Eugenia Messina | Industrial Biotechnology | Best Researcher Award

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Dr. Eugenia Messina | Industrial Biotechnology | Best Researcher Award

Research Fellow at University of Bari Aldo Moro | Italy

Dr. Eugenia Messina is a distinguished researcher in biotechnology and microbial metabolic engineering, currently serving as a Research Fellow at the Department of Biosciences, Biotechnologies, and Environment, University of Bari “Aldo Moro,” Italy. Her research focuses on the metabolic and genetic engineering of Yarrowia lipolytica and other non-conventional yeasts for the sustainable synthesis of industrially relevant compounds and the bioconversion of plastic-derived monomers. She has made significant contributions to the development of microbial platforms for plastic upcycling, biochemical characterization of mitochondrial transporters, and metabolic pathways related to fatty acid and isocitric acid production. Messina has authored 8 scientific documents indexed in Scopus, which have collectively received 110 citations from 108 documents, reflecting an h-index of 6-demonstrating the growing influence and recognition of her research. Her publications include papers in Bioresource Technology, Pharmaceutics, Microbial Cell Factories, Frontiers in Microbiology, Metabolic Engineering, FEBS Letters, and Biochemical Journal, along with a European patent on the metabolic engineering of Yarrowia lipolytica for isocitric acid production. She has been recognized with the HERITAGE 2024 Award from the Biotechnology Group of the Italian Society of Biochemistry and Molecular Biology (SIB), as well as multiple travel grants and presentation awards. Through interdisciplinary research and innovative approaches in bioengineering, Messina’s work contributes to advancing circular bioeconomy strategies, microbial biotechnology, and sustainable bioprocess development.

Profile: Scopus | ORCID

Featured Publications

Brito, D. S., Agrimi, G., Charton, L., Brilhaus, D., Bitetto, M. G., Lana-Costa, J., Messina, E., Nascimento, C. P., Feitosa-Araújo, E., Pires, M. V., Pérez-Díaz, J. L., Obata, T., Porcelli, V., Palmieri, L., Araújo, W. L., Weber, A. P. M., Linka, N., Fernie, A. R., Palmieri, F., & Nunes-Nesi, A. (2020). Biochemical and functional characterization of a mitochondrial citrate carrier in Arabidopsis thaliana. Biochemical Journal, 477(9), 1759–1777.

Yuzbasheva, E. Y., Scarcia, P., Yuzbashev, T. V., Messina, E., Kosikhina, I. M., Palmieri, L., Shutov, A. V., Taratynova, M. O., Amaro, R. L., Palmieri, F., Sineoky, S. P., & Agrimi, G. (2021). Engineering Yarrowia lipolytica for the selective and high-level production of isocitric acid through manipulation of mitochondrial dicarboxylate-tricarboxylate carriers. Metabolic Engineering, 65, 156–166.

Messina, E., de Souza, C. P., Cappella, C., Barile, S. N., Scarcia, P., Pisano, I., Palmieri, L., Nicaud, J. M., & Agrimi, G. (2023). Genetic inactivation of the carnitine/acetyl-carnitine mitochondrial carrier of Yarrowia lipolytica leads to enhanced odd-chain fatty acid production. Microbial Cell Factories, 22(1), 128.

Castellani, S., Iaconisi, G. N., Tripaldi, F., Porcelli, V., Trapani, A., Messina, E., Guerra, L., Di Franco, C., Maruccio, G., Monteduro, A. G., Corbo, F., Di Gioia, S., Trapani, G., & Conese, M. (2024). Dopamine and citicoline co-loaded solid lipid nanoparticles as multifunctional nanomedicines for Parkinson’s disease treatment by intranasal administration. Pharmaceutics, 16(8), 1048.

Messina, E., Zbigniew, L., Barile, S., Moroz, P., Scarcia, P., Palmieri, L., Pisano, I., & Agrimi, G. (2025). Acetate co-feeding increases ethylene glycol assimilation and glycolic acid production in Yarrowia lipolytica. Bioresource Technology. (Accepted October 2025).

