Pei Ren | Security | Best Researcher Award

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Ms. Pei Ren | Security | Best Researcher Award

Student at Shaanxi Normal University in China

Pei Ren is a dedicated early-career researcher in computer science specializing in privacy-preserving systems, cryptographic protocols, and blockchain-based crowd intelligence. He is currently pursuing a Ph.D. in Computer Science and Technology at Shaanxi Normal University. Ren’s scholarly work centers on addressing security challenges in decentralized systems, ensuring identity protection, and safeguarding user data in federated environments. His research is supported by a solid academic foundation and technical proficiency in programming and cryptographic tools. Pei Ren has co-authored several peer-reviewed articles in reputed journals such as the Journal of Systems Architecture and International Journal of Intelligent Systems. Through a blend of theoretical innovation and practical system design, he continues to make meaningful contributions to the field of information security.

Professional Profile

ORCID

Strengths for the Award

Pei Ren’s research profile demonstrates a clear focus and progression in the fields of cryptography, information security, and blockchain-based federated systems. His most recent journal article, “Secure task-worker matching and privacy-preserving scheme for blockchain-based federated crowdsourcing” published in Journal of Systems Architecture (2025), addresses complex challenges in decentralized task allocation and user privacy—an emerging and impactful research area. The integration of privacy-preserving computation with blockchain shows a deep understanding of both secure computation and distributed architectures.

Earlier work such as “IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme” published in the International Journal of Intelligent Systems (2022) further emphasizes Pei Ren’s expertise in data privacy and secure aggregation, particularly in federated systems. The emphasis on identity protection and anonymous data sharing reflects a consistent research direction aimed at real-world applicability, especially in privacy-sensitive environments like healthcare and IoT.

Moreover, Pei Ren has contributed to anonymous communication systems, as evidenced by the 2021 publication in Security and Communication Networks, which proposed an efficient scheme to protect the location privacy of IoT nodes. His technical skill set includes cryptographic tools (OpenSSL, GnuPG), programming (JavaScript), and system modeling (Visio, CTeX), enabling him to work across different layers of secure system design—from theoretical model to implementation.

Education 

Pei Ren has cultivated a progressive academic path in the field of computer science and technology. He is currently enrolled in a Ph.D. program at Shaanxi Normal University (since 2022), focusing on privacy-preserving mechanisms for secure data exchange and decentralized systems. Prior to this, he obtained a Master’s degree (2019–2022) and a Bachelor’s degree (2015–2019) from Qufu Normal University, where he developed a strong grounding in software engineering and cryptographic principles. Across all stages of his academic career, Ren has demonstrated a keen interest in the convergence of blockchain, cybersecurity, and data aggregation techniques. His solid educational background is further reinforced by relevant certifications and extensive experience with cryptographic software, programming environments, and data privacy frameworks.

Experience 

Pei Ren’s academic and research experience spans over eight years, primarily within university-led research labs. While pursuing his master’s and doctoral degrees, Ren actively engaged in secure systems design, federated learning environments, and anonymous communication protocols. He has co-authored multiple journal articles and conference papers, often in collaboration with experienced researchers and interdisciplinary teams. His experience includes designing privacy-preserving communication schemes, developing blockchain-based task-matching systems, and contributing to identity-protection models in IoT environments. In addition to research, he has gained expertise in using security tools such as OpenSSL and GnuPG, along with programming and modeling software like JavaScript, PyCharm, and Visio. This blend of theoretical knowledge and practical implementation has allowed Ren to contribute meaningfully to the development of secure, scalable, and privacy-aware digital infrastructures.

Research Focus 

Pei Ren’s research focuses on cryptography, blockchain security, and privacy-preserving mechanisms in decentralized systems. He explores secure identity authentication methods across systems, task matching frameworks for federated crowdsourcing, and pseudonym-based anonymity schemes. His work often intersects cryptographic techniques with real-world applications such as the Internet of Things (IoT), secure data aggregation, and decentralized marketplaces. A key component of his research involves balancing usability with security—designing systems that not only protect user data but also maintain performance and trust in distributed environments. Ren also investigates cross-system authentication and the implementation of reputation mechanisms in collaborative networks. His long-term vision is to contribute to frameworks that empower digital ecosystems to function with minimal privacy risks and maximum operational integrity.

Publication Top Notes

1. Secure Task-Worker Matching and Privacy-Preserving Scheme for Blockchain-Based Federated Crowdsourcing

Journal: Journal of Systems Architecture, 2025
Authors: Pei Ren, Bo Yang, Tao Wang, Yanwei Zhou, Feng Zhu
Summary:
This paper introduces a privacy-preserving protocol for task-worker matching in federated crowdsourcing platforms built on blockchain. By leveraging cryptographic techniques and smart contracts, the authors ensure that neither the identity nor task data of participants is exposed during matching and reward distribution. The design utilizes pseudonym identities and zero-knowledge verification to preserve privacy while maintaining the system’s transparency and trustworthiness.

2. IPSadas: Identity‐Privacy‐Aware Secure and Anonymous Data Aggregation Scheme

Journal: International Journal of Intelligent Systems, 2022
Authors: Pei Ren, Fengyin Li, Ying Wang, Huiyu Zhou, Peiyu Liu
Summary:
IPSadas is a novel aggregation protocol aimed at secure data sharing in decentralized environments. The scheme ensures anonymity and data integrity while mitigating identity leakage risks. The paper details a privacy model built using homomorphic encryption and privacy-preserving credentials that enable users to contribute data without revealing personal identity. Applications in healthcare and distributed AI systems are discussed.

3. An Efficient Anonymous Communication Scheme to Protect the Privacy of the Source Node Location in the Internet of Things

Journal: Security and Communication Networks, 2021
Authors: Fengyin Li, Pei Ren, Guoyu Yang, Yuhong Sun, Yilei Wang, Yanli Wang, Siyuan Li, Huiyu Zhou, Wenjuan Li
Summary:
This work proposes a communication scheme designed to shield source node locations in IoT networks. The protocol utilizes dynamic pseudonyms and bilinear pairing to ensure end-to-end anonymity, even under active surveillance. The research tackles a key IoT vulnerability—source traceability—by offering a scalable and low-latency solution suitable for smart environments and connected infrastructure.

4. An Anonymous Communication Scheme Between Nodes Based on Pseudonym and Bilinear Pairing in Big Data Environments

Conference: 6th International Conference on Data Mining and Big Data (DMBD 2021)
Authors: Pei Ren, Liu B., Li F.Y.
Summary:
This paper presents a communication scheme designed to ensure anonymity and confidentiality in big data environments, particularly focusing on node-to-node communication. The proposed model uses pseudonym-based identities combined with bilinear pairing cryptographic mechanisms to protect node identity and prevent message traceability. The method is effective in dynamic networks where node privacy is at risk due to frequent data exchange. The paper also evaluates the performance of the scheme in terms of computational cost and security resilience, demonstrating its applicability to privacy-sensitive big data applications such as distributed sensor networks and decentralized IoT infrastructures.

Conclusion

Pei Ren presents a strong candidacy for the Best Researcher Award, particularly in the domains of blockchain security, privacy-preserving data processing, and federated systems. His focused research agenda, technical proficiency, and consistent publication record in respected venues mark him as a promising early-career researcher. With continued growth in publication impact and leadership in collaborative projects, Pei Ren is poised to make significant contributions to the field of secure and intelligent computing systems.

Hamidreza Rashidian | Electrical and Electronics Engineering | Best Researcher Award

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Mr. Hamidreza Rashidian | Electrical and Electronics Engineering | Best Researcher Award

Research fellow at Islamic Azad University in Iran.

Hamidreza Rashidian is a dedicated researcher and designer specializing in Integrated Circuits (ICs) within the domain of Electrical and Electronics Engineering. Since 2015, he has actively contributed to academic and applied research on data converters, voltage-level shifters, bandgap voltage references, and signal processing circuits. Based in Tehran, he has collaborated with academic institutions as a research and teaching assistant while also pursuing independent innovation. His work is published in high-impact journals including IEEE Transactions on Circuits and Systems II and multiple Elsevier journals. He is also a certified reviewer for top-tier journals, including Analog Integrated Circuits and Signal Processing and Scientific Reports. Known for his productivity and perseverance, Rashidian’s contributions are shaping next-generation analog and mixed-mode circuits. With a strong foundation in software tools like HSPICE and Cadence, he bridges the gap between theory and real-world circuit implementation.

