Thi Huynh Anh Le | Smart Grid | Editorial Board Member

Published on by Applied Scientist

Dr. Thi Huynh Anh Le | Smart Grid | Editorial Board Member

The university of Danang, University of Science and Technology | Vietnam

Dr. Le Thi Huynh Anh is a rising researcher in renewable energy systems whose work has gained notable scholarly impact, reflected in 104 citations, an h-index of 5, and an i10-index of 4. Her research centers on sustainable microgrid design, peer-to-peer (P2P) energy trading, uncertainty modeling, and computational intelligence for optimizing modern power systems. She has published influential studies in leading journals such as Renewable and Sustainable Energy Reviews, Renewable Energy, Expert Systems with Applications, and Applied Sciences. Dr. Anh’s contributions emphasize advanced multi-microgrid planning frameworks that integrate seasonal demand patterns, government subsidies, stochastic uncertainties, and digital technologies to enhance economic, operational, and environmental performance. Her collaborative works on blockchain-enabled P2P energy trading propose innovative pathways for decentralized and consumer-centric energy markets. Alongside system optimization, she has developed intelligent methodologies for improving data quality and decision-making in energy systems, including dynamic-programming-based time-series anomaly detection using the longest common subsequence approach, as well as advanced clustering techniques that blend fuzzy logic, possibilistic modeling, and genetic algorithms for handling mixed data types. Her research also extends to maintenance optimization for offshore wind systems, demonstrating a broader commitment to the renewable energy ecosystem. Across her body of work, Dr. Anh integrates sustainability principles, AI-driven modeling, and robust optimization techniques to support the development of next-generation, resilient, and intelligent energy infrastructures. Her recent studies continue advancing sustainable multi-microgrid systems by addressing the challenges of uncertainty, seasonality, and evolving energy trading mechanisms.

Profile: Google Scholar

Featured Publications

  • Vincent, F. Y., Le, T. H. A., & Gupta, J. N. D. (2022). Sustainable microgrid design with multiple demand areas and peer-to-peer energy trading involving seasonal factors and uncertainties. Renewable and Sustainable Energy Reviews, 161, 112342.

  • Nguyen, T. P. Q., Phuc, P. N. K., Yang, C. L., Sutrisno, H., Luong, B. H., Le, T. H. A., … (2023). Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data. Expert Systems with Applications, 213, 118902.

  • Yu, V. F., Chiang, F. Y., Le, T. H. A., & Lin, S. W. (2022). Using the ISM method to analyze the relationships between various contractor prequalification criteria. Applied Sciences, 12(8), 3726.

  • Vincent, F. Y., Le, T. H. A., & Gupta, J. N. D. (2023). Sustainable microgrid design with peer-to-peer energy trading involving government subsidies and uncertainties. Renewable Energy, 206, 658–675.

  • Nguyen, T. P. Q., Kuo, R. J., Le, M. D., Nguyen, T. C., & Le, T. H. A. (2022). Local search genetic algorithm-based possibilistic weighted fuzzy c-means for clustering mixed numerical and categorical data. Neural Computing and Applications, 34(20), 18059–18074.

Shengjie Li | Internet of Things | Editorial Board Member

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Assist. Prof. Dr . Shengjie Li | Internet of Things | Editorial Board Member

