Ahmadreza Khodayari | Industrial and Manufacturing Applications | Excellence in Research

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Mr. Ahmadreza Khodayari | Industrial and Manufacturing Applications | Excellence in Research

PhD Candidate | The University of Adelaide | Australia

Mr. Ahmadreza Khodayari is a mining engineering researcher whose work integrates rock mechanics, blasting science, fracture mechanics, and machine learning to advance precision modelling and optimization in mining operations. With a Published Documents 8 citation index comprising 110 citations, an h-index of 4, and an i10-index of 4, his contributions span experimentally grounded studies, data-driven prediction models and mechanistic simulations that address key challenges in rock breakage and material flow behaviour.His research portfolio includes several completed and ongoing projects focused on blast modelling rock fracture characterization and artificial intelligence applications in geo-materials engineering. Notable works include the calibration of mechanistic blast models using Ernest Henry Mine datasets the development of machine learning models for predicting blast-induced fragment sizes and advanced Blender Physics Engine simulations to assess sublevel caving (SLC) material flow. He has also executed misfire impact analyses on SLC gravity flow supporting safer and more predictable caving performance. Additionally his studies on AI-driven prediction of concrete and rock fracture toughness contribute to bridging traditional fracture mechanics with modern computational intelligence.Ahmadreza’s publications are featured in respected outlets such as Engineering Fracture Mechanics Theoretical and Applied Fracture Mechanics Steel and Composite Structures and the Journal of Mining and Environment. His 2022 work on predicting mixed-mode fracture toughness using extreme gradient boosting and metaheuristic optimization has accumulated significant citations reflecting strong community interest in AI-enhanced fracture modelling. His earlier experimental studies on freeze–thaw effects in Lushan Sandstone provided valuable insights into strength degradation mechanisms in cold-region geomaterials.He collaborates with researchers from the Lebanese French University Imam Khomeini International University and other international institutions strengthening global knowledge exchange in blasting and rock mechanics. His contributions to major conferences including FragBlast MassMin and ARMA demonstrate active engagement with both scientific and industry practitioners.Through a combination of high-fidelity numerical modelling physics-based simulations and advanced data-driven techniques Ahmadreza’s research aims to enhance fragmentation predictability mine productivity and geomechanical safety. His work continues to shape emerging methodologies in intelligent mining systems contributing to more efficient and sustainable resource extraction practices worldwide.

Profiles: Googlescholar | ORCID | ResearchGate 

Featured Publications

1.Fakhri, D., Khodayari, A., Mahmoodzadeh, A., Hosseini, M., Ibrahim, H. H., & Others. (2022). Prediction of mixed-mode I and II effective fracture toughness of several types of concrete using the extreme gradient boosting method and metaheuristic optimization algorithms. Engineering Fracture Mechanics, 276, 108916. Cited By: 39

2.Khodayari, A. R. (2019). Effect of freeze–thaw cycle on strength and rock strength parameters (A Lushan sandstone case study). Journal of Mining and Environment, Cited By: 27

3.Fakhri, D., Mahmoodzadeh, A., Mohammed, A. H., Khodayari, A., Ibrahim, H. H., & Others. (2023). Forecasting failure load of sandstone under different freezing–thawing cycles using Gaussian process regression method and grey wolf optimization algorithm. Theoretical and Applied Fracture Mechanics, 125, 103876. Cited By: 24

4.Hosseini, M., & Khodayari, A. R. (2018). Effects of temperature and confining pressure on mode II fracture toughness of rocks (Case study: Lushan sandstone). Journal of Mining and Environment, 9(2), 379–391. Cited By: 17

5.Khodayari, A., Fakhri, D., Mohammed, A. H., Albaijan, I., Mahmoodzadeh, A., & Others. (2023). The gene expression programming method to generate an equation to estimate fracture toughness of reinforced concrete. Steel and Composite Structures, 48(2), 163–177. Cited By: 3

His research advances intelligent blasting and rock-mass behaviour prediction, enabling safer, more efficient, and data-driven mining practices that strengthen global resource sustainability.