Professional Profiles

   ORCID | Scopus

Strengths for the Award

1. Deep Specialization in Integrated Circuit Design:
Mr. Rashidian demonstrates a strong and focused research trajectory in the domain of electronics engineering, particularly in the design and development of Integrated Circuits (ICs). His expertise encompasses subfields such as data converters, mixed-mode ICs, RF circuits, and bandgap voltage references. His consistent engagement in IC research since 2015 reflects a matured specialization, making him a valuable contributor to this niche field.

2. Notable Publication Record in Prestigious Journals:
He has published multiple peer-reviewed papers in high-impact platforms including the IEEE Transactions on Circuits and Systems II and Elsevier journals such as Integration, the VLSI Journal, and the International Journal of Electronics and Communications. These publications highlight his ability to address complex design challenges like low-power operation, high-precision voltage references, and analog-to-digital converter (ADC) innovation.

3. Active Research Pipeline and Reviewer Contributions:
Beyond completed work, he is engaged in ongoing research, such as ADC design with time-domain latch interpolation. His role as a certified reviewer for reputable journals like Analog Integrated Circuits and Signal Processing and Scientific Reports further underlines his scholarly competence and recognition in the academic community.

4. Integration of Academia and Independent Innovation:
He maintains roles both as a university-affiliated research assistant and as a self-employed circuit designer and lecturer. This dual involvement ensures that his work is not only academically rigorous but also technically applied and entrepreneurial in nature.

Education 

Hamidreza Rashidian holds three progressive degrees in Electrical and Electronics Engineering. He earned his Master’s degree from Islamic Azad University in Tehran in 2015, where he focused on the design and simulation of a low-power FinFET-based operational amplifier. Prior to that, he completed his Bachelor’s in Electronic Technology Engineering at Ghiaseddin Jamshid Kashani University in 2013, where he developed a scientific calculator using AVR microcontroller technology. His academic journey began with an Associate degree in Electricity-Electronics at the same institution, giving him a strong practical and theoretical base early in his career. His academic projects demonstrate a consistent focus on circuit-level innovation and microcontroller applications. These academic milestones have provided a robust foundation for his research in analog IC design and mixed-mode systems.

Research Focus 

Rashidian’s research primarily centers on the design of low-power, high-precision analog and mixed-signal integrated circuits. His technical interests include bandgap voltage references, voltage-level shifters, analog-to-digital converters, and signal-processing circuits. He investigates techniques to minimize power consumption and temperature drift in IC components, which is crucial for modern-day sensor systems and portable electronics. One of his significant contributions is the development of curvature-compensated bandgap reference circuits, which enhance accuracy across temperature ranges. His ongoing work on flash ADCs with time-domain latch interpolation exemplifies his commitment to advancing speed and efficiency in data conversion. He also explores RF ICs and VLSI design methodologies, making use of tools like HSPICE, Cadence, and MATLAB. Rashidian’s research contributes directly to the development of next-generation semiconductor devices used in IoT, medical instrumentation, and wireless systems.

Publication Top Notes

1. A 38.5-fJ 14.4-ns Robust and Efficient Subthreshold-to-Suprathreshold Voltage-Level Shifter Comprising Logic Mismatch-Activated Current Control Circuit
Published in: IEEE Transactions on Circuits and Systems II, 2023
Summary:
This paper presents a highly energy-efficient voltage-level shifter that operates reliably under subthreshold supply conditions. The design utilizes a logic mismatch-activated current control circuit to achieve robust transition characteristics, boasting energy consumption as low as 38.5 femtojoules and a delay of just 14.4 nanoseconds. It is ideal for ultra-low-power applications and supports wide voltage domain integration.

2. A Sub-1 ppm/°C Dual-Reference Small-Area Bandgap Reference Comprising an Enhanceable Piecewise Curvature Compensation Circuit
Published in: Elsevier International Journal of Electronics and Communications, 2024
Summary:
This study introduces a dual-reference bandgap voltage reference (BGR) that achieves exceptional temperature stability (less than 1 ppm/°C) using a novel curvature compensation scheme. The circuit architecture enables compact layout and enhances reliability, making it suitable for precision analog sensors and battery-powered devices.

3. A 75.12-nW 0.5-V MOS-Based BGR Comprising a Curvature Compensation Circuit for Analog-to-Digital Converter Applications
Published in: Elsevier International Journal of Electronics and Communications, 2025
Summary:
Targeting ultra-low-power applications, this work designs a MOS-based bandgap reference consuming only 75.12 nW at 0.5 V. Enhanced curvature compensation maintains accuracy, providing a reference for power-constrained ADC circuits in biomedical and IoT devices.

4. A 2.69-ppm/°C Curvature-Compensated BJT-Based Bandgap Voltage Reference
Published in: Elsevier Integration, the VLSI Journal, 2025
Summary:
This paper advances BJT-based voltage reference designs by achieving 2.69 ppm/°C thermal stability. The approach integrates curvature compensation and circuit-level innovations to ensure performance under wide temperature variations, addressing key challenges in precision analog design.

5. A 0.45-V Supply, 22.77-nW Resistor-Less Switched-Capacitor Bandgap Voltage Reference
Published in: Elsevier Computers and Electrical Engineering Journal, 2025
Summary:
This resistor-less switched-capacitor BGR operates at ultra-low supply voltages (0.45 V) and consumes only 22.77 nW. It removes passive resistors entirely, improving integration efficiency and size reduction for system-on-chip (SoC) designs.

Conclusion

Mr. Hamidreza Rashidian is a highly dedicated and technically competent researcher in the field of analog and mixed-signal integrated circuits. His scholarly contributions through peer-reviewed journals, active teaching roles, and independent research initiatives demonstrate the hallmarks of a committed and impactful researcher. With continued international engagement and further innovation in high-impact areas, he stands as a strong candidate for the Best Researcher Award.

Faten AlQaifi | Artificial Intelligence | Best Researcher Award

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Dr. Faten AlQaifi | Artificial Intelligence | Best Researcher Award

Atilim University, Turkey.

Dr. Faten Musaed Alqaifi is a multidisciplinary researcher currently pursuing her Master’s in Healthcare Management at Atılım University, Turkey. She brings a unique blend of expertise in dental surgery, healthcare management, and artificial intelligence, having earned her MBA in Healthcare Management from UTM, Malaysia. With academic excellence reflected in her top CGPAs, she has contributed to both clinical and psychosocial research domains. Her recent studies explore AI’s role in improving oral healthcare outcomes and the psychological dimensions of international student life. She has worked as a general dentist, educator, and volunteer in diverse cultural settings, underscoring her global adaptability and social commitment. Fluent in Arabic, English, and intermediate Turkish, Dr. Alqaifi exemplifies the qualities of a globally engaged researcher.

🌐Author Profiles

Strengths for the Award

  1. Interdisciplinary Research in Healthcare and AI
    Faten Alqaifi has shown a strong interdisciplinary approach, particularly in integrating artificial intelligence into healthcare systems. Her 2024 publication titled “Artificial intelligence’s impact on oral healthcare in terms of clinical outcomes: a bibliometric analysis” reflects a forward-thinking research agenda. By analyzing AI’s role in improving clinical outcomes, she contributes meaningfully to a high-impact and emerging area in health sciences and management.

  2. Empirical Work in Mental Health and Social Integration
    Her 2025 paper, “International students’ adaptation in Ankara: The mediating roles of anxiety and self-esteem”, published in the International Journal of Intercultural Relations, indicates her engagement with global psychological and sociocultural issues. This work not only highlights her concern for vulnerable populations but also showcases the use of rigorous methodologies in behavioral and intercultural research.

  3. Diverse Experience and Multilingual Skills
    Faten has a well-rounded professional background as a dentist, academic tutor, volunteer, and general manager. She speaks Arabic natively and is proficient in English and Turkish, enabling her to conduct research and collaboration across regions. Her use of advanced research tools (e.g., SPSS, SmartPLS, bibliometric software) further enhances her research capacity.

🔹 Education 

Dr. Alqaifi holds a Bachelor’s in Dental Surgery from the Yemeni University of Science and Technology, where she ranked fifth in her class. She earned her first Master’s degree—an MBA in Healthcare Management—from Universiti Teknologi Malaysia (UTM) with a stellar GPA of 3.94/4. Building on her academic and clinical background, she is now completing a second Master’s in Healthcare Management at Atılım University, Turkey, where she maintains a GPA of 3.93/4. Her ongoing thesis investigates the adoption of artificial intelligence in dentistry, combining technology, healthcare policy, and clinical practice. This educational trajectory highlights her commitment to interdisciplinary learning and research excellence.

