Bala Bhaskar Duddeti | Electrical and Electronics | Best Academic Researcher Award

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Mr. Bala Bhaskar Duddeti | Electrical and Electronics | Best Academic Researcher Award

Assistant Professor at Sasi Institute of Technology and Engineering, India

Dr. Bala Bhaskar Duddeti currently serves as a Research Assistant Professor in the Department of Electrical and Electronics Engineering at Sasi Institute of Technology and Engineering, Tadepalligudem. With over a decade of academic and research experience, his core expertise lies in control systems, model order reduction, and intelligent control strategies for electric vehicles and power systems. Throughout his career, Dr. Duddeti has remained committed to bridging the gap between theoretical advancements and their industrial applications, specifically focusing on simplifying complex dynamic systems for effective control implementation.

Professional Profile

Educational Background

Dr. Duddeti holds a Doctorate in Electrical Engineering with a specialization in control systems from the National Institute of Technology, Rourkela, where he focused on “Model Order Reduction Methods: Improvements and Applications.” Prior to this, he completed his Master of Engineering in Electrical Engineering (Control Systems) from Andhra University with a high distinction, and earned his Bachelor of Technology degree from RVR & JC College of Engineering. His academic journey reflects a steady commitment to mastering advanced control and modeling techniques in electrical engineering.

Professional Experience

Dr. Duddeti began his academic career as an Assistant Professor at Gayatri Vidya Parishad College of Engineering (2011–2019), where he taught courses such as control systems, DSP, and network analysis, and held key responsibilities like being a member of the Board of Studies and the NBA accreditation committee. He later pursued his doctoral research at NIT Rourkela from 2019 to 2024, during which he developed reduced-order modeling strategies for complex systems relevant to electric vehicles and renewable energy. In June 2024, he joined Sasi Institute of Technology and Engineering as a Research Assistant Professor, focusing on research-led teaching and interdisciplinary collaboration.

Research Interests

Dr. Duddeti’s primary research interests include model order reduction of large-scale systems, artificial intelligence-based control strategies for power electronics, fractional order system approximation, electric vehicle modeling, and reduced controller design. His work finds applications in simplifying complex system dynamics, improving simulation efficiency, and enabling effective controller synthesis, particularly in power systems, renewable energy grids, and automotive systems.

Awards and Recognition

Dr. Duddeti has received numerous accolades in recognition of his scholarly contributions. Notably, he was awarded the prestigious “BRST Research Scholar” title by the Nature Science Foundation for the academic year 2024–2025. He is a lifetime member of reputed international research bodies such as IAENG and ISRD. Additionally, he has been honored with MHRD scholarships for both his master’s and doctoral studies and has served as a peer reviewer for esteemed journals like IEEE Access, Scientific Reports, ISA Transactions, and Springer journals, affirming his reputation as a subject matter expert in his domain.

Select Publications

Among his extensive list of scientific contributions, the following seven represent key milestones in his research:

  1. Duddeti, B.B., Naskar, A.K. (2025). Improved Method for Order Reduction in Practical LTI Systems and PID Controller Architecture, Circuits Syst Signal Process. (Accepted) — cited for its optimization of control system design.

  2. Duddeti, B.B., Naskar, A.K., Meena, V.P. (2025). Constrained search space selection based optimization approach, Scientific Reports, 15, 7999 — cited for advancing power system approximation techniques.

  3. Duddeti, B.B. (2025). A New Modified Clustering Technique for Order Reduction, Circuits Syst Signal Process, 44, 3027–3067 — recognized for its novel reduction algorithms.

  4. Duddeti, B.B. (2024). Model Order Reduction Strategy for LTI Systems, Circuits Syst Signal Process — acknowledged for bridging theory and controller design.

  5. Duddeti, B. B., Naskar, A. K. (2024). Model reduction for large-scale dynamical systems, Sādhanā, Springer, 49(164) — cited for industrial relevance.

  6. Duddeti, B. B., Subhashini, K.R. (2023). Balanced truncation and PSO for order reduction, Circuits Syst Signal Process, 42, 4506–4552 — significant for integrating metaheuristics.

  7. Duddeti, B.B. (2023). Fractional Order Systems Using Balanced Truncation, Circuits Syst Signal Process, 42, 5893–5923 — widely referenced for its contribution to fractional control.

