Sumaira Manzoor | Chemistry | Editorial Board Member

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Ms. Sumaira Manzoor | Chemistry | Editorial Board Member

Student | Institute of chemical sciences, Bahauddin zakariya university, Multan | Pakistan

Sumaira Manzoor is an accomplished researcher specializing in robotics, computer vision, and artificial intelligence, with a focus on developing advanced frameworks for autonomous systems and intelligent robots. Her work encompasses the design and deployment of edge-based vision models, human-following robots, and real-time inference systems, emphasizing efficiency and practical applicability in dynamic environments. She has made significant contributions to single and multi-object tracking, face mask recognition, semantic environment modeling, and robotic perception, leveraging both traditional machine vision and modern deep learning techniques. Manzoor has also explored ontology-based knowledge representation for cognitive robotic systems, providing comprehensive surveys and frameworks to enhance robot decision-making and interaction capabilities. Her research includes performance evaluations of cutting-edge detection models such as YOLOv3 and YOLOv4 in real-world applications, and she has developed innovative semantic SLAM and autonomous navigation frameworks aimed at high-level interaction and planning in complex environments. Throughout her career, she has published extensively in high-impact journals and presented her findings at leading international conferences, including Sensors, IEEE Access, Applied Sciences, and the International Conference on Control, Automation, and Systems (ICCAS). Her collaborative work with multidisciplinary teams has advanced the integration of AI-driven perception and reasoning in robotic systems, addressing practical challenges in mobile robotics and human-robot interaction. By bridging theoretical research and applied robotics, Manzoor’s contributions support the development of intelligent systems capable of autonomous decision-making, environment understanding, and adaptive interaction, positioning her as a leading figure in the field of AI-enabled robotics and computer vision. Her research not only advances technical knowledge but also has broad implications for real-world applications, including service robots, surveillance, healthcare, and autonomous navigation technologies.

Profile: ORCID

Featured Publications

  1. Manzoor, S., An, Y.-C., In, G.-G., Zhang, Y., Kim, S., & Kuc, T.-Y. (2023). SPT: Single pedestrian tracking framework with re-identification-based learning using the Siamese model. Sensors, 23(10), 4906. https://doi.org/10.3390/s23104906

  2. Manzoor, S., Kim, E.-J., Joo, S.-H., Bae, S.-H., In, G.-G., Joo, K.-J., Choi, J.-H., & Kuc, T.-Y. (2022). Edge deployment framework of GuardBot for optimized face mask recognition with real-time inference using deep learning. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3190538

  3. Manzoor, S., Joo, S.-H., Rocha, Y. G., Bae, S.-H., Kim, E.-J., Joo, K.-J., & Kuc, T.-Y. (2021). Ontology-based knowledge representation in robotic systems: A survey oriented toward applications. Applied Sciences, 11(10), 4324. https://doi.org/10.3390/app11104324

  4. Manzoor, S., Joo, S.-H., Rocha, Y. G., Lee, H.-U., & Kuc, T.-Y. (2019). A novel semantic SLAM framework for humanlike high-level interaction and planning in global environment. CEUR Workshop Proceedings.

  5. Manzoor, S., Joo, S.-H., & Kuc, T.-Y. (2019). Comparison of object recognition approaches using traditional machine vision and modern deep learning techniques for mobile robot. International Conference on Control, Automation and Systems. https://doi.org/10.23919/ICCAS47443.2019.8971680

Sumaira Manzoor’s work advances intelligent robotics and AI-driven perception, enabling autonomous systems to interact safely and efficiently with complex environments. Her research bridges cutting-edge computer vision and real-world applications, driving innovation in robotics, healthcare, surveillance, and autonomous navigation while contributing to global technological progress.