Beijing University of Posts and Telecommunications | China

Assist. Prof. Dr. Li Shengjie is a researcher specializing in robust visual object tracking, focusing on resolving key challenges such as occlusion, noise, scale variation, drift, and real-time computational constraints in dynamic visual environments. His body of work integrates particle filter methodologies, siamese network models, and correlation filter (CF)-based approaches to develop adaptive and resilient tracking frameworks. He has introduced several influential models, including a part-aware tracking framework that leverages local target structures, a dynamic particle filter architecture for improved failure correction, a noise-aware similarity measurement framework, and an efficient particle scale-space strategy designed to more accurately estimate target size under challenging scale variations. Assist. Prof. Dr. Li’s research also advances lightweight and accelerated particle-filter solutions that significantly reduce computational cost while preserving tracking accuracy, supporting real-time implementation. Additionally, his work integrates ensemble deep features and decision fusion mechanisms to combat model drift and enhance robustness during sudden appearance changes or out-of-view scenarios. His earlier contributions include physical modeling studies, reflecting a progression toward highly refined hybrid deep-learning and filter-based tracking systems. With eight publications, more than 100 citations, and nearly 300 reads, Assist. Prof. Dr. Li Shengjie’s research is recognized for systematically improving the reliability and efficiency of visual tracking algorithms. His innovations consistently address the limitations of traditional tracking methods by introducing adaptive, context-sensitive, and computationally optimized approaches that advance the field toward more effective real-world application.

Profile: Research Gate

Featured Publications

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2023). Part-aware framework for robust object tracking.

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2021). Dynamic particle filter framework for robust object tracking.

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2020). Noise-aware framework for robust visual tracking.

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2020). Efficient particle scale space for robust tracking.

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2018). Robust visual tracking via hierarchical particle filter and ensemble deep features.

Leqing Lin | Indoor Air Quality Monitoring | Best Researcher Award

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Ms. Leqing Lin | Indoor Air Quality Monitoring | Best Researcher Award

Jinan University | China

Ms. Leqing Lin’s research is centered on advanced optical and photoacoustic gas-sensing technologies, with a strong emphasis on developing ultra-sensitive, compact, and real-time detection systems for environmental and industrial applications. Her work spans quartz-enhanced photoacoustic spectroscopy (QEPAS), conductance spectroscopy, hybrid conductance-photoacoustic approaches, and distributed-feedback (DFB) laser–based sensing, enabling precise detection of gases such as CO, CO₂, H₂, hydrocarbons, water vapor, and humidity at extremely low concentrations. She has made notable contributions to multi-species gas analysis by integrating acoustic and electrical detection pathways, greatly improving speed, selectivity, and sensitivity. Ms. Lin’s innovations include cost-effective hydrogen sensors based on quartz crystal tuning forks, non-contact QEPAS architectures, clamp-type tuning-fork–enhanced systems, and miniature multipass optical cells featuring novel beam-shaping designs. Her studies also extend into unconventional sensing enhancements, such as spider-silk-assisted conductance spectroscopy for improved humidity detection in medical masks. Committed to sensor miniaturization and robust field deployment, she designs compact, low-power architectures suitable for portable, wearable, or embedded platforms. Recent breakthroughs—such as her 2.3 μm DFB-laser CO sensor using MPC-LITES for real-time cigarette-smoke monitoring and a ppb-level CO₂ sensor using an eight-petaled spot-pattern multipass cell—highlight her focus on high-impact, practical gas-sensing solutions. With a growing body of publications, 99 citations, and an h-index of 5, Ms. Lin’s research is advancing the frontier of optical and photoacoustic sensing by refining detection mechanisms, hybridizing technologies, and enabling highly sensitive, cost-efficient monitoring systems for a wide range of real-world applications.

Profile: Scopus | Orcid

Featured Publications

  • Lin, L., Lin, H., Hong, G., He, J., Wang, L., Zhuang, R., Zhu, W., Zhong, Y., Yu, J., & Zheng, H. (2025). A compact 2.3 μm DFB-laser CO sensor using MPC-LITES for real-time monitoring of cigarette smoke. Sensors, 25.

  • Zhuang, R., He, J., Lin, H., Luo, H., Lin, L., Wang, L., Liu, B., Zhu, W., Zhong, Y., Yu, J., et al. (2025). Conductance-photoacoustic spectroscopy for fast and concurrent sensing of hydrogen and hydrocarbons. Photoacoustics, 2025.

