Abirami Karthikeyan | Engineering | Young Scientist Award

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Dr. Abirami Karthikeyan | Engineering | Young Scientist Award

Assistant Professor | SASTRA Deemed University | India.

Dr. Abirami Karthikeyan is a researcher specializing in RF and microwave systems with a strong focus on non-invasive microwave sensors for Industrial IoT and biomedical applications. Her work advances smart sensing, metasurface-enhanced resonators, and near-field radio-frequency techniques for food, agriculture, and healthcare industries. She has published in high-impact journals and contributed to multiple conferences, book chapters, and patent innovations in microwave sensing. Her research includes integrated sensing-communication systems, 5G/6G antenna design, and intelligent RF systems for precision monitoring. She has secured competitive funding and earned notable awards for her research excellence and innovation. Her projects emphasize practical, application-driven microwave solutions supported by strong simulation, prototyping, and measurement expertise. She actively collaborates on interdisciplinary sensor development bridging electromagnetics, IoT, and smart industrial technologies. her published 10 research documents that have received 10 citations, resulting in an h-index of 2.

Citation Metrics (Scopus)

20

15

10

5

0

Citations
10
Documents
10

h-index
2


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Featured Publications

 

Keabetsoe Manosa | Chemical Engineering | Young Researcher Award

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Mr. Keabetsoe Manosa | Chemical Engineering
| Young Researcher Award

Mersin University | Turkey

Mr. Keabetsoe Manosa  study investigates the hydrogen-storage potential of AB₂-type cluster systems based on Magnesium–Titanium (Mg–Ti) and Magnesium–Nickel (Mg–Ni), focusing on their economic feasibility, effectiveness, safety profile, and proximity to optimal thermodynamic and physicochemical conditions for maximum hydrogen retention. The research evaluates key material parameters including enthalpy of formation, activation energy, hydride stability, charge distribution, atomic radii compatibility, and lattice behavior under varying temperature–pressure conditions. Comparative computational analyses reveal how alloying magnesium with transition metals enhances hydrogen diffusion pathways, reduces desorption barriers, and influences reversible storage capacity. The Mg–Ti system is examined for its lightweight composition, favorable thermodynamic window, and potential cost efficiency, while the Mg–Ni system is assessed for catalytic enhancement, structural robustness, and effective hydrogen absorption–desorption kinetics. The study integrates principles of materials thermodynamics, solid-state chemistry, and cluster theory to determine which system aligns more closely with optimal storage metrics required for scalable applications in clean-energy technologies. Overall, the analysis provides insight into the tunability of Mg-based alloys, highlighting their comparative strengths and limitations in meeting industrial hydrogen-storage demands and contributing to the broader pursuit of high-performance, safe, and economically viable energy-storage materials.

Featured Publications

Manosa, K. (2025, July 30). The comparison in the degree of economic feasibility, effectiveness, safety and the proximity to the optimum conditions needed for the maximum storage of hydrogen gas in AB₂-type cluster systems of Magnesium–Titanium and Magnesium–Nickel based on the relevant physical and chemical properties: The Mpoetsi Manosa study (Version 2) [Preprint]. ChemRxiv. https://doi.org/10.26434/chemrxiv-2025-wkpn4-v2

Manosa, K. (2025, June 23). The comparison in the degree of economic feasibility, effectiveness, safety and the proximity to the optimum conditions needed for the maximum storage of hydrogen gas in AB₂-type cluster systems of Magnesium–Titanium and Magnesium–Nickel based on the relevant physical and chemical properties: The Mpoetsi Manosa study [Preprint]. ChemRxiv. https://doi.org/10.26434/chemrxiv-2025-wkpn4

Heba Afify | Engineering | Editorial Board Member

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Dr. Heba Afify | Engineering | Editorial Board Member

