Ji Zhu | Energy Policy | Research Excellence Award

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Mr. Ji Zhu | Energy Policy | Research Excellence Award

Mr. Ji Zhu | School of International Relations and Public Affairs, Shanghai International Studies University | China

Mr. Ji Zhu’s research focuses on advancing the understanding of renewable energy transition in Arab countries, particularly in regions where the economic structure remains heavily dependent on fossil fuels. His scholarly contributions examine the underlying political, economic, and technological dynamics that influence the pace and direction of clean energy adoption. By exploring policy frameworks, governance challenges, and market mechanisms, his work identifies leverage points that can drive effective transformation of energy systems in resource-dependent economies. His publication, “Decoding the Paradoxical Drivers of Renewable Energy Transition in Arab Countries,” highlights the complex interplay of motivations behind renewable energy investments, including the pursuit of economic diversification, environmental commitments, and geopolitical considerations. Through rigorous analysis, he emphasizes the risks of carbon lock-in, demonstrating how current dependencies on conventional energy can hinder progress if not addressed through proactive policymaking. His research provides valuable insights into how nations can overcome these obstacles by implementing strategic reforms and fostering innovation within the energy sector. By combining data-driven evaluation with policy-oriented recommendations, his work contributes to strengthening national and regional climate strategies aligned with global sustainability goals. The practical implications of his findings make his research highly relevant to governments, international organizations, and stakeholders striving to accelerate low-carbon development. His contributions enhance collective understanding of the challenges and opportunities in transitioning from fossil fuel reliance to renewable energy futures across the Arab region, positioning him as a strong contributor to ongoing global discussions on sustainable energy transformation.

Profile: ORCID

Featured Publication

Qian, X., & Zhu, J. 2025. Decoding the paradoxical drivers of renewable energy transition in Arab countries.

 

Yingying Wu | Genetic Research | Research Excellence Award

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Assoc. Prof. Dr. Yingying Wu | Genetic Research | Research Excellence Award

Research Director‌ | Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences | China

Assoc. Prof. Dr. Yingying Wu is a dedicated researcher whose work focuses on the genetic breeding of industrialized edible mushrooms and the synthetic biology of active natural products from macrofungi, contributing significantly to biotechnology, functional food development, and mushroom-based pharmaceutical innovation. Her research explores the improvement of mushroom strains to enhance production efficiency, nutritional composition, and bioactive properties valuable for human health. She has contributed to metabolic profiling studies of Cordyceps militaris, identifying optimal consumption periods to maximize functional health effects and sensory qu`ality, providing scientific foundations for promoting mushroom products as high-value nutraceuticals. Her work on the cloning, characterization, and functional analysis of novel fungal immunomodulatory protein genes, such as those found in Ganoderma leucocontextum, demonstrates the therapeutic potential of macrofungi-derived biomolecules in immune regulation and cancer-related cellular functions. Additionally, she has advanced understanding of reproductive biology and nuclear behavior in edible mushroom strains through studies on protoplast monokaryotization and asexual spore isolation in Flammulina filiformis, supporting efficient breeding technologies for industrial cultivation. By integrating molecular biology, metabolic analysis, and applied breeding strategies, she contributes to creating sustainable and innovative mushroom-derived products, strengthening the link between food technology and biomedical research. Her scientific outputs reflect meaningful contributions to both fundamental mycological research and practical industrial applications, positioning her as a promising leader in the field of macrofungi biotechnology, functional active compound discovery, and future health-focused bioindustries.

Profile:  Scopus  |  ORCID

Featured Publications

Wang, Y., Zhang, R., Li, Y., Wu, Y., Gong, M., Shao, Y., Wang, L., Li, W., & Zou, G. (2026). Metabolic profiles reveal the preferable consumption time of Cordyceps militaris dried products with the optimal health effects and flavors. Journal of Future Foods, 6(4), 642–655.

