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Mechanical, durability, and thermal performance of concrete incorporating coffee biochar and raw and calcined montmorillonite
(Frontiers, 2026-04-21) Hepautwa, Amani; Hilonga, Askwar; Mrosso, Register; Alfredy, Tusekile; Lesafi, Fina; Jande, Yusufu
Introduction: Montmorillonite is a natural aluminosilicate clay with potential as a supplementary cementitious material, although its reactivity in the raw state is limited. This study investigates the effect of raw and calcined montmorillonite on the performance of concrete incorporating 15% pyrolyzed coffee grounds (PCG) at 350 °C.Methods: Montmorillonite calcined at 400, 600, and 800 °C replaced cement at levels of 5%–20%. Mechanical and durability properties were evaluated under acidic, saline, and thermal exposure conditions. Microstructural characterization was conducted using FTIR, XRD, and SEM, and statistical validation was performed using two-way ANOVA.Results: Calcination enhanced montmorillonite reactivity through amorphization and pozzolanic reactions, resulting in improved pore refinement and matrix densification. Specimens with calcined montmorillonite at 600 °C–800 °C showed superior strength and durability performance.Discussion: The combined use of calcined montmorillonite and 15% PCG biochar at 350 °C provides a sustainable approach for improving concrete performance.
Mathematical model for the control of HIV/TB coinfection incorporating dual stigma
(Bulletin of Biomathematics, 2026-04-29) Lengiteng'i, Lalashe; Mbalawata, Isambi; Luboobi, Livingstone; Mirau, Silas; Nyerere, Nkuba
The co-epidemics of HIV and Tuberculosis, intensified by dual stigma, pose major challenges to effective disease control. Understanding the interaction between coinfection dynamics and stigma is essential for designing targeted interventions. This study develops a deterministic mathematical model incorporating dual stigma and control strategies to analyze HIV/TB transmission dynamics. Separate analyses of HIV-only, TB-only, and coinfection sub-models are conducted. Numerical simulations assess the impact of treatment and stigma-reduction interventions, while sensitivity analysis and contour plots evaluate the influence of key parameters on the basic reproduction numbers. Results indicate that increased treatment rates and awareness campaigns significantly reduce infection prevalence and stigma, thereby lowering transmission. Stigma is shown to exacerbate disease spread by limiting treatment uptake and increasing susceptibility. Sensitivity analysis identifies treatment efficacy and awareness-related parameters as the most influential factors in reducing reproduction numbers. Overall, integrating medical treatment with stigma-reduction strategies is highly effective in controlling HIV/TB coinfection. These findings highlight the importance of combined biomedical and social interventions and provide practical insights for policymakers.
Managerial competence and production capability as drivers of SME performance in resource-constrained environments: does government support matter?
(World Scientific Connect, 2026-04-07) Luge, Jafari; Mkunda, Josephine; Sassi, Akinyi
Small and medium enterprises (SMEs) in developing countries are often resource-constrained, making the identification and effective utilization of critical internal resources essential for their survival and growth. Tanzania’s leather sector illustrates this paradox, whereby despite abundant livestock resources and government-led initiatives, SMEs struggle to achieve sustainable market performance. Grounded in the Resource-Based View (RBV) and informed by Penrosean and Institutional perspectives, this study examines the effects of managerial competence and production capability on market performance and the moderating role of government support. Survey data from 145 leather manufacturing SMEs were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that both managerial competence and production capability significantly enhance market performance, with production capability exerting a stronger effect. Government support strengthens the relationship between managerial competence and firm performance, but does not significantly moderate the link between production capability and firm performance, suggesting a misalignment between public support mechanisms and firms’ operational realities. The study advances RBV and Penrosean perspectives by demonstrating how internal capabilities interact differently with external institutional support in shaping SME performance within an under-researched African manufacturing context. Practically, the findings emphasize the need for managerial competence and production-focused capability development and more targeted policy interventions aligned with sector-specific operational needs.
Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance
(SAGE Publications, 2026-02-28) Lyimo, Beatus
Africa continues to bear a disproportionate burden of infectious diseases, particularly antimicrobial-resistant (AMR) infections, which significantly affect public health and socio-economic development. Addressing these complex health threats requires innovative approaches to data analysis, pathogen surveillance, and intervention design. The emergence of advanced computational tools especially artificial intelligence (AI) is expected to reduce turnaround times for AMR prediction from days to hours by leveraging whole-genome sequencing (WGS)-based models. This article explores the synergistic integration of AI and bioinformatics, focusing on their application in combating AMR in Africa. It details how AI techniques, particularly machine learning (ML) and deep learning (DL) algorithms, can enhance genomic research by automating the analysis of large-scale sequence datasets, predicting resistance patterns, and modeling infections transmission dynamics. In regions with limited laboratory capacity, AI models can detect resistance genes rapidly and assist clinicians in selecting appropriate antibiotics, offering a faster and more scalable alternative to traditional diagnostics. Tools such as convolutional neural networks (CNNs) and support vector machines (SVMs) are examples of models capable of classifying pathogen strains based on genetic data. Furthermore, the article highlights the emerging role of large language models (LLMs) in supporting bioinformatics workflows. These tools aid researchers by generating analysis scripts, interpreting complex outputs, troubleshooting code errors, summarizing literature, and preparing manuscripts or grant proposals particularly benefiting early-career scientists who may lack access to advanced training or mentorship. Despite notable progress, significant challenges remain, including limited infrastructure, barriers to data sharing, and the urgent need for ethical guidelines and policies to govern AI integration. Ultimately, this article underscores the transformative potential of AI in advancing bioinformatics across Africa and advocates for sustained investment in infrastructure, capacity-building, and responsible policy frameworks to harness AI for improved health research and disease control outcomes. We propose 3 priority actions: building African AMR genomic datasets, investing in AI-ready infrastructure, and developing responsible data-governance frameworks.
Less is more: modelling the impact of species-targeted versus broadcast larviciding approaches for malaria control in rural settings
(medRxiv, 2026-03-05) Msugupakulya, Betwel; Okumu, Fredros; Wilson, Anne; Selvaraj, Prashanth
Background Larval source management (LSM) was once central to malaria control before insecticide-treated nets and indoor residual spraying dominated. Renewed interest in LSM raises questions about its effectiveness in rural Africa, where habitats are dispersed, and vector species contribute unequally, and whether species-targeted larviciding could offer greater gains than broadcast approaches.
Methods This modelling study quantified the potential impact of larviciding in African settings where multiple vector species contribute unequally to malaria transmission. We modeled malaria transmission in southeastern Tanzania using agent-based simulations incorporating seasonal dynamics, insecticide resistance, and semi-field biolarvicide efficacy. Outcomes were entomological inoculation rate, malaria incidence in under-fives, and operational larviciding costs.
Findings Large-scale deployment of biolarvicides with >1-week residual activity substantially reduced malaria transmission, with disproportionately greater gains when control efforts were preferentially focused on the dominant vector species, Anopheles funestus, compared to broadcast approaches treating both An. funestus and An. arabiensis habitats. In the absence of ITNs, a four-month fortnightly larviciding campaign targeting An. funestus at 80% coverage reduced EIR by 58% and incidence by ∼40%, versus ∼55% incidence and ∼70% EIR reductions under broadcast strategies; targeting An. arabiensis alone yielded ≤30% EIR and ≤13% incidence reductions. Starting with pre-existing 80% ITN coverage, funestus-targeted larviciding further reduced peak EIR by ∼70% and incidence by ∼77%, versus ∼90% and ∼85%, respectively, with broadcast strategies, suggesting broadcast larviciding provided limited additional reductions beyond those achieved by the funestus–targeted approach. At 40% ITN coverage, additional reductions were ∼62% of EIR and ∼46% in incidence (funestus-targeted) versus ∼76% and 63%, respectively (broadcast). The targeted campaigns preserved a 30–50% cost advantage while sustaining >50% dry-season transmission reductions. Finally, high-coverage (e.g., 80%) funestus-targeted larviciding campaigns achieved greater impacts than lower-coverage (e.g., 40-60%) targeting both species.
Conclusions In settings where multiple vector species contribute unequally to malaria transmission, preferentially targeting larviciding against the dominant vector species can deliver substantial epidemiological impact, with greater resource efficiency than broadcast approaches targeting multiple vectors. In Tanzania, where An. funestus drives most transmission; concentrating larviciding efforts on its characteristic aquatic habitats may offer a scalable, low-cost complement to established tools such as ITNs.