  • Zhuang, R., Huang, J., Zhao, Y., Liao, H., He, J., Lin, L., Zhu, W., Zhong, Y., Liu, B., Yu, J., et al. (2025). Cost-effective hydrogen sensor employing a quartz crystal tuning fork. Microwave and Optical Technology Letters, 2025.

  • Wang, C., Lin, L., Zeng, X., Xie, J., Luo, H., Lin, H., Wang, L., Tang, J., Cui, R., & Zhu, W., et al. (2024). PPB-level CO₂ sensor based on a miniature multipass cell with eight-petaled spot pattern. Microwave and Optical Technology Letters, 2024.

  • Lin, H., Liu, Y., Lin, L., Zhu, W., Zhou, X., Zhong, Y., Giglio, M., Sampaolo, A., Patimisco, P., & Tittel, F. K., et al. (2023). Application of standard and custom quartz tuning forks for quartz-enhanced photoacoustic spectroscopy gas sensing. Applied Spectroscopy Reviews, 2023.

Hisao Nakai | Disaster Management | Applied Scientist Award

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Dr. Hisao Nakai | Disaster Management | Applied Scientist Award

University of Kochi | Japan

Dr. Hisao Nakai’s research centers on public health, nursing science, mental health care, and disaster-related health outcomes, with a strong focus on vulnerable populations such as older adults, individuals with psychiatric disorders, family caregivers, and frontline healthcare workers. His work provides critical insights into how major public health emergencies—including the COVID-19 pandemic and the 2024 Noto Peninsula Earthquake—shape behavioral health patterns, continuity of medical care, evacuation decisions, and psychological well-being. Dr. Hisao Nakai’s has contributed impactful studies on outpatient care adherence among psychiatric patients following earthquakes, the factors associated with worsening psychiatric symptoms in home-based patients during disasters, and the complex proxy decision-making processes experienced by care managers supporting older adults living alone. His research also advances understanding of healthcare workforce sustainability by examining turnover intentions among nurses, employees assisting older adults living independently, and hospital staff caring for COVID-19 patients, identifying organizational, psychological, and environmental determinants that influence job retention. Additionally, he investigates caregiver burden among families of elderly cancer survivors and evaluates the relationship between quality of life, treatment experiences, and life satisfaction among aging populations undergoing medical care. Spanning disaster medicine, gerontology, community health, mental health nursing, and healthcare management, Dr. Hisao Nakai’s work frequently employs cross-sectional and retrospective methodologies to generate evidence-based recommendations for policy and clinical practice. With more than 110 citations, numerous peer-reviewed publications, and extensive interdisciplinary collaborations, his research consistently aims to strengthen patient safety, disaster preparedness, mental health continuity, and the overall well-being of patients, caregivers, and healthcare professionals within Japan’s rapidly aging society.

Profile: Orcid | Scopus

Featured Publications

  • Nitta, Y., Hashimoto, R., Shimizu, Y., Nakai, Y., & Nakai, H. (2025). Adherence to outpatient care among individuals with pre-existing psychiatric disorders following the 2024 Noto Peninsula Earthquake: A retrospective study. Psychiatry and Clinical Neurosciences Reports.

  • Itatani, T., Nakai, H., Takahashi, Y., & Togami, C. (2024). Factors associated with behavioral and weight changes across adult to elderly age groups during the COVID-19 pandemic. Nutrition Research and Practice, 18(4), 544–556.*

  • Nakai, H., Oe, M., & Nagayama, Y. (2024). Factors related to evacuation intention when a Level 4 evacuation order was issued among people with mental health illnesses using group homes in Japan: A cross-sectional study. Medicine.