Yang, J., Jin, M., Zhang, L., Wu, Y., & Zhou, X. (2025). Characterization and functional analysis of a novel fungal immunomodulatory protein gene from Ganoderma leucocontextum in B16-F10 mouse melanoma cells. International Journal of Molecular Sciences, 26(11), 5063.

Wu, Y., et al. (2025). Nuclear partial segregation occurred in protoplast monokaryotization and asexual spore monosporous isolation of wild strains of Flammulina filiformis. Mycosystema.

 

Xu Zhao | Edible Mushroom Resources | Research Excellence Award

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Mr. Xu Zhao | Edible Mushroom Resources | Research Excellence Award

Associate Researcher | Institution of Urban Agriculture, Chinese Academy of Agricultural Sciences | China

Mr. Xu Zhao is a highly promising researcher whose work demonstrates strong innovation, technical depth, and industry relevance across edible fungi biotechnology, intelligent cultivation systems, and functional product development. His research focuses extensively on edible fungus genetic breeding, where he integrates modern molecular biology, genomics, gene editing, protoplast fusion, mutation breeding, and molecular marker–assisted strategies to develop specialized varieties suited for industrialized and intensive production. Through comprehensive collection and evaluation of domestic and international germplasm resources, he establishes trait-specific gene libraries, core germplasm repositories, and cultivation trait databases that support high-yield and high-quality breeding programs. In parallel, he advances the field of factory-scale edible fungi cultivation by designing intelligent equipment systems, IoT-based monitoring tools, and automated environmental control technologies for substrate preparation, inoculation, mycelial growth, fruiting management, harvesting, and preservation. His work in optimizing environmental parameters such as temperature, humidity, light, and CO₂ enables precise and efficient production models, supported by collaborations with industrial partners to promote technology integration and real-world application. Additionally, he contributes to the extraction and characterization of bioactive components from edible fungi, focusing on physicochemical properties, physiological functions, and pharmacological mechanisms. His efforts include development of advanced extraction processes, improvement of processing technologies, and exploration of new functional products spanning foods, nutraceuticals, pharmaceuticals, and cosmetics. Although his citation record is still at an early stage, his research achievements demonstrate significant potential for advancing agricultural biotechnology, sustainable production systems, and the health product industry, positioning him as a strong emerging contributor to excellence in research.

Profile: Scopus

Featured Publications

Zhao, X. (n.d.). Research progress on nutritional components, functional active components, and pharmacological properties of Floccularia luteovirens.

 

Dan li | Smart Energy System | Research Excellence Award

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Dr. Dan li | Smart Energy System | Research Excellence Award

Dr. Dan li | China Three Gorges University | China

Dr. Dan Li is an accomplished researcher in intelligent energy systems, with a strong focus on optimal scheduling, risk measurement, and intelligent decision-making for multi-energy systems operating under a high proportion of renewable energy. His work addresses the increasing complexity and uncertainty of modern power grids by developing advanced computational approaches that integrate artificial intelligence with domain-specific physical principles. Through three significant completed and ongoing research projects, he has contributed to flexible resource regulation and control technology for new-energy pumped storage power stations, the development and application of key technologies for grid-connected performance testing and analysis of renewable energy stations, and the functional debugging and data-processing framework for intelligent network-source coordinated control and risk early-warning decision-making platforms. His key scientific contribution lies in accurately defining the physical constraints, operational limits, and security boundaries of unit commitment and economic dispatch problems, and translating these elements into optimization objectives and state spaces suitable for advanced algorithms. Dr. Li introduced a physics-informed exploration strategy for deep reinforcement learning by embedding prior knowledge, including power-flow equations and unit ramp-rate constraints, directly into the reward design, reducing invalid exploration and improving algorithmic convergence by over 50 percent. This approach demonstrates a powerful integration of domain expertise and advanced computational tools, contributing significantly to enhancing the reliability, efficiency, and intelligence of next-generation power systems. His published research, including his recent article on optimization solutions for power generation planning, reflects strong scientific rigor and technological relevance, reinforcing his position as a promising contributor to the advancement of intelligent and sustainable energy systems.

