Ali Haji Elyasi | Earth Sciences | Best Researcher Award

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Mr. Ali Haji Elyasi | Earth Sciences | Best Researcher Award

Mr. Ali Haji Elyasi | Earth Sciences | PhD at University of Tehran | Iran

Mr. Ali Haji Elyasi is a researcher in Civil and Water Resources Engineering with a strong academic foundation and applied research orientation in hydrological systems, groundwater sustainability, and hydraulics. He is pursuing his Ph.D. in Civil Engineering (Water and Hydraulic Structures) at the University of Tehran, where he has developed expertise in advanced hydro-environmental modeling, geospatial intelligence, machine learning applications, and remote sensing-driven environmental monitoring. His education combines rigorous theoretical training with hands-on field research, enabling him to address complex challenges related to groundwater quality, flood risk assessment, watershed hydrology, and wetland ecosystem dynamics. Professionally, Mr. Ali Haji Elyasi has contributed to several interdisciplinary research projects in collaboration with leading institutions and research groups, focusing on groundwater potential analysis, land-use change detection, aquifer vulnerability assessment, and integrated water resource management strategies. His research interests include sustainable hydrology, resilience-based water infrastructure planning, satellite-based environmental observation, climate impact assessments, and data-driven decision support systems in semi-arid and ecologically sensitive regions. He is skilled in GIS, Remote Sensing, machine learning modeling, hydrological simulation, spatial data analysis, and environmental data interpretation, and he consistently integrates computational methods to enhance predictive accuracy and resource planning outcomes. Mr. Ali Haji Elyasi has published multiple peer-reviewed articles indexed in Scopus and reputable engineering journals, and his scientific contributions have been recognized through increasing citations and scholarly collaborations across universities and research institutions. He has also engaged in academic community development through teamwork, joint authorships, mentoring, and research dissemination activities. Awards and recognitions relate to his contributions in water resource research, collaborative scientific output, and commitment to advancing environmental sustainability. Overall, Mr. Ali Haji Elyasi demonstrates a strong commitment to scientific integrity, research innovation, and practical solutions for sustainable water system management, positioning him as a promising researcher capable of contributing meaningfully to academic scholarship, environmental policy, infrastructure planning, and future scientific leadership.

Academic Profile: ORCID | Google Scholar

Featured Publications:

Eftekhari, M., Mobin, E., Akbari, M., & Elyasi, A. H. (2021). Application assessment of GRACE and CHIRPS data in the Google Earth Engine to investigate their relation with groundwater resource changes (Northwestern region of Iran). Journal of Groundwater Science and Engineering, 9(2), 102–113. Cited 18 times

Eftekhari, M., Eslaminezhad, S. A., Akbari, M., DadrasAjirlou, Y., & Elyasi, A. H. (2021). Assessment of the potential of groundwater quality indicators by geostatistical methods in semi-arid regions. Journal of Chinese Soil and Water Conservation, 52(3), 158–167. Cited 7 times

Eslaminezhad, S. A., Eftekhari, M., Mahmoodizadeh, S., Akbari, M., & Elyasi, A. H. (2021). Evaluation of tree-based artificial intelligence models to predict flood risk using GIS. Iran-Water Resources Research, 17(2), 174–189. Cited 7 times

Eftekhari, M., Eslaminezhad, S. A., Elyasi, A. H., & Akbari, M. (2021). Geostatistical evaluation with Drinking Groundwater Quality Index (DGWQI) in Birjand Plain aquifer. Environment and Water Engineering, 7(2), 267–278. Cited 7 times

Eslaminezhad, S. A., Eftekhari, M., Akbari, M., Elyasi, A. H., & Farhadian, H. (2022). Predicting flood-prone areas using advanced machine learning models (Birjand Plain). Water and Irrigation Management, 11(4), 885–904. Cited 3 times

 

Marcela Correa | Oral Cancer | Best Researcher Award

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Prof. Marcela Correa | Oral Cancer | Best Researcher Award

