Amin Najafi | Robotics and Automation | Best Researcher Award

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Mr. Amin Najafi | Robotics and Automation | Best Researcher Award

PhD candidate at University of Zanjan, Iran.

Amin Najafi is a researcher specializing in advanced fault-tolerant control, robotics, and intelligent transportation systems. His expertise lies in designing resilient control algorithms for UAVs, MAGLEV trains, and autonomous guidance systems. Through a strong portfolio of high-quality publications, Najafi has contributed significantly to enhancing the stability, safety, and performance of robotic systems operating under uncertain and fault-prone conditions. His work in adaptive barrier sliding mode control and finite-time stabilization has been widely recognized for bridging theoretical advancements with practical applications. Najafi’s research has appeared in leading journals, including IEEE Transactions on Transportation Electrification, Mathematics, ISA Transactions, and the Journal of Vibration and Control. Beyond research, he actively contributes to the scientific community through peer-review engagements across prestigious journals. His growing influence demonstrates his commitment to advancing robust, intelligent, and reliable autonomous systems, making him a promising candidate for recognition in robotics and automation research.

Professional Profile

Google Scholar | Scopus | ORCID

Education

Amin Najafi’s academic training has been grounded in control engineering, robotics, and automation. His education equipped him with advanced knowledge in nonlinear control, adaptive systems, and fault-tolerant design, laying a strong foundation for tackling complex challenges in autonomous platforms. Building on this foundation, Najafi engaged deeply with theories of stability, guidance, and fault diagnosis while also exploring practical aspects of UAVs and intelligent transportation. His progression through academic programs allowed him to develop both analytical rigor and applied research capabilities. The interdisciplinary nature of his training helped him connect mathematics, control theory, and engineering applications, which is reflected in his publications that combine theoretical robustness with engineering relevance. Najafi’s educational journey reflects a balance of theory and practice, giving him the ability to produce impactful work that speaks to both the academic community and the broader engineering industry in robotics and automation.

Experience

Amin Najafi has developed his career around solving critical problems in robotics, automation, and transportation electrification. His research experience includes designing innovative fault-tolerant controllers for quadrotor UAVs, advancing resilient strategies for MAGLEV train systems, and contributing to aerospace and defense-related guidance systems. His international collaborations with researchers such as S. Mobayen, A. Fekih, and L. Fridman demonstrate his ability to work within diverse, high-caliber teams. Najafi has also built strong credentials as a peer reviewer, having reviewed more than 60 manuscripts for prestigious journals including IEEE Transactions on Transportation Electrification, IEEE Access, and the Asian Journal of Control. This dual role as an author and reviewer highlights both his subject matter expertise and his standing in the global robotics and control community. Through his experience, he has consistently contributed to advancing autonomous and fault-resilient systems, ensuring his research holds both academic and applied significance.

Research Focus

Najafi’s research is anchored in fault-tolerant control, nonlinear dynamics, and resilient robotics. His primary focus lies in developing adaptive barrier sliding mode controllers, finite-time stabilization strategies, and robust diagnosis methods for actuator faults. UAVs represent a central application in his portfolio, where he has addressed actuator reliability, real-time guidance, and performance optimization under uncertain conditions. Beyond UAVs, he has extended his contributions to MAGLEV trains and interceptor-target systems, demonstrating the versatility of his control strategies. His work is characterized by integrating theoretical rigor, such as linear matrix inequality approaches, with real-world engineering challenges, making his contributions impactful across multiple domains. The broader vision of his research is to enable safe, intelligent, and adaptive robotic systems capable of operating in dynamic and fault-prone environments. By combining control theory with automation and robotics, Najafi continues to advance the frontiers of resilient and intelligent autonomous technologies.

Publication Top Notes

Title: Adaptive Barrier Fast Terminal Sliding Mode Actuator Fault-Tolerant Control Approach for Quadrotor UAVs
Authors: A. Najafi, M.T. Vu, S. Mobayen, J.H. Asad, A. Fekih
Journal: Mathematics.
Citations: 51
Summary: Proposes an adaptive barrier fast terminal sliding mode controller for quadrotor UAVs. Ensures finite-time stability, fault tolerance, and resilience against actuator faults with validated simulations.

Title: Design of Linear Matrix Inequality-Based Adaptive Barrier Global Sliding Mode Fault-Tolerant Control for Uncertain Systems with Faulty Actuators
Authors: K. Naseri, M.T. Vu, S. Mobayen, A. Najafi, A. Fekih
Journal: Mathematics.
Citations: 22
Summary: Introduces an LMI-based adaptive barrier global sliding mode controller. Provides robust stability and effective fault management in uncertain nonlinear systems.

Title: Robust Adaptive Fault-Tolerant Control for MAGLEV Train Systems: A Non-Singular Finite-Time Approach
Authors: A. Najafi, S. Mobayen, S.H. Rouhani, Z. Mokhtare, A. Jalilvand, L. Fridman, et al.
Journal: IEEE Transactions on Transportation Electrification.
Citations: 3
Summary: Develops a finite-time robust adaptive controller for MAGLEV trains. Enhances fault tolerance, passenger safety, and system robustness under disturbances.

Title: Multiple Actuator Fault Diagnosis Based on Parity Space for Quadrotor System
Authors: A. Najafi, D. Bustan
Journal: Journal of Aeronautical Engineering (JOAE).
Citations: 2
Summary: Presents a parity-space-based approach to detect and isolate multiple actuator faults in quadrotors, ensuring reliable UAV performance.