Profile: ORCID

Featured Publications

Li, D., Zhang, L., Mi, N., & Zhong, H. (2025). Optimization solution for unit power generation plan based on the integration of constraint identification and deep reinforcement learning. Processes.

 

Lianghai Wu | Sustainable Development | Research Excellence Award

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Prof. Lianghai Wu | Sustainable Development | Research Excellence Award

Professor | Anhui University of Technology | China

Prof. Lianghai Wu is a highly accomplished and influential researcher whose work spans accounting research methods, quantitative analysis, financial management, and the emerging intersections of ESG disclosure, green finance, auditing theory, and sustainable development. He has demonstrated strong leadership by serving as the principal investigator for two National Social Science Fund projects, along with several major and key provincial-level humanities and social science teaching reform initiatives, an Anhui University of Technology postgraduate first-class textbook construction project, and multiple provincial soft science projects. His research significantly advances understanding in corporate environmental performance, sustainability reporting quality, and the role of financial governance in promoting green development. Prof. Wu has also contributed to the methodological advancement of the field through his exploration of intelligent text big data accounting, integrating tools such as Stata and Python to enhance the precision and scalability of empirical analysis. His extensive scholarly output includes more than ten monographs and textbooks and over one hundred peer-reviewed papers, reflecting both productivity and long-term dedication to research excellence. His work is regularly recognized by national and provincial academic institutions, evidenced by his involvement in expert databases for the National Social Science Fund, Ministry of Education dissertation evaluations, China Postdoctoral Science Foundation project reviews, and Anhui Provincial Science and Technology Department assessments. With impactful publications, including recent analyses of ESG information disclosure and corporate environmental performance, Prof. Wu’s contributions continue to shape contemporary discourse in accounting, sustainable finance, and environmental governance, establishing him as a leading figure highly suitable for competitive research awards.

Profile: ORCID

Featured Publications

Wu, L., Sun, H., & Chen, L. (2025). The impact of ESG information disclosure on corporate environmental performance: Evidence from China’s Shanghai and Shenzhen A-share listed companies. Sustainability.

 

Khurshid Hussain | Wireless Communication | Research Excellence Award

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Mr. Khurshid Hussain | Wireless Communication | Research Excellence Award

Research Assistant | Korea Institute of Ocean Science and Technology | South Korea

Mr. Khurshid Hussain is an accomplished multidisciplinary researcher whose work spans advanced artificial intelligence, optimization, cybersecurity, and next-generation energy-storage technologies, reflecting a strong alignment with contemporary scientific and industrial priorities. His research focuses on secure and privacy-preserving applications of large language models, generative AI for intelligent automation, anomaly detection, and predictive analytics, supported by the development of sophisticated optimization techniques for complex system design, resource planning, and resilient workforce scheduling. He has contributed extensively to machine learning-driven analytics, pattern recognition, and cybersecurity solutions, including data-driven HR analytics with an emphasis on ethical and privacy-compliant practices. Parallel to his AI contributions, he has produced impactful work in materials science, specifically in the development of high-performance nanostructures and energy-storage materials. His publications include binder-free cupric-ion-containing zinc sulfide nanoplates for flexible energy-storage devices and Mg-doped CeO₂ materials for advanced energy applications, demonstrating notable influence within the field. His research output also extends to integrated radar-communication frameworks, beamforming technologies, and OTFS-ISAC systems tailored for vehicular sensing and communication, reflecting a commitment to innovation across multiple technological domains. Publications in respected journals such as Chemosphere, Journal of Electroanalytical Chemistry, Electronics, and Journal of Industrial and Engineering Chemistry highlight the breadth and quality of his scientific contributions. With a strong record of innovation, interdisciplinary impact, and solutions-oriented research, Mr. Hussain advances the frontiers of AI, communication systems, and energy-storage technologies, making his work highly relevant to global research and industry needs.

Profile: Scopus | ORCID | Google Scholar

Featured Publications

Hussain, I., Shaheen, I., Ahmad, R., Ali, I., Hussain, K., Hussain, S. S., Alsaiari, N. S., et al. (2023). Binder-free cupric-ion containing zinc sulfide nanoplates-like structure for flexible energy storage devices. Chemosphere, 314, 137660.

