Kabale University

Compliance with maize grades and standards remains a significant challenge in Uganda, particularly among smallholder farmers. Despite the guidelines established by the East African Grain Council, adherence within local markets remains limited. Understanding the factors that influence the awareness and adoption of maize grading is crucial for enhancing market quality and improving farmer incomes. This study examined the determinants of smallholder farmers’ awareness, utilization, and intensity of use of recommended maize grades and standards in northern Uganda. Primary data were collected from 270 farmers through a cross-sectional survey using multistage sampling. Probit regression and the Heckman two-stage econometric model were used to identify factors influencing farmers’ awareness, adoption, and degree of compliance with maize grading standards. The results indicated that awareness and utilization of maize grades and standards were generally low, with only 40.4

Africa is home to over one-third of the world’s languages, yet remains severely underrepresented in multimodal AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. The benchmark offers parallel text and speech modalities and was entirely created by native speakers. We find that models show poor performance across evaluated cultures, with near-zero accuracy on open-ended VQA when queried through native language or speech. To test linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the pressing need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. We release Afri-MCQA to support more inclusive multimodal AI development.

Recent advances in AI have been driven by data abundance and computational scale, assumptions that rarely hold in low-resource environments. We examine how constraints in data, compute, connectivity, and institutional capacity reshape what effective AI should be. Using a structured mixed-methods review and PRISMA-inspired protocol over 300+ studies, we compare data-efficient approaches, physics-informed models, few-shot and self-supervised learning, parameter-efficient fine-tuning, TinyML, and federated learning, and evaluate them across deployment axes (data needs, compute footprint, latency, robustness, interpretability, and maintenance). Across health, agriculture, climate, and education, we show that lean, operator-informed, and locally validated methods often outperform conventional large-scale models under real constraints. We argue that data-efficient AI is not a stopgap but a foundational paradigm for equitable and sustainable innovation, and we provide a decision matrix and research-policy agenda to guide practitioners and funders in low-resource settings.

The performance of foamed concrete (FC) is significantly influenced by the type and proportion of supplementary cementitious materials (SCMs). However, comprehensive studies investigating the combined effects of engineered pozzolans on the thermal, mechanical, durability, and microstructural properties of FC are limited. This study addresses the scientific problem of optimizing the replacement of Ordinary Portland Cement (OPC) with blast-furnace slag (BFS), silica fume (SF), and surkhi (SK) at varying substitution levels (5–25

Low-income settings such as Uganda bear a disproportionately high burden of diseases such as HIV/AIDS, but contribute little to knowledge production to address their health challenges. The training of health professionals traditionally focuses on clinical skills with less emphasis on research; yet, in practice, health professionals are expected to do operational research. In this study, we evaluate the impact of a micro-research model on building the capacity of undergraduate health professions students to conduct locally relevant biomedical, behavioral, clinical, and operations research on HIV and its co-morbidities. This study was within the Health Professional Education Partnership Initiative - Transforming Ugandan Institutions Training Against HIV/AIDS (HEPI-TUITAH; 2018–2024), using a sequential explanatory mixed methods design. We collected quantitative data on sociodemographic, number of trainings, concepts and written manuscripts. Thereafter, we conducted in-depth interviews and analyzed them using a thematic content approach. We used the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework to evaluate the impact (number trainings, protocols written, manuscripts written, publications, and change in attitudes) of the HEPI-TUITAH micro-research model. Of the 24 participants that were interviewed, 15 (62.5