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.

 

 

Hongxiao Lv | Neuroscience | Best Researcher Award

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Dr. Hongxiao Lv | Neuroscience | Best Researcher Award

Dr. Hongxiao Lv | Neuroscience | Hebei University of Chinese Medicine | China

Dr. Hongxiao Lv is a dedicated and emerging researcher in the field of Traditional Chinese Medicine (TCM), particularly specializing in TCM syndrome differentiation and the treatment strategies for ischemic cerebrovascular diseases. Dr. Hongxiao Lv holds a comprehensive academic background, having completed a Bachelor’s degree in Chinese Materia Medica, followed by a Master’s degree in Acupuncture and Tuina, and currently pursuing doctoral research in TCM Diagnostics at Hebei University of Chinese Medicine, enabling her to integrate theoretical, clinical, and practical dimensions of TCM into her research endeavors. Her professional experience includes participating in academic research projects focused on the clinical mechanisms and therapeutic pathways of cerebrovascular disorders, contributing both analytical insights and evidence-based clinical viewpoints. She has authored and contributed to academic literature, including four research publications indexed in reputable scientific platforms and the book Practical Technologies of Traditional Chinese Medicine, which reflects her commitment to advancing clinical knowledge dissemination. Her research interests emphasize understanding pathogenesis patterns in ischemic cerebrovascular diseases, exploring diagnostic precision through syndrome differentiation, and developing treatment frameworks based on classical TCM theory combined with contemporary evidence-informed medical practice. Her research skills include TCM clinical syndrome analysis, integrative diagnostic interpretation, literature synthesis, experimental data review, research reporting, and collaboration in interdisciplinary research groups. She is a member of the Professional Committee on Integration of Traditional Chinese Medicine and Western Medicine, demonstrating her involvement in promoting the convergence of traditional therapeutic wisdom with modern biomedical practices. Awards and honors associated with her academic journey include recognition for scholarly merit, research performance, and program-based distinctions. In conclusion, Dr. Hongxiao Lv represents a committed young scholar with the ability to bridge traditional diagnostic systems with modern clinical challenges, contributing to the development of integrative treatment approaches in cerebrovascular disease management, and she continues to show promising potential for further academic growth, broader research collaboration, and international impact in the evolving field of Traditional Chinese Medicine research.

Academic Profile: ORCID

Featured Publication:

Lv, H. (n.d.). Research progress on the role of oxidative stress in the pathogenesis of vascular dementia and its treatment.

 

 

Hongtao Shi | Hydrology | Best Researcher Award

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Dr. Hongtao Shi | Hydrology | Best Researcher Award

Dr. Hongtao Shi | Hydrology | Lecturer at China University of mining and technology | China

Dr. Hongtao Shi is a lecturer at the School of Environment and Spatial Informatics, China University of Mining and Technology (CUMT) in Xuzhou, China, where he focuses on high-resolution microwave remote sensing of soil moisture, polarimetric SAR scattering modelling, and agricultural-hydrological applications of remote sensing data. He holds a Ph.D. in Photogrammetry and Remote Sensing from Wuhan University, China, where his doctoral research concentrated on multisource SAR and passive microwave methods for soil moisture retrieval; he also undertook joint doctoral training abroad at the University of Alicante, Spain. Prior to his current position, he completed his earlier degrees with an M.Sc. in Surveying Science and Technology from China University of Petroleum (East China) and a B.Sc. in Geographic Information Systems from the same institution. His professional experience includes his appointment at CUMT from mid-2021 onwards in the Environmental & Surveying Institute, during which time he has led and participated in national-level and laboratory-level research grants addressing multi‐angle, multi‐polarization SAR retrieval of soil moisture, high-resolution microwave downscaling, and airborne/spaceborne sensor data integration. His research interests span soil moisture inversion, multisource remote sensing for agriculture and hydrology, SAR polarimetry, passive microwave monitoring, time‐series image analysis, and machine-learning‐enhanced Earth-surface parameter retrieval. He has developed research skills in polarimetric SAR decomposition, multiscale data fusion, processing of microwave and optical remote sensing datasets, Python/Matlab/IDL/C# programming, time‐series modelling of hydrological variables, and uncertainty quantification in soil moisture retrieval. His honours include his role as Guest Editor for a special issue on “Soil Moisture Observation Using Remote Sensing and Artificial Intelligence” in the journal Remote Sensing, his membership in IEEE and the Chinese Society for Agricultural Meteorology, and his reviewer service for more than ten international journals including RSE, TGRS, JSTARS and Journal of Hydrology.

