Ravinesh Chand | Robotics | Best Researcher Award

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Mr. Ravinesh Chand | Robotics | Best Researcher Award

Lecturer at Fiji National University, Fiji

Mr. Ravinesh Chand is a dedicated Lecturer in Mathematics with over 21 years of teaching experience in Fiji’s academic and secondary education sectors. He has taught students from diverse backgrounds and is highly skilled in delivering engaging lessons, fostering an interactive learning environment, and conducting research in applied mathematics. Currently pursuing a PhD in Mathematics at the University of the South Pacific (USP), his expertise spans digital signature schemes, mathematical problem-solving, and curriculum development. He has held leadership roles as Head of Department at multiple institutions, contributing to academic excellence. Passionate about student success, he emphasizes innovative teaching methods and collaborative learning. As a researcher, he is committed to generating new knowledge in mathematics. His contributions extend beyond teaching, as he actively mentors students and supports extracurricular activities like soccer coaching. His work is marked by precision, analytical thinking, and a commitment to excellence.

Publication Profile

Scopus

 Orcid

 Google Scholar

Education 🎓📚

Mr. Ravinesh Chand is pursuing a PhD in Mathematics at the University of the South Pacific (USP), focusing on advanced mathematical research. He holds a Master of Science in Mathematics from USP, where he completed a thesis on “Digital Signature Scheme Over Lattices” under the supervision of Dr. M.G.M. Khan and Dr. Maheswara Rao Valluri. He earned a Postgraduate Diploma in Applied Mathematics from USP in 2013, further strengthening his expertise in mathematical modeling and cryptographic systems. His academic foundation was laid with a Bachelor of Education in Mathematics from USP in 2001, equipping him with strong pedagogical skills and mathematical proficiency. Throughout his educational journey, he has demonstrated a commitment to academic excellence, research, and innovative problem-solving. His education has been instrumental in shaping his career as a lecturer and researcher, allowing him to contribute meaningfully to the field of applied mathematics and mathematical education.

Experience 📖👨‍🏫

Mr. Ravinesh Chand has served as a Lecturer in Mathematics at Fiji National University since 2015. Previously, he held leadership roles as Head of the Mathematics and Physics Department at Dudley High School (2014-2015), Suva Grammar School (2013-2014), and Assemblies of God High School (2007-2013). His teaching career began at Waidina Secondary School (2002-2006) as an Acting Head of Department and Mathematics & Physics teacher. Throughout his career, he has led curriculum planning, student engagement, and performance evaluations while mentoring both students and teachers. As a leader, he ensured high-quality teaching standards, facilitated staff development, and implemented intervention strategies for student success. He has also promoted extracurricular activities, including coaching school soccer teams. His extensive experience in academia and secondary education showcases his expertise in mathematical instruction, research, and academic leadership, significantly impacting student learning and institutional development.

Awards and Honors 🏆🎖️

Mr. Ravinesh Chand has been recognized for his outstanding contributions to mathematics education and research. His commitment to academic excellence earned him accolades from Fiji’s leading educational institutions. As Head of Department, he received multiple commendations for his leadership in improving student performance in mathematics and physics. His research on digital signature schemes and applied mathematics has been acknowledged in academic circles, leading to invitations to present his work at conferences. His dedication to mentorship and student development has also been honored through appreciation awards from schools and tertiary institutions. In addition, his extracurricular involvement as a soccer coach has been recognized for fostering teamwork and discipline among students. His excellence in curriculum development, innovative teaching methods, and mathematical research continues to be celebrated, reinforcing his reputation as a distinguished educator and scholar in Fiji’s academic community.

Research Focus 🔬📊

Mr. Ravinesh Chand’s research primarily focuses on applied mathematics, digital signature schemes, cryptography, and mathematical modeling. His Master’s thesis explored “Digital Signature Scheme Over Lattices,” contributing to secure cryptographic protocols. His work involves analyzing the efficiency of mathematical structures in cybersecurity, aiming to enhance encryption techniques. His doctoral research further delves into computational mathematics and algorithmic problem-solving. His academic interests extend to mathematical pedagogy, exploring innovative methods to enhance student learning. His research integrates theoretical mathematics with practical applications, addressing real-world challenges in data security and computational modeling. Through independent studies and collaborative projects, he seeks to advance the understanding of mathematical frameworks in digital security. His contributions to applied mathematics continue to shape the academic discourse on cryptographic systems, making a lasting impact on both research and education in mathematics.