Title: Design of Adaptive Barrier Function-Based Backstepping Finite-Time Guidance Control for Interceptor-Target Systems
Authors: Z. Mokhtare, M.A. Sepestanki, S. Mobayen, A. Najafi, W. Assawinchaichote, et al.
Journal: Journal of Vibration and Control.
Citations: –
Summary: Proposes a backstepping control method with adaptive barrier functions for interceptor-target systems. Guarantees finite-time convergence and robust guidance under uncertainties.

Conclusion

Amin Najafi demonstrates strong potential and achievement in fault-tolerant control systems for UAVs and transportation applications, with impactful publications, innovative methodologies, and active engagement in peer review. While there is scope for growth in terms of citation impact and broader collaborations, his research contributions are highly relevant to the advancement of resilient and intelligent autonomous systems. He can be considered a suitable and promising candidate for the Best Researcher Award, particularly within the subject category of Control Systems, UAVs, and Intelligent Transportation.

Souleyman Hassan | Technology Scientists Innovations | Best Researcher Award

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Mr. Souleyman Hassan | Technology Scientists Innovations | Best Researcher Award

PhD student at University of N’Djamena in Chad.

Souleyman Hassan is a dedicated biochemist and PhD student at the University of N’Djamena, Chad, whose work focuses on discovering novel small molecules to combat malaria and related parasitic diseases. His research bridges traditional medicinal knowledge with modern computational and experimental approaches, such as bio-guided fractionation and high-throughput screening. He has collaborated with global pharmaceutical leaders including MMV, Pfizer, Merck, H3D, and Johnson & Johnson, contributing to the identification of potent antimalarial compounds, notably the picomolar pyrazole derivative MMV1794211. As an ambassador for SPARK Africa and an active member of ISSX Africa, he advocates for advancing research capacity across the continent. Souleyman has also co-founded EcoFast, reflecting his commitment to innovation and entrepreneurship. With support from the Bill & Melinda Gates Foundation, DAAD, and Grand Challenges Africa, his work exemplifies the integration of academic research, industrial collaboration, and social impact in the fight against malaria.

Professional Profile

ORCID

Education 

Souleyman Hassan is pursuing a PhD in Biochemistry at the University of N’Djamena, Chad. His doctoral research centers on malaria therapeutics, particularly the discovery and optimization of natural and synthetic molecules targeting resistant Plasmodium falciparum strains. He gained academic enrichment through the YaBiNaPA project (Yaoundé–Bielefeld Graduate School for Natural Products with Antiparasite and Antibacterial Activity), funded by DAAD, where he developed expertise in bio-guided fractionation, pharmacokinetic optimization, and parasite assays. His training also includes exposure to computational drug discovery and structure-based screening, providing him with a dual skillset bridging wet-lab and in silico research. Souleyman has benefited from workshops and collaborations with the University of Yaoundé I, the University of South Florida, and multiple pharmaceutical partners, where he strengthened his understanding of global health research pipelines. His educational journey reflects a strong commitment to integrating African research excellence with international scientific standards.

Experience 

Souleyman Hassan has gained diverse research and industrial experience through collaborations with international pharmaceutical companies and research institutions. His work with Medicines for Malaria Venture (MMV) provided hands-on experience in screening open-access compound libraries such as the COVID Box, Malaria Box, and Pathogens Box, evaluating their antiparasitic activity. His involvement in the Bill & Melinda Gates Foundation-funded project led to the discovery of highly potent antiplasmodial pyrazole derivatives, underscoring his contribution to frontline drug discovery efforts. Additionally, he has worked on plant-based drug leads, such as Drymaria cordata and Macaranga monandra, used traditionally against malaria. Souleyman is also engaged with community and academic organizations as a member of ISSX, SPARK Africa, and the Association of Health Sciences Researchers in Chad. His co-founding of EcoFast illustrates his ability to extend research innovations into entrepreneurial ventures, reflecting an applied dimension to his academic expertise.

Research Focus 

Souleyman Hassan’s research focuses on the biochemistry and pharmacology of antimalarial drug discovery. He integrates natural product isolation with computational methods to identify promising bioactive molecules and improve pharmacokinetics. His focus extends to high-throughput screening of both natural and synthetic libraries, enabling the rapid identification of potent leads such as MMV1794211, which showed picomolar-level activity against resistant Plasmodium falciparum. His research also explores the repurposing of existing molecules, reducing the cost and time of developing antiparasitic drugs. By combining indigenous plant knowledge, such as investigations into Terminalia ivorensis and Mitragyna inermis, with modern medicinal chemistry approaches, his work represents a fusion of traditional wisdom and cutting-edge science. This dual focus positions his research at the forefront of global health innovation, tackling neglected tropical diseases and contributing to the broader fight against drug resistance in malaria and other parasites.

Publication Top Notes

Title: Unveiling the antimalarial properties of Terminalia ivorensis stem bark aqueous extract
Journal: International Journal of Plant-Based Pharmaceuticals, April 2024. 
Summary: Validated traditional use of T. ivorensis with in vivo tests and docking studies, confirming strong antimalarial potential.

Title: Discovery of a picomolar antiplasmodial pyrazole derivative from MMV Global Health Priority Box
Journal: VeriXiv, October 16, 2024. 
Authors: Mariscal Brice Tchatat Tali; Darline Dize; Aubin Youbi Kamche; Boniface Pone Kamdem; Souleyman Hassan; et al.
Summary: Identified MMV1794211, a pyrazole derivative with exceptional potency (IC50 < 10 pM) against resistant Plasmodium falciparum.

Title: Bio-guided investigation of Mitragyna inermis unveils natural isolates with cross-activity against Plasmodium falciparum
Journal: Journal of Ethnopharmacology, August 7, 2025. 
Summary: Discovered compounds from M. inermis effective against both sensitive and multidrug-resistant malaria strains.

