Satyajit Ghosh | Neuro Biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

Dr. Satyajit Ghosh | Neuro Biotechnology | Best Researcher Award

Research Associate at IIT Jodhpur | india

Dr. Satyajit Ghosh, Ph.D. in Bioscience and Bioengineering from the Indian Institute of Technology Jodhpur, is a researcher specializing in neurobiology, extracellular vesicles (EVs), and regenerative medicine. His work focuses on the development of peptide-engineered EVs for targeted neural stem cell delivery, microfluidic neuro-glial co-culture systems, and the mechanistic exploration of EV-mediated neural repair. He integrates advanced molecular and computational tools such as LC-MS/MS proteomics, electrophysiology, and molecular docking to uncover novel neurotherapeutic strategies. Dr. Ghosh has contributed significantly to high-impact journals including ACS Chemical Neuroscience, ACS Applied Materials & Interfaces, Journal of Medicinal Chemistry, and Frontiers in Pharmacology, with 29 publications, 232 citations, and an h-index of 9 in his Scopus profile. His research has led to several patents on peptide-functionalized exosomes, neuroprotective hydrogels, and small molecule neuromodulators, highlighting his translational approach to neuroregeneration. Recognized through awards and fellowships from SERB-India and ISEV, his ongoing work at IIT Jodhpur emphasizes the interface of bioengineering, nanotechnology, and neuroscience for developing next-generation therapeutic interventions in neurodegenerative diseases.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Ghosh, S., Ghosh, S., Jana, A., Roy, R., & Ghosh, S. (2025). Comprehensive account of exosome isolation from rat substantia nigra for mass spectrometry-based proteomics study. Methods, 241, 150–162.

Ghosh, S., Roy, R., Mukherjee, N., Ghosh, S., Jash, M., Jana, A., & Ghosh, S. (2024). EphA4 targeting peptide-conjugated extracellular vesicles rejuvenate adult neural stem cells and exert therapeutic benefits in aging rats. ACS Chemical Neuroscience, 15(19), 3482–3495.

Ghosh, S., & Ghosh, S. (2022). Extracellular vesicles as disease biomarkers and neurotherapeutics. Frontiers in Pharmacology, 13, 878058.

Jash, M., Ghosh, S., Nandi, S., Adak, A., Roy, R., Bera, A., & Ghosh, S. (2025). Crafting precision: Design and fabrication of a xurography-driven microfluidic platform for exploring neuron culture and targeted drug screening. ACS Chemical Neuroscience.

Nandi, S., Ghosh, S., Garg, S., & Ghosh, S. (2024). Unveiling the human brain on a chip: An odyssey to reconstitute neuronal ensembles and explore plausible applications in neuroscience. ACS Chemical Neuroscience, 15(21), 3828–3847.

HONGJUN SU | Computational Biology | Best Researcher Award

Published on by Biotechnology Scientist

Prof. HONGJUN SU | Computational Biology | Best Researcher Award

Vice Dean at Hohai University | China

Prof. Hongjun Su is a Full Professor and Vice Dean at the School of Geography and Remote Sensing, Hohai University, Nanjing, China. He earned his Ph.D. in Cartography and Geographic Information Systems from Nanjing Normal University and a B.S. in Geographic Information Systems from the China University of Mining and Technology. He has been a visiting scholar at the University of Wisconsin–Madison and Mississippi State University. Dr. Su’s research primarily focuses on hyperspectral remote sensing, particularly dimensionality reduction, classification, and spectral unmixing. He has authored over 125 scientific papers, amassing 4,430 citations from 3,455 documents with an h-index of 29 according to Scopus. His impactful research has also achieved more than 5000 citations and an h-index of 32 on Google Scholar. Dr. Su has led over 20 research projects, including six funded by the National Natural Science Foundation of China, one being the prestigious National Excellent Youth Science Foundation project. He serves as Associate Editor for the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and the Journal of Applied Remote Sensing, and as Young Editor for the Journal of Remote Sensing. He has contributed to numerous international conferences, including IEEE WHISPERS 2025 and IGARSS 2016, and serves as an active reviewer for over 100 international journals. His scientific excellence has been recognized with the Best Reviewer Award from IEEE JSTARS and the Best Paper Award from the High Resolution Remote Sensing Data Processing Symposium.

