus | ORCID

Featured Publications

1. Kossi, K., Komi, K. K., Sandrine, S. T., Bouyo, T., & Tchadjobo, T. (2025). Bioactive molecules isolated from Phyllanthus amarus Schum & Thonn, Caesalpinia bonduc (L.) Roxb, Momordica charantia Linn and Xylopia aethiopica (Dunal) A. Rich used in the Diabeto-Dolvo® recipe in Togo: A review. Chemical Science International Journal.

2. Dossouvi, K. M., Bouyo, T., Sognonnou, S., Ibadin, E. E., Lv, L.-C., Sambe Ba, B., Seck, A., Dossim, S., Sellera, F. P., Camara, M., et al. (2025). Clarithromycin-resistant Helicobacter pylori in Africa: A systematic review and meta-analysis. Antimicrobial Resistance and Infection Control.

3. Bouyo, T., Komi, K. K., Salifou, S. T., Pissang, P., Gbekley, H. E., Hoekou, Y., M’boumba, B. E., Kpatagnon, J. K., Bidjada, B., & Dossouvi, K. M., et al. (2025). Phytochemical and biological studies of the hydroethanolic extract of Pteleopsis suberosa and Piliostigma thonningii used in herbal medicine in Togo. Research Square.

4. Tchakondo, S., Boleti, N., Gamayizome, A. E., Bouyo, T., Gbekley, E. H., Tchacondo, T., & Karou, S. D. (2025). Phytochemical, toxicological, and antimicrobial evaluation of the hydroethanolic extract of Calotropis procera (Ait.) leaves used in traditional medicine in the Maritime Region of Togo: An experimental study. Research Square.

5. Bouyo, T., Komi, K. K., Salifou, S. T., Pissang, P., Gbekley, E. H., Hoekou, Y., M’boumba, B. E., Kpatagnon, J. K., Bidjada, B., Sossou, K., et al. (2025). In vitro phytochemical and biological evaluation of hydroethanolic extract of Anchomanes difformis in Togo. Scientific Reports.

Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

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Mr. Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

Master of Engineering | Hubei Normal University | China

Mr. Xuewen Zhou is a developing researcher in medical signal processing, medical image segmentation, and intelligent optimization algorithms, with growing contributions to the fields of biomedical engineering and computational intelligence. Affiliated with Hubei Normal University, his research focuses on designing advanced fractional-order and optimization-driven neural network models to enhance the analysis of physiological signals such as ECG and EEG as well as dermatological image segmentation. With 5 scientific publications, 4 citations, and an h-index of 1, Dr. Zhou is steadily establishing a strong academic presence.Dr. Zhou’s notable achievements include the publication of multiple SCI-indexed journal papers and active participation in leading international conferences. His recent SCI Q2 paper Adaptive Fractional Order Pulse Coupled Neural Networks with Multi-Scale Optimization for Skin Image Segmentation introduces an innovative segmentation framework integrating fractional order optimization with pulse coupled neural networks. The method employs a novel entropy–edge fitness function significantly improving accuracy in skin lesion delineation.Another key contribution is the SCI Q2 paper Improved Sparrow Search Based on Temporal Convolutional Network for ECG Classification where Dr. Zhou explores hybrid fractional order algorithms to optimize ECG recognition. His work rigorously analyzes the influence of positive and negative fractional orders on optimization stability offering valuable insights into next-generation fractional learning systems.In the EI indexed China Automation Congress Dr. Zhou proposed an ECG classification model combining spatial–channel attention networks with an improved RIME optimization algorithm enhancing hyperparameter tuning for complex biomedical patterns. He also contributed to neuromorphic computing through the ICNC  paper on FRMAdam iTransformer KAN presenting a fractional order momentum optimizer for EEG and ECG prediction.Dr. Zhou maintains strong collaborations with researchers including Jiejie Chen Ping Jiang Xinrui Zhang Zhiwei Xiao and Zhigang Zeng contributing to interdisciplinary advancements across medical AI fractional order theory and neural computation. His research demonstrates meaningful societal impact by improving early disease detection supporting intelligent diagnostic tools and advancing clinical decision making technologies on a global scale.

Profiles: Scopus | ORCID | ResearchGate

Featured Publications

1.Zhou, X., Chen, J., Jiang, P., Zhang, X., & Zeng, Z. (2026). Adaptive fractional-order pulse-coupled neural networks with multi-scale optimization for skin image segmentation. Biomedical Signal Processing and Control, (February 2026).