Honghao Zhao | Agricultural Biotechnology | Best Researcher Award

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Dr. Honghao Zhao | Agricultural Biotechnology | Best Researcher Award

Doctor at College of fisheries | Tianjin Agricltural University | China

Dr. Honghao Zhao is a distinguished researcher at Tianjin Agricultural University specializing in sustainable aquaculture, fish nutrition, and aquatic physiology. Her research focuses on developing eco-friendly feed strategies using alternative protein sources such as defatted black soldier fly larvae and functional additives including bile acids and Chinese herbal compounds to enhance fish growth, health, and flesh quality. By integrating multi-omics techniques such as metabolomics and transcriptomics, she elucidates the molecular and physiological mechanisms underlying nutrient utilization and muscle development in aquatic species. As Principal Investigator, Dr. Zhao has successfully led multiple research projects funded by national and provincial programs, contributing significantly to the advancement of aquaculture nutrition science. Her prolific scholarly output includes numerous publications in high-impact international journals like Applied Food Research, Aquaculture, Frontiers in Physiology, and GigaScience, with her studies widely cited for their innovation and relevance to sustainable aquaculture practices. Her research excellence is further reflected in her Scopus record, with 566 citations, an h-index of 10, and a growing influence across global aquaculture and biotechnology communities. Through her interdisciplinary approach and applied research outcomes, Dr. Zhao continues to bridge scientific discovery and industry application, promoting environmentally sustainable and nutritionally optimized aquaculture systems that support global food security and resource conservation.

Profile: ORCID | Google Scholar

Featured Publications:

Zhao, H.*, Fu, Y., Zheng, X. J., Sun, X. L., Shen, J. W., & Fang, Z. Z. (2025). Gut microbiota mediates the improved growth, flesh quality and intestinal health of largemouth bass (Micropterus salmoides) fed defatted Hermetia illucens larvae meal. Applied Food Research, 101456.

Zhao, H.*, Fu, Y., Zheng, X. J., Huang, X. R., & Yan, K. (2025). The improvement of adding bile acid to high-level black soldier fly larvae meal-based diet on quality of largemouth bass (Micropterus salmoides). Aquaculture Nutrition, 2218372.

Duan, Y. N., Zhao, H., Qin, C. J., Ma, L., Bi, X. D., Song, T., & Sun, X. L. (2024). Astragalus polysaccharides, cinnamaldehyde and their complexes affected growth, physicochemical parameters, histomorphology and flesh quality of largemouth bass (Micropterus salmoides). Aquaculture Reports, 35, 101989.

Zhao, H., Chong, J., & Li, D. P. (2023). Integrated multiple-omics reveals the regulatory mechanism underlying the effects of artificial feed and grass feeding on growth and muscle quality of grass carp (Ctenopharyngodon idellus). Aquaculture, 562, 738808.

Li, X. T., Qin, C. J., Fang, Z. Z., Sun, X. L., Shi, H. Y., Wang, Q. K., & Zhao, H.* (2022). Replacing dietary fish meal with defatted black soldier fly (Hermetia illucens) larvae meal affected growth, digestive physiology and muscle quality of tongue sole (Cynoglossus semilaevis). Frontiers in Physiology, 13, 855957.

Habib Hamidinezhad | Nanoscience | Best Researcher Award

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Assoc. Prof. Dr. Habib Hamidinezhad | Nanoscience | Best Researcher Award