Academic Profile: ORCID | Scopus

Featured Publications:

Shi, H., Zhao, L., Yang, J., Lopez-Sanchez, J. M., Jinqi, Z., Sun, W., Lei, S., & Li, P. (2021). Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques. Remote Sensing of Environment, 261, 112485. (Citation 42)

Lang, F., Zhang, M., Zhao, J., Zheng, N., & Shi, H. (2024). Semantic segmentation for multisource remote sensing images incorporating feature slice reconstruction and attention upsampling.

Lang, F., Zhu, J., Qian, J., Dou, Q., Shi, H., Liao, L., & Zhao, L. (2025). Soil organic carbon estimation and transfer framework in agricultural areas based on spatiotemporal constraint strategy combined with active and passive remote sensing.

Zhao, J., Wang, Z., Sun, W., Yang, J., Shi, H., & Li, P. (2025). DMCF-Net: Dilated multiscale context fusion network for SAR flood detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Zhao, J., Zhang, M., Zhou, Z., Wang, Z., Wang, F., Shi, H., & Zheng, N. (2025). CFFormer: A cross-fusion transformer framework for the semantic segmentation of multisource remote sensing images. IEEE Transactions on Geoscience and Remote Sensing.

 

Yuehong Gong | Chip | Best Researcher Award

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Dr. Yuehong Gong | Chip | Best Researcher Award

Dr. Yuehong Gong | Chip | Associate Professor at Shandong Jiaotong University | China

Dr. Yuehong Gong earned her B.S. in Electronic Information Science and Technology, then M.S. in Microelectronics, and Ph.D. in Microelectronics and Solid‐State Electronics from Harbin Institute of Technology. She is currently a faculty member in the School of Navigation and Shipping at Shandong Jiaotong University. Over her career, Dr. Gong has developed strong professional experience in designing digital‐analog hybrid ASICs, working on chip reliability, radiation‐hardened circuits, and mixed signal integrated circuit design. Her research interests include digital‐analog hybrid ASIC design, chip reliability under extreme conditions (e.g., radiation), fractional-N PLLs for high energy physics experiments, and novel full-subtractor circuits hardened against double node upset. She has developed research skills in ASIC design and verification, mixed-signal circuit simulation, reliability analysis, ASIC fabrication flows, and low noise and low-power circuit techniques. Her leadership roles include coordinating international collaborations related to high energy physics electronics, peer review of IEEE and other journals, and mentoring graduate students and co-authors. She is a member of IEEE and associated societies (e.g. IEEE Nuclear & Plasma Sciences Society), and contributes to conference committees and journal reviewing. Among her awards and honors are recognition for her journal articles in IEEE Transactions and VLSI Integration, notable citations, and research grants supporting ASIC reliability and hybrid circuit design. While Dr. Gong has notable achievements in publication count and citation impact, there is potential to enhance visibility via more keynote talks or obtaining fellowships. In conclusion, Dr. Yuehong Gong is a highly skilled researcher in microelectronics and solid state electronics whose strong foundation in education, solid body of international research projects, and leadership in community activities position her to continue making impactful contributions to ASIC reliability, mixed-signal design, and high energy physics instrumentation. Her combination of technical expertise, publication record, and collaborative work suggests strong future growth and capacity to influence both academic and applied industrial domains.

Academic Profile: ORCID 

Featured Publications:

  1. Gong, Y., Yan, W., Luo, M., Wang, C., & Zhang, L. (2023). A Frequency-Tunable Fractional-N PLL for High-Energy Physics Experiments.

  2. Gong, Y., Luo, M., Wang, C., Yang, S., et al. (2025). Double-node-upset-hardened full-subtractor applying MTJ for the high energy physics experiments.

