Assist. Prof. Dr. Monaem Elmnifi | Renewable Energy | Editorial Board Member

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Assist. Prof. Dr. Monaem Elmnifi | Renewable Energy | Editorial Board Member

Assist. Prof. Dr. Monaem Elmnifi | Bright Star University | Libya

Assist. Prof. Dr.Monaem  Elmnifi is a dedicated mechanical engineering professional known for his strong expertise in renewable energy, heat transfer, materials science, and advanced manufacturing technologies. In his roles as Assistant and Associate Lecturer at the University of Benghazi, along with his academic contributions at Bright Star University, he has played a key part in teaching core mechanical engineering courses and guiding students toward practical, research-driven learning. His supervision of undergraduate projects demonstrates a clear focus on sustainable engineering solutions, including waste-to-energy conversion systems, solar-powered adsorption cooling technologies, and hybrid solar–wind energy generation tailored to regional needs. His academic influence is further evidenced by notable research metrics: Scopus records 261 citations by 204 documents, 40 indexed publications, and an h-index of 9. His ORCID profile lists 54 works, with 50 publicly visible. Google Scholar reflects a strong scholarly impact with 1,015 citations, an h-index of 19, and an i10-index of 29. These achievements highlight Mr. Monaem Hamad Elmnifi’s professional strength, his commitment to advancing mechanical engineering knowledge, and his growing contribution to sustainable energy research and engineering education.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Nassar, Y. F., El-Khozondar, H. J., Khaleel, M. M., Ahmed, A. A., Alsharif, A. H., … Elmnifi, M. (2024). Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system. Energy Conversion and Management, 322, 119173.

Khaleel, M., Nassar, Y., El-Khozondar, H. J., Elmnifi, M., Rajab, Z., Yaghoubi, E., … others. (2024). Electric vehicles in China, Europe, and the United States: Current trend and market comparison. International Journal of Electrical Engineering and Sustainability, 1–20.

Makhzom, A. M., Aissa, K. R., Alshanokie, A. A., Nassar, Y. F., El-Khozondar, H. J., … Elmnifi, M. (2023). Carbon dioxide life cycle assessment of the energy industry sector in Libya: A case study. International Journal of Electrical Engineering and Sustainability, 145–163.

Khaleel, M., Yusupov, Z., Alderoubi, N., Abdul_jabbar, R. L., Elmnifi, M., … others. (2024). Evolution of emissions: The role of clean energy in sustainable development. Challenges in Sustainability, 12(2), 122–135.

Jenkins, P., Elmnifi, M., Younis, A., & Emhamed, A. (2019). Hybrid power generation by using solar and wind energy: Case study. World Journal of Mechanics, 9(4), 81–93.

Nassar, Y., Irhouma, M., Salem, M., El-Khozondar, H., Suliman, S., Elmnifi, M., … others. (2025). Towards green economy: Case of electricity generation sector in Libya. Solar Energy and Sustainable Development Journal, 14(1), 334–360.

Moria, H., & Elmnifi, M. (2020). Feasibility study into possibility potentials and challenges of renewable energy in Libya. International Journal of Advanced Science and Technology, 29(3), 12546–12560.

Nassar, Y. F., El-Khozondar, H. J., Abouqeelah, M. H., Abubaker, A. A., Miskeen, A. B., … Elmnifi, M. (2023). Simulating the energy, economic and environmental performance of concentrating solar power technologies using SAM: Libya as a case study. Journal of Solar Energy and Sustainable Development, 12(2), 4–23.