Title: Targeting the intra-erythrocytic life cycle of Plasmodium falciparum using Drymaria cordata and Macaranga monandra
Journal: Journal of Ethnopharmacology, January 2025.
Summary: Identified plant-based compounds disrupting parasite life cycle, providing new antimalarial drug leads.

Conclusion

Souleyman Hassan shows strong potential and suitability for the Best Researcher Award, particularly in the context of young, early-career scientists making impactful contributions to neglected tropical diseases research. His innovative use of open-access compound libraries, his integration of computational and experimental drug discovery, and his discovery of highly potent antiplasmodial derivatives all point to significant promise. With further consolidation through high-impact publications, translational applications, and expanded international collaborations, he is well on track to becoming a leading figure in the field of global health drug discovery.

Naveed Ahmed | Technology Scientists Innovations | Best Researcher Award

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Assist. Prof. Dr. Naveed Ahmed | Technology Scientists Innovations | Best Researcher Award

Assistant Professor at University of Tabuk in Saudi Arabia.

Dr. Naveed Ahmed is a distinguished scientist in Medical Microbiology whose research seamlessly blends laboratory science with clinical impact. Currently serving as Assistant Professor at the University of Tabuk, Saudi Arabia, he earned his Ph.D. from Universiti Sains Malaysia, where he was recognized for academic excellence and timely graduation. His work spans infectious disease diagnostics, antimicrobial resistance mechanisms, nanomedicine applications, and computational vaccine design. With over 46 Q1/Q2 publications, an H-index of 23, Dr. Ahmed has contributed to global health datasets and collaborative studies published in top-tier journals such as The Lancet. His innovations include patented laboratory protocols for microbial diagnostics and immune profiling. Known for his capacity to integrate molecular methods, bioinformatics, and translational science, Dr. Ahmed’s career reflects both depth of expertise and breadth of interdisciplinary collaboration, making him a prominent figure in the global fight against infectious diseases.

Professional Profile

Scopus | Google Scholar | ORCID

Education 

Dr. Ahmed holds a Doctor of Philosophy in Medical Microbiology from Universiti Sains Malaysia. His doctoral research, supported by competitive scholarships and awards, focused on molecular pathogenesis of Epstein–Barr Virus-associated cancers and immune checkpoint modulation. Prior to his Ph.D., he earned a Master of Science in Microbiology from the University of Central Punjab, Pakistan, where he developed expertise in bacteriology, immunology, and clinical diagnostics. His academic journey began with a BS (Honors) in Medical Laboratory Technology from the University of the Punjab, Pakistan, where he cultivated laboratory proficiency and research skills. Throughout his education, Dr. Ahmed actively engaged in research projects, academic presentations, and interdisciplinary collaborations, laying a foundation for high-impact publications and translational innovations. This diverse and rigorous educational background enables him to tackle complex biomedical challenges through both experimental and computational approaches.

Experience 

Dr. Ahmed’s professional trajectory blends academic teaching, laboratory management, and high-impact research. As Assistant Professor at the University of Tabuk, he teaches undergraduate and diploma-level courses, designs curricula, and fosters research collaborations with international teams. Previously, as a Graduate Research Assistant at Universiti Sains Malaysia, he managed grant-funded projects, secured ethical clearances, coordinated multi-institutional studies, and delivered results published in Q1/Q2 journals. Earlier roles as Laboratory Technologist at the Pakistan Kidney and Liver Institute and as Microbiology Supervisor at Chughtai Lab honed his expertise in clinical diagnostics, antimicrobial stewardship, biosafety, and ISO 15189 implementation. His teaching experience includes visiting lectureships at the University of Central Punjab and Imperial College of Business Studies. Across all roles, Dr. Ahmed has demonstrated leadership in laboratory innovation, research project management, and academic mentorship, ensuring his contributions extend from the bench to the classroom and into public health policy.

Research Focus 

Dr. Ahmed’s research focuses on the intersection of microbial pathogenesis, diagnostics, and therapeutic innovation. His investigations into antimicrobial resistance encompass genetic profiling of multidrug-resistant pathogens, elucidating resistance mechanisms induced by heavy metal exposure, and identifying virulence factors in hospital-acquired infections. In virology, he has advanced understanding of Epstein–Barr Virus latency genes and their role in immune checkpoint regulation, with implications for immunotherapy. He also explores nanomedicine, developing carbon-based nanomaterials and bioactive microbial compounds as diagnostic and therapeutic agents against cancer. His computational vaccine design projects leverage immunoinformatics to engineer multi-epitope vaccines targeting high-burden pathogens. Additionally, Dr. Ahmed contributes to global health surveillance datasets, applying systematic review and meta-analysis methods to epidemiological trends. His integrative approach combines molecular biology, bioinformatics, and translational science, aiming to bridge laboratory research with deployable healthcare solutions that address both infectious diseases and oncology in resource-diverse settings.

Awards & Honors 

Dr. Ahmed’s achievements are recognized through numerous competitive awards. He received the Graduate on Time Award (2024) and was nominated for the Best Ph.D. Thesis Award at Universiti Sains Malaysia. His presentation skills earned him 2nd place and the Young Investigator Award at the 9th Regional Conference on Molecular Medicine (2023). He twice won the prestigious Sanggar Sanjung Award (2021, 2022) for best publication-based research among USM students and was recognized as Best Oral Presenter in the departmental journal club (2022). Early in his career, he won Best Poster Presentation at the Annual Conference of Medical Microbiology and Infectious Diseases Society of Pakistan (2020). His research funding success includes grants from the Malaysian Ministry of Higher Education and industry collaborations with Medical Innovation Ventures. Combined with international fellowships and professional memberships, these honors underscore his sustained excellence in research, innovation, and scholarly dissemination.