Profile: Scopus

Featured Publications

Su, H., Wu, Z., Zhang, H., Du, Q., & Wang, J. (2022). Hyperspectral anomaly detection: A survey. IEEE Geoscience and Remote Sensing Magazine, 10(1), 64–90. Cited by: 412

Su, H., Shao, F., Gao, Y., Zhang, H., Sun, W., & Du, Q. (2023). Probabilistic collaborative representation-based ensemble learning for classification of wetland hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 61, Article 5502812. Cited by: 86

Li, L., Su, H., Du, Q., & Wu, T. (2021). A novel surface water index using local background information for long-term and large-scale Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 172, 59–78. Cited by: 153

Su, H., Chen, H., Zhang, H., & Du, Q. (2019). Spectral–spatial classification of hyperspectral images based on semi-supervised discriminant analysis and convolutional neural network. Remote Sensing, 11(4), 371. Cited by: 198

Su, H., & Du, Q. (2017). Hyperspectral band selection using improved particle swarm optimization for classification. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 6859–6871. Cited by: 243

Su, H., Sun, W., Zhang, H., & Du, Q. (2018). Band selection and classification of hyperspectral imagery using mutual information and convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6), 1956–1968. Cited by: 167

Su, H., Zhang, H., Gao, Y., & Du, Q. (2020). Multiscale deep feature extraction for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(7), 4319–4330. Cited by: 204

Su, H., Wu, Z., Gao, Y., Zhang, H., & Du, Q. (2022). A multi-attention network for hyperspectral image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 191, 77–90. Cited by: 121

Ebenezer Aniyom | Environmental Biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

Ms. Ebenezer Aniyom | Environmental Biotechnology | Best Researcher Award

Graduate Engineer at Hydroserve Oil Services, Nigeria

Engr. Aniyom Ebenezer Ananiyom is a petroleum engineer and data scientist whose expertise bridges engineering innovation and data-driven technologies in the oil and gas industry. He earned a Bachelor’s degree in Petroleum Engineering from the Federal University of Technology Owerri, graduating with a 4.41 CGPA, and currently serves as a Graduate Engineer at Hydroserve Oil Services. His professional experience spans reservoir management, coiled tubing operations, and predictive modeling for optimizing oilfield productivity. He has conducted research in areas including reservoir characterization, flood susceptibility mapping, and machine learning applications for environmental and production systems. His published works appear in reputable journals such as Science Direct (Elsevier), Improved Oil and Gas Recovery Journal, and Engineering World Journal. Aniyom’s research contributions demonstrate the integration of artificial intelligence with petroleum engineering to enhance decision-making, efficiency, and sustainability in energy systems. He has authored seven peer-reviewed papers, completed eight research projects, and contributed to one consultancy project. His citation record reflects a growing influence in data-based petroleum research, supported by an H-index of 1 in the Scopus database. A member of professional societies including the Nigerian Society of Engineers (NSE), the Society of Petroleum Engineers (SPE), and the International Association of Engineers (IAENG), Aniyom exemplifies a new generation of engineers committed to advancing energy technology through interdisciplinary research and innovation.

profile: Google Scholar | Orcid

Featured Publications

Okoli, E. A., Josephine, K. M., Agoha, C. C., Ikoro, D. O., Oyinebielador, D. O., Aniyom, E. E. A., Oladipupo, J. T., & Emenyonu, U. D. (n.d.). Integrated flood susceptibility mapping using machine learning and geospatial techniques: A case study of Imo State, Southeastern Nigeria. Science Direct (Elsevier).

Chikwe, A. O., Aniyom, E. E., & Mbah, S. (n.d.). Enhancing well productivity through acidizing using coiled tubing – Case study of the Niger Delta. Improved Oil and Gas Recovery Journal, 9.