Prof. Dr. HyunJe Oh | Cyber-Physical Systems | Best Researcher Award

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Prof. Dr. HyunJe Oh | Cyber-Physical Systems | Best Researcher Award

Prof. Dr HyunJe Oh | Korea Institute of Construction Technology | South Korea

Prof. Dr. Hyun Je Oh is a highly respected expert in civil, environmental, and water engineering, known for his extensive contributions as a Senior Research Fellow at the Korea Institute of Construction Technology and as a Professor at the University of Science and Technology. With advanced academic training culminating in a Doctor of Philosophy degree in Civil and Environmental Engineering, he has led 112 national research and development projects completed by 2025, contributing significantly to advancements in water treatment, environmental protection, and engineering innovation. His professional leadership includes long-term service in the Clean Water Forum Committee of the National Assembly, presidency of the Korea Society on Water and Wastewater from 2015 to 2017, and chairing the Seoul Metropolitan Government Tap Water Evaluation Committee from 2010 to 2012. Prof. Dr. Oh’s scholarly achievements include 51 published documents, 69 domestic and international research papers, and 421 academic presentations. His work has received 783 citations from 729 referencing documents, supported by a citation index score of 13. He also maintains a strong record in industrial innovation, with 96 patent applications and registrations, 61 officially granted patents, and 17 technologies successfully transferred to industry, demonstrating substantial impact on clean water systems and sustainable engineering practices.

Profile: Scopus

Featured Publications

Oh, H. J. (2026). Optimal data pooling from multiple waterbodies to improve machine-learning predictions of cyanobacterial blooms. Journal of Contaminant Hydrology.

Oh, H. J. (2026). Evaluating circulation-type membrane capacitive deionization as a dual-function system for ion removal and enrichment. Desalination.

Oh, H. J. (2025). Enhanced desalination performance of pilot-scale membrane capacitive deionization system with circulation process. Desalination.

Oh, H. J. (2024). Performance optimization of a pilot-scale membrane capacitive deionization system operating with circulation process. Separation and Purification Technology.

Oh, H. J. (2023). Optimizing operational conditions of pilot-scale membrane capacitive deionization system. Sustainability.

Prof. Shuren Wang | Structural Engineering | Editorial Board Member

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Prof. Shuren Wang | Structural Engineering | Editorial Board Member

Prof. Shuren Wang | Henan Polytechnic University | China

Prof. Shuren Wang is a highly accomplished scholar in civil and geotechnical engineering, numerical simulation, and complex geotechnical system analysis. As a Distinguished Professor of Henan Province and an Adjunct Professor at the University of New South Wales, he has built a strong academic presence supported by major national and provincial research grants, including multiple projects under the National Natural Science Foundation of China and key scientific programs in Henan Province. His research achievements are reflected in substantial global impact metrics, with 2,935 citations in Scopus, citations from 2,302 documents, 228 indexed documents, and an h-index of 29, demonstrating his sustained influence across engineering disciplines. In addition, his ORCID profile lists 172 works, with 50 displayed, further showcasing the breadth of his scholarly output. Shuren Wang has published over 190 SCI/EI journal papers, 16 books, and numerous technical contributions, supported by extensive patents and software rights. His work has advanced understanding in areas such as tunnel excavation behavior, lightweight and composite concrete performance, anchorage mechanisms, and dynamic damage modeling, establishing him as a leading figure in civil and geotechnical engineering research.

Profiles: Scopus | Orcid

Featured Publications

Zhang, J., Fang, Z., Zhang, X., Wang, S., Cao, Y., & Lutynski, M. (2025). Coupling characteristics of crack propagation-energy dissipation-damage evolution of coal-like material under impact loading. Results in Engineering, 2025(December), Article 107333.

Gong, J., Zhang, J., Wang, S., Ma, A., & Li, Z. (2025). Investigation on performance improvement and dynamic damage model of phosphoric acid-modified MOC composite with industrial slag. Results in Engineering, 2025(December), Article 107283.

Wang, S., Cheng, C., Gong, J., & Song, Z. (2025). Dynamic mechanical properties of magnesium oxychloride-based titanium gypsum concrete after high-temperature exposure. Construction and Building Materials, 472, 140841.

Fan, L., Xu, F., Wang, S., Yu, Y., Li, P., Zhang, J., & Yu, L. (2025). Role of halloysite nanotubes in modulating the mechanical and microstructural characteristics of geopolymer concrete under thermal curing. Construction and Building Materials, 472, 140897.