  • Nakai, H., Ishii, K., & Sagino, T. (2024). Turnover intention among staff who support older adults living alone in Japan: A cross-sectional study. Social Sciences, 13(9), 463.*

  • Kitamura, Y., & Nakai, H. (2023). Factors associated with turnover intentions of nurses working in Japanese hospitals admitting COVID-19 patients. Nursing Reports, 13(2), 69–80.*

Binghui Xu | Machine Learning Assisted Computation | Best Researcher Award

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Prof. Binghui Xu | Machine Learning Assisted Computation | Best Researcher Award

Taizhou Vocational and Technical College | China

Prof. Binghui Xu is a researcher whose recent work focuses on machine learning, data-driven modeling, and computational intelligence, with applications across remote sensing, chemical engineering, environmental systems, and digital risk management. His research advances intelligent algorithms for solving complex real-world problems, emphasizing improved accuracy, predictive performance, and system-level optimization. In remote sensing, he has developed enhanced convolutional neural network architectures to achieve higher-precision image classification, addressing challenges related to feature extraction and large-scale datasets. His studies on cloud-based platforms highlight their critical role in supporting cancer-related research by enabling scalable data management, computational analytics, and collaborative scientific workflows. Prof. Xu has also applied supervised learning techniques to chemical and energy engineering, particularly for accurately predicting hydrogen content in bio-oil—an essential factor in assessing biofuel quality. His contributions to communication networks include proposing a quality-of-service control model for overlay convergence transmission, aimed at improving reliability and efficiency. Recently, he has extended machine learning methods to environmental research through the computation of water activity in ionic liquid–based aqueous ternary systems. He has also developed early warning systems for public risk perception and explored data association rule mining using genetic optimization, alongside graph attention–based image classification models. Collectively, Prof. Xu’s work integrates artificial intelligence, optimization algorithms, and applied modeling to deliver innovative solutions across multiple engineering and technological domains.

Profile: Scopus

Featured Publications

  • Xu, B. (2025). Machine learning-assisted computation of water activity for ionic liquid-based aqueous ternary elements. Desalination and Water Treatment, 10.

  • Xu, B. (2021). Improved convolutional neural network in remote sensing image classification. Neural Computing & Applications, 7, 8169–8180.

  • Xu, B., & Fengcheng. (2022). The roles of cloud-based systems on cancer-related studies: A systematic literature review. IEEE Access, 10, 64126–64145.

  • Xu, B., Chen, T.-C., & Danial, A. (2021). Application of a supervised learning machine for accurate prognostication of hydrogen contents of bio-oil. International Journal of Chemical Engineering, 7.

  • Xu, B., & Yan, W. (2020). A QoS control model in overlay convergence transmission. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science, 82(4), 103–118.

Agata Kołodziejczyk | Microgravity Research | Best Paper Award

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Dr. Agata Kołodziejczyk | Microgravity Research | Best Paper Award

AGH University of Kraków | Poland

Dr. Agata Maria Kołodziejczyk is a distinguished neurobiologist and space life sciences researcher whose interdisciplinary expertise integrates neurobiology, astrobiology, and space technology. She earned her Ph.D. in Neurobiology from the University of Stockholm and has since become a leading innovator in bioastronautics and analog space research. Currently, Dr. Agata Maria Kołodziejczyk leads the Bioastronautics Group–Habitat at the Faculty of Space Technologies, AGH University of Krakow, where she develops advanced laboratories focused on studying biological responses to microgravity, radiation, and other space-like conditions. Her research encompasses plant physiology under microgravity, microbial adaptation in stratospheric environments, the development of biomaterials such as bacterial cellulose for radiation shielding, and human physiological responses during simulated space missions. A recipient of the prestigious FNP Rojszczak Prize for combining biology with astronomy, she has also been recognized as a three-time first-prize winner in the Global Space Balloon Challenge for pioneering astrobiological experiments conducted in the stratosphere. Dr. Agata Maria Kołodziejczyk’s contributions include high-impact publications in Food Chemistry, Cellulose, Applied Sciences, and European Journal of Applied Physiology. Beyond academia, she is the founder and co-owner of the Analog Astronaut Training Center in Rzepiennik Strzyżewski—the first analog space base simulator in Poland—where she trains analog astronauts and space professionals. Through her visionary research and leadership, Dr. Agata Maria Kołodziejczyk is shaping the future of space biology, sustainable life-support systems, and human adaptation for long-duration missions beyond Earth.