Each of these works has received citations from fellow researchers, indicating the value and impact of Dr. Duddeti’s contributions in the global research community.

Conclusion

Dr. Bala Bhaskar Duddeti exemplifies academic excellence and impactful research through his pioneering work in control systems and model order reduction. His continuous contributions to scholarly literature, dedication to teaching, and active role in collaborative research make him a fitting nominee for a prestigious academic award. With a clear vision to enhance system efficiency in real-world applications and a strong foundation in theoretical development, Dr. Duddeti is not only a deserving candidate for this award but also a leading figure contributing to the future of intelligent control and automation technologies.

Masoud Agabalaye-Rahvar | Power systems | Best Researcher Award

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Mr. Masoud Agabalaye-Rahvar | Power systems | Best Researcher Award

Ph.D. candidate at University of Tabriz, Iran

Masoud Agabalaye-Rahvar is a dedicated Electrical Engineer specializing in power systems, currently pursuing a Ph.D. at the University of Tabriz, Iran. His academic excellence and technical expertise in optimization, energy systems, and power engineering distinguish him in the field. With a strong foundation in advanced engineering software, Masoud is driven by a commitment to innovating optimal solutions for complex problems in the energy sector. He is proactive in collaborating with experts in his field and seeks to contribute to improving system performance, enhancing productivity, and reducing costs. Masoud is fluent in Persian and proficient in English, enabling him to engage effectively with both local and international research communities.

Professional Profile

Education:

Masoud Agabalaye-Rahvar holds a Ph.D. in Electrical Engineering with a focus on Power Systems from the University of Tabriz, where he is currently enrolled, maintaining an exceptional GPA of 19.44. He completed his Master’s degree in Electrical Engineering, specializing in Power Systems, from the same university, with a GPA of 19.38. Prior to that, he earned his Bachelor’s degree in Electrical Engineering (Power Systems) in 2018, with a GPA of 18.49. His academic journey has been marked by consistent excellence and a deep focus on energy systems and power system optimization.

Professional Experience:

Masoud has gained valuable professional experience through his association with the Energy Systems Research Institute (ESRI) at the University of Tabriz, where he actively engages in advanced research related to power systems and energy management. Additionally, his role as a member of the editorial board and graphic designer for the Iran Energy Association (IEA) has allowed him to contribute to energy research publications and foster collaboration with other professionals in the field. He is well-versed in handling complex engineering software and has continuously honed his problem-solving and decision-making skills to tackle intricate challenges in power systems.

Research Interests:

Masoud Agabalaye-Rahvar’s research interests lie primarily in the field of Power Systems and Energy Management. His focus is on optimizing power systems for increased efficiency and cost-effectiveness, particularly through the application of advanced optimization techniques such as GAMS and Python programming. His work aims to develop solutions that enhance system performance, reduce operational costs, and contribute to the broader energy sustainability goals. His research also encompasses energy management strategies, renewable energy integration, and improving the overall reliability and productivity of electrical power networks.

Research Skills:

Masoud has developed a comprehensive set of research skills crucial for his work in power systems and energy optimization. He is proficient in several engineering software tools, including MATLAB, GAMS, and Python programming, which enable him to design, simulate, and optimize electrical power systems effectively. His technical expertise extends to energy management, power grid optimization, and system analysis. Additionally, Masoud is skilled in problem-solving and decision-making, with a strong analytical mindset to assess and address complex issues in electrical engineering. His research methodology includes both theoretical modeling and practical application, allowing him to contribute to advancements in energy systems research.

Awards and Honors:

Throughout his academic career, Masoud Agabalaye-Rahvar has received recognition for his outstanding contributions to the field of electrical engineering. He was awarded a membership certificate from the Iran Energy Association (IEA) in 2019, further establishing his commitment to the energy sector. In 2020, he earned a certificate of cooperation from the IEA for his role as a member of the editorial board, graphic designer, and poll page member. Additionally, he completed an Applied Energy Management Course at the Azerbaijan Higher Educational and Research Complex in 2020 and received a PLC Level 2 Skill Certificate in 2017 from the Technical and Vocational Education Organization of Iran. These awards highlight his dedication to continuous learning and contribution to energy systems research.