2.Zhou, X., Chen, J., Xiao, Z., Zhang, X., Jiang, P., & Zeng, Z. (2026). Improved sparrow search based on temporal convolutional network for ECG classification. Biomedical Signal Processing and Control, (February 2026).

3.Xiao, Z., Chen, J., Zhou, X., Wei, B., Jiang, P., & Zeng, Z. (2025). Monotonic convergence of adaptive Caputo fractional gradient descent for temporal convolutional networks. Neurocomputing, (December 2025).

4.Zhang, X., Chen, J., Zhou, X., & Jiang, P. (2024, December 13). FRMAdam-iTransformer KAN: A fractional order RMS momentum Adam optimized iTransformer with KAN for EEG and ECG prediction. In 2024 International Conference on Neuromorphic Computing (ICNC).

5.Zhou, X., Chen, J., Jiang, P., & Zhang, X. (2024, November 1). Electrocardiogram classification based on spatial-channel networks and optimization algorithms. In 2024 China Automation Congress (CAC).

Dr. Xuewen Zhou’s work advances science and society by developing fractional-order neural systems that significantly enhance the accuracy of biomedical signal and image analysis. His innovations support earlier disease detection, improved diagnostic reliability, and broader global access to intelligent healthcare technologies.

Şifa Gül Demiryürek | Generative Models for Computer Vision | Outstanding Scientist Award

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Dr. Şifa Gül Demiryürek | Generative Models for Computer Vision | Outstanding Scientist Award

Lecturer | Aksaray University | Turkey

Dr. Şifa Gül Demiryürek is a researcher specializing in acoustics, dynamics, vibration control, nonlinear structures, and metamaterials, with a growing body of work that bridges fundamental mechanics and applied engineering. Her research focuses on low-frequency broadband vibration damping, nonlinear passive particle dampers, and metamaterial-inspired structures aimed at improving stability, efficiency, and durability in modern mechanical systems.She has authored 11 scientific documents, accumulating 19 citations with an h-index of 3, reflecting the emerging impact of her contributions. Her early work includes the experimental study of thermal-mixing phenomena in coaxial jets published in the Journal of Thermophysics and Heat Transfer demonstrating her multidisciplinary foundation in fluid–thermal interactions. Transitioning toward structural dynamics  her doctoral research at the University of Sheffield advanced the understanding of periodically arranged nonlinear particle dampers under low-amplitude excitation providing new insights into damping mechanisms critical for lightweight and high-performance structures.Dr. Demiryürek has collaborated with notable researchers such as A. Krynkin and J. Rongong contributing to recognized venues including DAGA, ACOUSTICS Proceedings, and the Institute of Acoustics. Her studies on metamaterial-based dampers and locally resonating structures highlight innovative strategies for vibration mitigation particularly in the low-frequency regime where traditional dampers are less effective. Her works further expand this direction with investigations on dynamic behavior of thermoplastics and material resonance considerations for wind turbine towers addressing contemporary engineering challenges related to sustainability and structural reliability.In addition to research publications she has contributed educational materials including Introduction to Metamaterials  supporting broader knowledge dissemination in emerging engineering domains. Her collaborations in applied mechanics such as the numerical evaluation of electric motorcycle chassis demonstrate a commitment to integrating theoretical advances into practical real-world applications.Through her focused work at the intersection of vibration engineering and metamaterial science Şifa Gül Demiryürek is contributing to next-generation solutions for safer quieter and more efficient mechanical systems with potential societal impact across manufacturing transportation renewable energy and advanced materials engineering.

Profiles: Googlescholar | Scopus | ORCID

Featured Publications

1.Demiryürek, S. G., Kok, B., Varol, Y., Ayhan, H., & Oztop, H. F. (2018). Experimental investigation of thermal-mixing phenomena of a coaxial jet with cylindrical obstacles. Journal of Thermophysics and Heat Transfer, 32(2), 273–283. Cited By: 5

2. Demiryürek, S. G. (2022). Periodically arranged nonlinear passive particle dampers under low-amplitude excitation (Doctoral research, University of Sheffield). Cited By: 3

3. Demiryürek, S. G., & Krynkin, A. (2021). Low-frequency broadband vibration damping using the nonlinear damper with metamaterial properties. In DAGA 2021 Conference Proceedings (pp. 94–96). Cited By: 3

4.Demiryürek, S. G., Krynkin, A., & Rongong, J. (2020). Modelling of nonlinear dampers under low-amplitude vibration. In ACOUSTICS 2020 Proceedings. Cited By: 3

5.Demiryürek, S. G., Krynkin, A., & Rongong, J. (2019). Non-linear metamaterial structures: Array of particle dampers. Universitätsbibliothek der RWTH Aachen. Cited By: 3

Dr. Şifa Gül Demiryürek’s research advances next-generation vibration damping and metamaterial technologies, enabling safer, quieter, and more efficient mechanical systems across industries. Her contributions support innovation in sustainable engineering from wind energy structures to lightweight transportation strengthening global efforts toward resilient, high-performance designs.