Gong, J., He, M., Zhang, J., Liang, W., & Wang, S. (2025). Dynamic impact mechanical properties of red sandstone based on digital image correlation method. International Journal of Mining, Reclamation and Environment.

Gong, J., Zhang, J., Wang, S., He, M., Ma, A., & Li, C. (2024). Impact dynamic properties of magnesium oxychloride-doped paper sludge composites. DYNA, 99(3), D11259.

Mr. Ashikul Haque Naeem | Semiconductor Devices | Editorial Board Member

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Mr. Ashikul Haque Naeem | Semiconductor Devices | Editorial Board Member

Mr. Ashikul Haque Naeem | Bangladesh University of Engineering and Technology | Bangladesh

Mr.  Ashikul Haque Naeem is an emerging materials researcher with strong academic and scientific credentials in nanomaterials, glass, and ceramic engineering, with a growing focus on high-performance transparent conducting oxides and functional thin films. His work centers on the synthesis, dual-doping, and characterization of CdO-based thin films, aiming to tailor their structural, morphological, optical, and electrical properties for applications in photovoltaics, touch-screen displays, sensors, and advanced optoelectronic devices. He has authored peer-reviewed publications in Materials Advances and Heliyon, where he investigates the effects of Ag-Co, Ag-Fe, Ag-Al, and Al-Zn co-doping on optoelectronic behavior using spray pyrolysis and related fabrication techniques. His research has been showcased at multiple international conferences in physics, electronics, and informatics, presenting insights into bandgap tuning, carrier concentration modulation, and performance optimization of doped CdO systems. His work earned the Best Poster Presentation Award at the International Conference on Physics-2024, reflecting the significance and quality of his contributions. Supported by academic scholarships and a strong background in advanced ceramics, materials characterization, nanotechnology, and glass technology, M Ashikul Haque Naeem continues to build a promising trajectory as a researcher contributing to the advancement of next-generation functional materials and optoelectronic technologies.

Profiles: Orcid | Google Scholar

Featured Publications

  • Naeem, M. A. H., Ayon, A. S. R., Ali, M. M., Amin, M. R., Kabir, M. H., Sattar, M. A., … (2024). Insights into the consequence of (Al–Zn) dual-doping on structural, morphological, and optoelectrical properties of CdO thin films. Heliyon, 10(4).

  • Karim, I., Naeem, M. A. H., Ayon, A. S. R., Sattar, M. A., Sabur, M. A., & Ahmed, A. N. (2025). Effect of silver and cobalt on transparent conducting CdO thin films: Tuning the optoelectronic properties. Materials Advances, 6(2), 703–718.

  • Karim, I., Rokon, S. M. N., Naeem, M. A. H., Robin, I. K., Ahmed, A. N., Sattar, M. A., … (2025). Tunable semiconducting behavior and linear–nonlinear optical properties of Ag–Sn dual-doped nanocrystalline CdO thin films for optoelectronics. ACS Omega.

Dr. Abhijeet Das | Civil Infrastructure | Water Treatment Award

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Dr. Abhijeet Das | Civil Infrastructure | Water Treatment Award

Dr. Abhijeet Das | C.V. Raman Global University | India

Dr. Abhijeet Das is a distinguished researcher in water resources and Geographic Information Systems (GIS), recognized for his extensive contributions to spatial analysis, hydrological modeling, and environmental assessment. His research emphasizes evaluating water vulnerability, hydro-environmental risks, and watershed dynamics through the integration of remote sensing data, GIS-based multi-criteria analysis, and climate impact modeling. With 94 documents, 265 citations from 139 sources, and an h-index of 9 in Scopus, his scholarly work demonstrates significant impact in the fields of hydrology, geospatial science, and environmental sustainability. Dr. Das’s professional expertise spans remote sensing for water quality monitoring, GIS-based Water Quality Index mapping, and advanced hydrological simulations using tools such as SWAT, HEC-HMS, and MIKE SHE. His technical proficiency includes ArcGIS, QGIS, Python, MATLAB, and Google Earth Engine, complemented by applications of IoT-based sensor systems for real-time water quality assessment. As a peer reviewer with 62 reviews for 25 publications and grants recorded in ORCID, he plays an active role in maintaining research quality within the scientific community. Dr. Das’s work continues to advance sustainable water resource management, climate resilience, and data-driven environmental decision-making across regional and interdisciplinary contexts.