Xiangu Chen | Biomedical and Healthcare Applications | Best Research Article Award

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Prof. Xianguo Chen | Biomedical and Healthcare Applications | Best Research Article Award

Professor | Zhejiang University School of Medicine | China

Dr. Xianguo Chen is an active researcher in the field of lung cancer biology, molecular oncology, and precision medicine, with a strong focus on exploring genetic alterations, therapeutic resistance mechanisms, and biomarker-driven clinical translation. Affiliated with the Zhejiang University School of Medicine, Dr. Chen has established a robust research portfolio, contributing 16 scientific publications, accumulating 48 citations, and maintaining an h-index of 4, reflecting consistent scholarly impact within a rapidly evolving biomedical landscape.Dr. Chen’s research spans critical areas of lung adenocarcinoma, non-small cell lung cancer (NSCLC), oncogenic signaling pathways, and clinical molecular diagnostics. His work includes multiple contributions as first author, corresponding author, and co-corresponding author, demonstrating scientific leadership and collaboration across multidisciplinary teams. Notable publications include studies on miR-1293–mediated angiogenesis regulation, carbonic anhydrase 4 as a prognostic biomarker, and the identification of novel RET and ALK fusions in NSCLC, each contributing valuable insights into cancer progression, heterogeneity, and precision-targeted therapy.His commitment to translational oncology is further reflected in several research grants. These include major funded projects focused on acacetin-mediated SMYD2 inhibition and DNA damage repair, KMT3C-driven osimertinib resistance via ENO1-regulated glycolysis, and metabolomic discrimination of pulmonary nodules combined with fecal microbiota transplantation strategies. These funded studies highlight his expertise in integrating molecular biology, bioinformatics, and therapeutic research to address pressing clinical challenges in cancer diagnosis and treatment.In addition to his publication record, Dr. Chen engages in collaborative research involving over 130 co-authors, demonstrating broad interdisciplinary partnerships across medical, molecular, and computational sciences. His recent article on machine learning–based immune prognosis modeling for lung adenocarcinoma extends his contributions into the domain of AI-assisted oncology, reinforcing the relevance of computational technologies in modern cancer research.Dr. Chen’s scientific efforts collectively aim to enhance early cancer detection, refine prognostic tools, and illuminate new molecular targets for therapy. Through his funded projects, high-quality publications, and sustained collaborative activity, he continues to contribute significantly to the advancement of global lung cancer research and its transition toward more personalized, mechanism-driven clinical care.

Profiles: Scopus | ResearchGate

Featured Publication

1.Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning. (2025). Frontiers in Oncology.

Dr. Xianguo Chen research advances precision oncology by uncovering molecular mechanisms that drive lung cancer progression and therapeutic resistance, enabling more accurate diagnostics and targeted treatment strategies.

Sandip Kaledhonka | Biomedical and Healthcare Applications | Research Excellence Award

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Assoc. Prof. Dr. Sandip Kaledhonka | Biomedical and Healthcare Applications | Research Excellence Award

Associate professor | Indian Institute of Technology Bombay | India 

Dr. Sandip Kaledhonka is an accomplished structural biologist whose research focuses on time-resolved cryogenic electron microscopy (cryo-EM), ribosome dynamics, and molecular mechanisms underlying protein synthesis. With 38 published research documents 775 citations, an h-index of 10, and an i10-index of 10, he has established a strong global research presence through high-impact publications and sustained collaborations with leading scientists across structural biology biophysics and molecular microbiology.Dr. Kaledhonkar’s research has significantly advanced the understanding of dynamic events in translation initiation, elongation, termination, and ribosome recycling. His landmark work Late steps in bacterial translation initiation visualized using time-resolved cryo-EM published in Nature revealed critical structural intermediates that define the kinetics of ribosomal assembly. He has also contributed foundational methods including the widely used microfluidic spraying-plunging technique for ultrafast sample preparation enabling real-time visualization of rapid biochemical reactions.A notable aspect of his research is the integration of mixing-spraying microfluidics with high-resolution cryo-EM an approach that has provided unprecedented insights into transient conformations of biological macromolecules. His studies on ribosome subunit association release-factor activation and ribosome recycling published in journals such as Structure and Biophysical Journalhave shaped current understanding of translation control and fidelity. His contributions extend to photobiology with influential work on photoactive yellow protein (PYP) focusing on chromophore isomerization protonation hydrogen bonding networks and signaling kinetics.Beyond ribosome biology Dr. Kaledhonkar has collaborated on impactful multidisciplinary research including bacteriophage characterization microbial biofilm reduction structural components of jumbo phages and mechanisms of innate antimicrobial defense involving AAA-ATPases. His recent works further explore methodological innovations in cryo-EM pose estimation extracellular vesicle isolation and enzyme conformational regulation highlighting his broad scientific influence.He has co-authored publications with leading researchers such as Joachim Frank Ziao Fu Bo Chen Måns Ehrenberg and Robert A. Grassucci underscoring a strong record of international collaboration. With expertise spanning structural dynamics microfluidics and time-resolved structural biology Dr. Kaledhonkar’s research continues to contribute to the global advancement of molecular and biomedical sciences offering foundational knowledge that drives future therapeutic and biotechnological innovations.

