Saeed Amani | Civil Engineering | Best Researcher Award

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Dr. Saeed Amani | Civil Engineering | Best Researcher Award

Research Assistant | Tarbiat Modares University | Iran

Dr. Saeed Amani is a researcher at Tarbiat Modares University, Tehran, Iran, specializing in construction materials engineering with a focus on asphalt binders, pavement performance, and material durability under environmental and aging effects. He has authored 11 peer-reviewed publications, which have collectively received 191 citations, and holds an h-index of 7, reflecting a growing influence in the field of civil and transportation engineering. His research emphasizes the modification and characterization of asphalt materials to improve bonding strength, moisture resistance, and long-term sustainability, contributing significantly to the development of more durable and environmentally responsible pavement systems. His recent publication, “Characterizing the effects of aging and modification on asphalt binder bonding properties and moisture sensitivity” (2025, Case Studies in Construction Materials), exemplifies his focus on material optimization through empirical and analytical approaches. Collaborating with 17 co-authors from national and international institutions, Dr. Amani actively engages in multidisciplinary research integrating material science, environmental engineering, and infrastructure sustainability. His work advances innovative and cost-effective solutions that enhance the performance and longevity of transportation infrastructures while supporting global objectives in sustainable construction and resource efficiency, positioning him as a promising contributor to the advancement of resilient and eco-efficient civil engineering practices.

Profiles: Scopus | Google Scholar

Featured Publications

1. Amani, S., & Kavussi, A., & Karimi, M. M. (2020). Effects of aging level on induced heating-healing properties of asphalt mixes. Construction and Building Materials, 263, 120105.
Cited by: 72

2. Karimi, M. M., Amani, S., Jahanbakhsh, H., Jahangiri, B., & Alavi, A. H. (2021). Induced heating-healing of conductive asphalt concrete as a sustainable repairing technique: A review. Cleaner Engineering and Technology, 4, 100188.
Cited by: 52

3. Keymanesh, M. R., Amani, S., Omran, A. T., & Karimi, M. M. (2023). Evaluation of the impact of long-term aging on fracture properties of warm mix asphalt (WMA) with high RAP contents. Construction and Building Materials, 400, 132671.
Cited by: 31

4. Amani, S., Jahangiri, B., & Karimi, M. M. (2023). Performance characterization of asphalt mixtures under different aging levels: A fracture-based method. Construction and Building Materials, 383, 131126.
Cited by: 30

5. Amani, S., Jahanbakhsh, H., & Karimi, M. M. (2023). Influences of induced heating-healing on fracture properties and life extension of asphalt mixtures: Experimental investigations. Construction and Building Materials, 400, 132785.
Cited by: 8

Dr. Saeed Amani’s research advances the science of sustainable construction materials by developing innovative asphalt modification techniques that enhance pavement durability, reduce maintenance costs, and improve environmental performance. His work supports global infrastructure resilience and promotes eco-efficient, cost-effective solutions that benefit both industry and society through longer-lasting, safer, and more sustainable transportation systems.

Ikhlef Jebbor | Supply Chain | Best Innovation Award

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Dr. Ikhlef Jebbor | Supply Chain | Best Innovation Award