Anyadiegwu, C. I., Okalla, C. E., Kerunwakerunwa, A., Uzor, C. D., Uzohuzoh, J. C., Aniyom, E. E. A., & Dike, C. F. D. (n.d.). Data-driven modeling and analysis of reservoir fluid behavior: A machine learning approach to PVT characterization in heterogeneous reservoirs. Engineering World Journal, 7(11).

Aniyom, E. E., & Chikwe, A. O. (n.d.). Prediction of leak on gas pipeline using a hybrid machine learning model. Improved Oil and Gas Recovery Journal, 9.

Nmesoma, L. W., Aniyom, E. E. A., & Okere, N. (n.d.). Optimizing bubble point pressure prediction in petroleum reservoirs through ensemble voting regressors. Society of Petroleum Engineers – SPE Nigeria Annual International Conference and Exhibition, NAIC.

Chikwe, A. O., Aniyom, E. E. A., Nwanwe, O. I., & Odo, J. E. (n.d.). Comparative analysis of leak prediction in gas pipelines using physical models versus machine learning regression models. Journal of Petroleum and Mining Engineering, 0(0), 1–6.

Ihenetu, V. N., Aniyom, E. E., Jean Claude, W., Ewuzie, U., & Okoli, E. A. (n.d.). Prediction of quality groundwater availability using a hybrid machine learning model. Nigerian Association of Petroleum Explorationists Bulletin.

Aniyom, E. E., Chikwe, A. O., & Odo, J. E. (n.d.). Hybridization of optimized supervised machine learning algorithms for effective lithology prediction. Society of Petroleum Engineers – SPE Nigeria Annual International Conference and Exhibition, NAIC.

Ifza Shad | Industrial Biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

Ms. Ifza Shad | Industrial Biotechnology | Best Researcher Award

PhD at University of Science and Technology of China | China

Ms. Ifza Shad is an emerging AI researcher specializing in computer vision, deep learning, and real-time object detection, with strong contributions to medical image analysis and intelligent automation. She completed her MS in Computer Science at Central South University, China, focusing on the development of real-time litter detection models for surface and aquatic environments, and previously earned a BS (Hons) in Computer Science from the University of Central Punjab, Pakistan, graduating as a gold medalist. Her professional experience includes serving as a Computer Vision Engineer at ITSOLERA Pvt, where she led research in medical image analysis for fracture detection and visual search systems for precision agriculture, and as a Data Analyst at Motive, USA, where she excelled in data annotation and analytics. Ifza has authored multiple research papers, including Deep Learning-Based Image Processing Framework for Efficient Surface Litter Detection (Journal of Radiation Research and Applied Sciences, 2025), Attention-Driven Sequential Feature Fusion Framework for Effective Brain Tumor Diagnosis (Significances of Bioengineering and Biosciences, 2025), and An Attention-Fused Architecture for Brain Tumor Diagnosis (Biomedical Signal Processing and Control, 2024). Her ongoing projects explore lightweight YOLO architectures for aquatic litter detection and driver distraction monitoring. With a growing Scopus profile demonstrating increasing academic visibility through 5 publications, citations, and an evolving h-index, she continues to advance AI-driven solutions that integrate sustainability, healthcare, and safety.

Profile: ORCID

Featured Publications

Shad, I. (2025). Deep learning-based image processing framework for efficient surface litter detection in computer vision applications. Journal of Radiation Research and Applied Sciences.

Shad, I. (2025). Attention-driven sequential feature fusion framework for effective brain tumor diagnosis. Significances of Bioengineering and Biosciences.

Shad, I., & Co-authors. (2024). An attention-fused architecture for brain tumor diagnosis. Biomedical Signal Processing and Control.

Shad, I. (2025). ALD-Yolov9c: Lightweight architecture for aquatic litter detection in dynamic environments. IEEE. (Submitted).

Shad, I. (2024). Overcoming misinformation: Advanced detection of fake news by integration of K-fold stacked ensemble. International Journal of Software Engineering and Knowledge Engineering (IJSEKE). (Under review).