Profiles:  Googlescholar | Scopus

Featured Publications

1. Horst, M. A., Stalcup, T. P., Kaledhonkar, S., Kumauchi, M., Hara, M., & Xie, A. (2009). Locked chromophore analogs reveal that photoactive yellow protein regulates biofilm formation in the deep sea bacterium Idiomarina loihiensis. Journal of the American Chemical Society, 131(47), 17443–17451. Cited By : 61

2. Kaledhonkar, S., Fu, Z., White, H., & Frank, J. (2018). Time-resolved cryo-electron microscopy using a microfluidic chip. In Protein Complex Assembly: Methods and Protocols (pp. 59–71). Humana Press. Cited By : 52

3. Kumauchi, M., Kaledhonkar, S., Philip, A. F., Wycoff, J., Hara, M., Li, Y., & Xie, A. (2010). A conserved helical capping hydrogen bond in PAS domains controls signaling kinetics in the superfamily prototype photoactive yellow protein. Journal of the American Chemical Society, 132(44), 15820–15830. Cited By : 12

4. Das, S., & Kaledhonkar, S. (2024). Physiochemical characterization of a potential Klebsiella phage MKP-1 and analysis of its application in reducing biofilm formation. Frontiers in Microbiology, 15, 1397447. Cited By : 3

5. Ghosh, S., Roy, S., Baid, N., Das, U. K., Rakshit, S., Sanghavi, P., Hajra, D., Das, S., … & (include remaining authors if available). (2025). Host AAA-ATPase VCP/p97 lyses ubiquitinated intracellular bacteria as an innate antimicrobial defence. Nature Microbiology, 1–16. Cited By : 2

Dr. Kaledhonkar’s pioneering time-resolved cryo-EM work reveals molecular events in real time, advancing fundamental understanding of translation mechanisms. His innovations in microfluidic methodology continue to transform structural biology and accelerate discoveries in molecular medicine.

Shenglin Wang | Biomedical and Healthcare Applications | Young Scientist Award

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Dr. Shenglin Wang | Biomedical and Healthcare Applications | Young Scientist Award

Clinician-Scientist | Fujian Medical University | China

Dr. Shenglin Wang is a biomedical researcher whose work focuses on tumor biology, cancer metastasis, and molecular mechanisms underlying osteosarcoma, chondrosarcoma, and lung cancer bone lesions. He completed a Post-doctoral Fellowship at Fujian Medical University where he advanced single-cell sequencing, tumor microenvironment profiling, and molecular pathology research. In 2025, he joined The First Affiliated Hospital of Fujian Medical University as a Physician, continuing to integrate clinical oncology with translational cancer research.Over the last five years, Dr. Wang has secured three competitive research grants as Principal Investigator or Co-Investigator. His ongoing NSFC project  investigates SOX18-mediated endothelial senescence and HBEGF secretion in non-small cell lung cancer bone metastasis, aiming to define novel therapeutic targets within the senescent vascular niche. He also leads a provincial project exploring HMGA2-regulated ferroptosis resistance in chondrosarcoma, contributing to the understanding of tumor progression and cell death mechanisms. His completed NSFC project, focused on aptamer-based electrochemical sensing for rapid detection of circulating tumor cells, reflects his multidisciplinary approach combining bioengineering with oncology.Dr. Wang has authored 33 peer-reviewed publications, accumulating 651 citations, with an h-index of 11, demonstrating sustained research productivity and academic impact. His representative articles include studies in Cell Proliferation, Frontiers in Immunology, and Acta Biochimica et Biophysica Sinica, covering topics such as single-cell transcriptomics of metastatic bone microenvironments, immune regulatory networks in thyroid carcinoma, and STAT3/EGFR signaling in osteosarcoma drug resistance. His collaborative work spans more than 60 co-authors, highlighting strong interdisciplinary engagement.Collectively, Dr. Wang’s research advances understanding of tumor microenvironment remodeling, therapeutic resistance, and metastasis-associated cell senescence. His contributions support the development of precision oncology strategies and have broad implications for improving diagnostic, prognostic, and therapeutic outcomes in bone-related malignancies.