Industrial Engineering | Ibn Tofail University | Morocco

Dr. Ikhlef Jebbor is a leading researcher in Industrial Engineering and Operations Research, with a robust focus on sustainable supply chain management, circular economy models, and the adoption of emerging technologies in industrial settings. His extensive body of work includes over 664 publications, which have collectively garnered more than 59,656 citations, emphasizing the profound impact of his contributions on both academia and industry. His research spans diverse areas, such as multi-criteria decision-making frameworks, additive manufacturing, blockchain integration in healthcare, and decarbonization strategies for hydrogen supply chains. Dr. Jebbor’s impressive h-index of 123 highlights the enduring relevance and academic influence of his work. His collaborations with over 630 co-authors from across the globe further underscore his interdisciplinary approach and commitment to advancing knowledge through international partnerships. Notably, his research on sustainable business practices, particularly within the context of the circular economy and technological innovation, is shaping the future of industrial operations. Dr. Jebbor has consistently pushed the boundaries of knowledge by exploring the potential of advanced technologies like machine learning and blockchain to improve operational efficiency, sustainability, and supply chain resilience. The social and environmental impact of his research is significant, as his work provides actionable insights into the transition towards more sustainable industrial systems and practices. His contributions are instrumental in guiding policy development and shaping industry standards for greener, more resilient supply chains. Through his prolific research output, collaborative efforts, and dedication to sustainability, Dr. Jebbor has established himself as a key thought leader in the field, influencing both academic discourse and practical applications in global industrial sectors. His work continues to drive innovation, inspire future research, and foster sustainable practices worldwide.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Jebbor, I., Benmamoun, Z., & Hachmi, H. (2024). Revolutionizing cleaner production: The role of artificial intelligence in enhancing sustainability across industries. Journal of Infrastructure, Policy and Development, 8(10), 7455.
Cited by: 37

2. Jebbor, I., Benmamoun, Z., & Hachimi, H. (2024). Forecasting supply chain disruptions in the textile industry using machine learning: A case study. Ain Shams Engineering Journal, 15(12), 103116.
Cited by: 34

3. Khlie, K., Benmamoun, Z., Jebbor, I., & Serrou, D. (2024). Generative AI for enhanced operations and supply chain management. Journal of Infrastructure, Policy and Development, 8(10), 6637.
Cited by: 33

4. Jebbor, I., Benmamoun, Z., & Hachimi, H. (2023). Optimizing manufacturing cycles to improve production: Application in the traditional shipyard industry. Processes, 11(11), 3136.
Cited by: 28

5. Benmamoun, Z., Fethallah, W., Ahlaqqach, M., Jebbor, I., Benmamoun, M., & others. (2023). Butterfly algorithm for sustainable lot size optimization. Sustainability, 15(15), 11761.
Cited by: 25

Dr. Jebbor’s research bridges sustainability and advanced analytics to drive global industrial innovation. His work empowers industries to transition toward circular, carbon-neutral, and digitally intelligent operations, shaping a more resilient and sustainable future for science, society, and industry.

Tianyuan Liu | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr. Tianyuan Liu | Machine Learning | Best Researcher Award

Master’s Supervisor | Donghua University | China

Assoc. Prof. Dr. Tianyuan Liu, affiliated with Donghua University, Shanghai, China, is a distinguished researcher specializing in industrial intelligence, human-centric manufacturing, and vision-based quality inspection. With 43 publications, 1,103 citations, and an h-index of 17, Dr. Liu’s work reflects significant academic impact and steady scholarly growth in intelligent industrial systems. His research integrates cognitive computing, deep learning, and large language models to enhance manufacturing precision, reliability, and adaptability. Notably, his 2025 article “Analysis of causes of welding defects in bridge weathering steel based on large language models” in the Journal of Industrial Information Integration demonstrates his pioneering approach to applying AI-driven diagnostic systems in structural materials engineering. Another major contribution, “Causal deep learning for explainable vision-based quality inspection under visual interference” published in Journal of Intelligent Manufacturing, advances explainable AI (XAI) frameworks for real-time industrial inspection, ensuring transparency and accuracy in automated decision-making. His review, “Towards cognition-augmented human-centric assembly: A visual computation perspective”, underscores his vision for augmenting human intelligence with computational cognition to achieve collaborative, efficient, and sustainable manufacturing systems. Furthermore, his book chapter “Industrial Intelligence: Methods and Applications” provides a comprehensive view of the synergy between AI and industrial processes, shaping the academic and applied discourse in smart factories. Assoc. Prof. Dr. Liu’s contributions collectively enhance the fusion of AI, cognition, and industrial engineering, driving forward the next generation of intelligent, explainable, and human-oriented manufacturing ecosystems.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Zhang, R., Lv, Q., Li, J., Bao, J., Liu, T., & Liu, S. (2022). A reinforcement learning method for human-robot collaboration in assembly tasks. Robotics and Computer-Integrated Manufacturing, 73, 102227.
Cited by: 182.