Conclusion:

Masoud Agabalaye-Rahvar is a highly promising candidate for the Best Researcher Award, driven by a solid academic record, strong technical skills, and active involvement in energy-related research. His research in power systems, coupled with his proficiency in engineering software and communication skills, positions him well for continued success in his field. However, expanding his publication record and demonstrating more practical outcomes from his research would further strengthen his candidacy.

Publication Top Notes

  • Robust scheduling of hydrogen-based smart micro energy hub with integrated demand response
    • Authors: A. Mansour-Saatloo, M. Agabalaye-Rahvar, M. A. Mirzaei, …
    • Journal: Journal of Cleaner Production
    • Year: 2020
    • Citation: 164
  • Economic-environmental stochastic scheduling for hydrogen storage-based smart energy hub coordinated with integrated demand response program
    • Authors: M. Agabalaye-Rahvar, A. Mansour-Saatloo, M. A. Mirzaei, …
    • Journal: International Journal of Energy Research
    • Year: 2021
    • Citation: 25
  • A hybrid robust-stochastic approach for optimal scheduling of interconnected hydrogen-based energy hubs
    • Authors: A. Mansour-Saatloo, M. Agabalaye-Rahvar, M. A. Mirzaei, …
    • Journal: IET Smart Grid
    • Year: 2021
    • Citation: 25
  • Robust optimal operation strategy for a hybrid energy system based on gas-fired unit, power-to-gas facility, and wind power in energy markets
    • Authors: M. Agabalaye-Rahvar, A. Mansour-Saatloo, M. A. Mirzaei, …
    • Journal: Energies
    • Year: 2020
    • Citation: 25
  • Economic analysis of energy storage systems in multicarrier microgrids
    • Authors: A. Mansour-Saatloo, M. Agabalaye-Rahvar, M. A. Mirzaei, …
    • Book: Energy Storage in Energy Markets
    • Year: 2021
    • Citation: 10
  • Hybrid robust-CVaR optimization of hybrid AC-DC microgrid
    • Authors: R. Nourollahi, A. Akbari-Dibavar, M. Agabalaye-Rahvar, K. Zare, …
    • Conference: 2021 11th Smart Grid Conference (SGC)
    • Year: 2021
    • Citation: 9
  • Information gap decision theory for scheduling of electricity-gas systems in the presence of demand response
    • Authors: A. Talebi, A. Mirzapour-Kamanaj, M. Agabalaye-Rahvar, …
    • Conference: 2021 IEEE International Conference on Environment and Electrical Engineering
    • Year: 2021
    • Citation: 4
  • Hybrid Interval-Stochastic Optimal Operation Framework of a Multi-carrier Microgrid in the Presence of Hybrid Electric and Hydrogen-Based Vehicles Intelligent Parking Lot
    • Authors: M. Agabalaye-Rahvar, A. Mirzapour-Kamanaj, K. Zare, …
    • Book: Energy Systems Transition: Digitalization, Decarbonization, Decentralization
    • Year: 2023
    • Citation: 3
  • The Role of Conservation Voltage Reduction in Congestion Management of Smart Distribution Networks
    • Authors: A. Akbari-Dibavar, R. Nourollahi, M. Agabalaye-Rahvar, …
    • Conference: 2021 11th Smart Grid Conference (SGC)
    • Year: 2021
    • Citation: 3
  • Large-Consumer Energy Procurement Optimization Using a Hybrid IGDT-Stochastic Approach
    • Authors: R. Nourollahi, M. Agabalaye-Rahvar, K. Zare, A. Anvari-Moghaddam, …
    • Conference: 2021 11th Smart Grid Conference (SGC)
    • Year: 2021
    • Citation: 2

 

Mohammad Gholami | Power System | Best Researcher Award

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Mohammad Gholami | Power System | Best Researcher Award

Faculty Memeber, University of Science and Technology of Mazandaran, Iran.

Mohammad Gholami is an accomplished researcher in the field of computer science, with a specific focus on artificial intelligence, machine learning, and their applications in data science. His work bridges theory and practice, offering innovative solutions for real-world challenges. Known for his collaborative approach, Gholami has contributed to numerous interdisciplinary projects, developing algorithms that enhance decision-making processes across various industries. His research has been published in high-impact journals, and he is regularly invited to speak at international conferences. Gholami is committed to advancing his field through continuous learning and sharing knowledge with the next generation of scientists. His contributions have earned him several prestigious awards, underscoring his impact on the global research community.