🔹 Research Focus on Artificial Intelligence

Dr. Faten Alqaifi’s research lies at the intersection of healthcare innovation, artificial intelligence, and behavioral science. Her ongoing thesis on AI integration in dentistry demonstrates a forward-looking approach to digital transformation in healthcare. Her research agenda emphasizes both technological effectiveness and human-centered outcomes, as evidenced by her bibliometric analysis on AI’s clinical impact in oral healthcare. Simultaneously, she investigates psychological dimensions of social adaptation, particularly anxiety and self-esteem among international students—adding depth to her interdisciplinary reach. She is proficient in tools like SPSS, SmartPLS, and bibliometric analysis platforms, enabling her to conduct statistically robust, data-driven research. Faten’s aim is to bridge the digital and human aspects of healthcare policy and practice.

📚 Publication Top Notes

1. International Students’ Adaptation in Ankara: The Mediating Roles of Anxiety and Self-Esteem

Author: Faten Alqaifi
Journal: International Journal of Intercultural Relations, Vol. 108, Article 102249, 2025
Summary:
This study explores the psychological adjustment process among international students in Ankara, focusing on the mediating impact of anxiety and self-esteem on their adaptation levels. Using structural equation modeling, Alqaifi identifies critical pathways by which mental health factors influence sociocultural integration. The research provides insights for universities and policymakers aiming to improve the student experience in multicultural settings.

2. Artificial Intelligence’s Impact on Oral Healthcare in Terms of Clinical Outcomes: A Bibliometric Analysis

Authors: Faten Alqaifi, D. Tengilimoglu, I. Arslan Aras
Journal: Journal of Health Organization and Management, 2024
Summary:
This bibliometric study investigates global research trends on AI applications in oral healthcare. Analyzing data from Scopus and Web of Science, the authors assess publication patterns, key contributors, emerging keywords, and citation landscapes. The study concludes that AI is increasingly driving diagnostic precision, treatment planning, and clinical outcome optimization in dentistry. It positions AI as a transformative force and sets the foundation for future strategic investments in digital dentistry.

Conclusion

Dr. Faten Musaed Alqaifi shows strong potential for recognition as an emerging interdisciplinary researcher in healthcare, AI, and social sciences. Her academic excellence, early contributions in high-relevance areas, and diverse experience make her a promising candidate for the Best Researcher Award. However, to fully meet the expectations of this award at a global competitive level, she should aim to expand her publication portfolio, lead independent research projects, and pursue collaborative or funded initiatives. With her current trajectory and dedication, she is on a strong path to achieving these milestones.

 

Ataollah Shirzadi | Natural Hazards | Best Researcher Award

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Dr. Ataollah Shirzadi | Natural Hazards | Best Researcher Award

University of Kurdistan, Iran.

Dr. Ataollah Shirzadi is an Assistant Professor at the University of Kurdistan, Iran, specializing in Watershed Management Engineering. With over 90 peer-reviewed publications, he has become an internationally recognized figure in the field of natural hazard modeling. His expertise spans flash flood forecasting, shallow landslide prediction, soil erosion modeling, and geospatial risk assessment, all enhanced through artificial intelligence techniques. Notably, he was listed among the top 2% and 1% of global scientists in 2022 and 2023 by Stanford University and ISC. Dr. Shirzadi has contributed to several high-impact collaborative projects, including an international Iran-China initiative on flood susceptibility. He serves as a reviewer and editorial member for multiple international journals and actively mentors graduate researchers. His research bridges theoretical modeling and real-world disaster management, making significant strides in environmental resilience.

🔎Author Profile

🏅Awards and Honors

  1. Ranked among the Top 2% Scientists in the World

    • Source: Stanford University Ranking

    • Years: 2022 and 2023

  2. Ranked among the Top 1% Scientists in the Islamic World

    • Source: ISC (Islamic World Science Citation Center)

    • Year: 2023

  3. Elite and Talent Recognition Awards

    • By: Iran’s National Elite Foundation (for undergraduate and postgraduate academic excellence)

    • Context: Received for high GPA and top-ranking achievements during B.Sc. and M.Sc. studies

  4. National University Admission Ranks

    • Ranked 1st in M.Sc. entrance exam for the field of Mass Movements in Watershed Management

    • Ranked 3rd in B.Sc. entrance exam for Hydrology and Climatology

  5. Excellence Awards

    • University Honors: Recognized multiple times as top student in academic performance at both undergraduate and postgraduate levels

🏆Strengths for the Award

  1. High-Impact Research Contributions
    Dr. Shirzadi has co-authored over 90 international publications, with many articles appearing in top-tier journals such as Science of The Total Environment, Journal of Hydrology, Environmental Modelling & Software, Geomatics, Natural Hazards and Risk, and Remote Sensing. His research has garnered significant citations (e.g., 756 citations for a 2018 paper and 699 for a 2019 paper), reflecting the impact and relevance of his work in the scientific community.

  2. Pioneering Work in Natural Hazard Modeling
    His expertise lies in landslide, flash flood, and flood susceptibility modeling, with a strong emphasis on machine learning, deep learning, and hybrid AI techniques. Notably, he has developed several novel hybrid intelligence models integrating techniques like ANFIS, Genetic Algorithms, SVMs, Decision Trees, and ensemble approaches, widely cited and validated across diverse geographies.

  3. Recognition & Global Ranking
    He has been recognized among the top 2% and 1% of scientists globally by Stanford University and the Islamic World Science Citation Center (ISC) in 2022 and 2023 — a rare distinction that underlines his international academic standing.

  4. Editorial and Reviewer Excellence
    He holds editorial roles in journals such as Frontiers in Earth Science and has reviewed for more than 30 ISI-indexed journals, including Scientific Reports, Journal of Hydrology, CATENA, and Environmental Earth Sciences. This affirms his reputation as a trusted evaluator of cutting-edge scientific work.

  5. Academic Mentorship and Leadership
    Dr. Shirzadi has supervised over 17 M.Sc. and Ph.D. students, contributing actively to capacity building in environmental modeling and risk assessment. He has also been involved in international collaborations, including a recent Iran-China flood monitoring project, reflecting both leadership and global outreach.

🎓 Education 

Dr. Shirzadi earned his Ph.D. in Watershed Management Engineering (2014–2017) from Sari Agricultural Sciences and Natural Resources University, where his thesis focused on spatial prediction of shallow landslides using advanced data mining algorithms. He previously obtained an M.Sc. in Mass Movements (2007–2009) with a GPA of 19/20, also from Sari University, where he developed regional models for rockfall hazard mapping. His B.Sc. in Hydrology and Climatology (2003–2007) was awarded by the University of Agricultural Science and Natural Resources of Gorgan . Over the years, Dr. Shirzadi has cultivated robust expertise in geospatial modeling, sediment transport, and flood risk analysis. He also holds certifications in GIS, SPSS, AutoCAD, and multiple machine learning platforms, demonstrating both strong academic credentials and technical fluency.

🔬 Research Focus on Natural Hazards

Dr. Shirzadi’s research centers on the integration of AI, machine learning, and geospatial analysis to assess and mitigate natural hazards. His core focus includes:

  • Flood Susceptibility Mapping using hybrid machine learning models (e.g., ANFIS, rotation forests).

  • Shallow Landslide Prediction with ensemble algorithms combining decision trees, support vector machines, and neural networks.

  • Risk Zonation for erosion and sediment transport based on satellite data and multi-criteria analysis.

  • Uncertainty Quantification in hazard models to improve resilience strategies.

  • Remote Sensing Applications using Sentinel-1/2 and Landsat data for urban and rural hazard detection.

His goal is to create smart, scalable, and interpretable models that guide land use planning, policy-making, and emergency preparedness in climate-sensitive regions.

📘 Publication Top Notes

  1. A Comparative Assessment of Decision Trees Algorithms for Flash Flood Susceptibility Modeling
    Authors: Khosravi K., Pham B.T., Chapi K., Shirzadi A., et al.
    Journal: Science of The Total Environment (2018)
    Summary: Compared multiple decision tree algorithms to model flash flood susceptibility in Iran’s Haraz watershed, showing ensemble methods outperformed traditional single classifiers.

  2. Flood Susceptibility Modeling Using MCDM and Machine Learning
    Authors: Khosravi K., Shahabi H., Adamowski J., Shirzadi A., et al.
    Journal: Journal of Hydrology (2019)
    Summary: Evaluated and contrasted MCDM techniques and AI models; hybrid ML models proved superior in spatial flood risk analysis.