2. Zhou, B., Bao, J., Li, J., Lu, Y., Liu, T., & Zhang, Q. (2021). A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops. Robotics and Computer-Integrated Manufacturing, 71, 102160.
Cited by: 152.

3. Zhou, B., Shen, X., Lu, Y., Li, X., Hua, B., Liu, T., & Bao, J. (2023). Semantic-aware event link reasoning over industrial knowledge graph embedding time series data. International Journal of Production Research, 61(12), 4117–4134.
Cited by: 123.

4. Zhou, B., Li, X., Liu, T., Xu, K., Liu, W., & Bao, J. (2024). CausalKGPT: Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing. Advanced Engineering Informatics, 59, 102333.
Cited by: 114.

5. Liu, T., Bao, J., Wang, J., & Zhang, Y. (2018). A hybrid CNN–LSTM algorithm for online defect recognition of CO₂ welding. Sensors, 18(12), 4369.
Cited by: 105.

Assoc. Prof. Dr. Tianyuan Liu’s research bridges artificial intelligence and industrial engineering, advancing smart, explainable, and human-centric manufacturing solutions that empower global industry transformation.

Nurulazlina Binti Ramli | Nanogenerator | Best Researcher Award

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Dr. Nurulazlina Binti Ramli | Nanogenerator | Best Researcher Award

Senior Lecturer | SEGi University | Malaysia

Dr. Nurulazlina Bt Ramli, affiliated with SEGi University, Petaling Jaya, Malaysia, is an accomplished researcher whose work bridges microwave engineering, wearable electronics, energy harvesting, and smart technology integration. With 67 publications, 512 citations, and an h-index of 11, she has significantly contributed to the advancement of intelligent and sustainable electronic systems. Her research portfolio spans a wide spectrum—from textile metasurface antennas and RFID-based smart agriculture to triboelectric nanogenerators and adaptive virtual impedance control for distributed power systems—reflecting a strong multidisciplinary approach that unites materials science, electromagnetics, and the Internet of Things (IoT). Dr. Ramli’s recent works, such as the development of nylon-11/BaTiO₃-PVDF triboelectric nanogenerators and orbital angular momentum wearable antennas for sub-6 GHz 5G, underscore her commitment to practical innovation with societal and industrial relevance. Through collaborations with over 100 co-authors, she continues to expand the frontiers of smart textiles, sustainable energy solutions, and advanced communication systems. Her growing impact within both the academic and applied engineering communities demonstrates an ongoing pursuit to enhance human life through integrated, energy-efficient, and health-oriented technologies.

Profiles: Scopus | Google Scholar

Featured Publications

1. Noor, S. K., Yasin, M. N. M., Ismail, A. M., Osman, M. N., Soh, P. J., & Ramli, N., et al. (2022). A review of orbital angular momentum vortex waves for the next generation wireless communications. IEEE Access, 10, 89465–89484.
Cited by: 91

2. Adam, I., Yasin, M. N. M., Ramli, N., Jusoh, M., Rahim, H. A., Latef, T. B. A., et al. (2019). Mutual coupling reduction of a wideband circularly polarized microstrip MIMO antenna. IEEE Access, 7, 97838–97845.
Cited by: 69

3. Ramli, N., Noor, S. K., Rahman, N. H. A., & Khalifa, T. (2020). Design and performance analysis of different dielectric substrate based microstrip patch antenna for 5G applications. International Journal of Advanced Computer Science and Applications, 11(8).
Cited by: 65