Profiles:  Scopus | ResearchGate

Featured Publications

1. Author(s). (2025). The integrin α2–osteoclast axis: A key driver of bone destruction and therapeutic target in osteosarcoma. Journal of Translational Medicine.

2. Author(s). (2021). Corrigendum: Stattic sensitizes osteosarcoma cells to epidermal growth factor receptor inhibitors via blocking the interleukin 6-induced STAT3 pathway. Acta Biochimica et Biophysica Sinica, 53(12), 1670–1680.

3. Author(s). (2025). Single-cell transcriptomic analysis of the senescent microenvironment in bone metastasis. Cell Proliferation, 58(1), e13743. Cited By : 5

The nominee’s work advances precision oncology by uncovering key molecular and microenvironmental mechanisms that drive tumor progression, therapeutic resistance, and bone metastasis, enabling the development of more effective diagnostic and therapeutic strategies. By integrating single-cell analytics, molecular signaling research, and translational innovation, the nominee contributes to improved cancer outcomes and supports global efforts toward personalized, mechanism-driven cancer care.

Ibrahim Omara | Biometrics and Security | Research Excellence Award

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Assoc. Prof. Dr. Ibrahim Omara | Biometrics and Security | Research Excellence Award

Associated professor | Menoufia University  | Egypt 

Assoc. Prof. Dr. Ibrahim Omara is a dedicated researcher specializing in Cybersecurity, Artificial Intelligence, Machine Learning, Computer Vision, Multi-Biometrics, and Image Classification, with a growing influence across these interconnected domains. His scholarly contributions include 25 research documents, which have collectively earned 413 citations, supported by an h-index of 11 and i10-index of 12, highlighting both productivity and consistent scholarly impact. His work is highly recognized within the biometric research community, particularly for advancing ear recognition, multimodal biometric fusion, and deep feature learning, where several of his publications have become widely cited references.A significant portion of his contributions lies in pioneering geometric feature extraction, Mahalanobis distance learning, pairwise SVM classification, and distance-metric-driven multimodal authentication, including models that integrate deep CNNs, Vision Transformers, and feature-level fusion. His article A novel geometric feature extraction method for ear recognition stands among his most influential works, shaping subsequent research directions within biometric pattern recognition. In addition to ear biometrics, he has also contributed to remote sensing, SAR target classification, hyperspectral imagery transmission, and deep reinforcement learning, reflecting a multidisciplinary research approach.He has collaborated extensively with leading international researchers, including experts from Harbin Institute of Technology, Dublin City University, Nanyang Technological University, Benha University, Menoufia University, and Prince Sultan University. These collaborations have strengthened cross-institutional innovation in AI-driven security systems, robust biometrics, and intelligent vision technologies. His research outputs also include recent advancements in multi-biometric models, finger-knuckle recognition, and high-resolution scene classification, demonstrating continuous engagement with state-of-the-art machine intelligence.The social impact of his work is reflected in applications that enhance secure identification, digital authentication, and automated visual intelligence, contributing to safer digital ecosystems and improved trust in AI-enabled technologies. With a strong publication record and sustained research momentum, he remains committed to advancing next-generation intelligent security systems and expanding the frontiers of biometric artificial intelligence.