Cairo | Egypt

Dr. Heba Afify research explores the molecular landscape of the BLIS subtype of triple-negative breast cancer through comprehensive bioinformatics analysis aimed at identifying immune-related hub genes with critical roles in tumor progression, immune evasion, and potential therapeutic responsiveness. Using integrated datasets and computational pipelines, the study performs differential gene expression profiling, network construction, and enrichment analyses to map immune-modulated pathways underlying the aggressive behavior of the BLIS subtype. Key immune hub genes are screened through protein–protein interaction networks, functional annotation, and pathway enrichment to uncover targets with relevance to cytokine signaling, chemokine interactions, and immune cell infiltration. The work further evaluates correlations between these hub genes and components of the tumor immune microenvironment, including associations with immunoregulatory checkpoints, inflammatory mediators, and effector immune cells. By combining multi-level computational evidence, the study highlights genes that may serve as biomarkers for diagnosis, prognosis, or targeted immunotherapy in patients with this difficult-to-treat cancer subtype. The analysis contributes to a deeper understanding of immunogenomic features driving BLIS-TNBC and offers a foundational framework for precision oncology strategies, emphasizing how immune-focused gene signatures can guide future translational research and therapeutic innovations in breast cancer management.

Featured Publications

Adel, H., Abdel Wahed, M., & Afify, H. M. (2025). Bioinformatics analysis for immune hub genes in BLIS subtype of triple-negative breast cancer. Egyptian Journal of Medical Human Genetics. https://doi.org/10.1186/s43042-025-00745-0

Afify, H. M., Mohammed, K. K., & Hassanien, A. E. (2025). Stress detection based EEG under varying cognitive tasks using convolution neural network. Neural Computing and Applications, Advance online publication. https://doi.org/10.1007/s00521-024-10737-7

Afify, H. M., Mohammed, K. K., & Hassanien, A. E. (2024). Insight into automatic image diagnosis of ear conditions based on optimized deep learning approach. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-023-03422-8

Waleed Algriree | Engineering | Editorial Board Member

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Dr. Waleed Algriree | Engineering | Editorial Board Member

Putra university malaysia | Malaysia

Dr. Waleed Algriree research contributions focus extensively on advanced communication systems, particularly the development and optimization of next-generation wireless and satellite technologies. Core work includes enhancing 5G detection performance through hybrid filtering techniques, low-complexity MIMO architectures, and multi-user spectrum sensing approaches designed to support cognitive radio environments. Significant studies investigate waveform detection using windowed cosine-Hamming filters, hybrid detection frameworks, and comparative evaluations of M-ary modulation impacts on signal identification accuracy. Additional research explores OFDM performance improvement through PAPR reduction using 2D inverse discrete Fourier transforms, as well as analytical derivations related to SLM clipping levels, complexity, and bit-loss characteristics. Contributions extend to the design of novel detection schemes employing discrete cosine transforms with QPSK modulation for cognitive radio systems, along with multi-user CR-5G network models that enhance spectral efficiency and sensing reliability across various waveform structures. Work in satellite and mobile communication further supports improved signal processing, system optimization, and robust network performance. Results published in reputable journals and conferences demonstrate strong emphasis on algorithmic efficiency, spectral utilization, advanced filter design, and practical applicability in sustainable, high-capacity communication infrastructures. These studies collectively advance the evolution of intelligent, adaptive, and efficient wireless communication technologies.

Featured Publication

Algriree, W. K. H. (Year). Advancing healthcare through piezoresistive pressure sensors: A comprehensive review of biomedical applications and performance metrics.

Uwayesu Happy Edwards | Engineering | Excellence in Research Award

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Mr. Uwayesu Happy Edwards | Engineering | Excellence in Research Award

Suzhou university of science and technology | China

Mr. Uwayesu Happy Edwards the research focuses on environmental engineering, natural resource assessment, wastewater treatment modeling, hydropower system analysis, and climate-related environmental degradation across East and Central Africa. Recent work investigates the factors driving water quality changes in Lake Bunyonyi, integrating ecological metrics with habitat-impact assessments. Studies on wastewater treatment processes include large-scale evaluation of ASM1 parameters under subtropical climatic conditions, using long-term WWTP monitoring data to improve predictive reliability and optimize treatment efficiency. Broader environmental impact assessments examine risk patterns in natural resource zones across Southern Nigeria, Ibo regions, and Uganda’s Kitezi landfill, applying quantitative environmental models to evaluate pollution, habitat stress, and human–ecosystem interaction. Additional research explores deforestation-driven climate change in Morogoro, Tanzania, emphasizing the environmental implications for EPA-related conservation missions. Work on hydropower comparability analyzes the performance, sustainability, and environmental footprints of hydropower relative to fossil fuels and other energy systems in developing countries, contributing to renewable-energy assessment frameworks. Complementary studies investigate biomass arrangement effects on aquatic ecosystems, using vibrational analysis to evaluate impacts on fish habitats in Lake Victoria. Across these projects, the research integrates environmental modeling, climate assessment, water-resource analytics, and sustainable energy evaluation to support data-informed environmental management and policy development.