Prof. Marcela Correa | Oral Cancer | Professor at Universidad Mayor | Chile

Prof. Marcela Correa-Fernandez is a committed academic and researcher specializing in Oral Medicine, Oral Oncology, Oral Pathology, Dentistry, and Evidence-Based Medicine, currently affiliated with University Mayor in Temuco, Chile. Prof. Marcela Correa-Fernandez completed her dental and postgraduate training with a strong foundation in clinical diagnosis, oral disease prevention, and research methodology, shaping her expertise in the early detection and risk assessment of oral potentially malignant disorders. Throughout her academic and professional career, Prof. Marcela Correa-Fernandez has actively contributed to clinical education, oral pathology research, and community-based oral health programs, with clinical rotations and institutional responsibilities in dental teaching hospitals. Her research interests focus on the molecular and histopathological mechanisms underlying malignant transformation in oral tissues, the role of biomarkers such as podoplanin in disease progression, and the development of evidence-based strategies for early oral cancer screening. Prof. Marcela Correa-Fernandez demonstrates strong research skills in systematic review methodology, clinical data interpretation, meta-analysis, and interdisciplinary collaboration, supported by participation in research networks advancing oral oncology knowledge in Latin America. Her scholarly contributions include international peer-reviewed publications, highlighting the importance of biomarker expression patterns in clinical prognosis and diagnostic decision-making. In addition to research, she has been involved in mentoring dental students, contributing to academic curriculum activities, and supporting clinical training environments aligned with community oral health needs. Prof. Marcela Correa-Fernandez has received recognition for her participation in scholarly collaborations, oral health outreach initiatives, and academic service roles that contribute to knowledge dissemination and professional development within the dental field. Her academic profile reflects a balanced integration of clinical expertise, research-oriented inquiry, and commitment to public and professional oral health advancement. Moving forward, Prof. Marcela Correa-Fernandez is positioned to continue expanding her research scope through multi-center clinical collaborations, advanced biomarker-based diagnostic studies, and greater engagement in research dissemination activities at the international level, reinforcing her ongoing contribution to oral oncology and evidence-based dental practice.

Academic Profile: ORCID | Google Scholar

Featured Publications:

  1. Correa-Fernández, M., Ramos-García, P., Mjouel-Boutaleb, N., et al. (2025). Implications of podoplanin overexpression in the malignant transformation of oral potentially malignant disorders: A systematic review and meta-analysis. Cancers.

  2. Guerrero, A. B., Correa-Fernández, M., Pino, M. P. R., & Vargas, L. (1997). Informe internado asistencial Hospital Herminda Martín de Chillán. Universidad de Concepción.

 

 

Sonia Perez Diaz | Mathematics | Women Researcher Award

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Prof. Sonia Perez Diaz | Mathematics | Women Researcher Award

Prof. Sonia Perez Diaz | Mathematics | Professor at University of Alcala | Spain

Prof. Sonia Pérez-Díaz is a highly accomplished Full Professor in the field of Algebraic Geometry and Symbolic Computation, widely recognized for her contributions to the study, parametrization, and computational analysis of algebraic curves and surfaces. Prof. Sonia Pérez-Díaz completed her Ph.D. in Mathematics at the University of Alcalá, where she has developed a long-standing academic career and continues to serve as a leading faculty member and researcher. Her professional experience includes significant involvement in national and international research collaborations, interdisciplinary projects in computational mathematics, and academic leadership roles in curriculum development, graduate supervision, and methodological advancements in computer algebra systems. Prof. Sonia Pérez-Díaz’s research interests primarily focus on rational parametrization, implicitization algorithms, approximate parametrization techniques, characterization of algebraic surfaces, and computational strategies for singularity detection in geometric models. Her research skills include advanced symbolic computation, algebraic geometry modeling, computational design algorithms, mathematical software development, theoretical analysis, and collaborative project leadership. She has co-authored influential books published by Springer and contributed numerous high-impact journal articles indexed in Scopus, IEEE Xplore, and other international databases, indicating the strong visibility and relevance of her scholarly work. Prof. Sonia Pérez-Díaz has been recognized with research awards, invited talks at international conferences, and participation in scientific committees that advance computational and mathematical knowledge globally. She has also served in mentorship roles guiding postgraduate students, fostering academic exchange, and contributing to disciplinary growth within mathematics and applied computational sciences. Her work has gained worldwide recognition through extensive citations, international co-authorships, and inclusion in global research networks such as Google Scholar, Scopus, and ORCID. In conclusion, Prof. Sonia Pérez-Díaz continues to demonstrate distinguished academic excellence, impactful research dissemination, strong collaborative engagement, and a forward-looking approach to the advancement of symbolic computation and algebraic geometry, maintaining a respected presence as a leading scholar and contributor to mathematical research and education.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  1. Sendra, J. R., Winkler, F., & Pérez-Díaz, S. (2008). Rational algebraic curves: A computer algebra approach. Springer Berlin Heidelberg. Citations: 371.

