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.

 

 

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)