Uwayesu Happy Edwards | Engineering | Excellence in Research Award

Published on by Young Scientists Awards

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

Mehdi Saadallah | Computer Science | Best Researcher Award

Published on by Young Scientists Awards

Mr. Mehdi Saadallah | Computer Science
| Best Researcher Award

Vrije Universiteit Amsterdam | Netherlands

Mr. Mehdi Saadallah research focuses on advancing the integration of artificial intelligence (AI) and automation in cybersecurity operations, emphasizing the intersection between technology, human behavior, and organizational structures. It investigates how AI-driven tools influence professional identity, decision-making, and collaboration within Security Operations Centers (SOCs), where analysts and algorithms coexist in dynamic threat environments. By applying frameworks such as Paradox Theory, Organizational Routine Theory, and Identity Work Theory, the work uncovers the tensions, adaptations, and emergent practices that arise when automation transforms traditional cybersecurity routines. Empirical insights are drawn from multinational enterprises across diverse sectors, revealing how organizations balance efficiency, control, and trust in AI-augmented defense systems. The research also develops conceptual and operational models for AI-assisted vulnerability management and SOC modernization, providing a blueprint for improving detection, response, and resilience in complex digital ecosystems. Beyond theory, it delivers applied innovations that enhance cybersecurity governance, human–AI trust calibration, and automation ethics. Through interdisciplinary methods combining qualitative inquiry, computational analysis, and organizational modeling, the work contributes to redefining cybersecurity as a socio-technical discipline—bridging academic rigor and industrial application to guide the future of intelligent, adaptive, and human-centered cyber defense frameworks.

Featured Publications

Saadallah, M. (2025). Harmonizing paradoxical tensions in SOCs: A strategic model for integrating AI, automation, and human expertise in cyber defense and incident response. In Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS-58). https://doi.org/10.24251/HICSS.2025.723

Saadallah, M., Shahim, A., & Khapova, S. (2025). Reconciling tensions in Security Operations Centers: A Paradox Theory approach. Big Data and Cognitive Computing, 9(11), 278. https://doi.org/10.3390/bdcc9110278

Saadallah, M., Shahim, A., & Khapova, S. (2025). Optimizing AI and human expertise integration in cybersecurity: Enhancing operational efficiency and collaborative decision-making. PriMera Scientific Engineering, 6(1), 177. https://doi.org/10.56831/psen-06-177

Saadallah, M., Shahim, A., & Khapova, S. (2024). Multi-method approach to human expertise, automation, and artificial intelligence for vulnerability management. In Advances in Intelligent Systems and Computing (pp. xxx–xxx). Springer. https://doi.org/10.1007/978-3-031-65175-5_29

 Saadallah, M., Shahim, A., & Khapova, S. (2024). Synergizing human expertise, automation, and artificial intelligence for vulnerability management. PriMera Scientific Engineering, 5(10), 160. https://doi.org/10.56831/psen-05-160