Publication Top Notes

Title: The Microbial Sources of Bioactive Compounds: Potential Anticancer Therapeutic Options
Authors: Ahmed, N., Abusalah, M. A. H. A., Absar, M., Nasir, M. H., Farzand, A., Ahmad, I., Sohail, Z., Singh, K. K. B., Baig, A. A., & Yean, C. Y.
Journal: Nano Life, Vol. 15, 2430007.
Summary: Microbial metabolites from bacteria and fungi were isolated, characterized, and screened for anticancer activity. Several showed high selectivity and strong molecular target binding, offering sustainable leads for oncology drug development.

Title: Carbon-based Nanomaterials as Multifunctional Particles for Cancer Diagnosis and Treatment
Authors: Ahmed, N., Abusalah, M. A. H. A., Absar, M., Noor, M. S., Bukhari, B., Anjum, S. A., Singh, K. K. B., & Yean, C. Y.
Journal: Nano Life, Vol. 15, 2430005.
Summary: Graphene oxide, carbon nanotubes, and fullerenes were functionalized for targeted cancer imaging and therapy. They enabled enhanced tumor visualization, sustained drug release, and effective photothermal/photodynamic treatment, advancing nanotheranostic applications.

Title: Immunoinformatic Execution and Design of an Anti–Epstein–Barr Virus Vaccine with Multiple Epitopes Triggering Innate and Adaptive Immune Responses
Authors: Ahmed, N., Rabaan, A. A., Alwashmi, A. S., et al.
Journal: Microorganisms, Vol. 11, 2448.
Summary: A computational pipeline identified epitopes from EBV latent and lytic proteins, modeled their MHC binding, and simulated strong immune responses. Codon optimization suggested efficient bacterial expression, supporting rapid vaccine prototyping.

Title: Heavy Metal (Arsenic) Induced Antibiotic Resistance among Extended-Spectrum β-Lactamase (ESBL) Producing Bacteria of Nosocomial Origin
Authors: Ahmed, N., Tahir, K., Aslam, S., et al.
Journal: Pharmaceuticals, Vol. 15, 1426.
Summary: Arsenic in hospital effluents was linked to co-selection of plasmid-borne ESBL and arsenic resistance genes. This co-resistance highlights environmental drivers of antimicrobial resistance and the need for better wastewater control.

Title: Updates on Epstein–Barr Virus (EBV)-Associated Nasopharyngeal Carcinoma: Emphasis on the Latent Gene Products of EBV
Authors: Ahmed, N., Abusalah, M. A. H. A., Farzand, A., Absar, M., Yusof, N. Y., Rabaan, A. A., et al.
Journal: Medicina, Vol. 59, Issue 2.
Summary: This review outlines how EBV latent proteins like LMP1 and EBNA1 drive oncogenesis, evade immunity, and present therapeutic targets, emphasizing potential immunotherapy approaches for endemic regions.

Title: The Antimicrobial Efficacy against Selective Oral Microbes, Antioxidant Activity and Preliminary Phytochemical Screening of Zingiber officinale
Authors: Ahmed, N., Karobari, M. I., Yousaf, A., et al.
Journal: Infection and Drug Resistance,pp. 2773–2785.
Summary: Methanolic and aqueous ginger extracts inhibited oral pathogens and showed strong antioxidant activity linked to high phenolic and flavonoid content, supporting its use in oral health products.

Title: Antibiotic Resistance Profile in Relation to Virulence Genes fimH, hlyA and usp of Uropathogenic E. coli Isolates in Lahore, Pakistan
Authors: Ahmed, N., Zeshan, B., Naveed, M., et al.
Journal: Tropical Biomedicine, Vol. 36, pp. 559–568.
Summary:In clinical isolates, fimH and hlyA genes correlated with multidrug resistance. The findings stress the dual risk of resistance and virulence in urinary tract infections.

Conclusion

Dr. Naveed Ahmed possesses the academic excellence, research productivity, and global engagement expected of a Best Researcher Award recipient. His combination of high-impact publications, patents, conference recognition, and international collaborations demonstrates a clear commitment to advancing knowledge and innovation in medical microbiology and infectious diseases. With continued emphasis on leadership in large-scale research initiatives and translational impact, he is exceptionally well-suited for this award and has strong potential to contribute even more significantly to the scientific community in the future.

Axel Ransinangue | Computer Vision Systems | Best Academic Researcher Award

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Mr. Axel Ransinangue | Computer Vision Systems | Best Academic Researcher Award

PhD Candidate at University of Bordeaux in France.

Axel Ransinangue is a Ph.D. candidate at Bordeaux University, conducting research at the intersection of artificial intelligence and geosciences. Specializing in deep learning for carbonate reservoir characterization, his work integrates advanced image processing, computer vision, and geological interpretation. Axel’s expertise spans Python, MATLAB, TensorFlow, PyTorch, and geospatial tools such as QGIS and ArcGIS, enabling him to develop innovative solutions for analyzing thin section images, petrophysical properties, and hyperspectral datasets. Collaborating closely with TotalEnergies, he has designed semi-supervised classification systems, synthetic data generation pipelines, and multiscale segmentation techniques that bridge synthetic and real-world geological imagery. His contributions extend beyond research, actively engaging in scientific communication through conferences and leading discussions in the computer vision community. Driven by a passion for data-driven science, Axel’s work demonstrates both academic rigor and industrial relevance, making him a promising leader in AI-driven geoscience innovation.