Profiles: Google Scholar | Orcid | Scopus

Featured Publications

  • Markiewicz, M., Galanty, A., Kołodziejczyk, A. M., Żmudzki, P., Prochownik, E., Zagrodzki, P., & Paśko, P. (2025). Bioactive compounds accumulation in Brassica sprouts grown under microgravity and darkness: A novel approach to functional foods. Food Chemistry, 145324.

  • Markiewicz, M., Galanty, A., Prochownik, E., Kołodziejczyk, A. M., & Paśko, P. (2025). Innovative production of bioactive white clover sprouts under microgravity: Towards functional foods supporting prostate health. Applied Sciences, 15(21), 11668.

  • Kołodziejczyk, A. M., Lewandowski, M., Nawrot, A., & Sobota, I. (2025). Polar stations as testing platforms for space analogue simulations: Case study for Polish potential. Arctic, Antarctic, and Alpine Research, 57(1).

  • Kołodziejczyk, A. M., Silarski, M., Kaczmarek, M., Harasymczuk, M., Dziedzic-Kocurek, K., & Uhl, T. (2024). Shielding properties of the kombucha-derived bacterial cellulose. Cellulose, 32(12), 1–15.

  • Giacon, T. A., Mrakic-Sposta, S., Bosco, G., Vezzoli, A., Dellanoce, C., Campisi, M., Narici, M., Paganini, M., Foing, B., Martinelli, M., & Kołodziejczyk, A. M. (2024). Environmental study and stress-related biomarkers modifications in a crew during analog astronaut mission EMMPOL 6. European Journal of Applied Physiology, 124(10), 2231–2245.

 

Jacek Wilczyński | Posturology | Best Applied Science Award

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Prof. Jacek Wilczyński | Posturology | Best Applied Science Award

Jan Kochanowski University | Poland

Prof. Jacek Wilczyński is a prominent Polish scientist and academic recognized for his extensive research in posturology, physiotherapy, and corrective kinesiology. He serves as a Professor at the Jan Kochanowski University in Kielce, where he heads the Laboratory of Posturology within the Collegium Medicum. His distinguished academic journey includes a Master’s degree in Physical Education (1986), a Doctorate in Physical Culture Sciences (1997), a Habilitation (2010), and a Master’s in Physiotherapy (2012). Prof. Wilczyński’s research primarily explores posture defects, postural stability, balance and gait disorders, and the neurophysiological mechanisms involved in maintaining equilibrium. He investigates the complex interactions between vestibulospinal, vestibulo-optic, and vestibulo-oculomotor reflexes, as well as the autonomic nervous system’s role in children with scoliosis and postural abnormalities. His work also integrates spectral analysis of postural and cardiac responses, providing insights into the relationships between posture, stress resistance, and vagal tone. Author of more than 50 scientific works, including influential publications in Sensors, Journal of Clinical Medicine, and International Journal of Environmental Research and Public Health, Prof. Wilczyński employs advanced diagnostic technologies such as the Diers formetric III 4D system and Framiral Multitest Equilibre to analyze body alignment and stability. His innovative typology of body posture and evidence-based approaches have significantly influenced modern postural therapy, rehabilitation methods, and preventive health strategies, establishing him as a leading expert in the interdisciplinary field of postural and functional body assessment.

Profiles: Scopus | Orcid

Featured Publications

  • Wilczyński, J. (2025). Own typology of body posture based on research using the Diers Formetric III 4D system. Journal of Clinical Medicine, 14(2), 501.

  • Wilczyński, J., Habik Tatarowska, N., & Mierzwa-Molenda, M. (2023). Deficits of sensory integration and balance as well as scoliotic changes in young schoolgirls. Sensors, 23(3), 1172.

  • Wilczyński, J., Sowińska, A., & Mierzwa-Molenda, M. (2022). Physiotherapy as a specific and purposeful form of physical activity in children with idiopathic body asymmetry. International Journal of Environmental Research and Public Health, 19(22), 15008.