Ulf-Peter Apfel | Chemistry | Editorial Board Member

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Prof. Dr. Ulf-Peter Apfel | Chemistry | Editorial Board Member 

Professor | Ruhr University Bochum & Fraunhofer UMSICHT | Germany

Ulf-Peter Apfel is a leading figure in modern electrocatalysis and renewable energy chemistry, with a research career defined by major contributions to bio-inspired catalysis, hydrogen evolution, CO₂ reduction, and sustainable energy conversion. With over 8,300 citations, an h-index of 53, and more than 136 peer-reviewed publications, his work demonstrates both exceptional productivity and enduring scientific impact. His research bridges fundamental inorganic and bioinorganic chemistry with applied electrochemical energy technologies, particularly through the design of molecular and heterogeneous electrocatalysts inspired by natural metalloenzymes such as [FeFe]-hydrogenases. His highly cited studies in journals like Angewandte Chemie, Nature Communications, Chemical Society Reviews, and ACS Catalysis have shaped global understanding of hydrogen generation, oxygen reduction and evolution reactions, and carbon dioxide electroreduction. Notably, his work on enzyme-inspired iron porphyrins, pentlandite-based electrocatalysts, cobalt and manganese corrole complexes, and metal–organic framework supported catalysts has established new benchmarks for efficiency, selectivity, and sustainability in electrocatalysis. His interdisciplinary collaborations span spectroscopy, protein crystallography, materials science, and industrial chemistry, reflecting the broad translational relevance of his research. Beyond laboratory innovation, his scholarship also addresses the critical challenge of scaling fundamental discoveries toward real-world energy applications, as reflected in his influential work on bridging the “valley of death” between basic research and applied electrolysis. As a professor at Ruhr University Bochum and head of electrosynthesis at Fraunhofer UMSICHT, he plays a central role in shaping future directions in green hydrogen, artificial photosynthesis, and carbon-neutral fuel production. Overall, his career represents a powerful integration of molecular design, mechanistic insight, and technological relevance, positioning him as one of the most influential scientists in contemporary renewable energy and electrocatalysis research.

Profile: Google Scholar

Featured Publications

  1. Xie, L., Zhang, X. P., Zhao, B., Li, P., Qi, J., Guo, X., Wang, B., Lei, H., & Zhang, W. (2021). Enzyme-inspired iron porphyrins for improved electrocatalytic oxygen reduction and evolution reactions. Angewandte Chemie, 133(14), 7654–7659.

  2. Kleinhaus, J. T., Wittkamp, F., Yadav, S., Siegmund, D., & Apfel, U.-P. (2021). [FeFe]-hydrogenases: Maturation and reactivity of enzymatic systems and overview of biomimetic models. Chemical Society Reviews, 50(3), 1668–1784.

  3. Liang, Z., Guo, H., Zhou, G., Guo, K., Wang, B., Lei, H., Zhang, W., & Zheng, H. (2021). Metal–organic-framework-supported molecular electrocatalysis for the oxygen reduction reaction. Angewandte Chemie, 133(15), 8553–8557.

  4. Konkena, B., Junge Puring, K., Sinev, I., Piontek, S., Khavryuchenko, O., Dürholt, J. P., Schökel, A., Schuhmann, W., Muhler, M., & Apfel, U.-P. (2016). Pentlandite rocks as sustainable and stable efficient electrocatalysts for hydrogen generation. Nature Communications, 7(1), 12269.

  5. Gonglach, S., Paul, S., Haas, M., Pillwein, F., Sreejith, S. S., Barman, S., De, R., Müllegger, S., Apfel, U.-P., & Köller, S. (2019). Molecular cobalt corrole complex for the heterogeneous electrocatalytic reduction of carbon dioxide. Nature Communications, 10(1), 3864.

Ulf-Peter Apfel’s research advances the frontiers of bio-inspired electrocatalysis and sustainable energy conversion, delivering transformative solutions for green hydrogen production and CO₂ utilization. His work directly accelerates the global transition toward carbon-neutral energy technologies by bridging fundamental chemistry with scalable industrial applications.