Publication Top Notes

  • Linear manipulator: Motion control of an n-link robotic arm mounted on a mobile slider – 📖 Heliyon 9 (1), Cited by: 17, Year: 2023
  • Switch controllers of an n-link revolute manipulator with a prismatic end-effector for landmark navigation – 🤖 PeerJ Computer Science 8, e885, Cited by: 16, Year: 2022
  • Navigation of an n-link revolute robotic arm via hierarchal landmarks – 🗺️ NILES Conference, Cited by: 10, Year: 2021
  • Lyapunov-based controllers of an n-link prismatic robot arm – 🏗️ IEEE Asia-Pacific Conference, Cited by: 9, Year: 2021
  • A car-like mobile manipulator with an n-link prismatic arm – 🚗 IEEE Asia-Pacific Conference, Cited by: 9, Year: 2021
  • Embedded FPGA-based motion planning and control of a dual-arm car-like robot – 🔧 IEEE SPEC Conference, Cited by: 8, Year: 2022
  • LbCS navigation controllers of twining Lagrangian swarm individuals – 🐝 NILES Conference, Cited by: 8, Year: 2021
  • Digital signature scheme over lattices – 🔐 IEEE Circuits Conference, Cited by: 7, Year: 2021
  • Acceleration Feedback Controller Processor Design of a Segway – 🛴 IEEE SPEC Conference, Cited by: 5, Year: 2022
  • Vertically Sliding Revolute Robotic Arm intended for automated Pick-and-Place Industrial applications – 🏭 IEEE Asia-Pacific Conference, Cited by: 3, Year: 2022

Giuseppe Silano | Robotics | Best Researcher Award

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Dr. Giuseppe Silano | Robotics | Best Researcher Award

Dr. Giuseppe Silano, University of Washington, United States

Dr. Giuseppe Silano, a robotics and control expert, earned his Ph.D. in Information Technologies for Engineering from the University of Sannio, Italy, with a focus on path planning, software-in-the-loop, and unmanned aerial vehicles (UAVs). He collaborated internationally as a visiting Ph.D. student at CNRS, France, and is currently a Tenure Researcher at RSE S.p.A., Milan, Italy, and an Associate Researcher at Czech Technical University. Dr. Silano’s work spans motion planning, human-robot collaboration, and multi-robot systems. An open-source contributor, he develops cutting-edge robotics solutions and publishes widely. He is also a licensed drone pilot and an active IEEE member. ✈️📡📘

 

Publication Profile

Google Scholar

Education and Academic Journey 🎓🤖

Dr. Giuseppe Silano has a distinguished academic background in engineering and robotics. He earned his Ph.D. in Information Technologies for Engineering from the University of Sannio, Italy, as a Doctor Europaeus, focusing on robotics, control, path planning, and software-in-the-loop, under the guidance of Prof. Dr. Luigi Iannelli. He enhanced his expertise as a visiting Ph.D. student at CNRS, France, researching 6DoF robots with onboard sensors, supervised by Prof. Dr. Antonio Franchi. Dr. Silano also holds an M.Sc. in Electronic Engineering (2016) and a B.Sc. in Computer Engineering (2012) from the University of Sannio, specializing in robotics and control systems. 🛠️📡

 

Professional Affiliation 🌐🤖

Dr. Giuseppe Silano has been an active member of the IEEE (Institute of Electrical and Electronics Engineers) since December 2016. Starting as a Student Member (ST’17) and advancing to Member (M’21), he is associated with the IEEE Control Systems Society (CSS) and the IEEE Robotics and Automation Society (RAS). His involvement in these professional bodies underscores his commitment to advancing research and collaboration in robotics, automation, and control systems. Dr. Silano’s affiliation with IEEE highlights his dedication to staying at the forefront of technological innovation and contributing to the global engineering community. 📡📘