Profiles:  Googlescholar | Scopus | ORCID | ResearchGate

Featured Publications

1. Omara, I., Li, F., Zhang, H., & Zuo, W. (2016). A novel geometric feature extraction method for ear recognition. Expert Systems with Applications, 65, 127–135. Cited By : 100

2.Omara, I., Wu, X., Zhang, H., Du, Y., & Zuo, W. (2018). Learning pairwise SVM on hierarchical deep features for ear recognition. IET Biometrics, 7(6), 557–566. Cited By : 43

3.Omara, I., Hagag, A., Chaib, S., Ma, G., Abd El-Samie, F. E., & Song, E. (2020). A hybrid model combining learning distance metric and DAG support vector machine for multimodal biometric recognition. IEEE Access.
Cited By : 36

4.Omara, I., Wu, X., Zhang, H., Du, Y., & Zuo, W. (2017). Learning pairwise SVM on deep features for ear recognition. In Proceedings of the 2017 IEEE/ACIS 16th International Conference on Computer and Information. Cited By : 36

5.Omara, I., Hagag, A., Ma, G., Abd El-Samie, F. E., & Song, E. (2021). A novel approach for ear recognition: Learning Mahalanobis distance features from deep CNNs. Machine Vision and Applications, 32(1), 38. Cited By : 35

His contributions in AI-driven biometrics and intelligent security models provide industry with scalable, high-accuracy authentication solutions. This research accelerates technological innovation, enhances digital infrastructure reliability, and supports global transitions toward secure, intelligent, and automated systems.

Yu Zhou | Medical Image Analysis | Best Researcher Award

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Dr. Yu Zhou | Medical Image Analysis | Best Researcher Award

Lecturer | Henan University of Science and Technology | China

Dr. Yu Zhou is an emerging researcher in the intersecting domains of medical imaging, neuroscience, and artificial intelligence, recognized for advancing computational approaches that improve the understanding and diagnosis of neurological disorders. With 10 published research documents, 98 citations, an h-index of 7, and an i10-index of 6, his scholarly contributions reflect both productivity and growing international influence. His research has led to notable advancements in diffusion MRI analysis, white-matter connectivity modeling, and machine-learning-driven diagnostic frameworks, particularly within mild cognitive impairment (MCI), juvenile myoclonic epilepsy (JME), and neurobehavioral disorders.Yu Zhou’s most cited works demonstrate strong expertise in fiber-specific white matter analysis, CNN-based transfer learning, and automated classification systems, with contributions published in respected venues such as Cerebral Cortex, Frontiers in Aging Neuroscience, Frontiers in Neuroscience, and Journal of Neural Engineering. His research extends beyond human neuroscience to impactful cross-disciplinary applications, including AI-driven acoustic-based detection systems for livestock estrus identification, showcasing versatility and methodological depth.He has served as principal investigator for two provincial projects, participated in four additional provincial projects and one national project, and contributed to one consultancy/industry initiative, indicating growing leadership in funded research. His innovative capabilities are further evidenced by one granted patent and four patents under review, underscoring his commitment to translational and societally relevant technological development. With collaborations established across computational neuroscience and AI imaging research groups, he continues to contribute to global scientific networks.Yu Zhou’s ongoing work focuses on building interpretable deep-learning models, advancing multimodal data fusion for clinical diagnostics, and developing AI-assisted neuroimaging biomarkers for early disease identification. These contributions hold significant promise for clinical decision support, early-stage neurological assessment, and precision medicine applications. With increasing publication momentum and expanding collaborative research engagements, he is positioned to generate deeper scientific impact and contribute to the evolution of intelligent medical imaging and computational neuroscience.

Profiles:  Googlescholar | ResearchGate

Featured Publications

1.Zhou, Y., Si, X., Chen, Y., Chao, Y., Lin, C. P., Li, S., Zhang, X., Ming, D., & Li, Q. (2022). Hippocampus- and thalamus-related fiber-specific white matter reductions in mild cognitive impairment. Cerebral Cortex, 32(15), 3159–3174. Cited By : 23

2.Si, X., Zhang, X., Zhou, Y., Sun, Y., Jin, W., Yin, S., Zhao, X., Li, Q., & Ming, D. (2020). Automated detection of juvenile myoclonic epilepsy using CNN-based transfer learning in diffusion MRI. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. Cited By : 18

3.Zhou, Y., Si, X., Chao, Y. P., Chen, Y., Lin, C. P., Li, S., Zhang, X., Sun, Y., & Ming, D. (2022). Automated classification of mild cognitive impairment by machine learning with hippocampus-related white matter network. Frontiers in Aging Neuroscience, 14, 866230.Cited By : 13

