Darío Fernando Yépez Ponce | Robotics and Automation | Editorial Board Member

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Prof. Darío Fernando Yépez Ponce | Robotics and Automation | Editorial Board Member

Docente Investigador | Instituto Superior Universitario Central Técnico | Ecuador

Darío Fernando Yépez Ponce is a mechatronics and automation engineer and academic from Ecuador, currently working as a faculty member in electronics at Instituto Superior Universitario Central Técnico in Quito (since October 2024). His background includes an engineering degree in mechatronics (2016) from Universidad Técnica del Norte, plus ongoing postgraduate studies (Master’s in Electronics and Automation) at Universidad Politécnica Salesiana. Over the years he has served as a lecturer in various institutions across Ecuador teaching mechatronics, electronics, and automation engineering.Professor Yépez’s research interest concentrates on robotics, control systems (notably PID control), autonomous systems (including unmanned ground vehicles), microgrids and power electronics, IoT-based automation, and applications of mechatronics in agriculture and automation systems. His work shows a recurrent focus on optimization algorithms, control strategies, mobile robotics, and intelligent systems for automation and smart farming. Notable recent outputs include a 2025 journal article titled “Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges,” which analyses algorithms for path planning in autonomous ground vehicles. In terms of research productivity and impact: according to his publicly visible profile, he has a Google Scholar citation count of about 113 citations.Publications span journal articles, conference papers, and book chapters. For example, his 2025 UGV-optimization article is indexed in major journals. The breadth of his work — from control system tuning (e.g., PID controllers via hybrid optimization strategies) to IoT-based systems and robotics — reflects a versatile research skill set in automation, control, robotics, power electronics, and applied mechatronics.Although I could not find a definitive public value for his Scopus h-index or total Scopus-document count (his Scopus Author ID is 57220807265), the combination of his journal-indexed articles, book chapters and recent contributions suggests a growing research profile, particularly in robotics, automation, and sustainable/renewable-power applications.In conclusion, Darío Fernando Yépez Ponce represents a dynamic and interdisciplinary researcher bridging mechatronics, control systems, automation, and robotics — with an orientation toward real-world applications such as autonomous vehicles and smart farming. His emerging publication record and international-indexed works position him as an active contributor in automation and mechatronics research circles.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

  1. Yépez-Ponce, D. F., Salcedo, J. V., Rosero-Montalvo, P. D., & Sanchis, J. (2023). Mobile robotics in smart farming: Current trends and applications. Frontiers in Artificial Intelligence, 6, 1213330.
    Citations: 99

  2. Ponce, H. M. Y., & Yépez-Ponce, D. F. (2020). Control de modo deslizante para microrredes: Una revisión. Investigación Tecnológica IST Central Técnico, 2(1), 14–14.
    Citations: 5

  3. Yépez Ponce, D. F., & Montalvo López, W. M. (2021). Development of a hybrid optimization strategy based on a bacterial foraging algorithm (BFA) and a particle swarming algorithm (PSO) to tune the PID controller of a ball and plate system. In XV Multidisciplinary International Congress on Science and Technology (pp. 15–29).
    Citations: 3

  4. Yépez-Ponce, D. F., Montalvo, W., Guamán-Gavilanes, X. A., & Echeverría-Cadena, M. D. (2025). Route optimization for UGVs: A systematic analysis of applications, algorithms and challenges. Applied Sciences, 15(12), 6477.
    Citations: 2

  5. Yépez Ponce, H. M., Yépez Ponce, D. F., Proaño Lapuerta, E. A., Mosquera Bone, C. E., & Alarcón Angulo, M. L. (2022). Open-source platform for development of taximeters: Adjustment software. In International Conference on Applied Technologies (pp. 532–544).
    Citations: 1

 

Maedeh Azadi Moghadam | Artificial Intelligence | Best Researcher Award

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Dr. Maedeh Azadi Moghadam | Artificial Intelligence | Best Researcher Award