Etienne Perre | Human Computer Interaction and Augmented Reality | Best Innovation Award

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Prof. Etienne Perre | Human Computer Interaction and Augmented Reality | Best Innovation Award

Professor | Université Grenoble Alpes | France

Prof. Etienne Perret is an internationally recognized scholar in chipless Radio Frequency Identification (RFID), electromagnetic engineering, and microwave systems. With a distinguished research portfolio comprising 345 scientific documents, an h-index of 35, i10-index of 114, and over 5,740 citations, he stands among the foremost contributors to the global advancement of chipless RFID technologies. His pioneering work has shaped modern paradigms in identification, sensing, coding, and microwave-based signal processing.Dr. Perret’s research focuses on the design, optimization, and experimental realization of chipless RFID tags, hybrid and polarization-diverse coding methods UWB reader systems, RF encoding particles and group-delay–based encoding structures. His landmark publications such as hybrid-coded chipless tags, depolarizing tags for robust detection high-capacity polarization-insensitive tags and fully printable paper-based RFID systems are among the most cited works in the field each receiving between 150 and 400 citations. These contributions have provided the theoretical foundation and practical architectures enabling low-cost printable and flexible RFID solutions suitable for large-scale deployment.A notable part of his research explores RF sensing using silicon nanowires leading to breakthroughs in humidity sensing environmental monitoring and multifunctional RFID devices. His work on RCS magnitude-level coding noncommensurate transmission-line all-pass networks and analog signal processing further demonstrates his versatility across high-frequency electronics and electromagnetic engineering.Dr. Perret collaborates extensively with leading researchers including S. Tedjini A. Vena O. Rance, R. Nair, and others across Europe Australia and North America. His co-authored books and contributions to IEEE Magazines and Elsevier volumes continue to guide researchers engineers and industry practitioners.The societal impact of his research is profound enabling sustainable chipless and cost-effective identification technologies for logistics supply chain monitoring smart packaging structural health analysis and IoT sensing. His work accelerates the transition toward greener RFID solutions by eliminating semiconductor chips and promoting scalable printable technologies that benefit both industry and the environment.Dr. Perret remains a leading global voice advancing the science and engineering of next-generation RFID and wireless sensing systems.

Profiles: Googlescholar | Scopus

Featured Publications

1. Vena, A., Perret, E., & Tedjini, S. (2011). Chipless RFID tag using hybrid coding technique. IEEE Transactions on Microwave Theory and Techniques, 59(12), 3356–3364. Cited By: 404

2. Vena, A., Perret, E., & Tedjni, S. (2013). A depolarizing chipless RFID tag for robust detection and its FCC compliant UWB reading system. IEEE Transactions on Microwave Theory and Techniques, 61(8), 2982–2994. Cited By: 274

3.Vena, A., Perret, E., & Tedjini, S. (2012). High-capacity chipless RFID tag insensitive to the polarization. IEEE Transactions on Antennas and Propagation, 60(10), 4509–4515. Cited By: 261

4.Vena, A., Perret, E., & Tedjini, S. (2012). A fully printable chipless RFID tag with detuning correction technique. IEEE Microwave and Wireless Components Letters, 22(4), 209–211. Cited By: 209

5.Gupta, S., Parsa, A., Perret, E., Snyder, R. V., Wenzel, R. J., & Caloz, C. (2010). Group-delay engineered noncommensurate transmission line all-pass network for analog signal processing. IEEE Transactions on Microwave Theory and Techniques, 58(9), 2392–2407. Cited By: 194

The nominee’s pioneering work in Human–Computer Interaction and Augmented Reality bridges the gap between digital and physical worlds, enabling more intuitive and immersive user experiences. Their innovations contribute to advancing global technology, empowering industries, education, and society with next-generation interactive solutions.