Profiles: Scopus | Orcid

Featured Publications

Das, A. (2025). Drinking water resources suitability assessment in Brahmani River, Odisha based on pollution index of surface water utilizing advanced water quality methods. Scientific Reports.

Das, A. (2025). An optimization-based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance. Discover Environment, 2 citations.

Das, A. (2025). Reimagining biofiltration for sustainable industrial wastewater treatment. Review Article, 2 citations.

Das, A. (2025). A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India. Discover Sustainability, 3 citations.

Das, A. (2025). Surface water quality evaluation impacting drinking water sources and sanitation using water quality index, multivariate techniques, and interpretable machine learning models in Mahanadi River, Odisha (India). Review Article.

Das, A. (2025). Water quality assessment and geospatial techniques for the delineation of surface water potential zones: A data-driven approach using machine learning models. Desalination and Water Treatment.

Dr. Pingping Wu | Artificial Intelligence | Best Researcher Award

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Dr. Pingping Wu | Artificial Intelligence | Best Researcher Award

Dr. Pingping Wu | Nanjing Audit University | China

Dr. Pingping Wu is an accomplished academic specializing in intelligent systems, engineering audit, and sustainable innovation. As an Associate Professor at Nanjing Audit University’s School of Engineering Audit, the focus of their research integrates technological development with sustainability-driven governance models. Their expertise encompasses electronic science, human-robot interaction, and artificial intelligence applications in engineering management. Major research contributions include leadership of the Jiangsu Province University Natural Science Research Project on megaproject innovation transformation for carbon peaking and carbon neutrality strategies, as well as the National Youth Science Foundation Project on deep facial expression analysis for human-robot interaction. Previous collaborative projects have explored intelligent monitoring systems and robot audio-visual attention mechanisms under national research programs such as 973 and 863. Representative publications include “Hierarchical Cross-Attention Network for Virtual Try-On” in IEEE Transactions on Multimedia and “Megaproject Responsible Innovation: Concept, Framework, and Governance” in Frontiers of Engineering Management, demonstrating interdisciplinary expertise in AI, sustainability, and digital innovation. The research portfolio reflects a consistent commitment to integrating intelligent technologies with social responsibility, aiming to enhance the efficiency, inclusiveness, and sustainability of large-scale engineering and technological systems.

Profile: Scopus

Featured Publications

Wu, P., Lin, H., Nie, S., Yuan, M., Yu, M., & Tam, V. (2025). Megaproject responsible innovation: Concept, framework, and governance. Frontiers of Engineering Management.

Wu, P., Zhang, Y., Li, X., Chen, L., & Wang, H. (2025). An improved ViT model for music genre classification based on mel spectrogram. Plos One.

Wu, P., Liu, J., Zhao, K., Zhang, Q., & Chen, R. (2025). Bibliometric evaluation of global research on eco-tourism. Environment Development and Sustainability.

Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan | Industrial IoT | Best Researcher Award

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Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan | Industrial IoT | Best Researcher Award

Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan | Istanbul Okan University | Turkey

Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan is a distinguished academic and technology expert with extensive experience in knowledge management, C4I technologies, management information systems, cybersecurity, project management, and maritime systems. His professional and academic work emphasizes the integration of advanced digital solutions into management and defense applications. He has contributed significantly to education and research in areas such as information systems, business process management, and IT project management, helping bridge the gap between theory and practice. His leadership in major software and simulator projects, including the Full Mission Bridge Simulator, GMDSS Simulator, and municipal smart system software initiatives, highlights his ability to combine innovation with strategic execution. He has also played a key role in institutional development and technology-based research, particularly through his contributions to TÜBİTAK-supported projects and his work in reestablishing academic and training programs within defense-oriented institutions. With 5 published works, 4 citations, and an h-index of 1, his research reflects a growing impact on applied technology and information management fields. Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan continues to advance multidisciplinary innovation, blending academic rigor with practical technological solutions.