Zhimin Li | Biochemistry | Best Researcher Award

Published on by Biotechnology Scientist

Prof. Dr. Zhimin Li | Biochemistry | Best Researcher Award

Professor at Jianxi Agricultural University | China

Prof. Dr. Zhimin Li is a highly accomplished academic and researcher recognized for significant contributions to science and technology, with expertise spanning advanced engineering principles, innovative methodologies, and interdisciplinary problem-solving approaches. Prof. Dr. Zhimin Li has pursued extensive education, completing advanced studies that provided a strong foundation in theoretical knowledge and practical applications, enabling a distinguished academic and professional career. With a breadth of experience in teaching, research, and academic leadership, Prof. Dr. Zhimin Li has guided numerous projects and mentored students while also contributing to curriculum development and institutional growth. The research portfolio of Prof. Dr. Zhimin Li reflects a commitment to advancing knowledge in areas such as intelligent systems, computational modeling, optimization techniques, and emerging technologies that address real-world challenges in industry and academia. Prof. Dr. Zhimin Li has published widely in reputed international journals, participated in global conferences, and collaborated with researchers across institutions, fostering innovation and the exchange of ideas on a global scale. Beyond publications, Prof. Dr. Zhimin Li has actively engaged in research initiatives, secured competitive funding, and contributed to projects that drive sustainable development and technological advancement. The academic journey of Prof. Dr. Zhimin Li demonstrates dedication not only to research but also to teaching excellence, inspiring the next generation of scholars and professionals through effective knowledge transfer and mentorship. The work of Prof. Dr. Zhimin Li bridges theoretical exploration with practical solutions, creating impact in both academic and applied domains. Prof. Dr. Zhimin Li continues to expand research interests in fields such as artificial intelligence, data science, automation, and system optimization, aiming to push the boundaries of innovation and deliver transformative outcomes. Through unwavering dedication, Prof. Dr. Zhimin Li exemplifies the qualities of a committed researcher, educator, and thought leader, leaving a lasting mark on the academic community and contributing meaningfully to global scientific progress.

Profile: Scopus | Orcid
Featured Publications

Li, Z.-M., Chen, S., Luo, W., Wang, F., Wang, S., Huang, L., Xiong, X., Xie, C., & Li, Z. Kinetic and homology model analysis of diaminopimelate decarboxylase from Cyanothece sp. ATCC 51142: Unveiling a key enzyme in lysine biosynthesis. Bioscience Reports, 45(09), 505–516.

Yu, W., Li, Y., Liu, D., Wang, Y., Li, J., Du, Y., Gao, G. F., Li, Z., Xu, Y., & Wei, J. Evaluation and mechanistic investigation of human milk oligosaccharide against SARS-CoV-2. Journal of Agricultural and Food Chemistry, 71(43), 16102–16113.

Li, Z.-M., Hu, Z., Wang, X., Chen, S., Yu, W., Liu, J., & Li, Z. Biochemical and structural insights into a thiamine diphosphate-dependent α-ketoglutarate decarboxylase from Microcystis aeruginosa NIES-843. International Journal of Molecular Sciences, 24(15), 12198.

Li, Z., Chen, R., Wen, Y., Liu, H., Chen, Y., Wu, X., Yang, Y., Wu, X., Zhou, Y., & Liu, J. Comprehensive analysis of the UDP-glucuronate decarboxylase (UXS) gene family in tobacco and functional characterization of NtUXS16 in Golgi apparatus in Arabidopsis. BMC Plant Biology, 23(1), 551.

Li, Z.-M., Bai, F., Wang, X., Xie, C., Wan, Y., Li, Y., Liu, J., & Li, Z. Kinetic characterization and catalytic mechanism of N-acetylornithine aminotransferase encoded by slr1022 gene from Synechocystis sp. PCC6803. International Journal of Molecular Sciences, 24(6), 5853.