Ahmadreza Khodayari | Industrial and Manufacturing Applications | Excellence in Research

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Mr. Ahmadreza Khodayari | Industrial and Manufacturing Applications | Excellence in Research

PhD Candidate | The University of Adelaide | Australia

Mr. Ahmadreza Khodayari is a mining engineering researcher whose work integrates rock mechanics, blasting science, fracture mechanics, and machine learning to advance precision modelling and optimization in mining operations. With a Published Documents 8 citation index comprising 110 citations, an h-index of 4, and an i10-index of 4, his contributions span experimentally grounded studies, data-driven prediction models and mechanistic simulations that address key challenges in rock breakage and material flow behaviour.His research portfolio includes several completed and ongoing projects focused on blast modelling rock fracture characterization and artificial intelligence applications in geo-materials engineering. Notable works include the calibration of mechanistic blast models using Ernest Henry Mine datasets the development of machine learning models for predicting blast-induced fragment sizes and advanced Blender Physics Engine simulations to assess sublevel caving (SLC) material flow. He has also executed misfire impact analyses on SLC gravity flow supporting safer and more predictable caving performance. Additionally his studies on AI-driven prediction of concrete and rock fracture toughness contribute to bridging traditional fracture mechanics with modern computational intelligence.Ahmadreza’s publications are featured in respected outlets such as Engineering Fracture Mechanics Theoretical and Applied Fracture Mechanics Steel and Composite Structures and the Journal of Mining and Environment. His 2022 work on predicting mixed-mode fracture toughness using extreme gradient boosting and metaheuristic optimization has accumulated significant citations reflecting strong community interest in AI-enhanced fracture modelling. His earlier experimental studies on freeze–thaw effects in Lushan Sandstone provided valuable insights into strength degradation mechanisms in cold-region geomaterials.He collaborates with researchers from the Lebanese French University Imam Khomeini International University and other international institutions strengthening global knowledge exchange in blasting and rock mechanics. His contributions to major conferences including FragBlast MassMin and ARMA demonstrate active engagement with both scientific and industry practitioners.Through a combination of high-fidelity numerical modelling physics-based simulations and advanced data-driven techniques Ahmadreza’s research aims to enhance fragmentation predictability mine productivity and geomechanical safety. His work continues to shape emerging methodologies in intelligent mining systems contributing to more efficient and sustainable resource extraction practices worldwide.

Profiles: Googlescholar | ORCID | ResearchGate 

Featured Publications

1.Fakhri, D., Khodayari, A., Mahmoodzadeh, A., Hosseini, M., Ibrahim, H. H., & Others. (2022). Prediction of mixed-mode I and II effective fracture toughness of several types of concrete using the extreme gradient boosting method and metaheuristic optimization algorithms. Engineering Fracture Mechanics, 276, 108916. Cited By: 39

2.Khodayari, A. R. (2019). Effect of freeze–thaw cycle on strength and rock strength parameters (A Lushan sandstone case study). Journal of Mining and Environment, Cited By: 27

3.Fakhri, D., Mahmoodzadeh, A., Mohammed, A. H., Khodayari, A., Ibrahim, H. H., & Others. (2023). Forecasting failure load of sandstone under different freezing–thawing cycles using Gaussian process regression method and grey wolf optimization algorithm. Theoretical and Applied Fracture Mechanics, 125, 103876. Cited By: 24

4.Hosseini, M., & Khodayari, A. R. (2018). Effects of temperature and confining pressure on mode II fracture toughness of rocks (Case study: Lushan sandstone). Journal of Mining and Environment, 9(2), 379–391. Cited By: 17

5.Khodayari, A., Fakhri, D., Mohammed, A. H., Albaijan, I., Mahmoodzadeh, A., & Others. (2023). The gene expression programming method to generate an equation to estimate fracture toughness of reinforced concrete. Steel and Composite Structures, 48(2), 163–177. Cited By: 3

His research advances intelligent blasting and rock-mass behaviour prediction, enabling safer, more efficient, and data-driven mining practices that strengthen global resource sustainability.