4.Wang, J., Si, Y., Wang, J., Li, X., Zhao, K., Liu, B., & Zhou, Y. (2023). Discrimination strategy using machine learning technique for oestrus detection in dairy cows by a dual-channel-based acoustic tag. Computers and Electronics in Agriculture, 210, 107949.Cited By : 13

5.Wang, J., Chen, H., Wang, J., Zhao, K., Li, X., Liu, B., & Zhou, Y. (2023). Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag. animal, 17(6), 100811.Cited By : 12

Dr. Yu Zhou’s work advances global healthcare innovation by integrating medical imaging, neuroscience, and artificial intelligence to enable earlier, more accurate detection of neurological disorders. His research drives the development of interpretable, data-driven diagnostic tools that strengthen clinical decision-making and support precision medicine. Through cross-disciplinary innovation, he envisions AI-empowered neuroimaging solutions that improve patient outcomes and transform future healthcare systems.

Wolfgang Härdle | Industrial and Manufacturing Applications | Outstanding Contribution Award

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Prof. Dr. Wolfgang Härdle | Industrial and Manufacturing Applications | Outstanding Contribution Award

Humboldt-Universität zu Berlin | IDA Inst Digital Assets | Germany

Prof. Wolfgang Karl Härdle, Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin, is an internationally recognized leader in modern statistics, digital finance, machine learning, and smart data analytics. With an exceptional body of work spanning more than three decades, he has shaped the global landscape of statistical science through groundbreaking contributions to nonparametric statistics, multivariate analysis, econometrics, and quantitative finance. His academic influence is reflected in an outstanding scholarly output of 994 documents which have collectively amassed over 48,217 citations, supported by a remarkable h-index of 93 and i10-index of 311.A pioneer of applied nonparametric regression Prof. Härdle’s seminal works such as Applied Nonparametric Regression Applied Multivariate Statistical Analysis and Nonparametric and Semiparametric Models remain foundational references used across statistics econometrics  and data science. His highly cited research on smoothing techniques bandwidth selection average derivatives and optimal smoothing rules has advanced the theoretical and practical understanding of regression modeling. Additionally his contributions to wavelets financial econometrics copula theory tail-risk modeling and network risk analysis have had significant implications for financial stability risk assessment and decision analytics.Prof. Härdle has collaborated extensively with leading scholars worldwide producing influential publications that continue to guide contemporary methodological innovations. His interdisciplinary reach includes co-authoring major handbooks such as the Springer Handbook of Computational Statistics and the Handbook of Data Visualization which broaden access to advanced analytical methodologies for global researchers and practitioners.Beyond scholarly impact his work plays a vital societal role by strengthening statistical foundations for digital finance  high-dimensional modeling and smart data solutions helping institutions and industries make informed data-driven decisions. Through his research leadership mentorship and high-impact publications Prof. Härdle continues to advance statistical science and shape the future of data-centric research worldwide.

Profile:  Googlescholar

Featured Publications

1.Härdle, W. (1990). Applied nonparametric regression. Cambridge University Press. Cited By: 6559

2.Härdle, W., & Simar, L. (2007). Applied multivariate statistical analysis. Springer Berlin Heidelberg.Cited By: 3465

3.Härdle, W., Werwatz, A., Müller, M., & Sperlich, S. (2004). Nonparametric and semiparametric models. Springer Berlin Heidelberg.Cited By: 2006

4.Härdle, W., & Mammen, E. (1993). Comparing nonparametric versus parametric regression fits. The Annals of Statistics, 21(4), 1926–1947.Cited By: 1558

5.Härdle, W. (2012). Smoothing techniques: With implementation in S. Springer Science & Business Media.Cited By: 1529

Prof. Wolfgang Karl Härdle’s pioneering contributions in nonparametric statistics, digital finance, and machine learning have transformed data-driven decision-making across science, industry, and global financial systems. His methods for robust modeling, risk analytics, and smart data solutions empower researchers, policymakers, and institutions to navigate complex, high-dimensional data with greater accuracy, transparency, and resilience. He envisions a future where advanced statistical intelligence drives safer financial ecosystems and more equitable, evidence-based innovation worldwide.