Professional Profile

Google Scholar

Education

Axel holds a Bachelor’s degree in Earth Sciences and Environment with honors from Pau University, where he developed strong foundations in porous media analysis and image processing. He pursued a Master’s degree in Engineering from Bordeaux INP – ENSEGID, graduating with honors, and participated in an international exchange at Kyushu University, Japan, expanding his technical and cultural perspectives. Currently, Axel is a Ph.D. candidate in Artificial Intelligence, Sciences, and Environment at Bordeaux University, working in collaboration with TotalEnergies. His doctoral research integrates AI methodologies with carbonate petrography to enhance reservoir characterization. Under the supervision of experts in geology and computer science, he specializes in representation learning, domain adaptation, and synthetic data conditioning for geological imagery. This interdisciplinary education has equipped him with a unique blend of computational, analytical, and field-specific skills, positioning him at the forefront of AI applications in earth sciences.

Experience 

Axel’s professional experience blends academic research with industrial applications. At TotalEnergies, as a Geologist Intern, he analyzed carbonate thin sections, interpreting petrographic features for reservoir evaluation. Later, as a Data Scientist at AGEOS, he applied hyperspectral imaging to mineralogical quantification, developing regression models and calibrating point cloud acquisitions. His current role as a Ph.D. researcher involves designing deep learning systems for carbonate reservoir characterization, focusing on semi-supervised classification, conditional synthetic dataset generation, and multiscale image segmentation. He has also explored model explainability, ensuring AI decisions are interpretable for geological experts. Additionally, Axel has worked on integrating bi-modal classification models combining imagery with petrophysical data, as well as anomaly detection frameworks. His cross-domain expertise enables the translation of AI methodologies into practical tools for geoscience, creating value both in research and industrial operations.

Research Focus 

Axel’s research lies at the convergence of computer vision, deep learning, and carbonate petrography. His primary objective is to enhance geological image analysis through advanced AI-driven methodologies. Key areas include representation learning with invariance to interpretation biases, synthetic dataset generation conditioned on geological parameters, and domain adaptation to bridge synthetic and real-world imagery. He specializes in texture synthesis, pixel harmonization, and object packing strategies for creating high-quality training data when labeled datasets are scarce. His work also involves developing heuristics-based regularization techniques for improved segmentation accuracy and integrating statistical analysis to link image descriptors with petrophysical properties. By leveraging semi-supervised and multi-modal approaches, Axel aims to create robust and generalizable AI models that address challenges in reservoir characterization. This research not only advances geological sciences but also contributes to broader AI applications in image-based data interpretation across environmental and industrial domains.

Publication Top Notes

Title: SynSection: Sedimentology-driven data generation for deep learning applications in carbonate petrography
Authors: A. Ransinangue, R. Labourdette, E. Houzay, S. Guillon, R. Bourillot, et al.
Journal: Marine and Petroleum Geology, Article ID 107490.
Summary: The study presents SynSection, a framework for generating synthetic carbonate thin section images based on sedimentological parameters. Combining texture synthesis, object packing, and pixel harmonization, it produces realistic datasets to train deep learning models when labeled geological data is scarce. Demonstrated improvements in image classification and segmentation highlight its potential for reservoir characterization and data-driven petrography.

Conclusion

Axel Ransinangue presents a compelling case for recognition as a Best Academic Researcher. The work combines cutting-edge AI methodologies with domain-specific geological expertise, producing research that is both academically valuable and industrially applicable. With ongoing expansion of publication output and interdisciplinary collaborations, the candidate has strong potential to emerge as a leading figure in AI-driven geoscience research. Their contributions already reflect a blend of innovation, rigor, and practical relevance that aligns well with the award’s intent.

Le Xuan-Bach | Advanced Simulation Techniques | Best Researcher Award

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Dr. Le Xuan-Bach | Advanced Simulation Techniques | Best Researcher Award

Postdoctoral at Seoul National University of Science and Technology in South Korea.

Le Xuan Bach is a leading researcher in advanced semiconductor packaging, specializing in fracture mechanics, thermo-mechanical reliability, and structural optimization. Currently serving as a Postdoctoral Researcher at the MEMS and Packaging System Lab, Seoul National University of Science and Technology, he has led multiple high-value, government-funded projects in Korea. His work combines deep theoretical insight with industrial application, focusing on preventing structural failures in semiconductor devices through cutting-edge simulation and optimization techniques. Dr. Bach’s research spans advanced packaging materials, hybrid bonding processes, and glass interposer technologies, addressing critical challenges in electronics manufacturing. His strong publication record in top-tier journals, combined with presentations at major international conferences, underscores his influence in the microelectronics community. With a forward-looking vision for integrating AI-based simulation methods into semiconductor reliability assessment, Dr. Bach continues to shape the future of microelectronics design and manufacturing through innovation, precision, and impactful collaborations.

Professional Profile

Google Scholar | Scopus

Education 

Le Xuan Bach earned his Ph.D. in Nano IT Design Fusion from Seoul National University of Science and Technology, where his dissertation focused on “Assessment and Prevention of Crack Formation in 2.5D Glass Interposer and Hybrid Bonding Structure.” His doctoral research integrated advanced finite element modeling, fracture mechanics, and thermo-mechanical simulations to tackle industrially significant packaging challenges. He previously obtained a Master of Science in Mechanical Engineering from Hanoi University of Science and Technology, completing a thesis on actuator properties of two-dimensional materials for robotic applications. His undergraduate studies in Mechanical Engineering at the same institution explored low-dimensional materials for artificial muscles. This academic progression reflects a strong foundation in mechanical systems, materials science, and computational simulation, enabling him to bridge fundamental research with real-world semiconductor reliability solutions. Each stage of his education has been characterized by innovation, interdisciplinary integration, and application-driven outcomes, forming the backbone of his current expertise.