Associate professor at University of Mazandaran | Iran

Assoc. Prof. Dr. Habib Hamidinezhad, born on 30 March 1975 in Larim, Jouybar, Mazandaran, Iran, is a distinguished Iranian physicist specializing in nanophysics and nanomaterials. He obtained his B.Sc. in Applied Physics (Solid State) from Shahid Beheshti University, Tehran, his M.Sc. in Physics (Solid State and Electronics) from the University of Tabriz with a thesis on Bi-based high-(T_c) superconductors, and his Ph.D. in Nanophysics from the Ibnu Sina Institute for Fundamental Science Studies, Universiti Teknologi Malaysia (UTM), focusing on “Structural Characterization of Silicon Nanowires Grown by a 150 MHz Very High Frequency Plasma Enhanced Chemical Vapor Deposition.” He subsequently held a visiting researcher role and a postdoctoral fellowship at UTM’s Institute of High Voltage and High Current before joining the Department of Physics, Faculty of Basic Sciences, University of Mazandaran, Iran. His research interests encompass nanostructures, nanowires, nanofibers, thin films, photocatalysis, bionanoscience, semiconductors, and drug-delivery systems. Dr Hamidinezhad has authored 44 peer-reviewed publications in international journals indexed in Scopus, collectively cited 357 times across 326 documents, with an h-index of 11. His recent works include studies on TiO₂/ZnO composite nanofibers for photocatalytic applications and hybrid perovskite materials for solar cells. Recognized for his scholarly excellence, he received the Iran National Elite Foundation’s “Dr Kazemi Ashtiani Award for Young Assistant Professors” (2017) and the “Best Student Award” from UTM (2012). He continues to contribute significantly to nanoscience through his teaching, mentorship, and research innovations at the University of Mazandaran.

Profile: Scopus | Google Scholar

Featured Publications

Tabari, F. H., & Hamidinezhad, H. (2025). Effective photodegradation of organic pollutants and antibacterial activity by TiO₂/ZnO composite nanofibers as a direct Z-scheme heterojunction photocatalyst. Journal of Water Process Engineering, 77, 108562.

Hassan, A. K., Hamidinezhad, H., & Al-Bermany, E. (2025). Effectiveness of graphene-polymer nanocomposites on thermo-mechanical and cytotoxicity behavior for dental fillings applications. Iranian Polymer Journal, 1, 1–15.

Afra, F. G., Hamidinezhad, H., & Mozafari, H. (2025). Antibacterial activity of two-dimensional MoS₂ nanostructures: Effects of reaction time and temperature on morphology. Materials Chemistry and Physics, 343, 130945.

Narm, T. S., Hamidinezhad, H., Sabouri, Z., & Darroudi, M. (2025). Photodegradation of tetracycline antibiotic and organic dyes using green synthesized Ag-doped ZnO/CuO nanocomposite with Sclerorhachis leptoclada extract. Chemistry Africa, 1, 1–11.

Dakhil, T., Hamidinezhad, H., & Baron, A. S. (2025). Fabrication and characterization of a hybrid organic-inorganic perovskite material and applications in solar cells. Optical Materials, 160, 116708.

Min Li | Radiotherapy | Biotechnology Scientist Award

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Dr. Min Li | Radiotherapy | Biotechnology Scientist Award

Director at 960th Hospital of PLA | China

Dr. Min Li, M.D., is a distinguished medical imaging expert specializing in coronary artery disease, nuclear medicine, and interventional radiology. He currently serves as the Deputy Chief Physician and Director of the Department of Nuclear Medicine at the 960 Hospital of the People’s Liberation Army in Jinan, Shandong, China. Dr. Li obtained his Bachelor’s degree from the First Military Medical University, Guangzhou, and his Master’s degree from the Second Military Medical University, Shanghai. His professional career spans over two decades, during which he has contributed significantly to diagnostic imaging innovations and interventional treatment strategies. Dr. Li has authored and co-authored numerous high-impact publications in leading journals such as Radiology, European Radiology, JACC Cardiovascular Imaging, and ACS Applied Materials & Interfaces. His work encompasses meta-analyses, diagnostic imaging studies, and novel therapeutic interventions, advancing precision in cardiovascular diagnostics and imaging protocols. Notable studies include analyses on myocardial perfusion imaging accuracy, dual-source CT coronary angiography, and interventional therapies for osteoporotic fractures and arterial injuries. His research contributions demonstrate a consistent focus on improving imaging accuracy, radiation safety, and patient outcomes.