Featured Publications

Uwayesu, H. E., & Mulangila, J. (2025). Factor contributing to change of water in Lake Bunyonyi [Dataset]. Figshare. https://doi.org/10.6084/m9.figshare.30041587

Uwayesu, H. E. (2025). Address of Edwards line of emissions in reducing/positive impact to climate [Dataset]. OSF. https://doi.org/10.17605/osf.io/csz8x

 Uwayesu, H. E. (2025). Environmental impact and risk assessment of natural resource areas around Southern Nigeria, particularly Ibo and Uganda in the Kitezi landfill [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/EJ4Z7E

 Uwayesu, H. E. (2025). Evaluation of ASM1 parameters using large-scale WWTP monitoring data from a subtropical climate in Entebbe [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/BG5VJB

Yunwen Xu | Engineering | Best Researcher Award

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Dr. Yunwen Xu l Engineering
| Best Researcher Award

Shanghai Jiao Tong University | China

Dr. Yunwen Xu’s research focuses on advancing intelligent transportation systems, autonomous driving control, and predictive control for complex and embedded systems. Her innovative work integrates graph-based spatial-temporal modeling, data-driven control algorithms, and real-time optimization to enhance vehicle trajectory prediction, traffic signal management, and collaborative control in large-scale dynamic environments. Through over 50 high-impact publications, including 15 in top-tier journals and several ESI highly cited papers, Dr. Xu has significantly contributed to the theoretical and practical foundations of predictive control and intelligent mobility. Her research achievements include developing FPGA-based predictive controllers, robust model predictive frameworks, and reinforcement learning-based control systems for V2X-enabled autonomous vehicles. By leading national and provincial research projects and collaborating internationally with institutions like Purdue University and industrial partners such as Shanghai Electric Wind Power Group, she bridges the gap between academic innovation and industrial application. Her patents and successful technology transfers in microgrid energy management and advanced temperature control demonstrate the translational strength of her research. Recognized with prestigious honors, including the Best Paper Award at the Chinese Process Control Conference and championship at the Autonomous Driving Algorithm Challenge, Dr. Xu continues to pioneer next-generation control and automation technologies that drive the evolution of intelligent, efficient, and sustainable transportation ecosystems.

Profile:  Google Scholar 

Featured Publications

Mujeeb Abiola Abdulrazaq | engineering | Young Scientist Award

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Mr. Mujeeb Abiola Abdulrazaq l engineering
| Young Scientist Award

University of North Carolina at Charlotte | United States

Mr. Mujeeb Abiola’s research focuses on advancing transportation safety and efficiency through data-driven methodologies and emerging technologies. His work extensively employs large-scale traffic and crash data, including millions of federal highway administration records, to investigate the spatiotemporal dynamics of pedestrian crashes and the evolution of crash hotspots. Utilizing advanced statistical and machine learning models, he has developed predictive frameworks that outperform traditional Highway Safety Manual standards, providing robust insights into risk factors and injury severity in both human-driven and autonomous vehicle contexts. His research on connected and autonomous vehicles (CAVs) has led to the development of traffic control algorithms that significantly enhance safety, operational efficiency, and environmental sustainability in freeway work zones. Furthermore, his studies integrate GPU-accelerated data processing, simulation-based optimization, and multi-level heterogeneity modeling to evaluate vulnerable road user behavior and assess dynamic collision risks. Through simulation platforms such as VISSIM and SUMO, combined with Python-based data analysis and GIS applications, his work systematically addresses complex traffic scenarios, including merging, diverging, and weaving segments, while also accounting for seasonal variations and temporal constraints in crash determinants. His contributions include empirical analyses of autonomous vehicle incidents, methodological advancements in microsimulation accuracy, and development of actionable strategies for real-world traffic management, ultimately aiming to improve roadway safety, inform policy, and guide evidence-based planning in modern transportation systems.