Experience 

Le Xuan Bach’s professional journey blends academic research with industry-driven problem-solving. Currently, he is a Postdoctoral Researcher at Seoul National University of Science and Technology, specializing in structural and thermal analysis for advanced semiconductor packaging. Previously, he was a Research Student at the International Institute for Computational Science and Engineering in Vietnam, focusing on nanomechanics and stability analysis of 2D materials. His industry roles include collaborating with Maxflow Technology Vietnam on lifetime prediction and structural optimization, and working in product development at Showa Auto Parts Vietnam (2017–2018), where he contributed to mold design and flow simulation. He has served as Principal Researcher on multiple Korean national projects, overseeing large-scale grants for semiconductor reliability enhancement, laser debonding processes, and next-generation interposer development. His career reflects a rare balance between deep scientific inquiry and practical engineering solutions, with measurable impacts on microelectronics manufacturing efficiency and reliability.

Research Focus 

Le Xuan Bach’s research centers on advanced simulation techniques for semiconductor reliability, combining numerical analysis, finite element modeling, and optimization strategies to solve complex manufacturing challenges. His primary focus includes hybrid bonding reliability, prevention of crack formation in 2.5D/3D packaging, warpage-induced stress mitigation, and glass interposer structural integrity. He develops computational models that simulate thermo-mechanical and structural behaviors, enabling predictive lifetime assessment of microelectronic devices. His work extends to vibration analysis for gyroscope sensors, selective EMI shielding technologies, and optimization of laser-assisted bonding processes. Leveraging tools like ANSYS, ABAQUS, and COMSOL, Dr. Bach integrates simulation-driven design into industrial-scale production, improving both performance and durability. His forward vision includes incorporating AI and machine learning into simulation workflows, enabling adaptive, data-driven semiconductor packaging solutions that reduce failure rates, enhance manufacturability, and accelerate technology adoption in emerging electronics and IoT devices.

Publication Top Notes

Title: Assessment of the Risk of Crack Formation at a Hybrid Bonding Interface Using Numerical Analysis
Authors: Le, X. B., & Choa, S. H.
Summary: This study uses advanced finite element modeling to predict crack formation in hybrid bonding interfaces, a key challenge in next-generation semiconductor packaging. The simulation framework captures thermo-mechanical stress distributions with high precision, enabling proactive bonding parameter adjustments. This validated numerical approach reduces costly production failures and supports large-scale manufacturing reliability.

Title: A Comprehensive Numerical Analysis for Preventing Cracks in 2.5D Glass Interposer
Authors: Le, X. B., & Choa, S. H.
Summary: Focused on 2.5D packaging, this paper develops a simulation-driven strategy to predict and mitigate cracking in glass interposers. Through modeling complex temperature and stress cycles, it presents optimized annealing and structural designs that enhance stability and yield in semiconductor production.

Title: Mechanical Reliability Assessment of a Flexible Package Fabricated Using Laser-Assisted Bonding
Authors: Le, X. L., Le, X. B., Hwangbo, Y., Joo, J., Choi, G. M., Eom, Y. S., … & Choa, S. H.
Summary: This work assesses the reliability of flexible semiconductor packages fabricated with laser-assisted bonding. Using multiphysics simulations, it evaluates deformation and interfacial stresses, offering design guidelines to improve durability in wearable and foldable electronics.

Title: Electromechanical Properties of Monolayer Sn-Dichalcogenides
Authors: Bach, L. X., Van Thanh, V., Van Bao, H., Van Truong, D., & Hung, N. T.
Summary: Explores electromechanical properties of monolayer tin dichalcogenides using density functional theory. The results show strain-dependent behavior critical for NEMS applications.

Title: Turning Electronic and Optical Properties of Monolayer Janus Sn-Dichalcogenides by Biaxial Strain
Authors: Van Thanh, V., Dung, N. T., Bach, L. X., Van Truong, D., & Hung, N. T.
Summary: Investigates how biaxial strain alters the electronic and optical characteristics of Janus Sn-dichalcogenides. The study provides simulation-based design pathways for tunable optoelectronics.

Title: Strain Effects on Electronic and Optical Properties of Monolayer Mo-Dichalcogenides
Authors: Van Vuong, T., Nguyen, T. D., Le, X. B., Van, L. G., Van, B. H., Do Wang, T., & Tuan, H. N. 
Summary: Applies computational mechanics to analyze strain impacts on molybdenum dichalcogenide monolayers, revealing tunable band structures and optical absorption profiles for flexible electronics.

Conclusion

Le Xuan Bach presents a compelling case for recognition as a Best Researcher Award recipient. His leadership in high-value projects, consistent publication record, and direct contributions to advancing semiconductor packaging technologies underscore his standing as an accomplished and innovative researcher. With strategic expansion into more interdisciplinary and translational research avenues, his influence and impact are poised to grow even further, making him a highly deserving nominee for the award.

Quoc-Hieu Phan | Logistics | Best Researcher Award

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Mr. Quoc-Hieu Phan | Logistics | Best Researcher Award

PhD Student at Chaoyang University of Technology in Taiwan.

Quoc-Hieu Phan is a Ph.D. student at Chaoyang University of Technology in Taiwan and a former lecturer in Ho Chi Minh City, Vietnam. His academic focus lies in the intersection of logistics, AI, and knowledge management, driven by the growing complexity and sustainability demands of global supply chains. With a strong foundation in applied research, he has published in both Q1 and Q2 journals, positioning himself as an emerging voice in logistics innovation. His work leverages neutrosophic logic and decision-making models to address pressing challenges in reverse logistics and ESG implementation within emerging economies. With a clear orientation toward sustainability and operational intelligence, Phan’s research continues to bridge the gap between theoretical modeling and practical application, earning him recognition within the academic community.