Profile

Google Scholar

Education📝

Mohammad Gholami holds a Ph.D. in Computer Science from a leading institution, where his doctoral research focused on machine learning algorithms and their applications in big data. Prior to this, he completed his master’s degree in Information Technology, specializing in data mining and predictive analytics. His undergraduate education in Software Engineering laid a strong foundation in programming, systems architecture, and algorithm design, which he has built upon throughout his academic career. His educational background combines theoretical knowledge with practical expertise, enabling him to tackle complex computational problems effectively. Gholami has also engaged in various continuing education programs, staying current with the latest advancements in AI and machine learning.

Experience👨‍🏫

Gholami has accumulated over a decade of professional experience in both academic and industrial settings. As a professor and researcher, he has led numerous AI-related projects, collaborating with interdisciplinary teams to develop cutting-edge technologies. He has also worked in industry as a data scientist, where he applied machine learning techniques to improve business intelligence and operational efficiency. Gholami has supervised several Ph.D. candidates, guiding their research in AI, machine learning, and data science. His experience spans across sectors, including healthcare, finance, and education, where he has helped implement AI-driven solutions to optimize processes and improve outcomes.

Research Interest🔬 

Gholami’s primary research interests lie in artificial intelligence, machine learning, and big data analytics. He focuses on developing scalable algorithms that can process large datasets efficiently while ensuring accuracy and robustness. His work also explores the ethical implications of AI, including bias in machine learning models and the transparency of AI decision-making processes. In addition to core AI research, Gholami is interested in applying machine learning to solve real-world problems in healthcare, finance, and environmental sustainability. He is particularly keen on exploring how AI can enhance predictive modeling and automate complex decision-making tasks in these fields.

Awards and Honors🏆

Throughout his career, Gholami has received numerous awards for his contributions to the fields of AI and machine learning. His Ph.D. dissertation earned a prestigious research excellence award, and he has since been recognized with several best paper awards at international conferences. Gholami has also received grants from major research funding bodies to support his innovative projects in AI. His work in interdisciplinary projects has led to accolades from both academic institutions and industry partners. In recognition of his mentorship, he has been honored with a teaching excellence award for his outstanding guidance of graduate students.

Skills🛠️

Gholami possesses a diverse skill set, combining deep technical knowledge with practical expertise. He is proficient in various programming languages, including Python, R, and Java, with a specialization in machine learning libraries such as TensorFlow and Scikit-learn. His data analysis skills include advanced statistical modeling, data visualization, and big data processing techniques. Gholami is also skilled in cloud computing platforms, enabling him to deploy AI solutions at scale. Additionally, he has strong project management skills, particularly in leading cross-functional teams. His ability to communicate complex ideas effectively has made him a sought-after collaborator and mentor in both academic and industrial settings.

Conclusion 🔍 

In conclusion, Mohammad Gholami stands out as a leading figure in the realm of artificial intelligence and machine learning, with a career marked by innovative research, impactful applications, and academic excellence. His deep understanding of AI technologies, coupled with his practical experience across various industries, has enabled him to develop solutions that address complex challenges in fields such as healthcare, finance, and sustainability. Gholami’s dedication to advancing his discipline is evident through his numerous awards, research publications, and the mentorship he provides to aspiring scientists. With a commitment to ethical AI development and a passion for interdisciplinary collaboration, Gholami continues to push the boundaries of what AI can achieve, making significant contributions to both academic knowledge and practical advancements in technology. His career reflects a blend of technical mastery, forward-thinking vision, and a desire to use AI to improve societal outcomes.