ShihJung Juan | Human Computer Interaction and Augmented Reality | Best Researcher Award

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Dr. ShihJung Juan | Human Computer Interaction and Augmented Reality | Best Researcher Award

Post Doctoral Researcher | National Taichung University of Science and Technology | Taiwan

Dr. Shihjung Juan is a dedicated researcher at the National Taichung University of Science and Technology, Taiwan, recognized for his scholarly contributions in the fields of knowledge management, tacit knowledge acquisition, and organizational learning behaviors. With a research portfolio comprising 8 peer-reviewed publications, 114 citations, and an h-index of 2, his work reflects a steadily growing influence within the global academic community. His studies frequently examine how individuals and organizations acquire share and utilize knowledge to enhance performance improve innovation capacity and foster sustainable competitive advantages in dynamic environments.A notable highlight of his academic contribution is his research on tacit knowledge acquisition and absorptive capability providing key insights into how knowledge seekers internalize experiential insights and convert them into higher work effectiveness. This line of inquiry contributes to the broader discourse on human resource development organizational capability building and the strategic role of knowledge in modern enterprises. His publications demonstrate methodological rigor with conceptual clarity and empirical depth that position his research as a valuable reference in management and organizational studies.Dr. Juan has collaborated with researchers across interdisciplinary domains strengthening research networks and helping advance collaborative learning and knowledge-sharing ecosystems. His co-authorship with scholars from diverse backgrounds reflects an openness to integrating multi-perspective approaches supporting both theoretical advancements and practical implications for businesses educational institutions and public sector organizations.Beyond academic publishing the societal relevance of his research lies in its potential to guide organizations in enhancing employee performance building adaptive cultures and navigating knowledge-driven challenges in the digital age. By focusing on how individuals develop absorptive capacity his work informs strategies for upskilling innovation management and cultivating learning-oriented workplaces.Overall Dr. Shihjung Juan’s scholarly trajectory showcases a commitment to advancing knowledge management research while contributing meaningful insights to support organizational growth human development and evidence-based managerial practices worldwide.

Profile:  Scopus

Featured Publications

1. Juan, S. (2025). The impact of tacit knowledge acquisition and absorptive capability on individual performance: From the knowledge seeker’s perspective. Journal of Knowledge Management.

Dr. Shihjung Juan’s work advances global knowledge management practices by revealing how tacit knowledge and absorptive capability enhance individual performance and organizational adaptability. His insights support evidence-based strategies that strengthen innovation, workforce development, and sustainable competitiveness across education, industry, and knowledge-driven societies.

Divya Nimma | Applications of Computer Vision | Women Researcher Award

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Assist. Prof. Dr. Divya Nimma | Applications of Computer Vision | Women Researcher Award

Assistant Professor | Arkansas Tech University | United States

Dr. Divya Nimma is an accomplished researcher and Assistant Professor at Arkansas Tech University, specializing in Computer Vision, Artificial Intelligence, Image Processing, and Machine Learning. With a strong interdisciplinary footprint, she has contributed extensively to domains spanning environmental monitoring, healthcare analytics, intelligent transportation cybersecurity and immersive technologies. She has published 46 scholarly works and accumulated over 326 citations, with an h-index of 10 and i10-index of 10, underscoring her growing global research influence.Dr. Nimma’s research portfolio reflects a commitment to developing intelligent systems for real-world impact. Her notable contributions include climate-responsive modeling of freshwater ecosystems remote sensing–based marine life assessment for food security transformer-driven object detection , and advanced deep learning frameworks for image forensics and semantic segmentation. She has led and co-authored high-impact studies published in Scientific Reports IEEE Transactions Alexandria Engineering Journal Desalination and Water Treatment Remote Sensing in Earth Systems Sciences and other reputed journals.Her collaborative research spans international teams across the United States  Europe the Middle East  and Asia. Significant works include attention-based models for real-time surveillance explainable AI pipelines for fingerprint recognition IoT-enabled energy management for EV charging predictive maintenance in Industry 4.0 and multisource wearable data analytics for human activity recognition.Dr. Nimma has also made influential contributions to biomedical informatics including cancer detection using optimized deep learning osteoporosis classification and non-invasive brain stimulation–based sleep stage modeling. Additionally her research extends to precision agriculture integrating drone imagery AI and consumer electronics to enhance crop optimization and sustainability.Committed to societal and technological advancement Dr. Nimma’s work demonstrates a unique synthesis of deep learning innovation domain-driven applications and cross-disciplinary collaboration positioning her as a rising scholar and impactful global contributor in modern AI-driven intelligent systems.