  2. Pérez-Díaz, S. (2006). On the problem of proper reparametrization for rational curves and surfaces. Computer Aided Geometric Design, 23(4), 307–323. Citations: 75.

  3. Pérez-Díaz, S., Sendra, J., & Sendra, J. R. (2004). Parametrization of approximate algebraic curves by lines. Theoretical Computer Science, 315(2–3), 627–650. Citations: 50.

  4. Pérez-Díaz, S., Schicho, J., & Sendra, J. R. (2002). Properness and inversion of rational parametrizations of surfaces. Applicable Algebra in Engineering, Communication and Computing, 13(1), 29–51. Citations: 49.

  5. Pérez-Díaz, S. (2007). Computation of the singularities of parametric plane curves. Journal of Symbolic Computation, 42(8), 835–857. Citations: 44.

 

Bryan Nagib Zambrano Manzur | Business Development | Best Researcher Award

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Prof . Bryan Nagib Zambrano Manzur | Business Development | Best Researcher Award

Prof . Bryan Nagib Zambrano Manzur | Business Development | Professor at Universidad de Granada | Ecuador

Prof. Bryan Nagib Zambrano Manzur is an emerging academic and research scholar affiliated with Universidad de Granada, recognized for his developing contributions in the areas of artificial intelligence, innovation systems, and data-driven organizational decision-making. He has pursued advanced academic training and research scholarship that bridge interdisciplinary domains of computational intelligence and management sciences, enabling him to examine how data, algorithms, and analytical models shape decision behaviors in uncertain and complex environments. His professional involvement includes participation in collaborative research clusters, academic knowledge-sharing environments, and multi-institutional academic panels, where he has contributed to discussions on sustainable innovation strategies and emergent AI methodological frameworks. His research interests include artificial intelligence applications in organizational contexts, machine learning-driven decision-making systems, digital transformation strategies, and knowledge modeling for innovation ecosystems. He demonstrates strong research skills in structured literature synthesis, qualitative and conceptual research methodologies, academic writing, knowledge interpretation, and interdisciplinary reviewing of scientific literature. His published work in a Scopus-indexed journal highlights his developing academic impact, supported by citations that indicate relevance within contemporary research conversations. He has also contributed to working papers, conference discussions, and collaborative academic exchanges. While his career stage is still progressing, he has shown academic excellence and engagement through project participation, contributions to institutional research networks, and involvement in scholarly dialogues that foster future research collaborations. His awards, academic recognition, and scholarly acknowledgment are reflected through his association with reputable academic research environments and growing citation visibility. The development of expanded empirical research contributions, broader methodological diversification, and increased participation in externally funded international collaborations will further elevate his academic influence. Prof. Bryan Nagib Zambrano Manzur is committed to advancing research that strengthens the integration of computational intelligence and organizational innovation, aiming toward scalable research contributions, high-impact journal publication strategies, and active leadership roles within academic networks. In conclusion, he represents a promising and forward-focused academic researcher whose dedication, scholarly discipline, and research clarity position him for continued contributions within global academic and research communities.