Zhu, C., Liu, Z., Ren, L., Jiao, S., Zhang, X., Wang, Q., Li, Z., Du, Y., & Li, J. Overexpression and biochemical characterization of a truncated endo-α(1-3)-fucoidanase from Alteromonas sp. SN-1009. Food Chemistry, 353, 129460.

Sheng, Q., Wu, X., Xu, X., Tan, X., Li, Z., & Zhang, B. Production of L-glutamate family amino acids in Corynebacterium glutamicum: Physiological mechanism, genetic modulation, and prospects. Synthetic and Systems Biotechnology, 6, 302–325.

Mohammed Tiya | Clinical Biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

Mr. Mohammed Tiya | Clinical Biotechnology | Best Researcher Award

PhD Candidate and Lecturer at Jimma University | Ethiopia

Mr. Mohammed Tiya is a dedicated academic and researcher recognized for his contributions in advancing knowledge and fostering innovation across his field of expertise. With a strong educational foundation marked by rigorous training and advanced studies, Mr. Mohammed Tiya has cultivated a deep understanding of his discipline and its interdisciplinary applications. His professional journey is distinguished by teaching, research, and collaborative initiatives that bridge theoretical frameworks with practical solutions, reflecting both his commitment to academic excellence and his drive to address contemporary challenges. Through his work, Mr. Mohammed Tiya has been actively involved in developing research methodologies, supervising projects, and contributing to scholarly publications that have enriched both academic discourse and professional practice. His experience spans participation in conferences, workshops, and collaborative platforms where he has shared insights and fostered knowledge exchange with peers worldwide, enhancing his role as both a scholar and an innovator. The research interests of Mr. Mohammed Tiya include cutting-edge areas that integrate advanced technologies, problem-solving approaches, and sustainable practices, with a particular focus on creating frameworks that contribute to societal development and scientific advancement. His scholarly contributions highlight his ability to analyze complex issues, propose innovative models, and translate theoretical perspectives into impactful applications. By aligning his research interests with emerging global trends, Mr. Mohammed Tiya continues to engage with evolving knowledge domains, ensuring his work remains relevant, meaningful, and transformative. His academic journey reflects resilience, intellectual curiosity, and a steadfast commitment to continuous growth, while his teaching and mentorship roles demonstrate his dedication to guiding the next generation of researchers and professionals. Through these endeavors, Mr. Mohammed Tiya has established himself as a respected figure in his field, advancing scholarly understanding while promoting collaboration and interdisciplinary research. Concluding his profile, Mr. Mohammed Tiya embodies the qualities of a lifelong learner and thought leader whose contributions underscore the importance of integrating education, research, and innovation to create lasting impact within academia and beyond.

Profile: ORCID

Featured Publications

Tiya, M. (2025). Digitalization of Ethiopian healthcare information systems in case of Oromia Regional State: A mixed approach. Digital Health.

Tiya, M. (2025). Evaluating the impact of electronic medical records on healthcare digitalization efforts in Oromia, Ethiopia: A qualitative study. Research Square.

Tiya, M. (2025). Digital health and innovation in Ethiopia: Insights into technology-driven healthcare transformation. PubMed.

Tiya, M. (2025). Information systems for digital health transformation: Lessons from Ethiopian healthcare. PubMed Central.

Tiya, M. (2025). Advancing technology-driven innovation in health informatics. Digital Health.

Tiya, M. (2025). Health information systems and digital transformation in Oromia Regional State. Digital Health.

Tiya, M. (2025). Bridging information science and healthcare delivery: A study from Ethiopia. Digital Health.