 

Professional Experience 💼👨‍💻

Dr. Giuseppe Silano has amassed a decade of experience across various technical roles. From 2014 to 2024, he worked as a Technical Writer for leading Italian platforms, including Win Magazine and EOS Book. In 2016, as a Junior Software Engineer at Software Engine S.r.l., he specialized in front-end web development, database management, and debugging, completing key projects like a document management system for Mirabella Eclano, Italy. Earlier, in 2012, as a Control System Integrator at Mosaico Monitoraggio Integrato S.r.l., he designed industrial automation systems, including soda autoclave storage and turbine blade leaching processes, adhering to safety requirements. 📜⚙️

 

Research Activities 🤖📚

Dr. Giuseppe Silano’s research spans robotics, control, and UAV systems. He developed motion-planning algorithms for multi-robot systems in civilian infrastructure inspections, emphasizing obstacle avoidance and UAV constraints within the Aerial-Core project. His work on communication-aware robotics enhances robust wireless connectivity for UAVs in challenging environments. Dr. Silano advanced Model Predictive Control (MPC) strategies for collision avoidance and target tracking, and decentralized swarm navigation in UAVs. His studies in autonomous vehicles include MPC-based control for small-scale racing cars. Additionally, he explored human-aerial robot interaction to assist humans in critical tasks while prioritizing safety and ergonomics, contributing extensively to UAV software and simulators. 🚁💻

 

Teaching and Mentorship Experience 🎓📚

Dr. Giuseppe Silano has an extensive teaching background, including leading PhD courses such as “Fundamentals for Robot Programming with ROS” (University of Sannio, 2024). He served as a Teaching Assistant for courses like “Discrete Systems,” “Automatic Control,” and “Advanced Controls” in Computer and Electronics Engineering programs. As a Subject Matter Expert, he contributed to topics like “Sistemi Discreti” and “Controlli Automatici.” Dr. Silano co-supervised innovative research projects under MIT programs and guided numerous Bachelor’s and Master’s theses on UAVs, control systems, and robotics. His mentorship showcases his dedication to fostering technical and academic excellence. ✈️🤖

 

Awards and Achievements 🏆🤖

Dr. Giuseppe Silano has been recognized in prestigious international robotics competitions. He was part of the UNISANNIO team that won the “MathWorks Minidrone Competition” at IFAC 2020 in Berlin, Germany. Additionally, he contributed to the LAAS team, finalists in the “Mohamed Bin Zayed International Robotics Challenge (MBZIRC)” held in Abu Dhabi, UAE. Dr. Silano also showcased his expertise as a finalist in the “Aerial Robotics Control and Perception Challenge” during the 26th Mediterranean Conference on Control and Automation in Zagreb, Croatia. His accolades highlight his excellence in robotics and control systems. 🌍✈️

 

Research Focus

Dr. Giuseppe Silano specializes in robotics, with a focus on unmanned aerial vehicles (UAVs) for precision agriculture, power line inspections, and multi-robot systems. His work integrates advanced path-planning algorithms, software-in-the-loop platforms, and signal temporal logic for mission planning. Key areas include collision avoidance, perception-aware navigation, and real-world deployment of aerial robotics. Dr. Silano’s contributions extend to drone swarm coordination, non-linear model predictive control, and autonomous target tracking. His research advances UAV applications in environmental monitoring, communication-aware robotics, and physical security optimization, positioning him at the forefront of aerial robotics innovation. 🌱⚡🚁

 