Academic Profile: Scopus

Featured Publications:

  1. Zambrano Manzur, B. N. (2024). In what ways do AI techniques propel decision-making amidst volatility? Annotated bibliography perspectives. Journal of Innovation and Entrepreneurship. (3 citations)

 

 

Carlo Cavaliere | Cardiovascular | Best Researcher Award

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Dr. Carlo Cavaliere | Cardiovascular | Best Researcher Award

Dr. Carlo Cavaliere | Cardiovascular | Researcher at IRCCS SYNLAB SDN | Italy

Dr. Carlo Cavaliere is a distinguished radiologist and medical imaging researcher recognized for his contributions to neuroimaging, hybrid PET/MRI diagnostics, and advanced MRI methodologies in both clinical and research settings. Dr. Carlo Cavaliere completed his Bachelor in Medicine and Surgery followed by a Specialization in Radiology, and later earned his Ph.D. in Medical Sciences, where he strengthened his expertise in neuroimaging and cognitive disorders. He has served in multiple research, advisory, and scientific coordination roles at IRCCS SDN in Naples, contributing to high-impact clinical research in radiology, neurodegenerative disease imaging, and translational neuroscience. His professional experience reflects strong interdisciplinary collaboration across radiology, neuroscience, medical physics, and biomedical engineering, especially in the study of consciousness disorders, neuroplasticity, and biomarker-based disease stratification. His research interests include neuroimaging of altered states of consciousness, diffusion tensor imaging for brain network mapping, hybrid PET/MR integration for precision diagnostics, radiomics for oncology and cardiovascular disease profiling, and the emerging use of artificial intelligence in image-based clinical decision-making. Dr. Carlo Cavaliere demonstrates advanced research skills in quantitative imaging analysis, connectome analytics, multimodal image interpretation, radiogenomic integration, and clinical trial imaging workflow development. He has contributed extensively to national and international research initiatives and has authored numerous peer-reviewed publications in Scopus-indexed and high-impact clinical journals. His work has supported evidence-based advancements in diagnostic imaging, imaging biomarkers for neurological disorders, and functional neuroanatomy assessment. He has been recognized for academic and research excellence through collaborative project leadership, editorial and peer review activities, and participation in multidisciplinary scientific networks. Dr. Carlo Cavaliere’s career reflects a balance of clinical expertise, academic scholarship, and commitment to innovation-driven patient care. In conclusion, Dr. Carlo Cavaliere continues to be a leading figure in medical imaging research, demonstrating strong potential for further contributions to the fields of radiology, neuroscience, and translational diagnostic science.

Academic Profile: ORCID | Scopus

Featured Publications:

  1. Cavaliere, C., Aiello, M., Perri, C. D., et al. (2015). Diffusion tensor imaging and white matter abnormalities in patients with disorders of consciousness. Frontiers in Human Neuroscience. (Cited ~300 times)

  2. Aiello, M., Cavaliere, C., & Salvatore, M. (2016). Hybrid PET/MR imaging and brain connectivity. Frontiers in Neuroscience. (Cited ~200 times)

  3. Castaldo, R., Cavaliere, C., Soricelli, A., et al. (2021). Radiomic and genomic machine learning method performance for prostate cancer diagnosis: Systematic review. Journal of Medical Internet Research. (Cited ~150 times)

  4. Di Gregorio, E., Papi, C., Conti, L., Cavaliere, C., et al. (2024). A magnetic resonance imaging-chemical exchange saturation transfer method for detecting water cycling across cellular membranes. Angewandte Chemie. (Cited ~40 times)

 

 

Silvina Fadda | Environment | Best Researcher Award

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Dr. Silvina Fadda | Environment | Best Researcher Award