  • Wilczyński, J., Sobolewski, P., Zieliński, R., Kabała, M., & Lipińska-Stańczak, M. (2020). Body posture defects and body composition in school-age children. Children, 7(11), 204.

  • Wilczyński, J., Karolak, P., Janecka, S., Kabała, M., & Habik-Tatarowska, N. (2019). The relationship between the angle of curvature of the spine and SEMG amplitude of the erector spinae in young school-children. Applied Sciences, 9(15), 3115.*

Vladimir Chigrinov | Nanotechnology Innovations | Best Paper Award

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Prof. Dr. Vladimir Chigrinov | Nanotechnology Innovations | Best Paper Award

Hong Kong University of Science and Technology | Russia

Prof. Dr. Vladimir G. Chigrinov is a distinguished scientist and Professor Emeritus in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST). Renowned worldwide for his groundbreaking contributions to liquid crystal science and electro-optical technology, he has played a pivotal role in advancing photoalignment, photopatterning, and ferroelectric liquid crystal (FLC) research. His pioneering work has led to the development of innovative liquid crystal display (LCD) configurations, optically rewritable electronic paper, and adaptive photonic devices. Prof. Dr. Chigrinov’s research spans electro-optical effects in liquid crystals, surface alignment mechanisms, and modeling and optimization of liquid crystal device performance, contributing fundamentally to both theory and application in display and photonic technologies. He has authored or co-authored over 470 scientific publications, with a remarkable 19,312 citations, an h-index of 63, and an i10-index of 298, reflecting his profound global research impact. His studies have enabled major advances in fast-switching display technologies, tunable optical systems, and energy-efficient photonic elements, effectively bridging the gap between fundamental physics and modern engineering applications. Through extensive international collaborations, Prof. Dr. Chigrinov continues to lead innovation in liquid crystal photonics, smart optical materials, and next-generation display technologies, cementing his reputation as one of the foremost authorities in liquid crystal physics and device engineering.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Schadt, M., Schmitt, K., Kozinkov, V., & Chigrinov, V. G. (1992). Surface-induced parallel alignment of liquid crystals by linearly polymerized photopolymers. Japanese Journal of Applied Physics, 31(7 R), 2155–2164.

  • Blinov, L. M., & Chigrinov, V. G. (1994). Electrooptic effects in liquid crystal materials. Springer.

  • Chigrinov, V. G., Kozenkov, V. M., & Kwok, H.-S. (2008). Photoalignment of liquid crystalline materials: Physics and applications. Wiley.

  • Chigrinov, V. G. (1999). Liquid crystal devices: Physics and applications. Artech House.

  • Kiselev, A. D., Pozhidaev, E. P., & Chigrinov, V. G. (2019). Polarization gratings and electro-optics of deformed helix ferroelectric liquid crystals. Photonics Letters of Poland, 11(1), Article 182.

Fatemeh Rezaei | Agricultural Technology | Best Review Paper Award

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Assoc. Prof. Dr. Fatemeh Rezaei | Agricultural Technology | Best Review Paper Award

K. N. Toosi University of Technology | Iran

Assoc. Prof. Dr. Fatemeh Rezaei is an Associate Professor of Physics at K.N. Toosi University of Technology (KNTU), Iran, whose research centers on laser–plasma interactions, photonics, and biophotonics. Her primary focus lies in Laser-Induced Breakdown Spectroscopy (LIBS), where she investigates the mechanisms of self-absorption, optically thick plasma formation, and plasma parameter determination through both experimental and computational approaches. She has developed advanced models and correction methods for plasma emission spectra and applied machine learning and deep learning algorithms to enhance the accuracy of LIBS-based elemental analysis. Dr. Rezaei’s interdisciplinary work extends to laser-assisted cancer therapy, integrating photothermal and photodynamic techniques with nanoparticles such as gold and curcumin–magnetic composites for effective tumor treatment. Her expertise also includes laser Doppler vibrometry, LIDAR systems, plasma diagnostics, and nano–bio laser interactions, bridging optical physics with biomedical and industrial applications. Through her extensive research and collaborations, Dr. Rezaei has made significant contributions to the advancement of laser spectroscopy, plasma science, and laser-based therapeutic technologies, with her findings published in leading international journals such as Spectrochimica Acta B, Scientific Reports, and Applied Optics.