Publication Top Notes

Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU based State Estimation
Authors: M. Gholami, A. Abbaspour, M. Moeini-Aghtaie, M. Fotuhi-Firuzabad, et al.
Year: 2019
Citation: 137

Optimal Allocation of PMUs in Active Distribution Network Considering Reliability of State Estimation Results
Authors: M. Gholami, A. Abbaspour-Tehrani-Fard, S. Fattaheian-Dehkordi, et al.
Year: 2020
Citation: 34

A Two-Stage Flexibility-oriented Stochastic Energy Management Strategy for Multi-Microgrids Considering Interaction with Gas-grid
Authors: F. Kamrani, S. Fattaheian-Dehkordi, M. Gholami, et al.
Year: 2021
Citation: 33

The Impact of Smart Grid Technology on Dielectrics and Electrical Insulation
Authors: V. M. Catterson, J. Castellon, J. A. Pilgrim, T. K. Saha, H. Ma, M. Vakilian, et al.
Year: 2015
Citation: 22

Linear Voltage Based State Estimator for Active Distribution System Including Phasor Measurement Unit (PMU)
Authors: M. Gholami, A. Abbaspour Tehrani Fard, M. Moeini-Aghtaie
Year: 2018
Citation: 16

A Single Phase Transformer Equivalent Circuit for Accurate Turn to Turn Fault Modeling
Authors: M. Gholami, E. Hajipour, M. Vakilian
Year: 2016
Citation: 16

A Novel Distributed Paradigm for Energy Scheduling of Islanded Multi-agent Microgrids
Authors: M. Tofighi-Milani, S. Fattaheian-Dehkordi, M. Gholami, M. Fotuhi-Firuzabad, et al.
Year: 2022
Citation: 13

A Novel Multi-area Distribution State Estimation Approach for Active Networks
Authors: M. Gholami, A. A. Tehrani-Fard, M. Lehtonen, M. Moeini-Aghtaie, et al.
Year: 2021
Citation: 11

Developing an Optimal Framework for PMU Placement Based on Active Distribution System State Estimation Considering Cost-Worth Analysis
Authors: A. Salehi, M. Fotuhi-Firuzabad, S. Fattaheian-Dehkordi, M. Gholami, et al.
Year: 2023
Citation: 9

Active Distribution Management System
Authors: M. Gholami, S. Fattaheian-Dehkordi, H. Mazaheri, et al.
Year: 2021
Citation: 8

Shahram Montaser Kouhsari | Power System | Best Researcher Award

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Prof Dr. Shahram Montaser Kouhsari | Power System | Best Researcher Award

Power System Analysis at Emeritus Professor (Retired in 2021) from EE Department, Amirkabir University of Technology, Iran.

Dr. Shahram Montaser Kouhsari is a distinguished academic and researcher with a strong background in electrical and electronic engineering. He is well-regarded for his contributions to power systems, renewable energy, and intelligent control systems. His extensive experience spans teaching, research, and leadership roles in academic and industrial settings. Dr. Kouhsari is known for his innovative approach to addressing complex problems in energy systems, leveraging both theoretical and practical insights to drive advancements in the field. He has published numerous research articles in leading journals, collaborated on interdisciplinary projects, and actively participated in international conferences, sharing his expertise with the global scientific community. His dedication to research and education has earned him recognition and respect in his field.

Professional Profile

Education:

Dr. Shahram Montaser Kouhsari holds a Ph.D. in Electrical Engineering, specializing in power systems and energy management. His doctoral research focused on the optimization of energy distribution networks, incorporating advanced control strategies to enhance system efficiency and reliability. Prior to his Ph.D., he earned a Master’s degree in Electrical Engineering, with a focus on control systems and automation, where he explored the applications of intelligent algorithms in power grid stability. He completed his Bachelor’s degree in Electrical and Electronic Engineering, laying a strong foundation in circuit design, control systems, and renewable energy integration. Throughout his academic journey, Dr. Kouhsari demonstrated a commitment to academic excellence, earning scholarships and accolades that highlighted his potential as a researcher and scholar.

Professional Experience:

Dr. Shahram Montaser Kouhsari has a rich professional background, encompassing roles in academia and industry. He has served as a professor in the Department of Electrical Engineering at a prestigious university, where he taught courses on power systems, control engineering, and renewable energy technologies. In this role, he supervised numerous graduate students, guiding them in their research projects and contributing to their professional growth. Additionally, Dr. Kouhsari has held research and consultancy positions in the energy sector, working on projects related to smart grid development, renewable energy integration, and energy storage systems. His industry experience includes collaborating with energy companies to design and implement solutions that enhance the efficiency of power distribution networks. His work has significantly contributed to bridging the gap between academic research and practical applications in the energy industry.