Xiangu Chen | Biomedical and Healthcare Applications | Best Research Article Award

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Prof. Xianguo Chen | Biomedical and Healthcare Applications | Best Research Article Award

Professor | Zhejiang University School of Medicine | China

Dr. Xianguo Chen is an active researcher in the field of lung cancer biology, molecular oncology, and precision medicine, with a strong focus on exploring genetic alterations, therapeutic resistance mechanisms, and biomarker-driven clinical translation. Affiliated with the Zhejiang University School of Medicine, Dr. Chen has established a robust research portfolio, contributing 16 scientific publications, accumulating 48 citations, and maintaining an h-index of 4, reflecting consistent scholarly impact within a rapidly evolving biomedical landscape.Dr. Chen’s research spans critical areas of lung adenocarcinoma, non-small cell lung cancer (NSCLC), oncogenic signaling pathways, and clinical molecular diagnostics. His work includes multiple contributions as first author, corresponding author, and co-corresponding author, demonstrating scientific leadership and collaboration across multidisciplinary teams. Notable publications include studies on miR-1293–mediated angiogenesis regulation, carbonic anhydrase 4 as a prognostic biomarker, and the identification of novel RET and ALK fusions in NSCLC, each contributing valuable insights into cancer progression, heterogeneity, and precision-targeted therapy.His commitment to translational oncology is further reflected in several research grants. These include major funded projects focused on acacetin-mediated SMYD2 inhibition and DNA damage repair, KMT3C-driven osimertinib resistance via ENO1-regulated glycolysis, and metabolomic discrimination of pulmonary nodules combined with fecal microbiota transplantation strategies. These funded studies highlight his expertise in integrating molecular biology, bioinformatics, and therapeutic research to address pressing clinical challenges in cancer diagnosis and treatment.In addition to his publication record, Dr. Chen engages in collaborative research involving over 130 co-authors, demonstrating broad interdisciplinary partnerships across medical, molecular, and computational sciences. His recent article on machine learning–based immune prognosis modeling for lung adenocarcinoma extends his contributions into the domain of AI-assisted oncology, reinforcing the relevance of computational technologies in modern cancer research.Dr. Chen’s scientific efforts collectively aim to enhance early cancer detection, refine prognostic tools, and illuminate new molecular targets for therapy. Through his funded projects, high-quality publications, and sustained collaborative activity, he continues to contribute significantly to the advancement of global lung cancer research and its transition toward more personalized, mechanism-driven clinical care.

Profiles: Scopus | ResearchGate

Featured Publication

1.Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning. (2025). Frontiers in Oncology.

Dr. Xianguo Chen research advances precision oncology by uncovering molecular mechanisms that drive lung cancer progression and therapeutic resistance, enabling more accurate diagnostics and targeted treatment strategies.

Sandip Kaledhonka | Biomedical and Healthcare Applications | Research Excellence Award

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Assoc. Prof. Dr. Sandip Kaledhonka | Biomedical and Healthcare Applications | Research Excellence Award

Associate professor | Indian Institute of Technology Bombay | India 

Dr. Sandip Kaledhonka is an accomplished structural biologist whose research focuses on time-resolved cryogenic electron microscopy (cryo-EM), ribosome dynamics, and molecular mechanisms underlying protein synthesis. With 38 published research documents 775 citations, an h-index of 10, and an i10-index of 10, he has established a strong global research presence through high-impact publications and sustained collaborations with leading scientists across structural biology biophysics and molecular microbiology.Dr. Kaledhonkar’s research has significantly advanced the understanding of dynamic events in translation initiation, elongation, termination, and ribosome recycling. His landmark work Late steps in bacterial translation initiation visualized using time-resolved cryo-EM published in Nature revealed critical structural intermediates that define the kinetics of ribosomal assembly. He has also contributed foundational methods including the widely used microfluidic spraying-plunging technique for ultrafast sample preparation enabling real-time visualization of rapid biochemical reactions.A notable aspect of his research is the integration of mixing-spraying microfluidics with high-resolution cryo-EM an approach that has provided unprecedented insights into transient conformations of biological macromolecules. His studies on ribosome subunit association release-factor activation and ribosome recycling published in journals such as Structure and Biophysical Journalhave shaped current understanding of translation control and fidelity. His contributions extend to photobiology with influential work on photoactive yellow protein (PYP) focusing on chromophore isomerization protonation hydrogen bonding networks and signaling kinetics.Beyond ribosome biology Dr. Kaledhonkar has collaborated on impactful multidisciplinary research including bacteriophage characterization microbial biofilm reduction structural components of jumbo phages and mechanisms of innate antimicrobial defense involving AAA-ATPases. His recent works further explore methodological innovations in cryo-EM pose estimation extracellular vesicle isolation and enzyme conformational regulation highlighting his broad scientific influence.He has co-authored publications with leading researchers such as Joachim Frank Ziao Fu Bo Chen Måns Ehrenberg and Robert A. Grassucci underscoring a strong record of international collaboration. With expertise spanning structural dynamics microfluidics and time-resolved structural biology Dr. Kaledhonkar’s research continues to contribute to the global advancement of molecular and biomedical sciences offering foundational knowledge that drives future therapeutic and biotechnological innovations.

