Kyambogo University

This study evaluated the plausibility of the pectin-cation-phytate mechanism of hard-to-cook (HTC) defect development during storage in diverse common bean accessions with different cooking behaviour. Fresh (non-aged) and aged beans of five accessions were classified into narrow texture (hardness) classes to enhance detection of small biochemical changes that can otherwise be obscured by the large intra-accession bean-to-bean variability. Cell wall material (CWM) from non-soaked bean cotyledons in the most frequent hardness class were characterised for pectin and calcium content. Inositol hexaphosphate (InsP6) content and cooking behaviour of the same batch of beans used in this study were sourced from previous work. Significant differences in the parameters were observed among bean accessions and whether they are fresh or aged, reflecting genetic and ageing effects. Cell wall-bound calcium significantly increased upon ageing in 4 out of 5 bean accessions and correlated strongly with their InsP6 content (correlation coefficient, r = −0.91, p = 0.0002), suggesting phytate hydrolysis as the primary source of the calcium. Strong correlations were found between cell wall-bound calcium and softening rate constant (r = −0.70, p = 0.025) and time to cook (r = 0.68, p = 0.029), indicating that cell wall integrity reinforcement during ageing delays softening during cooking. Ageing significantly reduced the pectin degree of methylesterification (DM) in three bean accessions, which moderately correlated with cell wall-bound calcium and cooking behaviour. These findings provide compelling evidence supporting the pectin-cation-phytate mechanism of HTC development across diverse bean accessions in (at least partly) explaining their cooking behaviour.

Abstract Bone tissue engineering scaffolds must provide structural support while permitting fluid transport and maintaining safe hydrodynamic conditions for cell activity. Triply Periodic Minimal Surface (TPMS) architectures such as Gyroid, Schwarz-P, and Diamond offer continuous curvature and tunable porosity, yet identifying configurations that simultaneously satisfy mechanical, transport, and manufacturability requirements remains computationally expensive. This study presents a constraint-aware computational framework for rapid exploration of TPMS scaffold design spaces by combining procedural geometry generation, analytical physics-consistent property estimation, and a geometry-aware deep learning surrogate model. A dataset of 1000 voxelized scaffolds (porosity 0.55–0.80; unit-cell size 0.8–1.2 mm) was used to train a multitask 3D convolutional neural network to approximate apparent modulus, permeability, effective diffusivity, and shear-exposure indicators derived from established mechanistic relations. The surrogate achieved a mean absolute error of approximately 3.9 GPa for predicted stiffness and reproduced transport trends on the order of 10−11 m2/s, enabling screening of more than 3000 candidate geometries without performing high-fidelity simulations. Pareto analysis revealed strong stiffness–transport trade-offs across TPMS families. Manufacturability constraints, particularly a minimum printable wall thickness of approximately 0.30 mm, eliminated many high-porosity designs. A near-feasible Schwarz-P configuration (ϕ ≈ 0.86, a ≈ 2.6 mm) exhibited moderate predicted stiffness (~ 2.1–2.5 GPa after thickness adjustment), effective diffusivity ≈3 × 10−11 m2/s, and permeability on the order of 10−10 m2, illustrating the competing requirements of structural support and perfusion. The proposed framework functions as a geometry-aware design-screening and prioritization tool that identifies candidate scaffold configurations prior to detailed finite-element, computational-fluid-dynamics, or experimental validation. The work provides a reproducible approach for accelerating early-stage scaffold design exploration and guiding subsequent biomechanical evaluation.

This paper presents a factory-level, data-informed Industry 4.0 readiness and upgrade framework for steel manufacturing plants operating in low digital maturity environments, using Uganda as a representative case. Field data was collected at three medium-scale operational steel plants. Customized Digital Maturity Index (DMI) and cybersecurity risk (CSR) assessment criteria were developed and applied. Results show an overall DMI score of 1.8, indicating very low digital maturity with predominantly manual operations, absence of industrial robots, advanced automation, and integrated digital data systems. CSR assessment results show limited formal protection mechanisms. A phased, ROI-driven transition roadmap is proposed. A worked case using plant-level production data demonstrates that selective automation of four bottleneck stations could nearly double annual billet output and achieve an incremental payback period of approximately seven weeks under stated assumptions. Workforce transition modeling using a Markov approach indicates gradual role transformation over an expected horizon of about ten transition cycles rather than abrupt displacement.

As one of the largest hosts of refugees in Africa and globally, Uganda has been studied from a variety of perspectives. However, one major research gap is the domestic political role of refugees in Uganda-how are refugees considered in domestic narratives, and to what political gain? We show that two dominant, politically influential positive narratives focus on the benefits of community development and shared Pan-African identity. These narratives are especially impactful at the local level, where economic gains from supporting refugees often translate into political capital. Negative narratives do exist, but focus more on perceived deprivations for Ugandans, not on blaming refugees. Political elites only give limited attention to concerns like security, land, and environmental degradation, as positive narratives on refugee protection offer greater political capital. The paper draws on a variety of data, including parliamentary debates and newspapers sources in addition to interviews and focus group discussions.

This work presents a theoretically optimized multilayer surface plasmon resonance (SPR) biosensor for quantitative hemoglobin detection using the Kretschmann configuration. The sensor integrates a BK-7 prism, silver plasmonic layer, graphene enhancement layer, zirconium nitride (ZrN) protective layer, and aqueous sensing medium. This architecture synergistically combines enhanced electromagnetic confinement with chemical stability, addressing silver's oxidation vulnerability while maintaining superior plasmonic performance. Electromagnetic analysis via transfer matrix method and finite-element simulations demonstrates exceptional sensitivity metrics: maximum angular sensitivity of 500 degrees/RIU, figure of merit of 92.25 RIU-1, and detection limit of 0.006 RIU across clinically relevant hemoglobin concentrations (10-40 g/L). Localized electric field enhancement (similar to 10(6) V/m) at the sensing interface confirms optimal light-matter interaction amplification. Machine learning models predict sensor responses to graphene thickness and refractive index variations with R-2 > 0.99, enabling rapid optimization. This design advances SPR biosensor technology for sensitive, label-free biochemical detection applications.