Publication Top Notes

  • 🌾 “A review on the use of drones for precision agriculture” – Cited by: 212 – Year: 2019
  • 🚁 “A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture” – Cited by: 66 – Year: 2022
  • ⚡ “Power line inspection tasks with multi-aerial robot systems via signal temporal logic specifications” – Cited by: 60 – Year: 2021
  • 🛠️ “CrazyS: a software-in-the-loop platform for the Crazyflie 2.0 nano-quadcopter” – Cited by: 50 – Year: 2018
  • 🚀 “MRS Modular UAV Hardware Platforms for Supporting Research in Real-World Outdoor and Indoor Environments” – Cited by: 42 – Year: 2022
  • ✈️ “Software-in-the-loop simulation for improving flight control system design: a quadrotor case study” – Cited by: 37 – Year: 2019
  • 🤖 “MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems” – Cited by: 33 – Year: 2023
  • 🔧 “CrazyS: A Software-in-the-Loop Simulation Platform for the Crazyflie 2.0 Nano-Quadcopter” – Cited by: 29 – Year: 2019
  • 🔌 “A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot Systems in Power Line Inspection Tasks” – Cited by: 18 – Year: 2021
  • 📋 “Mission Planning and Execution in Heterogeneous Teams of Aerial Robots supporting Power Line Inspection Operations” – Cited by: 17 – Year: 2022

 

 

 

 

 

 

 

 

Shijie Wang | Robotics | Best Researcher Award

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Dr. Shijie Wang | Robotics | Best Researcher Award

Dr. Shijie Wang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, China

Dr. Shijie Wang is a PhD Candidate at Hebei University of Technology and a joint researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. With a background in engineering (B.Eng, M.Eng), Dr. Wang’s research focuses on robotics and mechanical systems. He has authored impactful publications in Applied Mathematical Modelling and contributed to several patents on construction robotics and high-load manipulators. Dr. Wang has won multiple awards, including the China “Challenge Cup” and 3D Digital Innovative Design Competition. His work on construction robotics has earned substantial funding, highlighting his innovative contributions. 🤖🔧📚📑

 

Publication Profile

Scopus

Orcid

Academic & Professional Qualifications

Dr. Shijie Wang is a PhD candidate at Hebei University of Technology and a joint training researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He holds a B.Eng and M.Eng from Hebei University of Technology. His academic journey has been complemented by professional experience, including his role as a research assistant at the Shenzhen Institutes and as a chief process engineer at Beijing Jingdiao Group. Dr. Wang has also worked as an R&D engineer at Shinetek Instruments Research Institute. His diverse expertise contributes significantly to advancements in engineering and robotics. 🎓🔬🤖

 

Representative Works and Awards

Dr. Shijie Wang has made significant contributions to the fields of robotics and mechanical engineering. His recent publication, Unified Recursive Kinematics and Statics Modeling (2024) in Applied Mathematical Modelling (IF 4.4), presents innovative work on high-load manipulators. Other notable publications include work on flapping-wing micro air vehicles (2023) and kinematic analysis of parallel manipulators (2022). He holds several patents, including inventions in construction robots and hydraulic manipulators. Dr. Wang has received prestigious awards, including the second prize in the 2015 China “Challenge Cup” and first prize in the 2014 3D Digital Innovative Design Competition. 🏆📚🤖

 

Research Focus

Dr. Shijie Wang’s research focuses on robotics, automation, and advanced manufacturing technologies. He explores kinematics and statics modeling of manipulators, with applications in redundantly actuated systems and functionally graded materials (FGMs). His work includes the optimization of robotic systems for construction and manufacturing, path planning strategies for 3D printing, and the modeling of dynamic mixing processes for materials fabrication. Dr. Wang is also deeply involved in machine learning applications in design and fabrication. His research has significant implications in construction robotics, material science, and robotic systems design. 🤖🔧📐📊

 

Publication Top Notes

  • Unified recursive kinematics and statics modeling of a redundantly actuated series-parallel manipulator with high load/mass ratio (2024) 🛠️
  • Process parameter modeling for the fabrication of functionally graded materials via direct ink writing (2024) 🖨️
  • Optimization of Pin Type Single Screw Mixer for Fabrication of Functionally Graded Materials (2024) 🔧
  • Numerical Simulation of a Dynamic Mixing Process of Ceramic-Grade Materials for Extruded 3D Printing (2023) 🏗️
  • Path planning strategy of functionally graded materials printed by material extrusion process (2023) 🌐
  • A Review: Applications of Machine Learning in Design-Fabrication of Functionally Graded Materials (2023) 🤖
  • Attitude Control of Flapping-Wing Micro Air Vehicles Based on Hyperbolic Tangent Function Sliding Mode Control (2023) ✈️
  • Digital prediction method for delay information for preparing FGMs parts by direct write forming (2023) ⏱️
  • Functionally graded materials model is constructed by B-spline surface and point gradient source (2022) 🧱
  • Kinematic Performance Analysis of Spatial 2-DOF Redundantly Actuated Parallel Manipulator (2022) 🔄