Profiles:  Googlescholar | Scopus

Featured Publications

1. Horst, M. A., Stalcup, T. P., Kaledhonkar, S., Kumauchi, M., Hara, M., & Xie, A. (2009). Locked chromophore analogs reveal that photoactive yellow protein regulates biofilm formation in the deep sea bacterium Idiomarina loihiensis. Journal of the American Chemical Society, 131(47), 17443–17451. Cited By : 61

2. Kaledhonkar, S., Fu, Z., White, H., & Frank, J. (2018). Time-resolved cryo-electron microscopy using a microfluidic chip. In Protein Complex Assembly: Methods and Protocols (pp. 59–71). Humana Press. Cited By : 52

3. Kumauchi, M., Kaledhonkar, S., Philip, A. F., Wycoff, J., Hara, M., Li, Y., & Xie, A. (2010). A conserved helical capping hydrogen bond in PAS domains controls signaling kinetics in the superfamily prototype photoactive yellow protein. Journal of the American Chemical Society, 132(44), 15820–15830. Cited By : 12

4. Das, S., & Kaledhonkar, S. (2024). Physiochemical characterization of a potential Klebsiella phage MKP-1 and analysis of its application in reducing biofilm formation. Frontiers in Microbiology, 15, 1397447. Cited By : 3

5. Ghosh, S., Roy, S., Baid, N., Das, U. K., Rakshit, S., Sanghavi, P., Hajra, D., Das, S., … & (include remaining authors if available). (2025). Host AAA-ATPase VCP/p97 lyses ubiquitinated intracellular bacteria as an innate antimicrobial defence. Nature Microbiology, 1–16. Cited By : 2

Dr. Kaledhonkar’s pioneering time-resolved cryo-EM work reveals molecular events in real time, advancing fundamental understanding of translation mechanisms. His innovations in microfluidic methodology continue to transform structural biology and accelerate discoveries in molecular medicine.

Shenglin Wang | Biomedical and Healthcare Applications | Young Scientist Award

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Dr. Shenglin Wang | Biomedical and Healthcare Applications | Young Scientist Award

Clinician-Scientist | Fujian Medical University | China

Dr. Shenglin Wang is a biomedical researcher whose work focuses on tumor biology, cancer metastasis, and molecular mechanisms underlying osteosarcoma, chondrosarcoma, and lung cancer bone lesions. He completed a Post-doctoral Fellowship at Fujian Medical University where he advanced single-cell sequencing, tumor microenvironment profiling, and molecular pathology research. In 2025, he joined The First Affiliated Hospital of Fujian Medical University as a Physician, continuing to integrate clinical oncology with translational cancer research.Over the last five years, Dr. Wang has secured three competitive research grants as Principal Investigator or Co-Investigator. His ongoing NSFC project  investigates SOX18-mediated endothelial senescence and HBEGF secretion in non-small cell lung cancer bone metastasis, aiming to define novel therapeutic targets within the senescent vascular niche. He also leads a provincial project exploring HMGA2-regulated ferroptosis resistance in chondrosarcoma, contributing to the understanding of tumor progression and cell death mechanisms. His completed NSFC project, focused on aptamer-based electrochemical sensing for rapid detection of circulating tumor cells, reflects his multidisciplinary approach combining bioengineering with oncology.Dr. Wang has authored 33 peer-reviewed publications, accumulating 651 citations, with an h-index of 11, demonstrating sustained research productivity and academic impact. His representative articles include studies in Cell Proliferation, Frontiers in Immunology, and Acta Biochimica et Biophysica Sinica, covering topics such as single-cell transcriptomics of metastatic bone microenvironments, immune regulatory networks in thyroid carcinoma, and STAT3/EGFR signaling in osteosarcoma drug resistance. His collaborative work spans more than 60 co-authors, highlighting strong interdisciplinary engagement.Collectively, Dr. Wang’s research advances understanding of tumor microenvironment remodeling, therapeutic resistance, and metastasis-associated cell senescence. His contributions support the development of precision oncology strategies and have broad implications for improving diagnostic, prognostic, and therapeutic outcomes in bone-related malignancies.