Profile:  Google Scholar 

Featured Publications

  • Abdulrazaq, M. A., & Fan, W. D. (2024). Temporal dynamics of pedestrian injury severity: A seasonally constrained random parameters approach. International Journal of Transportation Science and Technology, 9.

  • Abdulrazaq, M. A., & Fan, W. (2025). A priority based multi-level heterogeneity modelling framework for vulnerable road users. Transportmetrica A: Transport Science, 1–34. https://doi.org/10.1080/23249935.2025.2516817

  • Abdulrazaq, M. A., & Fan, W. (2025). Seasonal instability in crash determinants: A partially temporally constrained modeling analysis. SSRN 5341417. https://doi.org/10.2139/ssrn.5341417

Sheharyar Khan | Engineering | Young Scientist Award

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Dr. Sheharyar Khan l Engineering
| Young Scientist Award

Shandong University | Pakistan

Dr. Sheharyar Khan is a distinguished computer scientist and software engineer with extensive expertise in software engineering, artificial intelligence, and cybersecurity, specializing in IoMT edge-cloud frameworks and network intrusion detection systems. Currently a Postdoctoral Research Fellow at Shandong University, he leads independent and collaborative research initiatives, designing experiments, analyzing data, and publishing findings in high-impact journals. His doctoral research at Northwestern Polytechnical University focused on optimization-based hybrid offloading frameworks for IoMT in edge-cloud healthcare systems, demonstrating the integration of advanced computing techniques with practical healthcare applications. Dr. Khan has made significant contributions to explainable AI and hybrid ensemble machine learning, as seen in publications such as “HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems” and “Consensus hybrid ensemble machine learning for intrusion detection with explainable AI”. With prior experience as a lecturer and IT specialist, he combines academic rigor with practical software development expertise. Dr. Khan has 104 citations across 10 documents, an h-index of 6, an i10-index of 5, is indexed under Scopus Author ID 57221647889, and holds ORCID 0000-0002-0089-0168, reflecting his impact on the field. Recognized for his analytical skills, innovation, and interdisciplinary research, he continues to advance secure, intelligent, and explainable computing systems for both academic and real-world applications.

Profile: Scopus | Google Scholar | Orcid | Researchgate 

Featured Publications

Khan, S., Liu, S., Pan, L., & Mei, G. (2025). Optimization-based hybrid offloading framework for IoMT in edge-cloud healthcare systems. Future Generation Computer Systems, 108163. https://doi.org/

Ahmed, S. K. M. T. S., Jiangbin, Z., & Khan, S. (2025). HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems. Cluster Computing, 28(343). https://doi.org/

Ahmed, M. T. S., Jiangbin, Z., & Khan, S. (2024). Consensus hybrid ensemble machine learning for intrusion detection with explainable AI. Journal of Network and Computer Applications, 5*. https://doi.org/

Khan, S., Jiangbin, Z., & Ali, H. (2024). Soft computing approaches for dynamic multi-objective evaluation of computational offloading: A literature review. Cluster Computing, 27(9), 12459–12481. https://doi.org/

Heyu Peng | Engineering | Best Researcher Award

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Mr. Heyu Peng | Engineering | Best Researcher Award

Xi’an Jiaotong University | China

Heyu Peng is an emerging researcher in the field of nuclear science and technology, currently pursuing his doctoral studies at the School of Nuclear Science and Technology, Xi’an Jiaotong University, China, since March . His research primarily focuses on the development and application of advanced computational methods in nuclear engineering, particularly Monte Carlo particle-transport simulations and coupled deterministic–stochastic modeling approaches. He has contributed to significant advancements in the refinement of nuclear simulation tools, demonstrating his expertise in improving accuracy, efficiency, and applicability for nuclear reactor analysis and radiation transport problems. he co-authored a paper published in IEEE Transactions on Nuclear Science that presented a coupled deterministic and Monte Carlo method for modeling and simulating self-powered neutron detectors, a study that addressed critical aspects of detector response modeling and its implications for nuclear instrumentation and monitoring. More recently, a cutting-edge computational tool designed to enhance nuclear reactor physics simulations and broaden its utility in research and practical applications. Through these publications, Peng has established himself as a promising researcher contributing to the advancement of computational nuclear science. His work reflects a strong commitment to bridging theoretical development with real-world applications, offering tools and methodologies that can improve safety, efficiency, and innovation in nuclear energy systems. As a doctoral candidate, Peng continues to expand his research profile, collaborating with experts in the field and contributing to interdisciplinary efforts in nuclear engineering. His growing academic contributions highlight his potential to become a leading researcher in nuclear science, with a focus on computational methods that can shape the future of nuclear technology and its safe, sustainable applications.