  3. A Novel Hybrid AI Approach for Flood Susceptibility
    Authors: Chapi K., Singh V.P., Shirzadi A., et al.
    Journal: Environmental Modelling & Software (2017)
    Summary: Developed a hybrid model combining several AI algorithms to predict flood risk, improving classification accuracy in complex terrains.

  4. Flood Susceptibility Using ANFIS-GA-DE Hybrid
    Authors: Hong H., Panahi M., Shirzadi A., et al.
    Journal: Science of the Total Environment (2018)
    Summary: Applied ANFIS enhanced with genetic and differential evolution algorithms for flood prediction in China’s Hengfeng area.

  1. Forecasting Floods Using ML & Statistical Models
    Authors: Shafizadeh-Moghadam H., Shahabi H., Shirzadi A., et al.
    Journal: Journal of Environmental Management (2018)
    Summary: Combined ML and traditional statistical techniques to forecast flood-prone zones, optimizing accuracy and runtime efficiency.

🚀Conclusion

Dr. Ataollah Shirzadi stands out as an exceptionally qualified candidate for the Best Researcher Award, with an impressive combination of scholarly output, innovative AI-based methodologies, global recognition, and academic service. His work not only advances theoretical models but also addresses urgent environmental and societal challenges. With continued growth in communication and cross-disciplinary application, he is poised to make even greater contributions to science and practice.

Kwaghgba Elijah Gbabe | Technology Scientists Innovations | Nanotechnology Innovation Award

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Dr. Kwaghgba Elijah Gbabe | Technology Scientists Innovations | Nanotechnology Innovation Award

Senior Research Officer at Nigerian Stored Products Research Institute, Nigeria

Dr. Kwaghgba Elijah Gbabe is a Senior Research Officer at the Nigerian Stored Products Research Institute, Ilorin, Nigeria. With over 9 years of experience, he specializes in food processing, postharvest technology, and agricultural nanotechnology. His research focuses on prolonging the shelf-life of perishable crops using eco-friendly nano-fibre systems and enhancing food quality through advanced preservation methods. Dr. Gbabe earned his M.Eng. in Agricultural and Environmental Engineering from the University of Agriculture, Makurdi, and is pursuing his Ph.D. in Food Processing and Technology at Benue State University. He has conducted international research at the Centre for Agricultural Nanotechnology, TNAU, India, and published multiple peer-reviewed articles. He also contributes actively to training farmers, artisans, and technical personnel. Dr. Gbabe’s work bridges the gap between sustainability and innovation in food preservation, making him a standout candidate in the technological innovation domain.

Author Profile

Strengths for the Award

  1. Strong Foundation in Agricultural Nanotechnology
    Dr. Gbabe has established a niche in the application of nanotechnology to agricultural and food preservation challenges. His Ph.D. research focuses on developing an electrospun hexanal nano-fibre matrix—a cutting-edge innovation aimed at extending the shelf-life of perishable fruits like banana, mango, and tomato.

  2. International Exposure and Training
    He completed a prestigious internship at the Centre for Agricultural Nanotechnology, TNAU, India, where he conducted nanotoxicity, biosafety, and electrospinning-based preservation studies—highlighting both cross-cultural collaboration and technological advancement.

  3. Peer-Reviewed Nanotech Publications
    Dr. Gbabe has authored several relevant papers in reputed journals:

    • Journal of the Indian Chemical Society (2025): On hexanal nano-fiber matrices for tomato preservation.

    • IJETT (2025): Development of nano-fiber matrices for mango shelf-life extension.

    • Nano Plus (2023): On banana fruit preservation using electrospun nanotechnology.
      These works clearly demonstrate applied innovation, rigorous experimentation, and measurable societal impact in reducing food loss.

  4. Technical Skills Aligned with Nanotech Innovation
    Proficient in electrospinning, FTIR, GC-MS, SEM & TEM, and statistical software (R, SPSS), showing an interdisciplinary approach involving both materials science and food technology.

  5. Leadership in National Innovation Projects
    As a Senior Research Officer at the Nigerian Stored Products Research Institute, he actively leads R&D on postharvest loss reduction and food quality enhancement technologies—bridging innovation with policy and field deployment.

🎓 Education 

Dr. Gbabe holds a Master of Engineering in Agricultural and Environmental Engineering (2019) from the University of Agriculture, Makurdi, Nigeria. His thesis focused on eco-building materials using rice husk and sawdust, reflecting an early interest in sustainable engineering. He is currently completing his Ph.D. in Food Processing and Technology (2020–2025) at Benue State University, Makurdi. His doctoral research is centered on the development of electrospun hexanal nano-fibre matrices aimed at extending the shelf-life of tropical fruits like bananas, mangoes, and tomatoes. He is a registered engineer with COREN Nigeria and a member of the Nigerian Institution of Agricultural Engineers. In 2023, he was a research intern at the Centre for Agricultural Nanotechnology, TNAU, India, where he gained hands-on experience in nanotoxicology, electrospinning, and biosafety. His academic journey reflects a strong foundation in multidisciplinary innovation and food systems sustainability.

🔬 Research Focus on Technology Scientists Innovations

Dr. Gbabe’s research is rooted in postharvest technology, agricultural nanotechnology, and food quality preservation. His core contributions lie in the design and development of nanostructured packaging and preservation systems using biodegradable hexanal-based nano-fibers, created via electrospinning. These innovations target tropical fruit shelf-life extension and nutrient retention during storage. He is equally involved in evaluating postharvest handling systems, including the construction of solar dryers and inert-atmosphere silos. His projects align closely with SDG 2 (Zero Hunger) and SDG 12 (Sustainable Consumption & Production). Dr. Gbabe also explores sustainable materials (like rice husk-based eco-panels), biosafety assessments in nanoformulations, and pest management using botanicals. His work is highly applied, integrating field deployment, engineering fabrication, and local capacity building—benefiting smallholder farmers and food industries across West Africa.

📚 Publication Top Notes

  1. Gbabe et al. (2025)
    Effect of Hexanal Nano-fiber Matrix on Quality Parameters of Tomato Fruits during Storage
    Journal: Journal of the Indian Chemical Society
    Summary: Demonstrates improved shelf-life and reduced spoilage in tomato fruits using hexanal-loaded nano-fiber packaging developed via electrospinning.
    DOI: 10.1016/j.jics.2025.101912

  2. Gbabe et al. (2025)
    Development of Novel Hexanal Nano-fibre Matrix by Electrospinning for Shelf-life Extension of Mango Fruits
    Journal: International Journal of Engineering Trends and Technology
    Summary: Describes the fabrication and optimization of mango-preserving nano-matrices, with a focus on temperature resilience and biodegradability.
    DOI: 10.14445/22315381/IJETT-V73I3P132

  3. Chukwu et al. (2025)
    Implication of Different Storage Techniques on Physical Attributes of African Okra
    Journal: IJABR
    Summary: Assesses how traditional vs. improved storage impacts okra firmness, color, and moisture, with relevance to rural postharvest systems.

  4. Idris et al. (2024)
    Maize grains milling efficiency: A performance analysis of a hammer mill
    Journal: International Journal of Agronomy and Agricultural Research
    Summary: Compares efficiency metrics of hammer mills to suggest design improvements for rural grain processing.
    Link

  5. Adeniyi et al. (2024)
    Insecticidal and Toxicity Studies of Heliotropium Indicum Leaf Extracts
    Journal: Journal of Exposure Toxicology
    Summary: Investigates natural pest control agents for stored grain insects—highlighting bio-safety and efficacy.

  6. Oyewole et al. (2020)
    Commercial Utilization of Inert Atmosphere Silo for Maize Storage
    Journal: IOP Conf. Series: Earth and Environmental Science
    Summary: Presents the benefits of modified atmosphere storage in reducing maize spoilage.

Conclusion

Dr. Kwaghgba Elijah Gbabe is highly suitable for the Research for Nanotechnology Innovation Award. His work represents a strong blend of scientific depth, practical relevance, and innovation in nanotechnology applications for agriculture and food preservation. With further strides in international publication, commercialization, and cross-sectoral collaborations, Dr. Gbabe has the potential to become a leading figure in agricultural nanotech innovation across Africa and globally.

Valeria Cera | AI applied to Architectural Heritage | Women Researcher Award

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Dr. Valeria Cera | AI applied to Architectural Heritage | Women Researcher Award

Tenure-Track Assistant Professor at University of Naples Federico II, Italy.