Biomedical Engineer | Semnan University | Iran

Dr. Maedeh Azadi Moghadam is an emerging researcher whose work advances the fields of biomedical engineering, neurotechnology, and human–machine interaction, with a particular focus on developing more reliable and human-centered brain–computer interface (BCI) systems. Her research interests span neural signal processing, SSVEP-based BCI optimization, cognitive fatigue detection, biomarker-based performance measurement, and the integration of physiological signals into more adaptive computational models. She is especially interested in understanding how fatigue and cognitive variability influence BCI accuracy, and her work aims to design intelligent systems capable of adjusting in real time to user states, ultimately improving usability for rehabilitation, assistive technologies, and next-generation neuroengineering applications. Dr. Moghadam’s research skills include biosignal analysis, EEG processing, feature extraction, algorithmic modeling, quantitative measurement techniques, and scientific writing, demonstrating her multidisciplinary strengths across engineering and neuroscience. According to Scopus, she has 3 indexed documents, 2 citations, and an h-index of 1, reflecting growing visibility and early academic impact in her domain. Although no formal awards or honors are listed for her in the available Scopus record, her contributions to innovative metrics—such as a continuous fatigue index for SSVEP-based BCI performance—highlight her potential for future recognition in neurotechnology and biomedical measurement science. Her publications demonstrate a commitment to improving the efficiency, accuracy, and adaptability of neuroengineering systems, particularly those intended for people with motor impairments or communication limitations. In conclusion, Dr. Maedeh Azadi Moghadam represents a promising researcher whose interdisciplinary work is helping shape the future of intelligent BCIs, cognitive state monitoring, and biomedical signal-driven technologies. Her expanding scientific contributions, combined with her advancing research skill set, position her for continued impact in the global scientific community and future leadership in neurotechnology innovation.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

Azadi Moghadam, M., & Maleki, A. (2023). Fatigue factors and fatigue indices in SSVEP-based brain–computer interfaces: A systematic review and meta-analysis. Frontiers in Human Neuroscience, 17, 1248474. Citations: 33

Maleki, A., & Azadimoghadam, M. (2022). Fatigue assessment using frequency features in SSVEP-based brain–computer interfaces. Iranian Journal of Biomedical Engineering, 16(3), 229–240.
Citations: 4

Moghadam, M. A., & Maleki, A. (2023). Fatigue detection in SSVEP-based BCIs using biomarkers: A comparative study. 2023 31st International Conference on Electrical Engineering (ICEE), 496–500. Citations: 2

Azadi Moghadam, M., & Maleki, A. (2024). Comparative study of frequency recognition techniques for steady-state visual evoked potentials according to the frequency harmonics and stimulus number. Journal of Biomedical Physics and Engineering. Citations: 1

Moghadam, M. A., & Maleki, A. (2025). A continuous fatigue index based on biomarkers for SSVEP-based brain–computer interfaces. Measurement, 118598.

The Dr. Maedeh Azadi moghadam’s research advances global innovation in neurotechnology by improving the accuracy, stability, and human-centered design of brain–computer interface systems through biomarker-driven fatigue detection and advanced signal analysis. By enhancing the reliability of assistive technologies and cognitive monitoring tools, the nominee’s work contributes meaningful benefits to science, healthcare, and industry, ultimately supporting more accessible, intelligent, and high-performing human–machine interaction solutions for society.

 

Farhan Nisar | Computer Science | Best Researcher Award

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Dr. Farhan Nisar | Computer Science | Best Researcher Award

Lecturer | The University of Agriculture | Pakistan

Dr. Farhan Nisar, affiliated with Qurtuba University of Science & Information Technology, Peshawar, Pakistan, is an emerging scholar and researcher in wireless communications, Internet of Things (IoT) networks, and machine learning applications for network optimization. He has made notable contributions to the field through his research on Low Power Wide Area Networks (LPWANs), particularly LoRaWAN, focusing on improving network efficiency, energy consumption, scalability, and reliability. Dr. Nisar’s educational background and professional trajectory have equipped him with a solid foundation in computer science and telecommunications, enabling him to apply advanced machine learning techniques for adaptive network parameter optimization, such as spreading factor adjustment, which enhances IoT network performance in dynamic real-world environments. Professionally, he has been involved in academic research, teaching, and applied projects that bridge theoretical knowledge with practical deployment of intelligent network solutions. His research interests include wireless communication protocols, IoT architectures, network security, data-driven network management, and intelligent device integration, reflecting a multidisciplinary approach that combines computer science, engineering, and data analytics. Dr. Nisar has developed strong research skills in machine learning modeling, algorithm development, network simulation, data analysis, and performance evaluation, contributing to both academic publications and open-access research outputs. His scholarly work has resulted in six published documents, with 18 citations to date and an h-index of 3, as indexed in Scopus, demonstrating early yet impactful contributions to his field. While still in the early stages of his career, he has received recognition for his innovative approaches to network optimization and IoT research, highlighting his potential for future academic and industrial leadership. In conclusion, Dr. Farhan Nisar represents a forward-looking researcher whose interdisciplinary expertise, rigorous methodology, and practical focus on intelligent, self-optimizing networks position him as a valuable contributor to the advancement of next-generation IoT and wireless communication technologies.