Profile: Orcid

Featured Publications

  • He, Q., Zheng, Q., Li, J., Huang, Z., Huang, J., Qin, S., Shu, H., Peng, H., Yang, X., Shen, J., et al. (2024). Overview of the new capabilities in the Monte-Carlo particle-transport code NECP-MCX V2.0. EPJ Nuclear Sciences & Technologies.

  • Zhou, Y., Cao, L., He, Q., Feng, Z., & Peng, H. (2022). A coupled deterministic and Monte-Carlo method for modeling and simulation of self-powered neutron detector. IEEE Transactions on Nuclear Science.

 

Rayk Fritzsche | Engineering | Best Scholar Award

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Dr. Rayk Fritzsche | Engineering | Best Scholar Award

Gruppenleiter at Fraunhofer IWU, Germany

Dr.-Ing. Rayk Fritzsche is a distinguished researcher and group leader at Fraunhofer IWU, specializing in adaptable assembly systems and intelligent manufacturing. With a Dr.-Ing. (magna cum laude) from TU Dresden, he has made significant contributions to automation, AI-driven assembly, and car body manufacturing. His six patents and numerous peer-reviewed publications in CIRP, IEEE, and Automatica highlight his innovative work in industrial automation. Dr. Fritzsche’s research integrates artificial intelligence, robotics, and software-assisted design, making impactful advancements in automotive, aerospace, and fuel cell production. His Best Paper Award at CIRP ICME 2022 underscores his excellence in academic contributions. Beyond research, his leadership at Fraunhofer IWU and collaborations with industry leaders drive innovation in smart manufacturing. To further enhance his global recognition, expanding interdisciplinary projects and academic mentorship could elevate his influence in the field. His expertise and contributions make him a strong candidate for the Best Scholar Award.

Professional Profile

Education

Dr. Rayk Fritzsche’s educational journey reflects a blend of athletic excellence and academic rigor. He graduated from the Sportgymnasium Chemnitz with an Abitur in 1996, after years of pursuing speed skating at a competitive level. Following this, he transitioned into mechanical engineering, earning a Dipl.-Ing. degree from TU Chemnitz in 2009, specializing in construction and drive technology. His academic path was marked by internships and practical experiences, including at BMW and IAV GmbH, where he gained hands-on exposure to quality management and powertrain development. Dr. Fritzsche’s commitment to further education led him to pursue a doctoral degree at TU Dresden, where he successfully completed his dissertation in 2022 with the distinction magna cum laude. His thesis focused on adaptable assembly systems, solidifying his expertise in advanced manufacturing technologies and positioning him as a leader in the field of intelligent production systems.

Professional Experience

Dr. Rayk Fritzsche has had a distinguished career at Fraunhofer IWU, where he has held several key positions since 2009. After starting as an assistant scientist in 2009, he quickly advanced to become a research associate and later a group leader in the Assembly Systems Department, focusing on body construction and assembly. By 2018, he was appointed deputy head of the department, leading research in adaptable assembly systems. Dr. Fritzsche’s leadership culminated in his current role as group leader in charge of adaptable assembly systems at Fraunhofer IWU. His professional experience is complemented by valuable internships and roles at BMW Leipzig and IAV GmbH, where he focused on quality management and powertrain development. Throughout his career, Dr. Fritzsche has consistently contributed to cutting-edge research and technological advancements in intelligent manufacturing, automation, and AI-driven assembly systems, influencing both industry and academia.