Profile: Google Scholar

Featured Publications

  1. Rezaei, F., Cristoforetti, G., Tognoni, E., Legnaioli, S., Palleschi, V., & Safi, A. (2020). A review of the current analytical approaches for evaluating, compensating and exploiting self-absorption in Laser Induced Breakdown Spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy, 169, 105878.

  2. Ashkbar, A., Rezaei, F., Attari, F., & Ashkevarian, S. (2020). Treatment of breast cancer in vivo by dual photodynamic and photothermal approaches with the aid of curcumin photosensitizer and magnetic nanoparticles. Scientific Reports, 10(1), 21206.

  3. Rezaei, F., Karimi, P., & Tavassoli, S. H. (2014). Effect of self-absorption correction on LIBS measurements by calibration curve and artificial neural network. Applied Physics B, 114(4), 591–600.

  4. Safi, A., Tavassoli, S. H., Cristoforetti, G., Legnaioli, S., Palleschi, V., & Rezaei, F. (2019). Determination of excitation temperature in laser-induced plasmas using columnar density Saha-Boltzmann plot. Journal of Advanced Research, 18, 1–7.

  5. Aberkane, S. M., Safi, A., Botto, A., Campanella, B., Legnaioli, S., Poggialini, F., Rezaei, F., & Palleschi, V. (2020). Laser-Induced Breakdown Spectroscopy for determination of spectral fundamental parameters. Applied Sciences, 10(14), 4973.

Gengfeng Jiang | Pollution Monitoring | Best Researcher Award

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Mr. Gengfeng Jiang | Pollution Monitoring |Best Researcher Award

Postgraduate | Guilin University of Technology |China

Mr. Gengfeng Jiang is an accomplished researcher affiliated with the China Education and Research Network and Guilin University of Technology, Beijing, China. His research primarily focuses on the complex interactions of high-temperature gases, combustion chemistry, and spectral modeling, contributing significantly to the advancement of low-carbon energy and environmental safety. Mr. Gengfeng Jiang’s work integrates experimental analysis with computational modeling to explore the spectral characteristics of chemical pool fires, offering valuable insights into pollutant formation, flame radiation, and combustion efficiency. His recent publication, “Investigating the Spectral Characteristics of High-Temperature Gases in Low-Carbon Chemical Pool Fires and Developing a Spectral Model,” published in Toxics, exemplifies his innovative approach to addressing pressing challenges in sustainable combustion and low-emission energy systems. Through interdisciplinary collaboration with experts in environmental science, chemical engineering, and materials research, Mr. Gengfeng Jiang contributes to developing predictive spectral models that can enhance the understanding and control of industrial combustion processes. His research outputs demonstrate a commitment to mitigating environmental risks associated with industrial emissions and advancing clean energy technologies. Recognized for his analytical precision and forward-thinking methodologies, Mr. Gengfeng Jiang continues to play a pivotal role in bridging scientific inquiry with practical applications for a greener, safer, and more energy-efficient future. His scholarly work embodies a synthesis of scientific rigor, environmental consciousness, and technological innovation, making him a distinguished figure in the field of combustion and low-carbon energy research.

Profile : ORCID

Featured Publication

Jiang, G., Chen, Z., Liang, Y., Li, P., Liu, Q., & Zhou, L. (2025). Investigating the spectral characteristics of high-temperature gases in low-carbon chemical pool fires and developing a spectral model. Toxics, 13(10), 877.