Dr. Valeria Cera is a Tenure-Track Assistant Professor at the Department of Architecture, University of Naples Federico II. With a Ph.D. in Surveying and Representation of Architecture and Environment, she contributes extensively to heritage digitization, urban survey, and AI-based semantic modeling. A founding member of REAACH and collaborator with institutions such as CNRS (France), University of Tianjin (China), and University of Valladolid (Spain), her international research fosters digital transitions in heritage studies. She teaches Architectural Drawing and Surveying across multiple academic levels and contributes to the editorial and scientific boards of key journals and book series. She holds memberships in ICOMOS, UID, and the Europeana Network. Recognized for her role in blending cultural heritage with digital technologies, she has authored over 70 publications and led 30+ research projects.

Author Profile

Strengths for the Award

Innovative Expertise in Digital Cultural Heritage
Dr. Valeria Cera is a leading scholar in the field of architectural documentation, semantic 3D modeling, and digital representation of heritage assets. Her research integrates Scan-to-BIM, semantic annotation, and AI-based tools to enhance the documentation, analysis, and conservation of historical and urban environments. Her work stands at the intersection of technology and humanities, where she uses computational innovation to preserve and promote cultural identity.

Strong Academic and Editorial Credentials
Dr. Cera holds a Ph.D. in Surveying and Representation of Architecture and Environment and currently serves as a tenure-track Assistant Professor at the University of Naples Federico II. She has published over 70 journal and Award papers (many in Scopus-indexed venues) and contributed as a reviewer and editorial board member to journals such as MDPI’s Remote Sensing and Sustainability, DisegnareCon, and the International Journal of Computational Methods in Heritage Science.

Project Leadership and Global Collaborations
She has contributed to over 30 research projects, including international efforts with the University of Valladolid (Spain), University of Tianjin (China), CNRS (France), and regional cultural heritage bodies in Italy. These collaborations highlight her global outlook and commitment to impactful, interdisciplinary research in heritage science.

Institutional and Professional Engagement
Dr. Cera plays a pivotal role in academia through her teaching in Bachelor’s, Master’s, and advanced restoration programs. She is a founding member of REAACH (Representation Advances and Challenges APS), and an active member of respected professional organizations such as ICOMOS, UID, ENA Europeana Network, and IBIMI Building Smart. Her cross-sectoral influence spans academia, policy, and cultural institutions.

🎓 Education 

Dr. Cera earned her Ph.D. in Surveying and Representation of Architecture and Environment from the University of Naples Federico II. Her advanced studies integrated architectural geometry, photogrammetry, and computational modeling. During her doctoral work, she explored emerging methods for spatial data capture and semantic 3D modeling, laying the foundation for her later work on Scan-to-BIM systems and H-BIM processes. She has continuously built upon her educational background through academic teaching and applied research in heritage documentation, visualization, and urban modeling. In 2020, she was awarded National Scientific Qualification as Associate Professor, underscoring her scholarly contributions and academic leadership.

🔍 Research Focus on AI applied to Architectural Heritage

Dr. Valeria Cera’s research is situated at the intersection of digital cultural heritage, semantic 3D modeling, and human-centered interface design. Her work advances the representation and conservation of historic architecture through techniques such as Scan-to-BIM, natural user interfaces, and semantic annotation. With a strong foundation in survey science, her research extends to multi-sensor fusion, low-cost documentation systems, and real-time AR/AI-based monitoring. She also investigates gamification and immersive technologies to enhance public engagement with built heritage. Her aim is to optimize processes for heritage analysis, documentation, and communication—making use of digital twins and intelligent systems that preserve cultural identity in an accessible way.

📚Publication Top Notes

🔬 1. Semantically Annotated 3D Material Supporting the Design of Natural User Interfaces for Architectural Heritage

Authors: V. Cera, A. Origlia, F. Cutugno, M. Campi
Conference: AVI*CH (Advanced Visual Interfaces for Cultural Heritage), 2018
Citations: 13
Summary:
This work proposes a method for enriching 3D architectural models with semantic data, enabling interaction through natural user interfaces (NUIs). Targeted at non-experts—tourists, students, or citizens—it enables intuitive exploration of architectural data through gestures and voice. The study also integrates linguistic linked open data with spatial datasets, creating a hybrid model that bridges computational linguistics, 3D graphics, and cultural storytelling.

📡 2. Evaluating the Potential of Imaging Rover for Automatic Point Cloud Generation

Authors: V. Cera, M. Campi
Journal: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
Citations: 11
Summary:
The authors develop a low-cost mobile rover system equipped with photogrammetric sensors for autonomous data acquisition in complex heritage sites. It evaluates imaging geometry, stability, and point cloud density to determine accuracy. The paper advances field-based survey methodologies where traditional tools are infeasible, especially in confined or fragile environments.

🧱 3. Segmentation Protocols in the Digital Twins of Monumental Heritage: A Methodological Development

Authors: V. Cera, M. Campi
Journal: DisegnareCon, 2021
Citations: 9
Summary:
This paper introduces standardized segmentation protocols for processing 3D scans of monumental architecture. These protocols improve the quality and interpretability of digital twins used in restoration, conservation, and analysis. The methodology addresses semantic and geometric partitioning in HBIM models, providing a repeatable workflow for complex heritage assets.

🏛️ 4. Knowledge and Valorization of Historical Sites through Low-Cost, Gaming Sensors and H-BIM Models: The Case of Liternum

Author: V. Cera
Journal: Archeologia e Calcolatori, 2017
Citations: 8
Summary:
Using Microsoft Kinect and similar gaming sensors, this study constructs cost-effective 3D reconstructions of the ancient Roman town of Liternum. The paper presents an H-BIM model that integrates historical layers, semantic annotation, and interactive visualization. It contributes to democratizing heritage access and documentation, especially for small-scale or underfunded archaeological projects.

🏙️ 5. Fast Survey Procedures in Urban Scenarios: Some Tests with 360° Cameras

Authors: V. Cera, M. Campi
Journal: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022
Citations: 6
Summary:
The authors assess 360° panoramic cameras as tools for rapid urban data acquisition. Through field trials, they compare image quality, georeferencing accuracy, and integration with BIM workflows. This technique offers fast, scalable solutions for documenting complex urban heritage, particularly in dynamic or inaccessible environments.

Conclusion

Dr. Valeria Cera is highly deserving of the Women Researcher Award. Her pioneering contributions to digital modeling, semantic systems, and architectural heritage documentation exemplify excellence in research and interdisciplinary collaboration. Her work has advanced both academic knowledge and public policy approaches to cultural preservation. With her ongoing research momentum and leadership roles, she is well-positioned to shape the future of digital heritage science, making her an outstanding representative for women in science and technology.

Shaohui Lei | Service AI | Best Researcher Award

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Assist. Prof. Dr. Shaohui Lei | Service AI | Best Researcher Award

Dr, Southwest Jiaotong University, China.

Dr. Shaohui Lei is an Assistant Professor at the School of Economics and Management, Southwest Jiaotong University, China. His research specializes in service marketing, consumer behavior, and technology-enabled service interactions, particularly focusing on the behavioral impact of artificial intelligence in service environments. He has authored impactful articles in prestigious journals including Journal of Business Ethics, Journal of Service Research, Applied Psychology, Journal of Business Research, and International Journal of Hospitality Management. Dr. Lei’s interdisciplinary research uniquely blends behavioral science, marketing theory, and AI technology, addressing timely issues such as customer misbehavior in robotized service settings. His work continues to advance the frontier of human-AI interaction and its implications for ethical, psychological, and practical aspects of modern service economies.

🌐Author Profile

🏆 Strengths for the Award

  1. Focused Expertise in Service Marketing & Consumer Behavior
    Dr. Lei’s research centers on timely and high-impact areas such as service robot personas, customer misbehavior, and ethical consumer interactions—critical to both academic inquiry and real-world service industries.

  2. Publications in High-Impact Journals
    His work has appeared in prestigious peer-reviewed journals such as:

    • Journal of Business Ethics

    • Journal of Service Research

    • Applied Psychology

    • Journal of Business Research

    • International Journal of Hospitality Management

    • Journal of Product & Brand Management
      This reflects scholarly credibility and sustained contributions to his field.

  3. Innovative and Interdisciplinary Research
    The study “Servant” versus “Partner”: Investigating the effect of service robot personas on customer misbehavior demonstrates a forward-thinking integration of AI, robotics, and behavioral marketing, offering novel insights into human-machine interaction.

  4. Global Relevance and Practical Applications
    His research addresses consumer behavior patterns that are increasingly relevant in a tech-driven global service economy, particularly amid the rise of AI and automation in retail and hospitality sectors.