4. Ramli, N., Ali, M. T., Islam, M. T., Yusof, A. L., & Muhamud-Kayat, S. (2015). Aperture-coupled frequency and patterns reconfigurable microstrip stacked array antenna. IEEE Transactions on Antennas and Propagation, 63(3), 1067–1074.
Cited by: 53

5. Ali, M. T., Ramli, N., Salleh, M. K. M., & Tan, M. N. M. (2011). A design of reconfigurable rectangular microstrip slot patch antennas. 2011 IEEE International Conference on System Engineering and Technology, 111–115.
Cited by: 51

Dr. Ramli envisions a future where intelligent, flexible, and sustainable electronic systems empower communities and industries toward a more connected and energy-efficient world. Her work bridges innovation and practicality, driving technological transformation in healthcare, agriculture, and renewable energy through interdisciplinary research.

Oleksii Kostenko | Metaverse Philosophy | Best Innovation Award

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Assist. Prof. Dr. Oleksii Kostenko | Metaverse Philosophy | Best Innovation Award

Head of Laboratory | National Academy of Legal Sciences of Ukraine | Ukraine

Assist. Prof. Dr. Oleksii Volodymyrovych Kostenko, affiliated with the State Scientific Institution “Institute of Information, Security and Law of the National Academy of Legal Sciences of Ukraine”, is a pioneering scholar whose research integrates digital transformation, information security law, and emerging technologies such as the Metaverse and AI governance. With 7 publications, 17 citations, and an h-index of 1, Dr. Kostenko has established a growing presence in the interdisciplinary field that bridges law, technology, and cybersecurity. His recent work, “Legal Perspectives of the International Scientific Sandbox Metaverse: Technologies and Foresights for Digital Transformation”, reflects a forward-looking approach to understanding how legal systems must adapt to the decentralization and virtualization of digital environments. His research critically examines privacy, data protection, and the ethical regulation of AI-driven platforms, particularly within the European and international contexts. By addressing the legal foundations of cyberspace governance, Dr. Kostenko contributes to shaping frameworks that ensure technological innovation remains aligned with democratic and ethical principles. His scholarly collaborations with experts across law, computer science, and digital ethics demonstrate a commitment to multidisciplinary knowledge exchange, vital for building resilient, transparent, and secure digital societies.

Profiles: Scopus | ORCID

Featured Publications

1. Kostenko, O., Volkova, Y., Ustynova, I., Shapenko, L., & Usenko, Y. (2025). Legal perspectives of the International Scientific Sandbox Metaverse: Technologies and foresights for digital transformation. In Advanced Metaverse Wireless Communication Systems (Chapter 18, pp. 519–548). IET / The Institution of Engineering and Technology.

Assist. Prof. Dr. Oleksii Volodymyrovych Kostenko’s work pioneers the intersection of law and digital technology, shaping legal frameworks that safeguard human rights and ethical governance in the era of AI and the Metaverse. His vision promotes a secure, transparent, and accountable digital society where innovation advances in harmony with justice and democratic values.

Jaouher Ben Ali | Prognostics | Best Paper Award

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Prof. Jaouher Ben Ali | Prognostics | Best Paper Award

Professor | Tunis University | Tunisia

Prof. Jaouher Ben Ali, from the École Nationale Supérieure d’Ingénieurs de Tunis, Tunisia, is a prominent researcher in machine learning, signal processing, and intelligent fault diagnosis, with impactful work spanning biomedical and industrial applications. He has authored 41 publications, accumulated 2,656 citations, and achieved an h-index of 18, reflecting the strong influence of his research. Prof. Ben Ali’s work focuses on developing advanced algorithms for fault detection, condition monitoring, and health prediction using cutting-edge computational methods such as Empirical Mode Decomposition (EMD), Higher-Order Statistics (HOS), and deep learning models like LSTM-XGBoost fusion. His recent study, “Optimization of blood glucose prediction with LSTM-XGBoost fusion and integration of statistical features for enhanced accuracy” (2025, Biomedical Signal Processing and Control), showcases his efforts to integrate artificial intelligence with biomedical signal analysis for more precise and reliable health monitoring. He has also contributed significantly to mechanical fault diagnosis, as seen in works such as “Fault Diagnosis in Rolling Element Bearings Using Bi-Spectrum-Based EMD and Simplified Fuzzy ARTMAP” and “Advanced Feature Extraction Techniques for Bearing Fault Diagnosis Using Higher-Order Statistics and Machine Learning.” By combining data-driven techniques, nonlinear modeling, and adaptive learning, Prof. Ben Ali advances both theoretical understanding and practical applications of intelligent diagnostics. His interdisciplinary research strengthens links between academia and industry, promoting innovations that enhance system reliability, healthcare accuracy, and sustainable industrial performance worldwide.

