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).

Monica Alvarez Manso | Energy | Best Researcher Award

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Mrs. Monica Alvarez Manso | Energy | Best Researcher Award

Mrs. Monica Alvarez Manso | Universidad De Leon | Spain

Mónica Alvarez Manso is a PhD candidate at the University of León and a Technical Mining Engineer with a Master’s in Occupational Risk Prevention. She serves as Vice-Dean of the Official College of Technical Mining Engineers and Mining and Energy Graduates of Madrid and Castilla y León, where she leads initiatives in energy, mining, and advanced materials. Her expertise spans hydrogen technologies, bioenergy, renewable systems, and sustainable water resource management. With experience in large-scale international energy projects, innovative software development, and leadership roles, she combines technical excellence with a strong commitment to sustainable innovation and climate change mitigation.

Publication Profile

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Academic Background

Mónica Alvarez Manso is a highly accomplished professional with a strong academic and technical foundation. She is a PhD candidate at the University of León, where her research focuses on sustainable energy and resource management. She earned her degree as a Technical Mining Engineer from the School of Mining Engineering in León and later completed a Master’s in Occupational Risk Prevention at UNED. Her education has been complemented by extensive professional experience in hydrogen technologies, bioenergy, renewable systems, and sustainable water management, enabling her to integrate advanced knowledge with practical innovation in the fields of energy and sustainability.

Professional Experience

Mónica Alvarez Manso has built a distinguished career that combines technical expertise, research, and leadership in the fields of energy, mining, and sustainability. She has directed and coordinated complex projects involving hydrogen technologies, bioenergy, renewable energy integration, and sustainable water resource management. Her professional work includes the design and implementation of innovative systems for green hydrogen and biomethane production, large-scale energy infrastructure, and industrial energy efficiency. As Vice-Dean of the Official College of Technical Mining Engineers and Mining and Energy Graduates, she plays a key role in advancing innovation, professional development, and technological progress in the energy sector.

Awards and Honors

Mónica Alvarez Manso has earned recognition for her outstanding contributions to energy engineering, sustainability, and applied research. Her innovative work in hydrogen technologies, bioenergy, and renewable systems has positioned her as a respected leader in advancing clean energy solutions. She has been acknowledged for her role in developing pioneering projects that address climate change mitigation and industrial sustainability. Her leadership as Vice-Dean of the Official College of Technical Mining Engineers and Mining and Energy Graduates highlights her commitment to professional excellence. Through her academic, technical, and institutional achievements, she continues to gain distinction and honors in her field.

Research Focus

Mónica Alvarez Manso centers her research on sustainable energy systems, with particular emphasis on hydrogen technologies, bioenergy, and renewable integration. Her work explores the complete cycle of green hydrogen production, storage, and transport, as well as innovative methods for biogas upgrading and digestate valorization to support circular economy principles. She also investigates advanced energy systems, including smart grids, energy storage, and industrial efficiency. Another significant aspect of her research involves sustainable water resource management and climate change mitigation strategies, where she develops practical and innovative solutions that bridge academic research with real-world energy and environmental challenges.

Publication Top Notes

Classification Framework for Hydrological Resources for Sustainable Hydrogen Production with a Predictive Algorithm for Optimization

Year: 2025

Feasibility study of an electrical power plant combined with a thermolysis process

Year: 2002

Conclusion

Mónica Alvarez Manso is an accomplished researcher whose expertise combines technical depth with leadership in hydrogen technologies, bioenergy, renewable systems, and sustainable water management. She has contributed to international infrastructure projects and advanced research in green hydrogen and biomethane production, while also serving as Vice-Dean of the Official College of Technical Mining Engineers. Her innovations, such as HYDROGREENSIM and registered inventions, showcase creativity and impact. With stronger academic publications, her profile reflects an outstanding candidate for the Best Researcher Award.