 

Isha Chauhan | Wireless Communication | Best Researcher Award

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Ms. Isha Chauhan | Wireless Communication | Best Researcher Award

Ms. Isha Chauhan | Wireless Communication | Research fellow at Polytechnic University of Turin | Italy

Ms. Isha Chauhan is an emerging researcher in the field of wireless and optical communication systems, currently affiliated as a Research Fellow with the Department of Electronics and Telecommunications at Politecnico di Torino, Italy. Her academic foundation is firmly rooted in Electrical and Electronics Engineering, and she holds a Ph.D. in Electrical Engineering with specialization in Free-Space Optical (FSO) Communication and Wireless Systems, where she focused on developing robust techniques to mitigate jamming, noise, and channel impairments in optical networks. Throughout her academic journey, Ms. Chauhan has cultivated a strong interdisciplinary approach that integrates signal processing, communication theory, and information security to enhance transmission efficiency and system reliability under adverse communication environments.Professionally, Ms. Chauhan has collaborated with esteemed researchers such as Prof. Manav R. Bhatnagar (IIT Delhi) and Dr. B. R. Manoj (IIT Guwahati), contributing to several high-impact international projects related to next-generation wireless systems and optical communications. She has published multiple peer-reviewed articles in reputed journals such as IEEE Transactions on Communications, Applied Optics, and Applied Sciences, which collectively demonstrate her scholarly excellence and technical insight. Her research portfolio covers a range of topics including coding theory, channel estimation, jammer mitigation, and information-theoretic security in wireless and FSO channels.

Academic Profile: Google Scholar

Featured Publications:

  1. Chauhan, I., Paul, P., Bhatnagar, M. R., & Nebhen, J. (2021). Performance of optical space shift keying under jamming. Applied Optics, 60(7), 1856–1863. (Cited by 11)

  2. Chauhan, I., & Bhatnagar, M. R. (2022). Performance of transmit aperture selection to mitigate jamming. Applied Sciences, 12(4), 2228. (Cited by 10)

  3. Chauhan, I., & Bhatnagar, M. R. (2022). UAV-based FSO communication under jamming. Proceedings of the IEEE 95th Vehicular Technology Conference (VTC2022-Spring), 1–5. (Cited by 7)

  4. Chauhan, I., & Bhatnagar, M. R. (2023). Information theoretic study of friendly jammer abating an eavesdropper in FSO communication. IEEE Transactions on Communications, 72(4), 2106–2123. (Cited by 6)

  5. Chaudhary, P., Chauhan, I., Manoj, B. R., & Bhatnagar, M. R. (2024). Linear regression-based channel estimation for non-Gaussian noise. Proceedings of the IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 1–6. (Cited by 2)

 

Eleni Moushi | Organic | Best Researcher Award

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Assoc. Prof. Dr. Eleni Moushi | Organic | Best Researcher Award

Assoc. Prof. Dr. Eleni Moushi | Organic | Associate Professor at European University Cyprus School of Sciences | Cyprus

Assoc. Prof. Dr. Eleni Moushi is a highly accomplished chemist and academic leader, currently serving as an Associate Professor of Chemistry at the European University Cyprus, where she has built a distinguished career in Inorganic and Coordination Chemistry. She earned her Ph.D. in Chemistry from a renowned European institution, focusing her doctoral research on single-molecule magnets (SMMs) and metal–organic frameworks (MOFs), with particular emphasis on their magnetic and structural properties. Over her career, Assoc. Prof. Dr. Eleni Moushi has cultivated extensive professional experience in synthetic chemistry, molecular magnetism, and material design, contributing significantly to international collaborations involving advanced magnetic materials and coordination polymers. Her research interests encompass the synthesis and characterization of high-nuclearity transition-metal clusters, supramolecular assembly, and the development of porous frameworks for gas storage and catalysis. She has been an integral part of numerous international research projects aimed at bridging quantum and classical magnetism, achieving global recognition for her innovative work on nanoscale molecular architectures. Demonstrating outstanding research skills, she specializes in crystallography, magnetometry, and spectroscopic analysis, contributing to the structural elucidation of complex inorganic systems. Her contributions to scientific literature include several high-impact publications indexed in Scopus and Web of Science, with more than 1,400 citations, an h-index of 18, and publications in prestigious journals such as Chemical Society Reviews, Angewandte Chemie International Edition, and Inorganic Chemistry. Assoc. Prof. Dr. Eleni Moushi’s awards and honors reflect her dedication to excellence, including institutional recognitions for research innovation, excellence in teaching, and contributions to European scientific collaborations. As an active member of professional societies such as the American Chemical Society (ACS) and the Royal Society of Chemistry (RSC), she continues to mentor emerging scientists and advance international cooperation in chemical research. Her leadership extends beyond academia, contributing to community engagement and policy-oriented research in environmental chemistry and sustainable materials.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  1. Papatriantafyllopoulou, C., Moushi, E. E., Christou, G., & Tasiopoulos, A. J. (2016). Filling the gap between the quantum and classical worlds of nanoscale magnetism: giant molecular aggregates based on paramagnetic 3d metal ions. Chemical Society Reviews, 45(6), 1597–1628. (Cited by 248)