K ASHWINI | Computer Science | Best Researcher Award

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K ASHWINI | Computer Science | Best Researcher Award

K ASHWINI, National Institute of Technology Rourkela, India

K. Ashwini is a dedicated Ph.D. candidate in Computer Science and Engineering at NIT Rourkela, specializing in deep learning applications for grading diabetic retinopathy. She holds an M.Tech. from VSSUT Burla and a B.Tech. from Synergy Institute of Engineering & Technology, Dhenkanal. Her research includes notable publications, such as her work on CNN-based diabetic retinopathy grading in Biomedical Signal Processing and Control. Skilled in Python, MATLAB, and LaTeX, she has actively participated in workshops on machine learning and signal processing. Ashwini is fluent in Hindi, Telugu, and English.

Publication profile

google scholar

Academic Background

Ms. K. Ashwini is a Research Scholar in Computer Science and Engineering (CSE) at NIT Rourkela, currently pursuing her Ph.D., with her research focused on diabetic retinopathy grading using deep learning techniques. Her advanced studies in deep learning, combined with an M.Tech. in CSE from VSSUT Burla, highlight her dedication to exploring complex topics within biomedical and computational research. She has maintained a strong academic record throughout her studies, underscoring her commitment and expertise in her field.

Research Focus and Publications

Ashwini’s primary research area is in biomedical signal processing, specifically targeting diabetic retinopathy grading using CNNs and soft attention mechanisms. She has contributed a journal article to Biomedical Signal Processing and Control and presented multiple conference papers at reputable IEEE and Springer conferences, indicating her active participation in disseminating her research findings. Notably, her publications demonstrate her capacity to employ and innovate with advanced computational methods for impactful health-related applications, a relevant focus for this award.

Technical Skills and Training

Her technical skill set, including Python, MATLAB, and LaTeX, complements her research competencies. Ashwini’s training in SQL and experience with clustering and fraud detection in mobile networks contribute to a robust and versatile research portfolio. Her academic research skills and fluency in programming languages further solidify her qualifications as a proficient researcher in her domain.

Workshops and Professional Development

Ms. Ashwini has participated in several workshops and short-term training programs across India, including those focused on biomedical signal processing, machine learning, and image processing applications. Her engagement in diverse professional development initiatives, such as faculty development programs and national seminars, showcases her continuous effort to enhance her knowledge base and technical skills.

Publication top notes

Grading diabetic retinopathy using multiresolution based CNN

Soft attention with convolutional neural network for grading diabetic retinopathy

Application of Generalized Possibilistic Fuzzy C-Means Clustering for User Profiling in Mobile Networks

Improving Diabetic Retinopathy grading using Feature Fusion for limited data samples

An intelligent ransomware attack detection and classification using dual vision transformer with Mantis Search Split Attention Network

Check for updates Modified Inception V3 Using Soft Attention for the Grading of Diabetic Retinopathy

Modified InceptionV3 Using Soft Attention for the Grading of Diabetic Retinopathy

Grading of Diabetic Retinopathy using iterative Attentional Feature Fusion (iAFF)

Conclusion

Ms. K. Ashwini exemplifies a suitable candidate for the Research for Best Researcher Award. Her specialized research in diabetic retinopathy grading, supported by a solid academic and technical background, positions her as a promising researcher. Her publications and active participation in workshops further validate her dedication and contributions to biomedical signal processing and computer vision applications, aligning well with the award’s criteria for excellence in research and innovation.