Dr. Silvina Fadda | Environment | Senior Researcher at CERELA | Argentina

Dr. Silvina Fadda is a highly accomplished female scientist specializing in food microbiology and biochemistry, currently serving as a Research Scientist at CERELA under the National Scientific and Technical Research Council (CONICET) in Argentina. Dr. Silvina Fadda earned her Doctorate in Biochemistry from the Universidad Nacional de Tucumán, where she developed extensive expertise in microbial physiology, fermentation systems, and the characterization of lactic acid bacteria relevant to food safety and preservation. Throughout her professional career, she has focused on studying beneficial and bioprotective microorganisms, particularly lactic acid bacteria and their metabolic, proteomic, and adaptive responses under food processing conditions. Her research interests include microbial ecology in meat processing environments, the antagonistic activity of probiotic strains against foodborne pathogens, and molecular techniques for assessing microbial adaptation and stress tolerance. Dr. Silvina Fadda possesses strong research skills in proteomics, molecular microbiology, bacterial strain characterization, fermentation technology, and the development of microbial-based food safety strategies. She has collaborated on internationally coordinated research projects aimed at improving safe food production practices and developing sustainable microbial biopreservation methods. Her scholarly contributions are reflected in her publications in high-impact, Scopus-indexed journals and her continued role as a reviewer for reputable journals in food chemistry, food microbiology, and biofilm research. Dr. Silvina Fadda has received recognition for her scientific contributions through her longstanding research appointment at CONICET, along with participation in scientific committees and professional networks that advance food microbiology research. Her work demonstrates leadership in laboratory-based research, interdisciplinary collaboration, and mentoring early career researchers. In conclusion, Dr. Silvina Fadda exemplifies professional excellence, scientific rigor, and an ongoing commitment to improving global food safety through microbiological innovation, making her a commendable academic and a strong contributor to both scientific research and community knowledge.

Academic Profile: ORCID | Scopus

Featured Publications:

  1. Cisneros, L., Baillo, A. A., Ploper, D., Valacco, M. P., Moreno, S., Yantorno, O., Fusco, V., & Fadda, S. (2025). Foodborne lactic acid bacteria inactivate planktonic and sessile Escherichia coli O157:H7 in a meat processing environment: A physiological and proteomic study. Foods. Citations: 5

  2. Terán, L. C., Orihuel, A., Bentencourt, E., Raya, R., & Fadda, S. (2023). Role of curing agents in the adaptive response of the bioprotective Latilactobacillus curvatus CRL 705 from a physiologic and proteomic perspective. Bacteria. Citations: 12

  3. Sandoval-Mosqueda, I. L., Llorente-Bousquets, A., Soto, C., Mercado Márquez, C., Fadda, S., & Del Río García, J. C. (2023). Ligilactobacillus murinus strains isolated from mice intestinal tract: Molecular characterization and antagonistic activity against food-borne pathogens. Microorganisms. Citations: 18

  4. Terán, L. C., Orihuel, A., Bentencourt, E., Raya, R., & Fadda, S. (2023). Role of curing agents in the adaptive response of the bioprotective Latilactobacillus Curvatus CRL 705 from a physiologic and proteomic perspective (Preprint). Citations: 3

 

 

Nasrin Gharaei | Horticulture | Best Researcher Award

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Mrs. Nasrin Gharaei | Horticulture | Best Researcher Award

Mrs. Nasrin Gharaei | Horticulture | PhD student at Isfahan University of Technology | Iran