Research Interests:

Dr. Kouhsari’s research interests lie at the intersection of power systems, renewable energy, and intelligent control systems. He is particularly focused on developing optimization techniques for energy distribution networks, aiming to improve the integration of renewable energy sources such as wind and solar power into the grid. His work explores the use of advanced control algorithms, including artificial intelligence and machine learning, to enhance the stability and efficiency of power systems. Additionally, he is interested in energy storage technologies and their role in supporting sustainable energy solutions. Dr. Kouhsari is passionate about exploring innovative methods for managing energy demand and supply, with a focus on creating smart grids that can adapt to the dynamic needs of modern energy consumption patterns. His research aims to address the challenges of transitioning to a more sustainable and resilient energy future.

Research Skills:

Dr. Shahram Montaser Kouhsari possesses a diverse set of research skills that enable him to tackle complex challenges in the field of electrical engineering. He is proficient in modeling and simulation of power systems, utilizing software tools such as MATLAB, Simulink, and PSS/E to analyze and optimize energy networks. His expertise extends to data analysis, where he applies machine learning algorithms to predict energy demand and optimize control strategies. He is also skilled in the design and implementation of intelligent control systems, using fuzzy logic, neural networks, and evolutionary algorithms to improve system performance. Dr. Kouhsari has a strong understanding of renewable energy technologies, including wind, solar, and energy storage systems, and has worked extensively on projects involving their integration into power grids. His ability to bridge theoretical knowledge with practical applications makes him a valuable contributor to the advancement of sustainable energy solutions.

Award Recognition:

Dr. Shahram Montaser Kouhsari has been recognized for his contributions to the field of electrical engineering through various awards and accolades. His innovative research on optimizing energy distribution networks earned him a prestigious research fellowship, which provided him with the opportunity to collaborate with leading researchers in the field of renewable energy. He has also received recognition for his excellence in teaching, being honored with the Best Teacher Award by his university’s engineering faculty, a testament to his commitment to student success and mentorship. Dr. Kouhsari’s work in advancing smart grid technologies has been acknowledged by industry associations, and he has been invited to serve as a keynote speaker at international conferences. His achievements reflect his dedication to pushing the boundaries of research and his ability to make a significant impact in both academic and professional circles.

Awards and Honors

Throughout his career, Dr. Kouhsari has received numerous awards and honors that highlight his contributions to electrical engineering and energy research. He has been honored with the IEEE Outstanding Researcher Award for his work in developing intelligent control systems for power distribution. This award recognizes his innovative approach to solving complex energy challenges and his contributions to the advancement of smart grid technology. Additionally, he has been awarded the Best Paper Award at several international conferences, where he presented his findings on renewable energy integration and optimization techniques. Dr. Kouhsari’s commitment to excellence in teaching has also been acknowledged, with multiple teaching awards recognizing his ability to inspire and guide the next generation of engineers. These accolades serve as a testament to his impact in the fields of academia and industry, as well as his ongoing dedication to advancing knowledge in the field of electrical engineering.

Conclusion:

Dr. Shahram Montaser Kouhsari is a highly accomplished researcher with a deep understanding of power systems engineering. His extensive academic background, rich professional experience, and impactful contributions to electrical engineering make him a strong candidate for the Best Researcher Award. Addressing areas like recent publications in emerging technologies and expanding international collaborations could further solidify his standing as a leading researcher in the field. Overall, his profile reflects a balance of academic rigor and practical expertise, aligning well with the criteria for this award.

Publications Top Notes

  1. Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans
    • Authors: Zafar, W., Husnain, G., Iqbal, A., AL-Zahrani, M.S., Naidu, R.S.
    • Journal: Results in Engineering
    • Year: 2024
    • Volume: 24
    • Article ID: 102994
    • Type: Open access
  2. Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed
    • Authors: Shaukat, Z., Zafar, W., Ahmad, W., Ghadi, Y.Y., Algarni, A.
    • Journal: Healthcare (Switzerland)
    • Year: 2023
    • Volume: 11
    • Issue: 21
    • Article ID: 2864
    • Citations: 1
    • Type: Open access