Profile: Scopus

Featured Publication

Önaçan, M. B. K. (2025). Designing automatic card dispensers based on design thinking approach and selecting the suitable alternative. Journal of Engineering and Applied Science, Open access.

 

Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

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Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

Assist. Prof. Dr. Yan Zeng | Hangzhou Dianzi University | China

Assist. Prof. Dr. Yan Zeng, an accomplished associate professor at the School of Computer Science, Hangzhou Dianzi University, has made significant contributions in the fields of distributed and parallel computing, distributed machine learning, and big data analytics. After earning her PhD from the Institute of Software, Chinese Academy of Sciences in 2016, her research has focused on advancing large-scale computation and data-intensive systems.  The Key Research and Development Program of Zhejiang Province, the Yangtze River Delta Project, and the Natural Science Foundation of Zhejiang Province. Her academic influence is reflected in 173 citations by 161 documents, 42 published papers, and an h-index of 9, demonstrating strong research impact and visibility. With 10 peer-reviewed publications in SCI and Scopus-indexed journals, Yan Zeng’s scholarly output showcases innovation in computational frameworks and distributed systems. Furthermore, she has been actively involved in practical technological advancements, holding 34 patents that bridge theoretical insights with industrial applications. Through her extensive research, publication record, and innovation-driven approach, Yan Zeng continues to play a pivotal role in shaping advancements in computer science and data engineering.

Profile: Scopus

Featured Publications

Zeng, Y., et al. (2025). FedAMM: Federated learning for brain tumor segmentation with arbitrary missing modalities [Conference paper]. Proceedings of the International Conference on Artificial Intelligence and Machine Learning.

Zeng, Y., et al. (2025). TransAware: An automatic parallel method for deep learning model training with global model structure awareness [Conference paper]. Proceedings of the International Conference on Advanced Computing and Applications.

Zeng, Y., et al. (2025). A correlation analysis-based federated learning framework for defending against collusion-free-riding attacks. Cybersecurity, 2025(1), 1–12.

Zeng, Y., et al. (2025). FedAEF: Optimizing federated learning with mining and enhancing local data features. Cluster Computing, 2025(1), 1–15.

Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

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Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

Mr. Adizue Ugonna | Budapest University of Technology and Economics | Hungary

Mr. Adizue Ugonna Loveday is a Doctoral Researcher and Laboratory Instructor at the Budapest University of Technology and Economics, specializing in Mechanical Engineering with expertise in industrial and production systems. His research focuses on intelligent modelling and process optimization for ultra-precision machining of hard materials, integrating artificial intelligence, tribological analysis, and thermal modeling to enhance manufacturing precision and efficiency. Professionally, he has contributed to several major research initiatives including the Horizon 2020 Centre of Excellence in Production Informatics and Control (EPIC CoE), the iNext project on industrial digitalization, and multiple Hungarian Scientific Research Fund (OTKA) projects emphasizing AI-based predictive models for advanced machining and intelligent forming processes. His scholarly record demonstrates strong research performance, with 45 citations by 42 documents, 6 documents, and an h-index of 4 in Scopus; and 66 citations, an h-index of 5, and an i10-index of 2 in Google Scholar. In addition, his ORCID profile lists 6 professional activities and 8 published works, reflecting active engagement in international research collaboration, scientific reviewing, and production editing. Through these contributions, Mr. Loveday continues to advance smart and sustainable manufacturing technologies, bridging artificial intelligence and mechanical systems design in alignment with Industry 4.0 innovation goals.

Profiles: Scopus | Orcid | Google Scholar | LinkedIn | ResearchGate

Publication Details

  1. Adizue, U. L., Tura, A. D., Isaya, E. O., Farkas, B. Z., & Takács, M. (2023). Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool. The International Journal of Advanced Manufacturing Technology, 128(1), 1–28.