Mrs. Nasrin Gharaei is a dedicated researcher in the field of Horticultural Science, currently pursuing her Ph.D. in Horticulture at Isfahan University of Technology with a focus on photosynthesis mechanisms, plant physiology, and the biochemical development of horticultural and medicinal plant species. Mrs. Nasrin Gharaei’s academic background reflects strong theoretical and practical training in plant biology, agronomy, and advanced horticultural practices, where she has contributed to both controlled laboratory experiments and applied field research. Throughout her academic journey, she has actively engaged in studies involving nutrient management, foliar application strategies, plant–soil–microbial interactions, and the employment of plant growth-promoting microorganisms and nano-enhanced compounds to improve plant productivity and quality. Her professional experience is primarily shaped through her role as a research scholar, where she has contributed to ongoing departmental research projects, supported horticultural analysis laboratories, and collaborated with senior academics on plant physiology and phytochemical profiling studies. Her research interests include photosynthetic performance optimization, secondary metabolite enhancement in medicinal and aromatic crops, the role of bio-fertilizers in sustainable cultivation, and innovative strategies for improving crop resilience under varying agronomic conditions. She has demonstrated proficiency in research skills including plant physiological parameter measurement, biochemical assay execution, experimental design, volatile compound profiling, statistical data interpretation, and academic writing. Although early in her academic career, Mrs. Nasrin Gharaei has already contributed publications to Scopus-indexed journals, demonstrating rigor in research methodology and interdisciplinary scientific collaboration. In terms of awards and honors, she has received academic recognition through departmental research support, graduate research encouragement programs, and participation in scientific seminars, reflecting her growing reputation in her area of specialization. Mrs. Nasrin Gharaei continues to expand her scientific profile by engaging in collaborative research, attending academic conferences, and contributing to knowledge dissemination activities within the horticultural science community. In conclusion, she shows strong potential for future research leadership, with interests aligned toward sustainable horticulture, medicinal crop improvement, and bio-based yield enhancement strategies, positioning her for meaningful academic and research contributions in her field.

Academic Profile: ORCID | Google Scholar

Featured Publications:

  1. Gharaei, N., Shamshiri, M. H., & Dehghani, M. R. (2022). Effect of repeated foliar application of urea on growth, fruit quantity and quality of Pistacia vera Cv. Kalleh-Ghuchi. (Citations: 1)

  2. Gharaei, N., Nikbakht, A., Rahimmalek, M., & Szumny, A. (2025). Co-application of arbuscular mycorrhizal fungi and silicon nanoparticles: A strategy for optimizing volatile profile, phenolic content, and flower yield in Rosa damascena genotypes.

  3. Gharaei Masjedi, N., Shamshiri, M. H., & Dehghani, M. R. (2019). Effect of repeated foliar applications of urea on photosynthetic parameters of pistachio trees cv. Kalleh-Ghuchi in different stages of fruit growth.

 

Mohammad Esmaeili | Wildfire Monitoring | Best Researcher Award

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Mr. Mohammad Esmaeili | Wildfire Monitoring | Best Researcher Award

Mr. Mohammad Esmaeili | Wildfire Monitoring – PHD Student at SBUK | Iran

Mr. M. Esmaeili is a dedicated researcher specializing in remote sensing, hyperspectral image processing, machine learning, and deep learning-based geospatial analytics. He is currently pursuing his Ph.D. in the Technical and Engineering Faculty of Shahid Bahonar University of Kerman, where his doctoral research focuses on developing advanced neural architectures for spatial–spectral feature extraction in Earth observation data with applications in environmental monitoring, wildfire detection, and precision agriculture. Throughout his academic journey, Mr. M. Esmaeili has been involved in collaborative international research projects with scholars from leading institutions across Iran, Europe, Central Asia, and the United States, contributing to major studies that utilize multi-sensor data fusion, morphological feature integration, and LiDAR-assisted crop classification frameworks. His professional experience includes research assistantship roles, participation in laboratory-based geospatial analysis teams, and contributions to multi-disciplinary research environments that integrate remote sensing, signal processing, and artificial intelligence. His research interests extend to spectral dimensionality reduction, spatial attention mechanisms, convolutional neural network model optimization, object-based image analysis, and wildfire burn area mapping from multispectral and hyperspectral satellite platforms. He possesses strong research skills in Python programming, deep learning model development, GIS software environments, spectral data preprocessing, quantitative accuracy evaluation, and scientific publishing. His academic contributions are reflected in multiple Scopus and IEEE indexed articles, which have gained citation recognition in the research community. Although early in his career, Mr. M. Esmaeili has received acknowledgment through research collaborations, peer-reviewed journal publications, conference participation, and contributions to emerging solutions in environmental remote sensing. His work demonstrates both scientific rigor and practical relevance in contemporary geospatial intelligence research. In conclusion, Mr. M. Esmaeili continues to strengthen his academic profile through high-impact research output, technical skill development, and sustained international research engagement, positioning him as a promising scholar capable of future leadership in remote sensing and intelligent Earth observation systems.