Profiles:  Scopus | ResearchGate

Featured Publications

1. Author(s). (2025). The integrin α2–osteoclast axis: A key driver of bone destruction and therapeutic target in osteosarcoma. Journal of Translational Medicine.

2. Author(s). (2021). Corrigendum: Stattic sensitizes osteosarcoma cells to epidermal growth factor receptor inhibitors via blocking the interleukin 6-induced STAT3 pathway. Acta Biochimica et Biophysica Sinica, 53(12), 1670–1680.

3. Author(s). (2025). Single-cell transcriptomic analysis of the senescent microenvironment in bone metastasis. Cell Proliferation, 58(1), e13743. Cited By : 5

The nominee’s work advances precision oncology by uncovering key molecular and microenvironmental mechanisms that drive tumor progression, therapeutic resistance, and bone metastasis, enabling the development of more effective diagnostic and therapeutic strategies. By integrating single-cell analytics, molecular signaling research, and translational innovation, the nominee contributes to improved cancer outcomes and supports global efforts toward personalized, mechanism-driven cancer care.

Ibrahim Omara | Biometrics and Security | Research Excellence Award

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Assoc. Prof. Dr. Ibrahim Omara | Biometrics and Security | Research Excellence Award

Associated professor | Menoufia University  | Egypt 

Assoc. Prof. Dr. Ibrahim Omara is a dedicated researcher specializing in Cybersecurity, Artificial Intelligence, Machine Learning, Computer Vision, Multi-Biometrics, and Image Classification, with a growing influence across these interconnected domains. His scholarly contributions include 25 research documents, which have collectively earned 413 citations, supported by an h-index of 11 and i10-index of 12, highlighting both productivity and consistent scholarly impact. His work is highly recognized within the biometric research community, particularly for advancing ear recognition, multimodal biometric fusion, and deep feature learning, where several of his publications have become widely cited references.A significant portion of his contributions lies in pioneering geometric feature extraction, Mahalanobis distance learning, pairwise SVM classification, and distance-metric-driven multimodal authentication, including models that integrate deep CNNs, Vision Transformers, and feature-level fusion. His article A novel geometric feature extraction method for ear recognition stands among his most influential works, shaping subsequent research directions within biometric pattern recognition. In addition to ear biometrics, he has also contributed to remote sensing, SAR target classification, hyperspectral imagery transmission, and deep reinforcement learning, reflecting a multidisciplinary research approach.He has collaborated extensively with leading international researchers, including experts from Harbin Institute of Technology, Dublin City University, Nanyang Technological University, Benha University, Menoufia University, and Prince Sultan University. These collaborations have strengthened cross-institutional innovation in AI-driven security systems, robust biometrics, and intelligent vision technologies. His research outputs also include recent advancements in multi-biometric models, finger-knuckle recognition, and high-resolution scene classification, demonstrating continuous engagement with state-of-the-art machine intelligence.The social impact of his work is reflected in applications that enhance secure identification, digital authentication, and automated visual intelligence, contributing to safer digital ecosystems and improved trust in AI-enabled technologies. With a strong publication record and sustained research momentum, he remains committed to advancing next-generation intelligent security systems and expanding the frontiers of biometric artificial intelligence.

Profiles:  Googlescholar | Scopus | ORCID | ResearchGate

Featured Publications

1. Omara, I., Li, F., Zhang, H., & Zuo, W. (2016). A novel geometric feature extraction method for ear recognition. Expert Systems with Applications, 65, 127–135. Cited By : 100

2.Omara, I., Wu, X., Zhang, H., Du, Y., & Zuo, W. (2018). Learning pairwise SVM on hierarchical deep features for ear recognition. IET Biometrics, 7(6), 557–566. Cited By : 43

3.Omara, I., Hagag, A., Chaib, S., Ma, G., Abd El-Samie, F. E., & Song, E. (2020). A hybrid model combining learning distance metric and DAG support vector machine for multimodal biometric recognition. IEEE Access.
Cited By : 36

4.Omara, I., Wu, X., Zhang, H., Du, Y., & Zuo, W. (2017). Learning pairwise SVM on deep features for ear recognition. In Proceedings of the 2017 IEEE/ACIS 16th International Conference on Computer and Information. Cited By : 36

5.Omara, I., Hagag, A., Ma, G., Abd El-Samie, F. E., & Song, E. (2021). A novel approach for ear recognition: Learning Mahalanobis distance features from deep CNNs. Machine Vision and Applications, 32(1), 38. Cited By : 35

His contributions in AI-driven biometrics and intelligent security models provide industry with scalable, high-accuracy authentication solutions. This research accelerates technological innovation, enhances digital infrastructure reliability, and supports global transitions toward secure, intelligent, and automated systems.

