ASRIC Journal of Engineering Sciences 2025 v5-i2
ISSN: 2795-3548
EISSN: 2795-3548
ASRIC Journal of Engineering Sciences 2025 v5-i2
ASRIC Journal of Engineering Sciences 2025 v5-i2
Published: 2025-12-31
Articles
Multi-Compartment PFAS Contamination and Risk Profiling in Kampala’s Urban Hydroscape: A Pre-Collapse Study of Kitezi, Lubigi, and Murchison Bay
Michael Ahimbisibwe, Patrick Ssebugere*, Christine Betty Nagawa, Henry Matovu, Godfrey Muhwezi, Prossie Nakawuka, Isa Kabenge, Ahamada Zziwa
Per- and polyfluoroalkyl substances (PFAS) have emerged as persistent pollutants of growing concern in urban hydrological systems. This study quantified PFAS concentrations across three environmental matrices open surface water bodies, landfill leachate from Kitezi, and Lubigi wetland treatment zones in central Uganda. Using liquid chromatography tandem mass spectrometry (LC-MS/MS), concentrations
Exploring the Fiscal of Wood-Based Renewable Biomass as an Energy Source
Nnadikwe Johnson, Onyewudiala Julius Ibeawuchi, Nwosi Hezekiah Andrew, Mbadikwe Columbus Asodike, Onwukwe Cajetan Ndubuisi
In Nigeria, the adoption of sustainable biomass for energy generation is rising. Moisture content significantly impacts biomass utilization efficiency. This study investigates the economic impact of moisture at different stages of the wood biomass distribution chain. The methodology includes a literature review, interviews, and economic calculations. The costs associated with moisture content in N
Technology Transfer in Africa: Unlocking Opportunities, Overcoming Barriers, and Mitigating Strategic Threats
Wael Badawy Samy Turk
Technology transfer plays a pivotal role in accelerating socio-economic development, especially in emerging economies. In Africa, the effective adoption of foreign technologies can drive industrialization, job creation, and innovation across critical sectors such as agriculture, health, energy, and education. However, the continent faces significant challenges in realizing the full potential of te
A Smart Food Delivery Platform with Machine Learning-Driven Intelligent Matching
Emmanuel Victoria Nkemjika, Enyinnaya Victor Chibunna
This study presents the design and implementation of a smart food delivery platform powered by machine learning-driven intelligent matching. The platform is developed in response to the growing need for efficient and personalized food ordering experiences, reducing the time, effort, and costs associated with visiting physical food outlets. Common issues such as long wait times, lack of real-time e
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