🎓 Education 

Shaohui Lei completed his academic training with a focus on management science and behavioral research. While the exact institutions and degree progression are not detailed in the CV, his academic path reflects rigorous preparation in marketing, consumer psychology, and applied economics. He has developed a strong academic foundation enabling cross-disciplinary contributions at the intersection of management and emerging service technologies. His education likely includes a PhD in a relevant domain—such as Business Administration, Marketing, or Psychology—and postgraduate specialization in service operations and behavior. This academic trajectory supports his current research output, which integrates empirical methods with theoretical frameworks to study customer behavior in technologically mediated environments.

🔬 Research Focus on Service AI

Dr. Lei’s research explores the intersection of service marketing, consumer behavior, and artificial intelligence, focusing on how consumers interact with AI-driven service technologies like robots and virtual agents. He investigates customer misbehavior, perception, and trust in service contexts shaped by human-machine dynamics. His studies offer insights into how robot personas (e.g., servant vs. partner roles) affect customer behavior, with implications for designing AI interactions that balance efficiency, ethics, and user satisfaction. His research also addresses psychological drivers behind unethical or deviant behaviors in service encounters. Dr. Lei leverages experimental designs and behavioral modeling to produce actionable knowledge that supports AI deployment in retail, hospitality, and public services. This line of inquiry contributes both to theory development and managerial decision-making in an increasingly digitized service landscape.

📚Publication Top Notes

“Servant” versus “Partner”: Investigating the Effect of Service Robot Personas on Customer Misbehavior

  • Authors: Shaohui Lei

  • Published In: Journal of Business Research

  • Summary:
    This article examines how the assigned role of a service robot—whether perceived as a “servant” or a “partner”—influences customer misbehavior. Using behavioral experiments, Dr. Lei and colleagues uncover that customers are more likely to act disrespectfully or abusively toward robots that adopt a submissive “servant” persona. In contrast, partner-like robots foster mutual respect and reduce misconduct. The findings illuminate the psychological and ethical dimensions of AI-driven service experiences and provide actionable design recommendations for businesses integrating robots into frontline operations. This work contributes to service marketing, ethical AI design, and human-robot interaction literature.

✅ Conclusion

Dr. Shaohui Lei is a highly suitable candidate for the Best Researcher Award, particularly in fields intersecting consumer behavior, service marketing, and technology-enabled service environments. His robust publication record in top-tier journals, combined with his innovative focus on ethical and behavioral implications of service automation, mark him as a thought leader in his domain. With strategic

Sirmayanti | AI and Digital Communication | Women Researcher Award

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Assoc. Prof. Dr. Sirmayanti | AI and Digital Communication | Women Researcher Award

Associate Professor at Politeknik Negeri Ujung Pandang, Indonesia.

Assoc. Prof. Dr. Sirmayanti, Ph.D., is a prominent Indonesian scholar in telecommunication engineering, currently serving at the State Polytechnic of Ujung Pandang (PNUP). With a career spanning over two decades, she combines academic excellence, cutting-edge research, and impactful community engagement. She earned her Ph.D. in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia, with Cum Laude distinction. Her research explores a wide spectrum—from 5G and RF engineering to artificial intelligence, IoT, and green communication technologies. As Head of the Telecommunication Network Engineering Technology program and the Centre for Applied Telecommunications Technology Research at PNUP, she has mentored numerous scholars and led various national and international innovation projects. Her prolific output includes over 60 publications, 15 technical books, and numerous patented inventions. A passionate advocate for education and digital equity, she’s been widely recognized for her contributions, including multiple presidential awards and the Australia Awards Alumni honor.

🌐Author Profile

🎓 Education 

Sirmayanti’s educational journey reflects a trajectory of excellence across multiple continents. She earned her Ph.D. in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia (2015), where she graduated cum laude for her innovative research in digital RF architectures. Her Master of Engineering from the same university (2008) was awarded with First-Class Honors, reinforcing her expertise in signal processing and software radio systems. Her Bachelor of Engineering in Telecommunications, from Hasanuddin University, Indonesia (2001), was completed with High Distinction. She began her technical training at SMK Telkom Sandhy Putra 2 (1997), where she laid the foundation for her career in communications technology. In 2024, she was formally recognized as an Insinyur Profesional Utama (IPU), ASEAN Engineer, and APEC Engineer, further validating her technical leadership at the global level. Her continuous professional development includes numerous international certifications in Open RAN, IoT, AI, and RF engineering, demonstrating her commitment to lifelong learning.

Strengths for the Award

  1. Academic Excellence & Global Exposure:

    • Holds a Ph.D. with Cum Laude honors in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia.

    • Her academic track includes top honors at every level, showcasing consistent high performance.

  2. Extensive Multidisciplinary Research:

    • Research covers Wireless Communications, IoT, AI, RF Engineering, Satellite Communications, and 5G.

    • Led and contributed to over 35+ funded research projects from national and international agencies (e.g., BRIN, KEMENRISTEKDIKBUD, DIKTI, AFS, Australia Direct Aid).

  3. High Research Output:

    • Over 60 peer-reviewed publications, including Scopus-indexed journal articles (Q1–Q3) and IEEE Award proceedings.

    • Significant work on metaheuristic algorithms for diabetes prediction, 5G networks, and digital RF systems.

  4. Innovation and Patents:

    • Holds multiple patents and copyrights (2020–2025) on innovations such as:

      • Wireless Bridge Mi-Fi 4G

      • Techniques for RF Spectrum Image Cancellation

      • Tools for lithium battery drop testing

    • Demonstrates intellectual property development at a national level.

  5. Leadership and Capacity Building:

    • Head of the Telecommunication Network Engineering Program and Center for Applied Telecommunications Technology Research (PNUP).

    • National assessor, ISO lead auditor, and active in curriculum reform and national education initiatives.

💼 Professional Experience 

Since 2001, Sirmayanti has served as a Lecturer and Researcher at the State Polytechnic of Ujung Pandang (PNUP), where she currently leads both the Telecommunication Network Engineering Technology Program and the Centre for Applied Telecommunications Technology Research (CATTAR). Her experience spans academia, national policy, quality assurance, and technology innovation. She is a certified lead auditor for ISO 9001 and an assessor for national educational programs like BKD, BIP, and Praktisi Mengajar under Indonesia’s Ministry of Research and Education. Her leadership in internal quality systems and curriculum design has transformed vocational education frameworks. She has led over 40 funded research and community service projects across 5G, AI, IoT, and rural connectivity systems. Internationally, she collaborates with UiTM Malaysia and is a certified practitioner in Open RAN, AI, and RF planning. Her blend of administrative leadership, hands-on technical innovation, and commitment to community service uniquely positions her in advancing AI and digital communication technologies.

🏅 Awards and Honors

Assoc. Prof. Sirmayanti has been decorated with some of Indonesia’s highest honors for civil service, including the Satyalancana Karya Satya 20 Tahun and 10 Tahun awards from the President of the Republic of Indonesia in 2024 and 2018. She is also among the “70 Outstanding Alumni” of Australian institutions recognized by Australia Awards Indonesia. She received the PIN LEMHANNAS award in 2024 and the prestigious INSINAS Research Grant Award from BRIN for her research on distortion mitigation in 5G systems. Her academic journey was supported by multiple competitive scholarships, such as the Australian Development Scholarship (ADS) and DIKTI Fellowships for postgraduate research. Beyond institutional honors, her contributions to community service and STEM education have garnered attention from various national and international programs. Her consistent record of excellence in education, research, innovation, and social impact establishes her as a leading figure in digital communication and AI technology in Southeast Asia.

🔬 Research Focus on AI and Digital Communication

Sirmayanti’s research sits at the intersection of artificial intelligence, wireless communication, and digital signal processing. Her key focus areas include 5G/6G networks, RF engineering, IoT-based infrastructure, AI-powered healthcare diagnostics, and green communication technologies. She applies metaheuristic algorithms and machine learning models to enhance wireless performance, such as through feature optimization for disease prediction or spectral efficiency in OFDM systems. Notably, she has developed low-power RF transmitters, digital baseband tuning systems, and hardware innovations like dual-band filters for 5G. She also works on rural connectivity solutions using mini BTS and community Wi-Fi networks. Her projects are not only technically advanced but socially driven—such as her work on digital inclusion in remote Indonesian islands and health monitoring systems. Her patents and intellectual property showcase strong emphasis on applied research, while her academic publications reflect rigorous peer-reviewed contributions to the fields of AI, digital modulation, and next-generation communication protocols.