Profiles: Scopus | Google Scholar

Featured Publications

1. Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., & Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16–27.
Cited by: 900

2. Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150–172.
Cited by: 625

3. Saidi, L., Ali, J. B., Bechhoefer, E., & Benbouzid, M. (2017). Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Applied Acoustics, 120, 1–8.
Cited by: 279

4. Saidi, L., Ali, J. B., & Fnaiech, F. (2015). Application of higher order spectral features and support vector machines for bearing faults classification. ISA Transactions, 54, 193–206.
Cited by: 237

5. Saidi, L., Ali, J. B., & Fnaiech, F. (2014). Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis. ISA Transactions, 53(5), 1650–1660.
Cited by: 192

Dr. Ben Ali’s research advances intelligent diagnostic technologies that enhance system reliability, healthcare precision, and industrial safety—driving progress toward a smarter, data-driven, and sustainable future.

George Efthimiou | Computational Fluid Dynamics | Best Researcher Award

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Dr. George Efthimiou | Computational Fluid Dynamics | Best Researcher Award

Senior Scientist | University of Western Macedonia | Greece

Dr. George C. Efthimiou, affiliated with the Chemical Process & Energy Resources Institute, Thessaloniki, Greece, is a distinguished researcher recognized for his extensive contributions to the fields of atmospheric dispersion modeling, environmental sustainability, and urban air quality analysis. Dr. Efthimiou has established a significant academic presence supported by 66 published documents and 899 citations, reflecting the wide impact and credibility of his scientific research. Holding an h-index of 17, his research demonstrates consistent scholarly influence through innovative modeling and applied environmental studies. His recent works, such as “An Empirical Theoretical Model for the Turbulent Diffusion Coefficient in Urban Atmospheric Dispersion” (Urban Science, 2025), “Predicting Extreme Atmospheric Conditions: An Empirical Approach to Maximum Pressure and Temperature” (Sustainability, 2025), and “Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains” (Atmosphere, 2024), showcase his commitment to bridging empirical and computational approaches for enhanced environmental predictions. Additional studies, including “An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment” (Atmosphere, 2024) and conference papers on indoor depollution modeling and photocatalytic paint applications, highlight his multidisciplinary engagement in atmospheric chemistry, pollutant transport, and sustainable engineering solutions. Overall, Dr. Efthimiou’s prolific research record and strong citation profile reflect his enduring contributions to advancing urban environmental modeling, air pollution control technologies, and computational fluid dynamics (CFD) in environmental engineering.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Hertwig, D., Efthimiou, G. C., Bartzis, J. G., & Leitl, B. (2012). CFD-RANS model validation of turbulent flow in a semi-idealized urban canopy. Journal of Wind Engineering and Industrial Aerodynamics, 111, 61–72.
Cited by: 121

2. De Sabatino, S., Buccolieri, R., Olesen, H. R., Ketzel, M., Berkowicz, R., Franke, J., … (2011). COST 732 in practice: The MUST model evaluation exercise. International Journal of Environment and Pollution, 44(1–4), 403–418.
Cited by: 102