Profiles: Scopus

Featured Publications

  1. Nisar, F., & [Co-authors]. (2016). Green cloud computing approaches with respect to energy saving to data centers. Journal of Information, 6(2).

  2. Nisar, F., & [Co-authors]. (2017). Native approach security issue. In Proceedings of the IEEE Comtech Conference.

  3. Nisar, F., & [Co-authors]. (2019). Location-based authentication service in smartphones. In Proceedings of the IEEE Comtech Conference.

  4. Nisar, F., & [Co-authors]. (2019). Apply ARIMA model for data center with respect to different architecture. In Proceedings of the IEEE Raees Conference.

  5. Nisar, F., & [Co-authors]. (2019). Resource utilization in data center by applying ARIMA approach. In INTAP 2019.

 

 

Zhaoxia Duan | Robotics and Automation | Best Researcher Award

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Assoc. Prof. Dr. Zhaoxia Duan | Robotics and Automation | Best Researcher Award

Associate Professor | Hohai University | China

Assoc. Prof. Dr. Zhaoxia Duan is a distinguished researcher whose academic journey began with a B.Sc. in Automation (2007-2011) and a Ph.D. in Control Science and Engineering (2011-2017) from Nanjing University of Science and Technology, including a visiting research fellowship at Shibaura University of Technology in Japan (2013-2014). Currently a tenured associate professor at the School of Artificial Intelligence and Automation, Hohai University, she earlier served as a lecturer in the College of Energy and Electrical Engineering (2017-2023) and has also been a postdoctoral researcher at the School of Mathematics, Southeast University since 2019. Her research interests encompass multi-agent network systems, path planning for robots and autonomous underwater vehicles (AUVs), multi-dimensional / 2D system theory and applications, switching system theory, and positive system theory; she has developed strong research skills in stability analysis, finite-frequency control, fuzzy hybrid system modeling, fault detection observers, optimization via machine-learning (e.g. multi-objective PSO + Q-learning), and observer/controller synthesis for 2D positive / Markov jump / delayed / hybrid systems. Over her career, she has led multiple funded projects including national, postdoctoral, and central university grants, supervised research, and contributed to theoretical advances in control and automation. Her publication record is robust, with over 30+ journal articles, two authorized invention patents, and the compilation of a university textbook. She has been recognized with numerous awards and honors such as teaching excellence, thesis supervision awards, lecture competition prizes, and reviewer excellence. According to Scopus metrics, she has authored approximately 64 documents, has received around 710 citations, and has an h-index of 17. In conclusion, Dr. Duan is a well-rounded scholar who bridges rigorous theoretical research with practical intelligent automation applications, and continues to make impactful contributions to control theory, robotics and multi-agent systems.

Profile: Scopus 

Featured Publications

Duan, Z., Zhang, Y., Xu, Z., & Xiang, Z. (2025). Path planning problem in rough terrain by multi-objective PSO algorithm combined with Q-learning and crossover operator. Applied Soft Computing Journal, 184, 113798.

Duan, Z., Fu, Y., Wang, R., & Xiang, Z. (2025). l₁-gain control for delayed 2-D Markov jump positive systems in the Fornasini–Marchesini model. Journal of the Franklin Institute, 362(13), 107907.

Duan, Z., Zhang, Y., Wang, R., Xu, Z., & Xiang, Z. (2025). Robot path planning in rough terrain based on multi-objective crossover-mutation particle swarm optimization. Evolutionary Intelligence, 18, 64.

Duan, Z., Shao, Z., & Xiang, Z. (2025). A collaborative command and allocation model for manned and unmanned maritime forces. Journal of Army Engineering University of PLA, 3(4), 98–104. (In Chinese).

Duan, Z., Chu, C., Ghous, I., & Xiang, Z. (2024). On the l∞-gain of 2-D positive Roesser systems with bounded time-varying delays. Journal of the Franklin Institute, 361, 106819.