Browsing by Author "Matungwa, William"

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Assessment of Spatial Water Quality Variations in Shallow Wells Using Principal Component Analysis in Half London Ward, Tanzania
(Scientific Research Publishing, 2025-02-21) Matungwa, William; Katambara, Zacharia
Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tun duma Town, Tanzania, using Principal Component Analysis (PCA) to iden tify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs ex plained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate
Examination seating optimization using graph coloring and combinatorial design
(ELSEVIER, 2026-05-12) Kibona, Isack E; January, Jeremiah,; Matimbwa, Hadija; Nchia, Edwin; Matungwa, William; Vuai, Said A.H
This paper presents an optimization approach for exam seating in universities with limited infrastructure, based on a mixed-course allocation model. Students in different courses share rooms while maintaining spatial separation to improve academic integrity. The model incorporates a theoretical probability of interaction, which decreases as the number of mixed courses in a room increases. Using real data with 5175 students, the proposed model significantly improves upon the traditional method. Although the traditional approach required 35 rooms with a total capacity of 7269, the proposed model utilized only 12 large rooms, leaving 23 rooms unused and saving about 2475 seats. The unused space within the occupied rooms was minimal (approximately 29 seats), indicating near-optimal utilization. The invigilation requirement was reduced from at least 70 to 36, achieving nearly 50% savings. Small-enrollment and carryover courses are efficiently integrated and sorted. The model is formulated using graph coloring and combinatorial optimization, supported by a simple allocation algorithm.
Examination Seating Optimization Using Graph Coloring and Combinatorial Design
(ELSEVIER, 2026) Kibona, Isack E.; January, Jeremiah; Matimbwa, Hadija; Nchia, Edwin; Matungwa, William; Vuai, Said A.H.
This paper presents an optimization approach for exam seating in universities with limited infrastructure, based on a mixed-course allocation model. Students in different courses share rooms while maintaining spatial separation to improve academic integrity. The model incorporates a theoretical probability of interaction, which decreases as the number of mixed courses in a room increases. Using real data with 5175 students, the proposed model significantly improves upon the traditional method. Although the traditional approach required 35 rooms with a total capacity of 7269, the proposed model utilized only 12 large rooms, leaving 23 rooms unused and saving about 2475 seats. The unused space within the occupied rooms was minimal (approximately 29 seats), indicating near-optimal utilization. The invigilation requirement was reduced from at least 70 to 36, achieving nearly 50% savings. Small- enrollment and carryover courses are efficiently integrated and sorted. The model is formulated using graph coloring and combinatorial optimization, supported by a simple allocation algorithm