3. Bartzis, J., Wolkoff, P., Stranger, M., Efthimiou, G., Tolis, E. I., Maes, F., … (2015). On organic emissions testing from indoor consumer products’ use. Journal of Hazardous Materials, 285, 37–45.
Cited by: 82

4. Tolias, I. C., Koutsourakis, N., Hertwig, D., Efthimiou, G. C., Venetsanos, A. G., … (2018). Large Eddy Simulation study on the structure of turbulent flow in a complex city. Journal of Wind Engineering and Industrial Aerodynamics, 177, 101–116.
Cited by: 70

5. Dimitroulopoulou, C., Trantallidi, M., Carrer, P., Efthimiou, G. C., & Bartzis, J. G. (2015). EPHECT II: Exposure assessment to household consumer products. Science of the Total Environment, 536, 890–902.
Cited by: 64

Dr. George C. Efthimiou’s research advances global environmental sustainability by enhancing predictive modeling of air quality and pollutant dispersion, enabling smarter urban planning and cleaner cities. His integration of empirical and computational methods drives innovation in environmental policy, industrial emission control, and climate-resilient urban development.

Monalisha Nayak | spectroscopy | Best Researcher Award

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Ms. Monalisha Nayak | spectroscopy | Best Researcher Award

Research Scholar | Banaras Hindu University | India

Ms. Monalisha Nayak from Banaras Hindu University, Varanasi, India, is an emerging researcher specializing in nanomaterials, semiconductor physics, and optical material engineering. Her work primarily focuses on the design and characterization of doped metal oxide nanostructures, particularly transition metal–doped ZnO nanoparticles, to enhance their optical, magnetic, and electronic functionalities. Ms. Nayak has authored 5 research publications that collectively reflect her growing contribution to the field of advanced materials and nanotechnology. Her research has attracted 3 citations and she holds an h-index of 1, marking the early yet impactful phase of her academic career. A notable publication, “Concentration-engineered structural and optical properties in Fe/Ni Co-Doped ZnO nanoparticles” (Materials Letters, 2026), highlights her innovative approach to concentration engineering, investigating how dual doping modifies the structural defects, bandgap behavior, and photoluminescent responses of ZnO-based systems. Through advanced characterization techniques such as X-ray diffraction (XRD), UV–Vis spectroscopy, and photoluminescence (PL) analysis, she explores the intricate relationship between crystal structure, defect chemistry, and optical emission properties. Her studies contribute significantly to the understanding of defect-controlled tuning of semiconductor materials, with potential applications in optoelectronics, spintronics, photocatalysis, and energy devices. By integrating synthesis innovation with materials characterization, Ms. Nayak’s research advances the scientific foundations of multifunctional nanomaterials, establishing her as a promising figure in the expanding domain of functional oxide nanoscience and photonic material engineering.

Profiles: Scopus | Google Scholar

Featured Publications

1. Patel, C. B., Nayak, M., Pandey, S., Prakash, O., Singh, S. K., & Singh, R. K. (2024). Spectroscopic study of coordination of Cu²⁺ with liquid crystal HBDBA: An investigation at ultra-trace level. Chemical Physics Impact, 8, 100649.
Cited by: 3

2. Nayak, M., Patel, C. B., Mishra, A., Singh, R., & Singh, R. K. (2024). Unveiling the influence of glutathione in suppressing the conversion of aspirin to salicylic acid: A fluorescence and DFT study. Journal of Fluorescence, 34(3), 1441–1451.
Cited by: 1

3. Seth, S., Mondal, B., Nayak, M., & Pal, S. P. (2025). Effect of different synthesis parameters on the structural, morphological and optical properties of hydrothermally synthesized Tungsten Diselenide (WSe₂) nanostructures. Materials Today Communications, Article 113793.

4. Mishra, M., Verma, A., Yadav, R., Singh, S., Singh, S., Srivastava, R., Singh, S. K., … (2025). Concentration-engineered structural and optical properties in Fe/Ni co-doped ZnO nanoparticles. Materials Letters, Article 139398.