  2. Moushi, E. E., Stamatatos, T. C., Wernsdorfer, W., Nastopoulos, V., & Christou, G. (2006). A family of 3D coordination polymers composed of Mn19 magnetic units. Angewandte Chemie International Edition, 45(46), 7722–7725. (Cited by 153)

  3. Moushi, E. E., Stamatatos, T. C., Wernsdorfer, W., Nastopoulos, V., & Christou, G. (2009). A Mn17 octahedron with a giant ground-state spin: occurrence in discrete form and as multidimensional coordination polymers. Inorganic Chemistry, 48(12), 5049–5051. (Cited by 144)

  4. Moushi, E. E., Lampropoulos, C., Wernsdorfer, W., Nastopoulos, V., & Christou, G. (2010). Inducing single-molecule magnetism in a family of loop-of-loops aggregates: heterometallic Mn40Na4 clusters and the homometallic Mn44 analogue. Journal of the American Chemical Society, 132(45), 16146–16155. (Cited by 134)

  5. Mishra, A., Tasiopoulos, A. J., Wernsdorfer, W., Moushi, E. E., & Moulton, B. (2008). Single-molecule magnets: A family of MnIII/CeIV complexes with a [Mn8CeO8]12+ core. Inorganic Chemistry, 47(11), 4832–4843. (Cited by 75)

 

Galina Malykhina | Sensors | Best Researcher Award

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Prof. Dr. Galina Malykhina | Sensors | Best Researcher Award

Prof. Dr. Galina Malykhina | Sensors | professor at Peter the Great St.Petersburg Polytechnic University | Russia

Prof. Dr. Galina Malykhina is a distinguished researcher and professor at Peter the Great St. Petersburg Polytechnic University, Russia, with extensive expertise in measurement information technologies and computational modeling. She completed her Ph.D. at the same university, establishing a strong foundation in applied engineering and computational sciences. Over the years, Prof. Dr. Malykhina has led and contributed to numerous international research projects, particularly in areas such as cerebral autoregulation assessment, two-phase flow control systems in oil production, and physics-informed neural network methods for modeling chemical reactors. Her professional experience encompasses academic leadership, mentoring graduate and doctoral students, and supervising high-impact research collaborations across interdisciplinary fields. Prof. Dr. Malykhina’s research interests include signal processing, computational engineering, neural network modeling, and the application of physics-based methods in complex engineering systems. She possesses advanced research skills in real-time data analysis, modeling of parameterized singular perturbation problems, wavelet cross-correlation techniques, and software-hardware integration for physiological assessments. Throughout her career, she has published multiple influential papers in reputable journals such as Sensors, Processes, and Computation, garnering significant citations that reflect the impact and recognition of her work in the global scientific community. She is an active member of IEEE and ACM, contributing to peer review activities, technical committees, and international workshops. Her dedication to advancing research excellence has been recognized with numerous awards and honors, celebrating her contributions to computational technologies, neural network modeling, and applied measurement systems. Prof. Dr. Malykhina’s ongoing work demonstrates remarkable potential for future contributions, particularly in integrating AI-driven methodologies with engineering measurement systems. Her leadership, innovative research, mentorship, and commitment to scientific advancement position her as an influential figure in her field, poised to continue shaping the development of cutting-edge computational and engineering technologies, while fostering collaboration and knowledge transfer across international research communities.