Academic Profile: Google Scholar

Featured Publications:

  1. Hyperspectral image band selection based on CNN embedded GA (CNNeGA). (2023). Citations: 104.

  2. ResMorCNN model: hyperspectral images classification using residual-injection morphological features and 3DCNN layers. (2023). Citations: 98.

  3. HypsLiDNet: 3-D–2-D CNN model and spatial–spectral morphological attention for crop classification with DESIS and LiDAR data. (2024). Citations: 73.

  4. DESSA-net model: Hyperspectral image classification using an entropy filter with spatial and spectral attention modules on DeepNet. (2024). Citations: 17.

 

 

Hassan Moomivand | Mechanics | Innovative Research Award

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Prof. Hassan Moomivand | Mechanics | Innovative Research Award

Prof. Hassan Moomivand | Mechanics | Professor at Urmia University | Iran

Prof. Hassan Moomivand is a highly respected scholar and Professor of Rock Mechanics and Rock Fragmentation by Blasting at Urmia University, recognized for his substantial contributions to mining engineering, underground space stability, and applied geomechanics. Prof. Hassan Moomivand completed his doctoral studies in Rock Mechanics with a focus on rock mass behavior under varying stress and structural conditions, strengthening his expertise in field-based experimentation, laboratory testing, and numerical modeling approaches. Throughout his academic and professional career, he has held progressive research and teaching positions, contributing not only to theoretical advancements but also to practical engineering solutions applied to tunneling, underground caverns, and blasting optimization in mining and civil engineering projects. His research interests include rock mass characterization, fracture propagation, blasting performance evaluation, empirical and numerical modeling, anisotropy in dynamic properties of rocks, and geotechnical stability enhancement, where he has successfully collaborated with interdisciplinary teams and international partners. Prof. Hassan Moomivand possesses strong research skills in advanced rock testing, image-based rock feature analysis, dynamic stress modeling, and the development of empirical and semi-mechanistic prediction models. His scholarly output includes multiple high-impact publications indexed in Scopus and Web of Science, with notable contributions in Engineering Geology, Rock Mechanics and Rock Engineering, and the Arabian Journal of Geosciences, reflecting growing citations and recognition in the global research community. His academic service extends to supervising graduate students, participating in academic committees, reviewing journal manuscripts, and supporting research dissemination in scientific conferences. Honors and recognitions attributed to Prof. Hassan Moomivand include institutional acknowledgments for research productivity, contributions to engineering education, and active involvement in professional scientific communities. His work continues to influence rock engineering practices, particularly through new empirical models used for predicting fragmentation outcomes, assessing rock mass stability, and designing safer excavation operations. In conclusion, Prof. Hassan Moomivand remains a prominent figure in the field of Rock Mechanics, demonstrating ongoing commitment to scientific advancement, academic leadership, and impactful engineering applications that contribute to both industry innovation and scholarly development.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  1. Hemmati, A., Ghafoori, M., Moomivand, H., & Lashkaripour, G. R. (2020). The effect of mineralogy and textural characteristics on the strength of crystalline igneous rocks using image-based textural quantification. (Citations: 80)

  2. Azizi, A., & Moomivand, H. (2021). A new approach to represent impact of discontinuity spacing and rock mass description on the median fragment size of blasted rocks using image analysis of rock mass. (Citations: 51)

  3. Habibi, R., Moomivand, H., Ahmadi, M., & Asgari, A. (2021). Stability analysis of complex behavior of salt cavern subjected to cyclic loading by laboratory measurement and numerical modeling. (Citations: 45)

  4. Moomivand, H., & Vandyousefi, H. (2020). Development of a new empirical fragmentation model using rock mass properties, blasthole parameters, and powder factor. (Citations: 41)

  5. Moomivand, H., Seadati, S., & Allahverdizadeh, H. (2021). A new approach to improve the assessment of rock mass discontinuity spacing using image analysis technique. (Citations: 27)

  6. Mokhtarian, H., & Moomivand, H. (2020). Effect of infill material of discontinuities on the failure criterion of rock under triaxial compressive stresses. (Citations: 27)