Hussain, K., Ali, I., Hasnain, S., Hussain, S. S., Hussain, B., Khan, M. S., Ammar, S. M., et al. (2020). Reagents assisted Mg-doped CeO₂ for high-performance energy-storage applications. Journal of Electroanalytical Chemistry, 873, 114401.

Hussain, K., & Oh, I. Y. (2024). Joint radar, communication, and integration of beam-forming technology. Electronics, 13(23).

Nawaz, T., Wen, Y., Ahmad, M., Hussain, K., Ali, A., Ullah, Q., Khan, S. A., et al. (2024). Development of vertically aligned NixCo₃₋ₓO₄ nanowires for supercapacitors. Journal of Industrial and Engineering Chemistry, 133, 498–504.

Hussain, K., Ali, E. M., Hussain, W., Raza, A., & Elkamchouchi, D. H. (2025). Robust OTFS-ISAC for vehicular-to-base station end-to-end sensing and communication. Electronics, 14(21), 4340.

Inese Skapste | Algal Biostimulants | Editorial Board Member

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Mrs. Inese Skapste | Algal Biostimulants | Editorial Board Member

Mrs. Inese Skapste | Latvia University of Life Sciences and Technologies | Latvia

Mrs. Inese Skapste is an emerging researcher specializing in sustainable agriculture, bioresource economics, and the innovative use of marine biomass to enhance agricultural productivity in the Baltic Sea Region. Her work centers on assessing the economic and agronomic potential of algae-based biostimulants, with a focus on transforming locally available marine resources into value-added agricultural inputs that support environmental resilience and circular bioeconomy principles. She investigates the efficiency, scalability, and cost–benefit dynamics of algae-derived products, emphasizing their role in reducing chemical fertilizer dependence and promoting eco-friendly crop enhancement strategies. Her research includes evaluating the digestate extract of Furcellaria lumbricalis, a Baltic Sea red algae species, and its impact on plant growth, demonstrated through applied studies such as basil cultivation trials that highlight measurable improvements in growth promotion. She contributes to understanding how algae biostimulants can integrate into regional farming systems by analyzing market potential, sustainability outcomes, and opportunities for local resource utilization. Her conference and journal publications explore both scientific performance and socioeconomic implications, positioning algae-based solutions as strategic tools for strengthening sustainable agriculture and regional bioresource management. With an interdisciplinary approach bridging environmental science and economic evaluation, her work advances the development of green agricultural technologies and supports regional efforts to foster innovation, reduce environmental pressures, and enhance long-term agricultural sustainability in the Baltic Sea area.

Profile: Scopus | ORCID

Featured Publications

Skapste, I. (2024, November 27). The potential of Baltic Sea algae as an agricultural resource enhancing sustainability in Latvia. Conference paper.

Skapste, I. (2025). Economic potential of algae biostimulant for sustainable agriculture in the Baltic Sea Region: Impact of Furcellaria lumbricalis digestate extract on basil growth promotion. Sustainability.

 

Zhen Guo | Renewable Energy | Editorial Board Member

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Dr. Zhen Guo | Renewable Energy | Editorial Board Member

