Mbarara University of Science & Technology (MUST), commonly known as Mbarara University, is a public university in Uganda. Mbarara University commenced student intake and instruction in 1989. It is one of the ten public universities and degree-awarding institutions in the country. MUST is accredited by the Uganda National Council for Higher Education.
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.
We present a new empirical vertical drift model developed using ground-based magnetometer, radar, and satellite data over equatorial latitude regions. We first implement an algorithm relating magnetometer derived equatorial electrojet (EEJ) and vertical ion plasma drift (equivalent to vertical drift within magnetic latitudes of and altitudes of about 400-550 km) from the Communications and Navigation Outage Forecasting System (C/NOFS) satellite at different longitude sectors. The relationship between EEJ and C/NOFS vertical drift is developed separately at different longitudes over the globe at coincidental times when both data sets are available. These relationships are then used to estimate continuous vertical drift at each epoch of EEJ observation over the respective longitude sectors during local daytime. The reconstructed vertical drift data are combined with global C/NOFS vertical drifts and JULIA data set to develop a global vertical drift model. Validation using Ion Velocity Meter (IVM) drifts from ICON satellite for January to August 2022 shows that our model improves vertical drift global modeling by over 20% compared to the current climatology representation.
Contemporary medical AI systems exhibit a critical vulnerability: they deliver confident predictions without mechanisms to express uncertainty or acknowledge limitations, leading to dangerous overreliance in clinical settings. This paper introduces the BODHI (Bridging, Open, Discerning, Humble, Inquiring) framework, a dual-reflective architecture grounded in two essential epistemic virtues: curiosity and humility, as foundational design principles for healthcare AI. Curiosity drives systems to actively explore diagnostic uncertainty, seek additional information when faced with ambiguous presentations, and recognize when training distributions fail to match clinical reality. Humility provides complementary restraint, enabling uncertainty quantification, boundary recognition, and appropriate deference to human expertise. We demonstrate how these virtues function synergistically in a dynamic feedback loop, preventing both reckless exploration and excessive caution while supporting collaborative clinical decision-making. Drawing from psychological theories of curiosity and cross-species evidence of epistemic humility, we argue that these capacities represent fundamental biological design principles essential for systems operating in high-stakes, uncertain environments. The BODHI framework addresses systemic failures in medical AI deployment, from biased training data to institutional workflow pressures, by embedding uncertainty awareness and collaborative restraint into foundational system architecture. Key implementation features include calibrated confidence measures, out-of-distribution detection, curiosity-driven escalation protocols, and transparency mechanisms that adapt to clinical context. Rather than pursuing algorithmic perfection through pure optimization, we advocate for human-AI partnerships that enhance clinical reasoning through mutual accountability and calibrated trust. This approach represents a paradigm shift from overconfident automation toward collaborative systems that embody the wisdom to pause, reflect, and defer when appropriate.
Abstract The integration of diverse energy sources and the advent of smart grids have intensified the challenges in load frequency management (LFM). Modern power systems are increasingly vulnerable to inherent nonlinearities, such as generation rate constraints, governor dead bands, boiler dynamics, and communication delays, as well as sophisticated cyber-attacks, which collectively threaten frequency stability and tie-line power balance. To address these challenges, this study proposes a novel cascade controller, designated as (1 + FOPI)-FOPI-TID, for robust automatic generation control in hybrid two-area power systems. The controller uniquely combines fractional-order (FO) dynamics with a tilt-integral-derivative stage and is optimized using a green metaheuristic, the weighted average algorithm (WAA). The WAA effectively balances exploration and exploitation to achieve superior parameter tuning. The proposed control architecture processes both area control error (ACE) and frequency deviation (ΔF) signals through dedicated stages, enabling enhanced disturbance rejection and transient response. The system model incorporates a comprehensive set of nonlinearities and evaluates resilience against resonance-based cyber-attacks. Comprehensive simulation studies under both AC and HVDC tie-line configurations demonstrate that the WAA-optimized (1 + FOPI)-FOPI-TID controller significantly outperforms existing schemes, including PD-PI, PIFOD-(1 + PI), and PIDF(1 + FOD). Key performance metrics show a 45.3% reduction in the integral of time-weighted absolute error (ITAE) and improvements in settling times of 47.7% for ΔF₁ and 32.8% for ΔF₂. Sensitivity analysis confirms robustness under ± 25% parameter variations and random load perturbations. During cyber-attacks, the controller maintains the lowest Rate of Change of Frequency (RoCoF), underscoring its dual capability in stabilizing grid dynamics and mitigating cyber-physical threats. These results validate the controller’s potential to enhance operational resilience and reliability in future smart grids.
BACKGROUND:Malnutrition significantly contributes to mortality among people with tuberculosis (TB). However, evidence on factors associated with the specific forms of malnutrition, specifically underweight and overweight/obesity, beyond clinical determinants, remains limited in many settings. We investigated the prevalence and determinants of underweight and overweight/obesity among people with pulmonary TB across five health facilities in Kampala, Uganda. METHODS:This analytic cross-sectional study involved people with pulmonary TB, either clinically diagnosed or bacteriologically confirmed, aged ≥18 years sampled across five health facilities in Kampala, Uganda. Nutritional status was assessed using body mass index (BMI, kg/m²) and categorized as underweight (<18.5), normal weight (18.5-24.9), and overweight/obese (≥25.0). To identify factors independently associated with nutritional status, normal weight was considered as the reference category in a multinomial logistic regression analysis, adjusting for multiple covariates and clustering at the health facility level. The measure of association was the adjusted relative risk ratios (aRRR) and the corresponding 95% confidence intervals (CI). RESULTS:Of the 818 participants studied, 417 (51.0%) had normal weight, 302 (36.9%) were underweight, and 99 (12.1%) were overweight or obese. Adjusted analysis showed that being underweight was associated with household food insecurity (aRRR 2.04, 95% CI: 1.48-2.80) while being overweight or obese was associated with self-employment (aRRR 2.26, 95% CI: 1.35-3.79) and being newly diagnosed with TB (aRRR 2.10, 95% CI: 1.30-3.41). CONCLUSION:This study, conducted among people with pulmonary TB in an urban setting in Uganda, showed that underweight and overweight/obesity are prevalent. Furthermore, the study showed that food insecurity is associated with being underweight, while being overweight or obese is associated with being self-employed or newly diagnosed with TB. Therefore, TB control programs need to regularly assess the nutritional status of people with TB to mitigate the effect of being underweight or overweight on treatment outcomes.