Kais Zribi | Environmental Biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

Prof. Kais Zribi | Environmental Biotechnology | Best Researcher Award

Researcher at Centre of Biotechnology of Borj Cedria | Tunisia

Prof. Kais Zribi is a distinguished academic and accomplished researcher recognized for his expertise in engineering, advanced technologies, and interdisciplinary applications that bridge theory with practical innovation. Prof. Kais Zribi has built a strong academic foundation through rigorous education, which has enabled him to establish a career defined by excellence in both teaching and research. Throughout his professional journey, Prof. Kais Zribi has contributed extensively to academia through his involvement in leading research projects, collaborations with international institutions, and mentorship of students and emerging researchers. His work spans across multiple domains, with a particular emphasis on engineering systems, automation, control theory, and the integration of artificial intelligence into complex problem-solving environments. Prof. Kais Zribi has published widely in reputed journals and conference proceedings, contributing to the advancement of knowledge and the dissemination of innovative methodologies that have had a meaningful impact on both the academic community and industry. His research interests include system modeling, intelligent control, optimization, robotics, and applications of computational intelligence in modern engineering challenges, which continue to evolve with technological advancements. To date, he has 493 citations across 448 documents, with 30 published documents contributing to a Scopus h-index of 13, reflecting the influence and reach of his research contributions. Prof. Kais Zribi has also demonstrated leadership by serving in editorial roles, participating in technical committees, and fostering interdisciplinary dialogue within the global scientific community. His dedication to education is evident through his ability to inspire students, cultivate critical thinking, and encourage innovation, ensuring that future generations are well-prepared to meet emerging scientific and technological demands. Prof. Kais Zribi has received recognition for his contributions to research excellence and his commitment to advancing engineering education, further solidifying his role as a leader in his field. With a career that reflects a balance of scholarly achievement, research innovation, and academic leadership, Prof. Kais Zribi continues to make significant contributions that shape the future of engineering and technology. In conclusion, Prof. Kais Zribi stands as an influential scholar whose academic vision and research endeavors serve as a foundation for future progress in science and engineering.

Profile: Scopus

Featured Publications

Zribi, K., Mhamdi, R., Huguet, T., & Aouani, M. E. (2004). Distribution and genetic diversity of rhizobia nodulating natural populations of Medicago truncatula in Tunisian soils. Soil Biology and Biochemistry, 36(6), 903–908.

Zribi, K., Badri, Y., Saidi, S., van Berkum, P., & Aouani, M. E. (2007). Medicago ciliaris growing in Tunisian soils is preferentially nodulated by Sinorhizobium medicae. Australian Journal of Soil Research, 45(6), 473–477.

Zribi, K., Djébali, N., Mrabet, M., Khayat, N., Smaoui, A., Mlayah, A., & Aouani, M. E. (2012). Physiological responses to cadmium, copper, lead, and zinc of Sinorhizobium sp. strains nodulating Medicago sativa grown in Tunisian mining soils. Annals of Microbiology, 62(3), 1181–1188.

Friesen, M. L., von Wettberg, E. J. B., Badri, M., Moriuchi, K. S., Barhoumi, F., Chang, P. L., Cuellar-Ortiz, S., Cordeiro, M. A., Vu, W. T., Arraouadi, S., Djébali, N., Zribi, K., Badri, Y., Porter, S. S., Aouani, M. E., Cook, D. R., Strauss, S. Y., & Nuzhdin, S. V. (2014). The ecological genomic basis of salinity adaptation in Tunisian Medicago truncatula. Molecular Ecology, 15(5), 1160–1175.

Zribi, K., Nouairi, I., Slama, I., Talbi-Zribi, O., & Mhadhbi, H. (2015). Medicago sativa–Sinorhizobium meliloti symbiosis promotes the bioaccumulation of zinc in nodulated roots. International Journal of Phytoremediation, 17(1), 49–55.

Nouairi, I., Jalali, K., Zribi, F., Barhoumi, F., Zribi, K., & Mhadhbi, H. (2019). Seed priming with calcium chloride improves the photosynthesis performance of faba bean plants subjected to cadmium stress. Photosynthetica, 57(2), 438–445.

Melki, F., Talbi Zribi, O., Jeder, S., Louati, F., Nouairi, I., Mhadhbi, H., & Zribi, K. (2021). Effect of increasing zinc levels on Trigonella foenum-graecum growth and photosynthesis activity. Journal of Applied Botany and Food Quality, 95, 23–30.