Profile: Orcid

Featured Publications

Li, M. (2020). Electrosprayed soft capsules of millimeter size for specifically delivering fish oil/nutrients to the stomach and intestines. ACS Applied Materials & Interfaces.

Li, M. (2020). Percutaneous vertebral augmentation using drill rotation for osteoporotic vertebral compression fractures with intravertebral vacuum cleft. Skeletal Radiology.

Li, M. (2019). Diagnostic performance of MRI for detecting intraplaque hemorrhage in the carotid arteries: A meta-analysis. European Radiology.

Li, M. (2014). Diagnostic accuracy of myocardial magnetic resonance perfusion to diagnose ischemic stenosis with fractional flow reserve as reference: Systematic review and meta-analysis. JACC: Cardiovascular Imaging.

Li, M. (2012). Prognostic value of coronary CT angiography for cardiac events. Radiology.

Yu Fang | Molecular biotechnology | Best Researcher Award

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Prof. Dr. Yu Fang | Molecular biotechnology | Best Researcher Award

Full Professor at Fujian Institute of Research on the Structure of Matter | China

Dr. Yu Fang is a distinguished chemist whose research lies at the interface of materials chemistry and supramolecular science, particularly in the design and synthesis of Porous Coordination Cages (PCCs) and Metal–Organic Frameworks (MOFs). His work focuses on developing advanced porous materials for applications in catalysis, gas storage, molecular recognition, and energy conversion. Dr. Fang has made significant contributions to understanding host–guest chemistry, self-assembly mechanisms, and structure–property relationships in coordination materials. His innovative research has been widely recognized in leading journals such as Nature Communications, Angewandte Chemie International Edition, Journal of the American Chemical Society, and Chemical Society Reviews. Through collaborative projects and pioneering insights, Dr. Fang’s studies have bridged molecular design and functional materials engineering, influencing both academic and industrial research in advanced materials. He has published extensively, contributing to the development of next-generation coordination architectures with tailored porosity and dynamic behavior for chemical and environmental applications. With 7132 citations and an h-index of 26 according to his Scopus profile, Dr. Fang’s scientific impact reflects both productivity and quality, positioning him as a leading figure in coordination chemistry and materials innovation.

Profile: Google Scholar | Orcid

Featured Publications

Liang, Y., Yang, X., Wang, X., Guan, Z.-J., Xing, H., & Fang, Y. (2023). Nature Communications, 14, 5223.

Su, Z., Liu, K.-K., Xu, Y.-Q., Yan, B., Wang, S., Guan, Z.-J., Zou, Y., & Fang, Y. (2024). Angewandte Chemie International Edition, 64(9), e202420945.

Peng, Y., Yuan, L., Liu, K.-K., Jin, S., & Fang, Y. (2024). Angewandte Chemie International Edition, 64(12), e202423055.

Liang, Y., Xie, G., Liu, K.-K., Jin, M., Chen, Y., Yang, X., Guan, Z.-J., Xing, H., & Fang, Y. (2024). Angewandte Chemie International Edition, 64(1), e202416884.

He, H.-H., Yuan, J.-P., Cai, P.-Y., Wang, K.-Y., Feng, L., Kirchon, A., Li, J., Zhang, L.-L., Zhou, H.-C., & Fang, Y. (2023). Journal of the American Chemical Society, 145, 17164–17175.

Fang, Y., Powell, J., Li, E., Wang, Q., Perry, Z., Kirchon, A., Yang, X., Xiao, Z., Zhu, C., Zhang, L., Huang, F., & Zhou, H.-C. (2019). Chemical Society Reviews, 48(17), 4707–4730.

Liang, Y., Li, E., Wang, K., Guan, Z.-J., He, H.-H., Zhang, L.-L., Zhou, H.-C., Huang, F., & Fang, Y. (2022). Chemical Society Reviews, 51, 8378–8405.

Satyajit Ghosh | Neuro Biotechnology | Best Researcher Award

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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

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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

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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

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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

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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.