Academic Profile: ORCID | Scopus

Featured Publications:

  1. Semenyutin, V., Antonov, V., Malykhina, G., Nikiforova, A., Panuntsev, G., Salnikov, V., & Vesnina, A. (2025). Software and hardware complex for assessment of cerebral autoregulation in real time. Sensors. Citation: 12

  2. Arseniev, D., Malykhina, G., & Kratirov, D. (2024). Wavelet cross-correlation signal processing for two-phase flow control system in oil well production. Processes. Citation: 18

  3. Tarkhov, D., Lazovskaya, T., & Malykhina, G. (2023). Constructing physics-informed neural networks with architecture based on analytical modification of numerical methods by solving the problem of modelling processes in a chemical reactor. Sensors. Citation: 22

  4. Lazovskaya, T., Malykhina, G., & Tarkhov, D. (2021). Physics-based neural network methods for solving parameterized singular perturbation problem. Computation. Citation: 30

 

Vishal Gupta | Artificial Intelligence | Best Researcher Award

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Dr. Vishal Gupta | Artificial Intelligence | Best Researcher Award

Dr. Vishal Gupta | Artificial Intelligence | Assistant Professor at CGC University, Mohali | India

Dr. Vishal Gupta is an accomplished researcher and academician specializing in Web Accessibility, Assistive Technologies, Website Usability, and AI-driven web evaluation frameworks. He earned his Ph.D. from Guru Nanak Dev University, where he developed expertise in accessibility evaluation and applied computing techniques. Dr. Gupta has extensive professional experience in higher education and research, currently serving at Chandigarh Group of Colleges, where he leads research initiatives and mentors students in computer science and web accessibility projects. His research interests focus on enhancing web usability, accessibility compliance for educational and healthcare institutions, and integrating artificial intelligence for industrial and security frameworks. Dr. Gupta possesses strong research skills in website quality assessment, bi-level decision tree methodologies, AI-based vulnerability analysis, and accessibility evaluation metrics, supported by a solid record of international publications and collaborations. He has collaborated with esteemed colleagues such as Hardeep Singh, Parminder Kaur, and I. Kaur on multidisciplinary projects, reflecting his ability to lead and contribute to global research initiatives. Dr. Gupta has actively participated in professional organizations including IEEE and ACM, contributing to conferences, peer reviews, and academic committees, highlighting his leadership and community engagement. His work has been recognized with multiple awards and honors for excellence in research, innovation, and contributions to accessibility studies, reflecting his impact in the academic community. Strengths include his consistent publication record, strong interdisciplinary collaboration, and practical implementation of research findings in real-world settings. Areas for improvement involve exploring larger-scale international projects and further integrating emerging technologies into web accessibility studies. Suggestions for future work include policy-level impact analysis, open-source accessibility frameworks, and AI-enhanced methodologies for inclusive digital platforms. Dr. Gupta’s dedication, scholarly rigor, and innovative approach position him as a leader in his field with promising potential for future research contributions and societal impact, making him a highly suitable candidate for recognition in research and academic excellence.

Academic Profile: ORCID | Google Scholar

Featured Publications:

  1. Gupta, V., & Singh, H. (2021). Web Content Accessibility Evaluation of Universities’ Websites-A Case Study for Universities of Punjab State in India. 8th International Conference on Computing for Sustainable Global Development, 9 citations.

  2. Gupta, V., & Singh, H. (2022). Website Readability, Accessibility, and Site Security: A Survey of University Websites in Punjab. International Journal of Mechanical Engineering, 7(6), 1-9, 3 citations.

  3. Gupta, V., Singh, H., & Kaur, P. (2024). Accessibility Evaluation of Hospital Websites in India. International Journal of Computer Applications & Information Technology, 14, 1 citation.

  4. Gupta, V., Kaur, I., Singh, S., Kumar, V., & Kaur, P. (2025). Artificial Intelligence-empowered Industrial Framework for Extreme Vulnerability Analysis. Future Generation Computer Systems, 108127, citation data not available.

  5. Gupta, V., Kaur, P., & Singh, H. (2024). Bi-Level Decision Tree Approach for Web Quality Assessment. IEEE Access, citation data not available.