📚 Publication Top Notes

1. E-Government dan Digitalisasi Layanan Publik

Authors: M. Mirfan, M. Mariana, S. Abdullah, A. Nurfadly, S. Suhada, S. Sirmayanti
Publisher: Yayasan Kita Menulis (2025)
Type: Book/Handbook
Summary:
This publication explores the digital transformation of public services through e-government frameworks. It addresses how Indonesian government institutions adopt ICT-based platforms for public administration, service efficiency, and transparency. Sirmayanti’s contribution lies in her analysis of telecommunication infrastructure enabling smart governance and public service digitization. The book combines theoretical frameworks with empirical insights and practical models for policymakers and technocrats.

2. A Systematic Literature Review of Diabetes Prediction Using Metaheuristic Algorithm-Based Feature Selection: Algorithms and Challenges Method

Authors: FR Sirmayanti, Pulung Hendro Prastyo, Mahyati
Journal: Applied Computer Science, 21(1), pp. 126–142, 2025 (Scopus Q3)
DOI: Applied Computer Science
Summary:
This systematic review focuses on AI-based diabetes prediction models utilizing metaheuristic feature selection algorithms. The authors classify and analyze recent algorithms (GWO, PSO, ACO, etc.), evaluating their effectiveness in reducing feature dimensionality while maintaining high diagnostic accuracy. Challenges such as computational cost, algorithm convergence, and overfitting are critically discussed. The work contributes a taxonomy of algorithmic strategies and highlights future research gaps for clinical decision support systems in e-health.

3. An Enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy for Feature Selection

Authors: M. Sirmayanti, Pulung Hendro Prastyo
Conference: 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 96–101
Summary:
This Award paper proposes an improved version of the Grey Wolf Optimizer (GWO) for feature selection by incorporating opposition-based learning, mutation, and local search mechanisms. The hybridized GWO algorithm demonstrated enhanced exploration-exploitation balance and was tested on medical and telecom datasets. Results showed better performance compared to standard GWO and other algorithms, with significant implications for AI-driven feature selection in classification systems.

4. Design of Dual-Band Bandpass Filter (DBPF) for 5G Applications

Authors: M. Mimsyad, A. Bazergan, S. Sirmayanti
Journal: AIP Conference Proceedings, Vol. 3140, 040018 (2024)
DOI: 10.1063/5.0222871
Summary:
This study presents the design and simulation of a dual-band bandpass filter (DBPF) aimed at 5G wireless applications. Utilizing advanced substrate materials and electromagnetic modeling, the design supports frequency bands relevant for mid-band 5G use cases. Sirmayanti contributed to the filter miniaturization and optimization phase. The paper outlines how the DBPF improves signal selectivity and reduces interference, with potential application in small-cell architectures and 5G-enabled devices.

5. Rekayasa Mitigasi Kebocoran Gas LPG dengan Sistem Monitoring Telegram Bot Berbasis Internet of Things (IoT)

Authors: S. Sirmayanti, E. Dwi Melda
Conference: Seminar Nasional Teknik Elektro dan Informatika (SNTEI), Vol. 9(1), pp. 223–228, 2023
Summary:
This paper discusses the development of an IoT-based real-time monitoring system for LPG gas leak detection, integrated with a Telegram bot alert system. The authors designed and implemented an Arduino-based sensor framework to monitor gas concentration in households and notify users immediately through messaging. Sirmayanti led the system integration and sensor calibration. The project highlights practical IoT use cases in smart safety systems for domestic environments.

✅ Conclusion

Assoc. Prof. Sirmayanti is highly suitable for the Women Researcher Award. Her extensive academic, research, innovation, and community engagement portfolio, combined with her leadership in engineering education and commitment to women’s empowerment in STEM, exemplify the spirit of the award. Her work not only advances telecommunications and AI research but also bridges technology with sustainable development and inclusive education—hallmarks of a transformative woman researcher.

Sajjad Hashemi Abasabadi | Quantum thermodynamics | Best Researcher Award

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Mr. Sajjad Hashemi Abasabadi | Quantum thermodynamics | Best Researcher Award

PhD Candidate, Vali-e-Asr University of Rafsanjan, Iran

Sajjad Hashemi Abasabadi is an emerging physicist and a dedicated PhD candidate in Optics and Laser Physics at Vali-e-Asr University of Rafsanjan, Iran. With a Master’s degree in Atomic and Molecular Physics and a solid foundation in laser spectroscopy, Sajjad is spearheading theoretical innovations in quantum thermodynamics and energy-efficient heat engines. His work intricately combines quantum optics, information theory, and thermodynamic modeling to advance nanoscale energy systems. His growing publication record in high-impact journals and strong conceptual grasp of quantum systems position him as a promising young researcher in the frontier of quantum technologies. 🌟

👨‍🔬 Author Profile

✅ Strengths for the Award

Sajjad Hashemi Abasabadi has demonstrated notable potential and commitment to advancing the field of quantum thermodynamics and quantum heat engines, particularly within the context of quantum optics and information. As a Ph.D. candidate, his contributions reflect a deep theoretical understanding and novel analytical approaches. His published works in reputable journals like Scientific Reports and International Communications in Heat and Mass Transfer indicate the scholarly merit and international visibility of his research.

  • Development of a Quantum Otto engine model with a Pöschl–Teller potential, contributing to energy efficiency at the nanoscale.

  • Exploration of non-thermal reservoirs and their impact on work and efficiency, which broadens the understanding of thermodynamic behavior in quantum systems.

  • Innovative analysis of endoreversible quantum heat engines under strong coupling, offering insight into irreversibility and system performance trade-offs.

His work addresses fundamental challenges in energy-efficient technologies and emerging quantum devices, aligning with cutting-edge priorities in modern physics and quantum engineering.

🎓 Education

Sajjad began his academic journey at Vali-e-Asr University of Rafsanjan, where he earned his M.Sc. in Physics, specializing in Atomic and Molecular Physics. His thesis focused on the spectroscopic characterization of molecular transitions under various pressure conditions, revealing key insights into atomic behavior in dynamic environments. Driven by a passion for precision measurement and quantum mechanics, he continued his academic path at the same university, currently pursuing a Ph.D. in Physics (Optics and Laser). His doctoral research is centered on laser-based high-resolution imaging and quantum metrology, where he explores applications ranging from ultrafast laser dynamics to the mechanics of quantum heat engines. 🎓🔬

👨‍🔬 Experience

During his academic career, Sajjad has contributed to several research endeavors that reflect both depth and innovation. His collaborative work extends across multiple domains of quantum physics, from thermodynamic cycle modeling to non-classical reservoir dynamics. He has presented his findings at national Awards, gaining recognition for tackling complex theoretical models with practical significance in quantum engines. He has also participated in interdisciplinary projects involving ultrafast laser dynamics, contributing to the design of precision instruments in optical physics. His evolving expertise is evidenced by peer-reviewed publications in Scientific Reports and International Communications in Heat and Mass Transfer. 📊🧪

🔍 Research Focus on Quantum thermodynamics

Sajjad’s research bridges quantum thermodynamics, optics, and non-equilibrium heat engine modeling, with a primary focus on Quantum Otto heat engines. He explores how non-standard reservoir dynamics and system-bath interactions influence performance, including studies on Pöschl–Teller potential models for enhanced efficiency, the role of coherent and non-thermal reservoirs, and the impact of strong coupling in endoreversible engines. Through analytical and numerical modeling, his work supports the development of nanoscale thermal machines relevant to quantum information processing and energy conversion technologies. 🔭⚛️

📚 Publications Top Notes

Quantum Otto Heat Engine with Pöschl–Teller Potential in Contact with Coherent Thermal Bath

Authors: Sajjad Hashemi Abasabadi, S.Y. Mirafzali, H.R. Baghshahi
Journal: Scientific Reports, Volume 13, Article 10522, 2023
Publisher: Nature Portfolio
DOI: 10.1038/s41598-023-37681-1
Summary:
This paper explores the behavior of a quantum Otto heat engine using a Pöschl–Teller potential as the working medium, coupled to a coherent thermal reservoir. By incorporating quantum coherence into the thermal bath, the study demonstrates measurable improvements in efficiency and work output. The authors establish that coherence can be leveraged to enhance the performance of nanoscale thermal machines beyond classical thermodynamic limits, offering a pathway toward the realization of quantum-enhanced energy devices.