Research Interest

Dr. Rayk Fritzsche’s research interests focus on advancing intelligent manufacturing and automation technologies with a particular emphasis on adaptable assembly systems. He is deeply engaged in the integration of artificial intelligence and robotics into industrial production, aiming to enhance flexibility, efficiency, and precision in assembly processes. His work addresses key challenges in automated fixture design, utilizing software-supported systems for positioning and clamping in car body manufacturing. Additionally, Dr. Fritzsche explores the use of mathematical algorithms and geometry-based search methods to optimize production workflows and reduce resource consumption. His research also extends to advanced AI applications, including machine learning for optimizing assembly system configurations and leveraging virtual reality and augmented reality for real-time process improvements. Dr. Fritzsche’s interests span across high-rate production, fuel cell manufacturing, and bio-inspired design, positioning him at the forefront of innovation in smart and sustainable manufacturing.

Award and Honor

Dr. Rayk Fritzsche has received several notable awards and honors in recognition of his groundbreaking contributions to intelligent manufacturing and automation. One of his most distinguished accolades is the Best Paper Award in 2022 at the CIRP ICME Conference, for his innovative work on software-assisted clamping point classification and position optimization for flexible car body fixtures. This recognition highlights his excellence in applying mathematical geometry-based algorithms to optimize production processes. In addition to this prestigious award, Dr. Fritzsche holds multiple patents for his inventions in automated fixture systems and adaptable assembly technologies, underscoring his impact on the industrial sector. His extensive contributions to both academic and practical advancements in automation, robotics, and AI in manufacturing have earned him recognition as a leader in his field. Dr. Fritzsche’s work continues to influence manufacturing practices, ensuring his place among top researchers in industrial engineering.

Conclusion

Dr. Rayk Fritzsche is highly suitable for the Best Scholar Award due to his strong research output, patents, industry impact, and academic excellence. His contributions to intelligent manufacturing, automation, and AI-driven assembly systems place him among top scholars in his field. While already highly accomplished, expanding international collaboration and interdisciplinary research could further enhance his scholarly profile.

Publications Top Noted

  • Title: Computer-based design and development of a fully automated assembly of aircraft doors made of thermoplastic composite material
    Authors: Fritzsche, R., Jäger, E.
    Year: 2024
    Citations: 0
  • Title: Development of a suction gripper network based on the biological role model of an octopus
    Authors: Fritzsche, R., Kunze, H., Jäger, E.
    Year: 2024
    Citations: 0
  • Title: Autonomous assembly and disassembly by cognition using hybrid assembly cells
    Authors: Frieß, U., Oberfichtner, L., Hellmich, A., Fritzsche, R., Ihlenfeldt, S.
    Year: 2023
    Citations: 0
  • Title: Software support for the development of flexible plant technology in highly automated and high-rate body-in-white production
    Authors: Fritzsche, R., Ahrens, A.
    Year: 2023
    Citations: 0
  • Title: Autonomous assembly and disassembly – Key technologies and links for the adaptive self-optimization of future circular production
    Authors: Ihlenfeldt, S., Lorenz, M., Frieß, U., Fritzsche, R.
    Year: 2023
    Citations: 0
  • Title: Automated gripper design | DesignAssistant – multikriterielle optimierte Konstruktion mit digitalen Baukästen Automatisierter Greiferentwurf
    Authors: Ahrens, A., Oberfichtner, L., Richter-Trummer, V., Frieß, U., Ihlenfeldt, S.
    Year: 2022
    Citations: 0
  • Title: Solving a multi-dimensional matching problem for grouping clamping points on car body parts
    Authors: Oberfichtner, L., Ahrens, A., Fritzsche, R., Richter-Trummer, V., Todtermuschke, M.
    Year: 2022
    Citations: 3
  • Title: Software assisted clamping point classification and position optimization for the efficient flexibilization of carbody fixtures using mathematical geometry-based search algorithms
    Authors: Fritzsche, R., Schaffrath, R., Todtermuschke, M.
    Year: 2021
    Citations: 4
  • Title: Automated design of product-flexible car body fixtures with software-supported part alignment using particle swarm optimization
    Authors: Fritzsche, R., Voigt, E., Schaffrath, R., Todtermuschke, M., Röber, M.
    Year: 2020
    Citations: 9
  • Title: Hololens AR-using vuforia-based marker tracking together with text recognition in an assembly scenario
    Authors: Knopp, S., Klimant, P., Schaffrath, R., Fritzsche, R., Allmacher, C.
    Year: 2019
    Citations: 11