 

Zhen Xiao | Nanotechnology | Best Researcher Award

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Dr. Zhen Xiao | Nanotechnology | Best Researcher Award

Dr. Zhen Xiao | Nanotechnology | Postdoctoral at Stanford University | United States

Dr. Zhen Xiao is a distinguished researcher in the field of nanomaterials, inorganic chemistry, magnetic materials, and bioimaging, currently affiliated with the Department of Radiology, Stanford University School of Medicine, where he contributes to advancing precision imaging and nanotechnology-enabled biomedical innovation. Dr. Zhen Xiao completed his Ph.D. in Chemistry, focusing on the rational design, synthesis, and characterization of multifunctional magnetic nanomaterials for biomedical and environmental applications, building a strong foundation in advanced materials fabrication, surface chemistry modification, and nanoscale functional property tuning. His professional experience includes participation in collaborative research groups integrating chemistry, materials science, radiology, and engineering, enabling him to translate nanomaterials into high-impact solutions for neural modulation, cancer treatment, MRI contrast enhancement, biosensing, and targeted drug delivery. His research interests center on magnetic nanoparticle assemblies, nano-bio interactions, neurostimulation technologies, magneto-thermal control mechanisms, and engineered nanoplatforms for multimodal imaging. Dr. Zhen Xiao possesses strong research skills including nanomaterial synthesis, spectroscopy, electron microscopy, magnetic property characterization, in vitro and in vivo imaging assays, and interdisciplinary experimental design. He has contributed to several influential international research projects and multidisciplinary consortia, producing peer-reviewed publications in leading journals such as Nature Materials, ACS Nano, Advanced Healthcare Materials, ACS Applied Materials & Interfaces, and Nano Research, which are widely cited across scientific communities. Dr. Zhen Xiao’s contributions have earned him recognition within academic and research networks, and he remains actively engaged in collaborative scientific exchange through conference presentations, institutional workshops, young-researcher mentoring, and scholarly review activities. His developing leadership includes supervising emerging researchers, fostering laboratory innovation workflows, and contributing to cross-institutional research grant initiatives. Dr. Zhen Xiao’s awards and honors reflect his growing influence in the global nanoscience community, acknowledging his role in developing transformative materials and imaging strategies with implications in neural engineering, oncology, and regenerative medicine. Looking ahead, Dr. Zhen Xiao aims to further expand scalable nanomaterial platforms, integrate nanotechnology with clinical translational research pathways, and strengthen interdisciplinary collaborations bridging materials innovation with human health applications, reinforcing his trajectory as a leading scientist in advanced biomedical nanotechnology.

Academic Profile: Google Scholar

Featured Publications:

  1. Stueber, D. D., Villanova, J., Aponte, I., Xiao, Z., et al. (2021). Magnetic nanoparticles in biology and medicine: Past, present, and future trends. Pharmaceutics. 223 citations.

  2. Sebesta, C., Torres Hinojosa, D., Wang, B., Asfouri, J., Li, Z., Xiao, Z., et al. (2022). Subsecond multichannel magnetic control of select neural circuits in freely moving flies. Nature Materials. 74 citations.

  3. Guo, Z., Xiao, Z., Ren, G., Xiao, G., Zhu, Y., Dai, L., Jiang, L. (2016). Natural tea-leaf-derived, ternary-doped 3D porous carbon as a high-performance electrocatalyst for the oxygen reduction reaction. Nano Research. 73 citations.

  4. Xiao, Z., Xiao, G., Shi, M., Zhu, Y. (2018). Homogeneously dispersed Co9S8 anchored on N and S co-doped carbon as bifunctional oxygen electrocatalysts and supercapacitor. ACS Applied Materials & Interfaces. 68 citations.

  5. Xiao, Z., Zhang, L., Colvin, V. L., Zhang, Q., Bao, G. (2022). Synthesis and application of magnetic nanocrystal clusters. Industrial & Engineering Chemistry Research. 30 citations.