Dr. Zhen Guo | Wuhan University of Technology | China

Dr. Zhen Guo is an emerging researcher specializing in intelligent fault diagnosis, deep learning, anomaly detection, and engineering signal processing, with emphasis on developing reliable data-driven methods for modern industrial systems. His work addresses key challenges in rotating machinery, gearboxes, propellers, and wind turbines by integrating advanced machine learning models with domain-specific signal processing techniques to enhance diagnostic accuracy under real-world constraints. Dr. Guo’s research explores multi-scale wavelet decomposition, adaptive feature fusion, and high-dimensional sampling strategies to mitigate multi-level class imbalance and improve the robustness of condition-monitoring frameworks. He has introduced innovative unsupervised anomaly detection approaches using deep convolutional support generative adversarial networks, enabling effective detection in scenarios where labeled data are insufficient or costly. His contributions also include transfer-learning architectures with channel-attention residual networks and LLM-assisted fine-tuning to achieve few-shot fault recognition in complex systems such as autonomous underwater vehicle propellers. Additionally, he has advanced wavelet-random-forest methods for modeling high-dimensional imbalance samples and proposed unified pattern-fusion strategies for mining alarm data in large-scale industrial facilities using adaptive discretization and time-clustering mechanisms. Dr. Guo’s work published in leading journals such as Mechanical Systems and Signal Processing, Scientific Reports, Ocean Engineering, Measurement, and Measurement Science and Technology demonstrates his commitment to bridging the gap between theoretical advances and practical reliability engineering. Supported by competitive research funding, he continues to design predictive frameworks that improve equipment health assessment, Remaining Useful Life estimation, and maintenance decision-making across industrial environments, contributing significantly to the advancement of intelligent diagnostic technologies.

Profile: Scopus | ORCID

Featured Publications

Guo, Z. (2025). Multi-scale wavelet decomposition and feature fusion for rotating machinery fault diagnosis under multi-level class imbalance. Mechanical Systems and Signal Processing.

Guo, Z. (2025). Unsupervised anomaly detection for gearboxes based on the deep convolutional support generative adversarial network. Scientific Reports, (Published July 1, 2025).

Guo, Z. (2025). Channel attention residual transfer learning with LLM fine-tuning for few-shot fault diagnosis in autonomous underwater vehicle propellers. Ocean Engineering.

Guo, Z. (2025). Fault diagnosis of rotating machinery with high-dimensional imbalance samples based on wavelet random forest. Measurement.

Guo, Z. (2025). Alarm data mining in complex industrial facilities using adaptive discretization based on time clustering and unified pattern fusion mining. Measurement Science and Technology, (Published January 31, 2025).

Farshad Sadeghpour | Underground Storage | Editorial Board Member

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Mr. Farshad Sadeghpour | Underground Storage | Editorial Board Member

Researcher | Petroleum University of Technology (PUT) | Iran

Farshad Sadeghpour is a geomechanics and reservoir engineering researcher at the Petroleum University of Technology (PUT), known for his contributions to underground gas storage, petrophysics, and CO₂ geological sequestration. He has authored multiple peer-reviewed publications covering geomechanical upscaling, fracture development under stress, anisotropic rock behavior, storage-efficiency modeling, and petrophysical parameter estimation. His current Google Scholar record lists 22 citations, an h-index of 3, and 6 indexed documents, reflecting his growing influence in subsurface engineering. He holds academic training in petroleum engineering from PUT and additional postgraduate research experience from the Islamic Azad University, Science & Research Branch. His research experience includes collaborative studies on elastic property prediction, machine-learning-based evaluation of CO₂ storage feasibility, and advanced triaxial testing for characterizing anisotropic formations. His work demonstrates strong expertise in integrating experimental, computational, and data-driven approaches to solve complex reservoir challenges. His research interests include geomechanics, underground storage, CO₂ sequestration, petrophysical modeling, machine learning, and rock mechanics. Although still early in his career, his contributions indicate promise for impactful advancements in sustainable subsurface energy systems. Overall, Farshad Sadeghpour is an emerging researcher dedicated to improving geological storage, reservoir characterization, and the scientific foundations of low-carbon energy technologies.

Profile: Google Scholar | ORCID

Featured Publications

Sadeghpour, F., Darkhal, A., Gao, Y., Motra, H. B., Aghli, G., & Ostadhassan, M. (2024). Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media. Geoenergy Science and Engineering, 239, 212915.

Aghli, G., Aminshahidy, B., Motra, H. B., Darkhal, A., Sadeghpour, F., … (2024). Effect of stress on fracture development in the Asmari reservoir in the Zagros Thrust Belt. Journal of Rock Mechanics and Geotechnical Engineering, 16(11), 4491–4503.