Profiles:  Scopus | ORCID | Googlescholar

Featured Publications

1. Nimma, D., Devi, O. R., Laishram, B., Ramesh, J. V. N., Boddupalli, S., Ayyasamy, R., et al. (2025). Implications of climate change on freshwater ecosystems and their biodiversity. Desalination and Water Treatment, 321, 100889. Cited By : 42

2. Srikanth, G., Nimma, D., Lalitha, R. V. S., Jangir, P., Kumari, N. V. S., & Arpita. (2025). Food security-based marine life ecosystem for polar region conditioning: Remote sensing analysis with machine learning model. Remote Sensing in Earth Systems Sciences, 8(1), 65–73. Cited By : 36

3. Nimma, D., Nimma, R., Rajendar, & Uddagiri. (2024). Image processing in augmented reality (AR) and virtual reality (VR). International Journal on Recent and Innovation Trends in Computing and Communication. Cited By : 27

4. Nimma, D., & Zhou, Z. (2024). IntelPVT: Intelligent patch-based pyramid vision transformers for object detection and classification. International Journal of Machine Learning and Cybernetics, 15(5), 1767–1778. Cited By : 25

5. Nimma, D., Nimma, R., & Uddagiri, A. (2024). Advanced image forensics: Detecting and reconstructing manipulated images with deep learning. International Journal of Intelligent Systems and Applications in Engineering.
Cited By : 24

Dr. Divya Nimma’s research advances intelligent vision systems that enhance environmental sustainability, healthcare diagnostics, and smart transportation. Her work integrates AI with real-world applications, driving scientific innovation that strengthens societal resilience and global technological progress.

Mesiya Mwakisoma | Traffic and Transportation Analysis | Best Researcher Award

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Mr. Mesiya Mwakisoma | Traffic and Transportation Analysis | Best Researcher Award

Assistant Lecturer | Ruaha Catholic University | Tanzania

Mr. Mesiya  Mwakisoma is a Tanzanian legal scholar and academic affiliated with Ruaha Catholic University (Tanzania), where his work spans contemporary legal theory technology-law intersections maritime law and public policy. With a growing scholarly footprint and an emerging international presence, he contributes to advancing legal understanding in areas shaped by rapid technological change. His Scopus-indexed research record Scopus ID: 59932444100 includes one peer-reviewed publication to date with citations and an h-index of reflecting an early-career research trajectory with significant potential for future development.His most recent and notable contribution is the article “Tortious Liability for Autonomous Marine Vehicle Collisions: A Suggestive Move from Fault-based to Strict Liability published in Ocean & Coastal Management. This work examines the evolving legal complexities associated with autonomous maritime systems and advocates for a shift from traditional fault-based liability to strict liability an approach that could strengthen accountability enhance marine safety and support responsible innovation in the autonomous shipping sector. The article demonstrates his ability to integrate legal reasoning with emerging technologies positioning him within an important global discourse on maritime autonomy and risk governance.Mr. Mwakisoma’s earlier scholarship includes studies on trademark–domain name conflicts in the ICT era published in Ruaha Law Review  public–private partnerships in higher education featured in the 6th Ruaha Catholic University Convocation Newsletter (2018); and an academic paper on the doctrine of doli incapax and its relevance to modern juvenile delinquency presented at faculty level. These works reflect his wide-ranging interests in intellectual property education policy and juvenile justice.Through research academic service and collaborative work with co-authors Mr. Mwakisoma contributes to the advancement of legal scholarship in Tanzania and offers insights relevant to regional and global policy-making. His interdisciplinary approach strengthens legal understanding in domains critical to societal development in the digital and technological age.

Profile:  Scopus

Featured Publications

1.Mwakisoma, M. P., & Ma, M. (2025). Tortious liability for autonomous marine vehicle collisions: A suggestive move from fault-based to strict liability.

Mr. Mesiya  Mwakisoma’s research advances legal adaptation in an era of rapid technological change, offering frameworks that strengthen accountability, safety, and governance in emerging domains such as autonomous maritime systems and digital intellectual property. His work supports evidence-based policymaking and contributes to a more resilient, just, and innovation-ready global legal environment.