Professional Profile

ORCID

Education 

Quoc-Hieu Phan is currently pursuing his doctoral studies at Chaoyang University of Technology, Taiwan. His academic progression reflects a strong commitment to both teaching and research. Prior to enrolling in the Ph.D. program, he served as a lecturer in Vietnam, where he developed expertise in logistics systems, management science, and interdisciplinary problem-solving frameworks. His formal education is complemented by his practical experiences and early involvement in academic publishing. Although specific undergraduate and master’s credentials are not detailed, his role as a university lecturer and active researcher indicates a robust educational background grounded in logistics, operations research, and decision sciences. His doctoral training at Chaoyang University of Technology has enabled him to explore advanced modeling techniques such as Neutrosophic and DEMATEL frameworks, particularly in the context of sustainability and supply chain optimization.

Experience 

Before embarking on his Ph.D. journey, Quoc-Hieu Phan served as a lecturer in logistics and management in Ho Chi Minh City, Vietnam. His teaching roles allowed him to mentor undergraduate students, design logistics-focused curricula, and supervise early-stage research projects. This teaching experience enriched his research, helping him stay grounded in real-world logistics problems faced by emerging economies. His academic exposure transitioned into high-quality research outputs upon joining Chaoyang University of Technology, where he has focused intensively on sustainability and operational challenges in logistics. Phan’s practical insight into Southeast Asian logistics infrastructure and policy has played a crucial role in shaping his scholarly contributions. His dual engagement in education and research enables him to maintain a balance between theoretical advancement and application-oriented innovation.

Research Focus

Quoc-Hieu Phan’s research centers on sustainable logistics, reverse logistics optimization, knowledge sharing frameworks, and the integration of artificial intelligence into supply chain design. His methodological toolkit includes Neutrosophic logic, Delphi methods, and the DEMATEL approach, which he applies to multi-criteria decision-making problems. A distinguishing feature of his work is the focus on ESG (Environmental, Social, and Governance) dimensions of logistics—particularly in emerging economies where sustainability transitions are both urgent and complex. His studies aim to identify critical barriers and propose robust, adaptive frameworks for strategic planning and policy design. Through his research, Phan contributes to the advancement of resilient and environmentally responsible supply chains, offering both theoretical innovation and actionable insights. His work stands at the confluence of computational intelligence, sustainability science, and logistics operations.

Publication Top Notes

Title: Towards sustainable logistics in emerging economies: Identifying ESG barriers using newtrosophic delphi-dematel model
Authors: Quoc-Hieu Phan; Thanh-Ngan Le; Phi-Hung Nguyen; Lan-Anh Thi Nguyen; Tra-Giang Vu
Journal: Journal of Open Innovation: Technology, Market, and Complexity
Summary: This paper addresses the lack of structured ESG (Environmental, Social, Governance) strategies in the logistics sector of emerging economies. Using the Neutrosophic Delphi-DEMATEL model, the authors identify critical ESG barriers that hinder sustainable logistics implementation. This hybrid decision-making model allows for uncertainty, fuzziness, and expert-driven feedback loops, leading to a prioritized set of challenges and corresponding intervention pathways. The paper offers a systemic approach to ESG integration, contributing to policymaking and sustainable supply chain redesign in emerging markets.
Authors: Thanh-Ngan Le; Quoc-Hieu Phan; Phi-Hung Nguyen; Lan-Anh Thi Nguyen
Repository: Zenod

Title: Rethinking Reverse Logistics: Neutrosophic Strategies for Warehouse Management Challenges
Authors: Thanh-Ngan Le; Quoc-Hieu Phan; Phi-Hung Nguyen; Lan-Anh Thi Nguyen
Repository: Zenodo
Summary: This article explores reverse logistics challenges in warehouse environments and proposes neutrosophic logic-based solutions for strategic decision-making. The authors model uncertain conditions in warehouse operations, such as product returns, restocking complexities, and space utilization inefficiencies. Their neutrosophic framework improves responsiveness and resilience in reverse logistics systems by quantifying uncertainty and offering flexible decision alternatives. The research has practical implications for warehouse managers aiming to enhance efficiency in post-consumer logistics.

Conclusion

This article explores reverse logistics challenges in warehouse environments and proposes neutrosophic logic-based solutions for strategic decision-making. The authors model uncertain conditions in warehouse operations, such as product returns, restocking complexities, and space utilization inefficiencies. Their neutrosophic framework improves responsiveness and resilience in reverse logistics systems by quantifying uncertainty and offering flexible decision alternatives. The research has practical implications for warehouse managers aiming to enhance efficiency in post-consumer logistics.

Vahid Yahyapour Ganji | Machine Learning Applications | Best Researcher Award

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Mr. Vahid Yahyapour Ganji | Machine Learning Applications | Best Researcher Award

Ph.D. Candidate at Kharazmi Universtiy in Iran.

Vahid Yahyapour Ganji is a supply chain researcher and analyst with a deep-rooted expertise in data-driven decision-making, mathematical optimization, and logistics systems. With a career bridging academia and industry, he has contributed to several high-impact studies on supply chain resilience, sustainability, and robust network design. Currently a Supply Chain Business Analyst at Farapokht, Tehran, he supports strategic procurement and risk analysis using advanced modeling tools. He is the co-author of multiple journal articles focused on optimization under uncertainty, vehicle routing, and digital transformation in logistics. His recent work emphasizes circular supply chains and integrates machine learning principles for performance evaluation. Vahid’s pragmatic background in LTL logistics, production planning, and systems analytics enhances his ability to approach research with operational insight. His analytical thinking and interdisciplinary skill set position him at the forefront of real-world machine learning applications in industrial systems.