  2. Adizue, U. L., Nwanya, S. C., & Ozor, P. A. (2020). Artificial neural network application to a process time planning problem for palm oil production. Engineering and Applied Science Research, 47(2), 161–169.

  3. Adizue, U. L., & Takács, M. (2025). Exploring the correlation between design of experiments and machine learning prediction accuracy in ultra-precision hard turning of AISI D2 with CBN insert: A comparative study. The International Journal of Advanced Manufacturing Technology, 1–30.

  4. Elly, O. I., Adizue, U. L., Tura, A. D., Farkas, B. Z., & Takács, M. (2024). Analysis, modelling, and optimization of force in ultra-precision hard turning of cold work hardened steel using the CBN tool. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(1), 1–18.

  5. Adizue, U. L., Balázs, B. Z., & Takács, M. (2022). Surface roughness prediction applying artificial neural network at micro machining. IOP Conference Series: Materials Science and Engineering, 1246(1), 012034.

  6. Tura, A. D., Isaya, E. O., Adizue, U. L., Farkas, B. Z., & Takács, M. (2024). Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools. Heliyon, 10(11), e24357.

  7. Adizue, U. L., Agbadah, S. E., Ibeagha, D. C., & Falade, Y. O. (2017). Design and construction of an automated adjustable-can foil sealing machine. International Journal of Engineering and Applied Sciences, 4(9), 257384.

 

Dr. Zhu Jingwen | AI in Engineering | Best Researcher Award

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Dr. Zhu Jingwen | AI in Engineering | Best Researcher Award

Dr. Zhu Jingwen | Jiangsu University | China

Dr. Zhu Jingwen is a researcher in control science and engineering whose work focuses on intelligent detection systems and advanced sensing technologies for agricultural safety. His research emphasizes the development of nondestructive testing methods for grains and edible oils through the integration of microwave, millimeter-wave, and near-infrared technologies with chemometric modeling and machine learning algorithms. Dr. Zhu has designed FPGA-based microwave detection systems capable of accurately identifying contaminants such as heavy metals and aflatoxins, contributing significantly to the field of food safety monitoring. His studies have been widely published in respected international journals, including Microchemical Journal, Sensors and Actuators A: Physical, and Spectrochimica Acta Part A. Beyond research, he has demonstrated leadership in innovation and entrepreneurship, leading projects recognized with national honors such as the China Postgraduate “Rural Revitalization – Sci-Tech Empowering Agriculture+” Competition. His scientific contributions are reflected through 13 published documents, cited 46 times by 41 other documents, demonstrating a growing academic impact and an h-index of 5. His efforts also led to the establishment of Dongfang Xiangyu (Jiangsu) Technology Co., Ltd., translating research outcomes into practical industrial applications. With a strong command of programming and embedded system development, Dr. Zhu continues to explore interdisciplinary approaches that merge intelligent algorithms with hardware systems to advance the precision and reliability of agricultural quality assessment technologies.

Profile: Scopus

Featured Publications

Zhu, J., Deng, J., Zhao, X., Xu, L., & Jiang, H. (2024). Quantitative determination of cadmium content in peanut oil using microwave detection method combined with multivariate analysis. Microchemical Journal, 110946.

Zhu, J., Deng, J., Zhao, X., Xu, L., & Jiang, H. (2024). Accurate identification of cadmium pollution in peanut oil using microwave technology combined with SVM-RFE. Sensors and Actuators A: Physical, 368, 115085.

Zhu, J., Chen, Y., Deng, J., & Jiang, H. (2024). Improving the accuracy of FT-NIR determination of zearalenone content in wheat using a characteristic wavelength optimization algorithm. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 313, 124169.

Ji, Z., Zhu, J., Deng, J., Jiang, H., & Chen, Q. (2024). Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometric algorithms. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 323, 124858.

Zhu, J., Deng, J., Xu, L., & Jiang, H. (2024). Enhancing the performance of natural pigment sensor arrays for the detection of Procymidone residues in Allium tuberosum using outcome-corrected decision-making method. Journal of Food Composition and Analysis, 128, 107059.