5. Nayak, M., Patel, C. B., Prakash, O., Singh, A. K., & Singh, R. K. (2025). Detection of a neurotoxin quinolinic acid at ultra-trace amount: SERS and DFT study. Journal of Raman Spectroscopy.

Yanping Mo | Image Restoration Algorithms | Best Researcher Award

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Ms. Yanping Mo | Image Restoration Algorithms | Best Researcher Award

Postgraduate Student | Xi’an University of Science and Technology | China

Ms. Yanping Mo is a researcher affiliated with the School of Communication and Information Engineering at Xi’an University of Science and Technology, China, and associated with the China Education and Research Network in Beijing. Her research primarily focuses on the intersection of computational imaging, signal processing, and optimization algorithms for image restoration and enhancement. In particular, her recent work titled “Research on plug-and-play image restoration algorithm based on dual weighted ADMM,” published in Optics & Laser Technology (December 2025), demonstrates her expertise in developing advanced optimization frameworks for image reconstruction. The study explores a dual weighted Alternating Direction Method of Multipliers (ADMM) approach that integrates plug-and-play priors to enhance the flexibility and accuracy of image restoration tasks. This approach effectively addresses common challenges in image denoising, deblurring, and super-resolution by adaptively balancing data fidelity and regularization terms. Her contribution lies in improving the convergence stability and computational efficiency of traditional ADMM-based algorithms while maintaining high-quality visual outputs. Through her collaborative work with researchers such as Wei Chen, Zhaohui Li, and Bin Fan, Mo advances the application of mathematical modeling and artificial intelligence techniques in optical and laser imaging technologies. Her research supports the broader goal of enhancing image processing methodologies for scientific imaging, remote sensing, medical imaging, and industrial inspection applications. Overall, Yanping Mo’s research reflects a strong commitment to the development of robust and intelligent algorithms that bridge theory and application in the field of computational optics and image restoration.

Profile: ORCID

Featured Publications

1. Chen, W., Mo, Y., Li, Z., & Fan, B. (2025, December). Research on plug-and-play image restoration algorithm based on dual weighted ADMM. Optics & Laser Technology, 113997.

Xingcai Wu | Agricultural Artificial Intelligence | Best Researcher Award

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Dr. Xingcai Wu | Agricultural Artificial Intelligence | Best Researcher Award

Ph.D. Candidate | Guizhou University | China

Dr. Xingcai Wu, an active researcher at Guizhou University, Guiyang, China, has established a growing research portfolio focused on the intersection of artificial intelligence, precision agriculture, and environmental sustainability. With 17 publications, over 141 citations, and an h-index of 7, his work emphasizes the integration of machine learning, computer vision, and data analytics to enhance agricultural productivity and resource efficiency. A notable contribution is his recent 2025 publication titled “Multimodal Weed Infestation Rate Prediction Framework for Efficient Farmland Management” in Computers and Electronics in Agriculture, which highlights his innovative use of multimodal sensing technologies to optimize weed detection and control systems. His research approach combines remote sensing data, UAV imagery, and AI-driven prediction models to enable real-time decision-making for crop management, soil monitoring, and sustainable land use. Dr. Wu’s broader research interests include smart farming systems, automation in agricultural operations, and environmental data modeling, with a particular focus on developing scalable, data-driven frameworks that address global food security and environmental challenges. Collaborating with a network of over 60 co-authors, his interdisciplinary work bridges computer science, environmental engineering, and agricultural science, contributing to the advancement of intelligent agricultural ecosystems. Through his studies, Dr. Wu aims to promote climate-resilient and technology-enabled farming solutions, positioning his research at the forefront of digital agriculture innovation and reinforcing the role of AI in sustainable rural development.

Profile: Scopus

Featured Publications

1. Wu, X., [Other authors]. (2025). Multimodal weed infestation rate prediction framework for efficient farmland management. Computers and Electronics in Agriculture.
Cited by: 1