Inam Ullah | Robotics Award | Young Scientist Award

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Prof Dr. Inam Ullah | Robotics Award | Young Scientist Award

Prof Dr. Inam Ullah, Shenzhen University, China

Based on the details provided, the candidate seems well-suited for the Research for Young Scientist Award. Here’s an evaluation of their profile, formatted with headings and conclusions for each section:

Publication profile

Professional Experience

The candidate has a robust professional background in research and academia. Their current role as an Assistant Professor at Gachon University, combined with recent postdoctoral research and consulting experience, showcases a strong blend of teaching, research, and industry application. This varied experience aligns well with the criteria for a Young Scientist Award, highlighting their capability in contributing to cutting-edge research and practical solutions.

Education

With a PhD in Information & Communication Engineering from Hohai University, the candidate has demonstrated excellence in their academic pursuits. Their research on mobile robot localization and underwater localization algorithms reflects a high level of expertise in advanced technological areas relevant to the award.

Research Interests

The candidate’s diverse research interests, including IoT, robotics, and AI, align with current trends in science and technology. Their focus on cutting-edge areas such as autonomous vehicles, network security, and machine learning showcases their commitment to advancing knowledge in these fields.

Citations and Impact

With an impressive cumulative impact factor of 268.20 and substantial Google Scholar citations, the candidate’s research output has significantly influenced their field. Their h-index of 28 and i10-index of 51 further attest to the high quality and impact of their work.

Awards, Funding, and Honors

The candidate has received multiple prestigious awards, including the Top-10 Outstanding Students Award and the Jiangsu Province Distinguish International Students Award. These recognitions, along with their consistent academic performance, highlight their exceptional achievements.

Other Experience and Projects

The candidate’s experience in supervising projects, teaching, and developing instructional methodologies showcases their commitment to education and research. Their involvement in student supervision and research activities further highlights their capability to contribute to both academic and practical advancements.

Publication Top Notes

  • A Review of Underwater Localization Techniques, Algorithms, and Challenges – S Xin, U Inam, L Xiaofeng, C Dongmin, Journal of Sensors 2020, 24 – Cited by 162 📘 (2020)
  • A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms – I Ullah, Y Shen, X Su, C Esposito, C Choi, IEEE Access 8, 2233-2246 – Cited by 147 🔍 (2019)
  • Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces – MT Sadiq, X Yu, Z Yuan, F Zeming, AU Rehman, I Ullah, G Li, G Xiao, IEEE Access 7, 171431-171451 – Cited by 145 🧠 (2019)
  • Localization and Detection of Targets in Underwater Wireless Sensor Using Distance and Angle Based Algorithms – I Ullah, J Chen, X Su, C Esposito, C Choi, IEEE Access 7, 45693-45704 – Cited by 132 🌊 (2019)
  • Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions – T Ahsan B, M Y-K, K M K. A, M F, J AR, U Inam, K Rahim, Computational and Mathematical Methods in Medicine 2021, 28 – Cited by 99 📈 (2021)
  • Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network – BK Yousafzai, SA Khan, T Rahman, I Khan, I Ullah, A Ur Rehman, M Baz, Sustainability 13 (17), 9775 – Cited by 86 🎓 (2021)
  • A Multi-Layer Cluster Based Energy Efficient Routing Scheme for UWSNs – W Khan, H Wang, MS Anwar, M Ayaz, S Ahmad, I Ullah, IEEE Access 7, 77398-77410 – Cited by 79 🔋 (2019)
  • Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review – M Tehseen, I Hafiz Muhammad, H Inayatul, U Inam, A Madiha, Electronics 12 (1), 26 – Cited by 74 💡 (2023)
  • Efficient and Accurate Target Localization in Underwater Environment – I Ullah, Y Liu, X Su, P Kim, IEEE Access 7, 101415-101426 – Cited by 72 🛰️ (2019)
  • Analysis of Cyber Security Attacks and Its Solutions for the Smart Grid Using Machine Learning and Blockchain Methods – M Tehseen, I Hafiz Muhammad, K Sunawar, H Inayatul, U Inam, Future Internet 15 (83), 1-38 – Cited by 67 🔐 (2023)

Conclusion

Overall, the candidate’s comprehensive experience, strong educational background, significant research contributions, and recognition in their field make them a highly suitable candidate for the Research for Young Scientist Award.