Yu Zhou | Medical Image Analysis | Best Researcher Award

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Dr. Yu Zhou | Medical Image Analysis | Best Researcher Award

Lecturer | Henan University of Science and Technology | China

Dr. Yu Zhou is an emerging researcher in the intersecting domains of medical imaging, neuroscience, and artificial intelligence, recognized for advancing computational approaches that improve the understanding and diagnosis of neurological disorders. With 10 published research documents, 98 citations, an h-index of 7, and an i10-index of 6, his scholarly contributions reflect both productivity and growing international influence. His research has led to notable advancements in diffusion MRI analysis, white-matter connectivity modeling, and machine-learning-driven diagnostic frameworks, particularly within mild cognitive impairment (MCI), juvenile myoclonic epilepsy (JME), and neurobehavioral disorders.Yu Zhou’s most cited works demonstrate strong expertise in fiber-specific white matter analysis, CNN-based transfer learning, and automated classification systems, with contributions published in respected venues such as Cerebral Cortex, Frontiers in Aging Neuroscience, Frontiers in Neuroscience, and Journal of Neural Engineering. His research extends beyond human neuroscience to impactful cross-disciplinary applications, including AI-driven acoustic-based detection systems for livestock estrus identification, showcasing versatility and methodological depth.He has served as principal investigator for two provincial projects, participated in four additional provincial projects and one national project, and contributed to one consultancy/industry initiative, indicating growing leadership in funded research. His innovative capabilities are further evidenced by one granted patent and four patents under review, underscoring his commitment to translational and societally relevant technological development. With collaborations established across computational neuroscience and AI imaging research groups, he continues to contribute to global scientific networks.Yu Zhou’s ongoing work focuses on building interpretable deep-learning models, advancing multimodal data fusion for clinical diagnostics, and developing AI-assisted neuroimaging biomarkers for early disease identification. These contributions hold significant promise for clinical decision support, early-stage neurological assessment, and precision medicine applications. With increasing publication momentum and expanding collaborative research engagements, he is positioned to generate deeper scientific impact and contribute to the evolution of intelligent medical imaging and computational neuroscience.

Profiles:  Googlescholar | ResearchGate

Featured Publications

1.Zhou, Y., Si, X., Chen, Y., Chao, Y., Lin, C. P., Li, S., Zhang, X., Ming, D., & Li, Q. (2022). Hippocampus- and thalamus-related fiber-specific white matter reductions in mild cognitive impairment. Cerebral Cortex, 32(15), 3159–3174. Cited By : 23

2.Si, X., Zhang, X., Zhou, Y., Sun, Y., Jin, W., Yin, S., Zhao, X., Li, Q., & Ming, D. (2020). Automated detection of juvenile myoclonic epilepsy using CNN-based transfer learning in diffusion MRI. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. Cited By : 18

3.Zhou, Y., Si, X., Chao, Y. P., Chen, Y., Lin, C. P., Li, S., Zhang, X., Sun, Y., & Ming, D. (2022). Automated classification of mild cognitive impairment by machine learning with hippocampus-related white matter network. Frontiers in Aging Neuroscience, 14, 866230.Cited By : 13

4.Wang, J., Si, Y., Wang, J., Li, X., Zhao, K., Liu, B., & Zhou, Y. (2023). Discrimination strategy using machine learning technique for oestrus detection in dairy cows by a dual-channel-based acoustic tag. Computers and Electronics in Agriculture, 210, 107949.Cited By : 13

5.Wang, J., Chen, H., Wang, J., Zhao, K., Li, X., Liu, B., & Zhou, Y. (2023). Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag. animal, 17(6), 100811.Cited By : 12

Dr. Yu Zhou’s work advances global healthcare innovation by integrating medical imaging, neuroscience, and artificial intelligence to enable earlier, more accurate detection of neurological disorders. His research drives the development of interpretable, data-driven diagnostic tools that strengthen clinical decision-making and support precision medicine. Through cross-disciplinary innovation, he envisions AI-empowered neuroimaging solutions that improve patient outcomes and transform future healthcare systems.