Sadeghpour, F. (2025). Storage efficiency prediction for feasibility assessment of underground CO₂ storage: Novel machine learning approaches. Energy, 324, 136040.

Iranfar, S., Sadeghpour, F., Manshad, A. K., Naderi, M., & Shakiba, M. (2025). An eigenvalue-driven framework for the ranking and selection of optimal geological CO₂ storage sites. Results in Engineering, 106770.

Sadeghpour, F., Motra, H. B., Sethi, C., Wind, S., Hazra, B., Aghli, G., … (2025). Elastic properties of anisotropic rocks using a stepwise loading framework in a true triaxial testing apparatus. Geoenergy Science and Engineering, 251, 213883.

Muhammad Musa Khan | Agricultural Entomology | Editorial Board Member

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Dr. Muhammad Musa Khan | Agricultural Entomology | Editorial Board Member

Associate Researcher | Hainan Institute of Zhejiang University | China

Dr. Muhammad Musa Khan’s research centers on advancing sustainable pest management through a detailed understanding of insecticide toxicology, resistance mechanisms, and the ecological risks posed by pollutants in agricultural systems. His work focuses extensively on major rice pests, particularly the brown planthopper (Nilaparvata lugens), and its natural predator, the rove beetle (Paederus fuscipes), emphasizing their physiological responses to insecticides and environmental contaminants. He investigates how heavy metals and other pollutants influence pest behavior, resistance development, and overall ecosystem stability, offering insights crucial for designing safer and more effective pest control strategies. A significant component of his research explores the role of insect gut microbiota in pesticide degradation, highlighting microbial contributions to resistance evolution and detoxification pathways. Dr. Khan also examines the lethal and sublethal impacts of widely used insecticides on beneficial predatory species, underscoring the need to balance pest suppression with the preservation of ecological services. His studies on dispersal patterns of Paederus fuscipes provide valuable guidance for managing outbreaks of Paederus dermatitis in rice-growing regions, linking agricultural management to public health protection. Additionally, his contributions include work on metabolic and digestive enzyme–related mechanisms driving host plant adaptation in polyphagous pests, as well as assessments of invasive ant species and their ecological impacts under changing environmental conditions. Through his diverse and high-impact research outputs, Dr. Khan delivers evidence-based strategies aimed at enhancing crop protection, reducing environmental harm, and promoting biologically informed and ecologically balanced pest management systems.

Profile: Google Scholar | ResearchGate | Staff Page

Featured Publications

Siddiqui, J. A., Khan, M. M., Bamisile, B. S., Hafeez, M., Qasim, M., Rasheed, M. T., … (2022). Role of insect gut microbiota in pesticide degradation: A review. Frontiers in Microbiology, 13, 870462.

Hafeez, M., Ullah, F., Khan, M. M., Li, X., Zhang, Z., Shah, S., Imran, M., Assiri, M. A., … (2022). Metabolic-based insecticide resistance mechanism and ecofriendly approaches for controlling beet armyworm (Spodoptera exigua): A review. Environmental Science and Pollution Research, 29(2), 1746–1762.

Siddiqui, J. A., Bamisile, B. S., Khan, M. M., Islam, W., Hafeez, M., Bodlah, I., Xu, Y. (2021). Impact of invasive ant species on native fauna across similar habitats under global environmental changes. Environmental Science and Pollution Research, 28(39), 54362–54382.

Khan, M. M., Nawaz, M., Hua, H., Cai, W., & Zhao, J. (2018). Lethal and sublethal effects of emamectin benzoate on the rove beetle (Paederus fuscipes), a non-target predator of the rice brown planthopper (Nilaparvata lugens). Ecotoxicology and Environmental Safety, 165, 19–24.

Hafeez, M., Li, X. W., Zhang, J. M., Zhang, Z. J., Huang, J., Wang, L. K., Khan, M. M., … (2021). Role of digestive protease enzymes and related genes in host plant adaptation of a polyphagous pest (Spodoptera frugiperda). Insect Science, 28(3), 611–626.