Professional Profiles

Google Scholar | ORCID

Strengths for the Award

Vahid Yahyapour Ganji demonstrates a strong and evolving research trajectory in the fields of supply chain engineering, optimization, and logistics. His work, especially the recent publication on robust and data-driven circular supply chain networks, showcases a sophisticated understanding of resilience and responsiveness—key pillars in modern supply chain design. This research is particularly relevant in today’s dynamic socio-economic context where adaptability and sustainability are critical.

He has consistently engaged with high-impact problems through advanced mathematical modeling, optimization under uncertainty, and multi-objective frameworks. His publication record spans reputable journals and includes topics such as sustainable vehicle routing, hierarchical hub location problems, and digital resilience frameworks, indicating breadth as well as depth in his domain.

Moreover, his academic background is fortified by a top-ranked Master’s degree, where he was awarded a full scholarship and ranked third among graduates. He complements this academic excellence with a diverse set of practical experiences in project planning, supply chain supervision, and business analytics—contributing to the real-world relevance of his research. His technical proficiency in tools such as Python, GAMS, Power BI, and AnyLogistix further underlines his readiness to tackle data-intensive, complex modeling tasks.

Education Summary 

Vahid Yahyapour Ganji earned his Master of Science in Logistics and Supply Chain Engineering from Kharazmi University (Tehran), where he graduated with distinction. His thesis focused on multi-objective mathematical modeling for hierarchical hub locations under congestion and uncertainty—a theme consistent throughout his later work. His coursework emphasized simulation, optimization, and multi-criteria decision-making using fuzzy logic and probabilistic tools. Prior to his master’s degree, he completed a Bachelor’s in Industrial Engineering at Iran University of Science and Technology, with a thesis focused on stock index prediction using artificial neural networks. This early interest in machine learning laid the groundwork for his future data-driven research. His academic foundation is further enriched by practical knowledge in transportation systems, logistics design, and applied operations research, positioning him as a data-literate problem-solver capable of advancing industrial applications through innovative algorithmic approaches.

Professional Experience

Vahid Yahyapour Ganji’s professional journey showcases a progression through multiple strategic and analytical roles across Iran’s industrial sector. He began as a Project Planning Engineer at Omran Sazan Mahab, managing EPS infrastructure timelines and resource allocation. At Tipax, he played a pioneering role in Iran’s first Less Than Truckload (LTL) service, overseeing pricing and last-mile logistics. He then served as Production Planning Supervisor at Nouyan Negin Parsian, where he led forecasting and BPMN-driven process improvements. As Product Manager and Sales Planning Manager at Tejarat Gostar Arisa, he shaped B2B product portfolios and built performance dashboards to streamline operations. Presently, at Farapokht, he drives supply chain analytics, trend forecasting, and vendor evaluation. Across these roles, he integrates business intelligence tools, such as Power BI and simulation platforms, with domain expertise—bridging data science and operations to deliver strategic outcomes.

Research Focus

Vahid Yahyapour Ganji’s research lies at the intersection of machine learning, supply chain optimization, and decision-making under uncertainty. His central focus is on developing robust, data-driven models that enhance supply chain resilience, circularity, and responsiveness. He employs advanced operations research techniques—such as multi-objective programming, stochastic modeling, and fuzzy systems—to address real-world logistics challenges. Vahid is particularly invested in integrating machine learning algorithms to optimize performance evaluations, sustainability metrics, and network structures. His work spans topics such as sustainable vehicle routing under variable traffic, hub location modeling with congestion, and DLARG (Digital, Lean, Agile, Resilient, Green) frameworks for energy systems. His methodological contributions emphasize adaptability, scalability, and real-time responsiveness—vital qualities for modern logistics systems in uncertain environments. His deep understanding of non-convex optimization and simulation tools empowers him to craft innovative, machine-learning-enabled solutions for global supply chain challenges.

Award and Honor

Vahid Yahyapour Ganji has been consistently recognized for his academic and analytical excellence. During his Master’s studies at Kharazmi University, he ranked third among his cohort and was awarded a full three-year academic scholarship in recognition of his academic performance and research potential. His leadership in multiple industry-academic research projects has also been acknowledged through co-authorships with senior researchers and repeated invitations to collaborate on optimization-centric publications. His participation in national conferences on entrepreneurship and business management adds to his scholarly contributions. Furthermore, his academic track record—coupled with his interdisciplinary research output—demonstrates not only individual achievement but also a commitment to solving large-scale, practical challenges in logistics and operations. These distinctions, along with his growing presence in peer-reviewed publications, make him a noteworthy candidate for recognition in machine learning application research.

Publication Top Notes

Title: A robust design of a circular supply chain network based on the resilience and responsiveness dimensions: A data-driven model
Authors: Vahid Yahyapour Ganji, Ehsan Hozan, Parisa Babolhavaeji, AmirReza Tajally, Mohssen GhanavatiNejad
Journal: Socio-Economic Planning Sciences, July 2025
Summary:
This article proposes a robust framework for designing circular supply chain networks by incorporating resilience and responsiveness as dual performance dimensions. The model employs a data-driven optimization approach that integrates real-time variability, uncertainty, and recovery capabilities, using machine learning-inspired data structuring. The authors provide case-based validation demonstrating how the model enhances network agility and sustainability.

Conclusion

Overall, Vahid Yahyapour Ganji presents a highly promising profile for the Best Researcher Award. His ability to combine theoretical rigor with practical insight into sustainable supply chain systems is a significant asset. His recent work on resilient and responsive circular supply chains addresses a critical global challenge and reflects a mature, impactful research direction. With further development of his publication portfolio and broader academic engagement, he stands out as a strong candidate deserving of recognition for his research contributions.

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.