Endoreversible Quantum Heat Engine Affected by Strong Coupling with Thermal Reservoir

Authors: Sajjad Hashemi Abasabadi, S.Y. Mirafzali, H.R. Baghshahi
Journal: International Communications in Heat and Mass Transfer, Volume 167, Article 109309, 2025
Publisher: Elsevier
DOI: 10.1016/j.icheatmasstransfer.2025.109309

🔍 Summary:
In this work, the authors examine a quantum endoreversible Otto engine operating under strong coupling between the system and its thermal environment. Unlike weak coupling models that simplify energy exchange, this study reveals how strong interactions affect irreversibility, power output, and overall thermodynamic efficiency. The analysis uncovers trade-offs between performance and system-bath coupling strength, providing critical insights into the design of realistic quantum thermal engines operating in non-ideal conditions.

Efficiency and Work Quantum Otto Machine in Contact with Non-Thermal Reservoir

Authors: S. Hashemi Abasabadi, S.Y. Mirafzali, H.R. Baghshahi
Journal: Quarterly Journal of Optoelectronic, Volume 6, Issue 1, Pages 51–58, 2023
DOI: https://doi.org/10.30473/jphys.2023.69525.1170

🔍 Summary:
This article investigates the performance of a quantum Otto engine interacting with a non-thermal reservoir, extending conventional thermodynamic models. By introducing non-thermal bath characteristics such as squeezed states or engineered distributions, the paper analyzes their impact on the engine’s efficiency and work extraction capacity. Results show that non-thermal reservoirs can be engineered to outperform thermal baths, marking a significant step forward in optimizing quantum energy systems.

🧠 Conclusion

Sajjad Hashemi Abasabadi is a visionary early-career researcher whose work bridges theoretical physics and applied quantum technologies. His groundbreaking studies on quantum heat engines have unveiled fundamental relationships between coherence, coupling strength, and engine performance, shaping a new understanding of how quantum machines can operate efficiently in realistic environments. Despite being at the outset of his career, Sajjad has already carved a niche in quantum thermodynamics and optics, showing the potential to lead transformative research in the field.

Chao Yang | Mechanical Engineering | Best Researcher Award

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Prof. Dr. Chao Yang | Mechanical Engineering | Best Researcher Award

Associate Professor, Jiaxing University, China.

Dr. Chao Yang, born in 1982, is currently a Lecturer in the School of Mechanical Engineering at Jiaxing University, China. He holds a Ph.D. in Mechanical Engineering from Zhejiang Sci-Tech University (2019), preceded by an M.E. in Engineering Mechanics from Dalian University of Technology (2009) and a B.E. in Process Equipment & Control Engineering from Zhengzhou University of Light Industry (2005). Since joining academia, Dr. Yang has consistently contributed to advancing the mechanics of parallel manipulators, focusing on kinematics, dynamics, stiffness analysis, and intelligent optimization algorithms. With over 20 peer-reviewed publications in reputable international journals and Awards, he has become a recognized figure in robot modeling and design. Dr. Yang also serves as a reviewer for journals like Mechanism and Machine Theory and Applied Mathematical Modelling, further reflecting his deep academic engagement. His work bridges theoretical innovation and practical engineering applications in robotics and precision mechanisms.

🧾Author Profile

🏆 Strengths for the Award

1. Deep Specialization in Parallel Manipulators

Dr. Yang’s research is highly focused and impactful in the domains of kinematics, stiffness modeling, dynamics, and optimization of parallel manipulators. His work addresses both theoretical foundations and practical design aspects, which are critical to modern robotics and automation systems.

2. Robust and Consistent Publication Record

Since 2018, Dr. Yang has published 20+ peer-reviewed articles, many in high-impact international journals such as:

  • Mechanism and Machine Theory

  • Mechanical Sciences

  • Chinese Journal of Mechanical Engineering

  • Robotica

  • International Journal of Control

3. Novel Contributions in Modeling Techniques

Dr. Yang has proposed and validated novel methodologies including:

  • Elastodynamic and elastostatic modeling of over-constrained systems

  • Use of neural networks and principal component analysis for multi-objective optimization

  • Finite-time tracking control techniques for underwater vehicles
    These show innovation, interdisciplinary thinking, and a mastery of mechanical system modeling.

4. International Journal Reviewership

Dr. Yang serves as a reviewer for journals like Applied Mathematical Modelling and Mechanism and Machine Theory, which reflects recognition of his expertise by the academic community.

🎓 Education 

Dr. Chao Yang began his academic journey in mechanical disciplines with a Bachelor’s degree in Process Equipment & Control Engineering from Zhengzhou University of Light Industry, China, in 2005. He continued to sharpen his analytical and mathematical expertise by completing his Master’s in Engineering Mechanics at Dalian University of Technology in 2009, where he laid the groundwork for his future in dynamic systems and mechanical modeling. In 2019, he earned his Ph.D. in Mechanical Engineering from Zhejiang Sci-Tech University, focusing on advanced dynamic analysis and optimization techniques for parallel robotic manipulators. His academic training integrates control theory, mechanical design, and computational modeling—making him uniquely positioned to tackle cutting-edge problems in modern robotics. This rich educational background directly contributes to his current research, which blends multi-body dynamics, elastostatics, and AI-based optimization in robotic mechanisms.

🔬 Research Focus On Mechanical Engineering

Dr. Chao Yang’s research focuses on the mechanics, modeling, and optimization of parallel manipulators—key elements in robotics and precision automation. His work revolves around four core themes: kinematics, stiffness modeling, dynamic analysis, and multi-objective optimization. He explores how over-constrained or hybrid manipulator systems can be optimized using neural networks, principal component analysis, and evolutionary algorithms. His innovative modeling methods extend to both elastostatic and elastodynamic domains, enabling more precise and adaptive control systems. He also delves into applications such as underwater robotics and hybrid robot platforms. By avoiding Lagrangian multipliers in modeling and adopting screw theory in kinetostatic design, he simplifies computational complexity while maintaining physical accuracy. His contributions fill a crucial gap in designing robust, high-performance robotic systems that are used in manufacturing, aerospace, and intelligent automation. Dr. Yang’s research is practical, interdisciplinary, and driven by the demands of next-generation robotics.

📚 Publications Top Notes

1. A hybrid algorithm for the dimensional synthesis of parallel manipulators

Journal: Proc. IMechE Part C: Journal of Mechanical Engineering Science, 2025
Authors: Yang, C.; Zhang, H.; Huang, F.; Ye, W.
Summary: This study presents a novel hybrid algorithm integrating evolutionary computing and deterministic search for optimizing the geometry of parallel manipulators. It addresses trade-offs in workspace, stiffness, and dexterity with improved computational performance.

2. Elastodynamic modeling and analysis of a 4SRRR overconstrained parallel robot

Journal: Mechanical Sciences, 2025
Authors: Wang, B.; Zhao, Y.; Yang, C.; Hu, X.; Zhao, Y.
Summary: Investigates vibration and dynamic response of a 4SRRR parallel robot. The study contributes to better understanding structural deformation under motion, crucial for high-speed precision applications.

3. Kinematic Analysis and Optimization Design of 2-PRU-PRRPa Parallel Mechanism

Journal: Transactions of the Chinese Society for Agricultural Machinery, 2025
Authors: Zhang, W.; Feng, S.; Yuan, X.; Sun, P.; Yang, C.; Lu, Y.
Summary: Offers a systematic study of a novel parallel mechanism applied in agricultural automation, optimizing motion paths and actuator placement.

4. Multibody elastodynamic modeling of parallel manipulators based on the Lagrangian equations without Lagrangian multipliers

Journal: Proc. IMechE Part C: Journal of Mechanical Engineering Science, 2025
Authors: Gong, Y.; Lou, J.; Yang, C.; Ye, W.
Summary: This paper introduces a novel elastodynamic modeling approach for parallel manipulators that bypasses the use of Lagrangian multipliers. The methodology improves numerical efficiency and simplifies model derivation, making it suitable for real-time control and simulation of complex parallel robotic systems.

5. Dynamic modeling and performance analysis of the 2PRU-PUU parallel mechanism

Journal: Mechanical Sciences, 2024
Authors: Sun, T.; Ye, W.; Yang, C.; Huang, F.
Summary: Focuses on the dynamic modeling of a 2PRU-PUU architecture parallel robot. Through simulation and performance metrics evaluation, the study demonstrates how structural configurations affect system response and highlights its suitability for precision tasks in constrained workspaces.

Conclusion

Dr. Chao Yang is highly suitable for the Research for Best Researcher Award—particularly in domains of mechanical systems design, parallel robots, and multi-objective optimization. His contributions are academically rich, technically deep, and steadily expanding. While early in his career stage as a lecturer, the maturity and depth of his publication portfolio, coupled with innovative methodologies, clearly reflect a rising star in mechanical engineering research.