University of Botswana

Recently, there has been growing research interest in the integration of generative artificial intelligence (GenAI) in educational contexts, particularly in academic writing. In multilingual contexts where students struggle with the complexities of academic writing, particularly at the graduate level, the adoption of GenAI may play a critical role in supporting graduate academic writing. This study aimed to conduct a scoping review to determine the benefits, challenges, concerns, and research gaps associated with GenAI adoption in L2 graduate academic writing within higher education. Articles that described the adoption of GenAI in L2 Graduate academic writing were searched across four databases: Scopus, Web of Science, Google Scholar, and EBSCOhost. Articles that were not specific to L2 graduate academic writing, not empirical studies and not written in English were excluded. Eight empirical studies (2024-2025) that focused on L2 graduate academic writing were selected for the review. The results revealed that all eight studies reported notable improvements in students’ academic writing, including enhanced grammar, spelling, coherence, and writing style. Tools such as Grammarly and ChatGPT were found to be particularly beneficial for non?native English?speaking graduate students. However, the review also identified key challenges including ethical concerns and the risk of over-reliance on GenAI-generated content. Overall, the review concludes that while GenAI tools show strong potential for enhancing L2 graduate academic writing skills, further research and policy development are needed to guide responsible and effective integration of GenAI within universities.

Infection prevention and control (IPC) is critical in neonatal units to prevent healthcare-associated infections, yet research on IPC has primarily focused on healthcare workers, overlooking the role of family caregivers. In low-resource settings, mothers often take on essential caregiving responsibilities including IPC-related tasks, both a logistic necessity and a cultural norm, but their perspectives and experiences remain underexplored. This qualitative descriptive study aimed to explore mothers' experiences and perceptions of IPC in a neonatal unit in Gaborone, Botswana. Understanding maternal involvement in IPC is essential for developing effective interventions, particularly in settings where health systems face workforce shortages and resource constraints. Fifteen semi-structured interviews were conducted with mothers of infants admitted to the neonatal unit between June and December 2022. Data were analysed using thematic analysis. Six themes were identified. Major themes related to (1) mothers' strong sense of responsibility for IPC and concerns about infection risks, (2) health system constraints (including supply shortages) that limited consistent IPC practice, (3) competing demands within restricted visiting times that required mothers to balance IPC with feeding and bonding, (4) variable communication with health care workers (HCWs) and limited comfort reminding staff about IPC, and (5) peer dynamics between mothers, including mutual reminders as well as tensions arising from shared feeding equipment. A minor theme that emerged was (6) mothers' emotional distress and its perceived impact on remembering and enacting IPC behaviours. Overall, mothers described high motivation to protect their infants but reported that time pressures, insufficient communication with HCWs, and resource limitations constrained IPC adherence, underscoring the need for structured IPC orientation and ongoing support for families in neonatal care. Mothers in this neonatal unit viewed themselves as key partners in IPC and were highly motivated to protect their infants, but their ability to consistently follow IPC practices was constrained by limited supplies, staffing pressures, restricted time for caregiving and bonding, and variable communication with HCWs. Mothers also expressed concerns about HCW adherence to IPC and described how emotional distress and peer dynamics could shape IPC behaviour. These findings underscore the need for structured IPC education and support for mothers in neonatal units, and the importance of integrating family-centred IPC policies into neonatal care. Future research should evaluate interventions that strengthen caregiver support, improve communication, and address contextual barriers and constraints to sustained IPC practice.

Tail risk assessment is crucial in financial markets, especially for commodities such as Brent crude oil, where extreme price fluctuations pose a risk for investors and policymakers. Risk models such as generalised autoregressive conditional heteroscedasticity (GARCH) often struggle to capture these extreme movements accurately, leading to potential underestimation of risk exposure. To solve this problem, we combine an alpha-recurrent neural network with a generalised Pareto distribution to better predict extreme price changes and improve tail risk estimation. Our findings demonstrate that this approach effectively captures downside risk, with backtesting results yielding high p-values, confirming its statistical reliability. The results from the shape parameter reveal that losses in crude oil markets are significantly riskier than gains, highlighting the asymmetric nature of price movements. Risk estimates indicate that the model provides robust assessments for both long- and short-term trading positions, making it a valuable tool for risk management. Nevertheless, these results have broader implications for financial risk modelling, particularly in commodity markets, where macroeconomic and geopolitical factors influence price volatility. Future work should focus on expanding the data set, enhancing computational efficiency and adding external risk factors such as liquidity constraints and regulatory shifts. Better calibration methods and the ability to adjust in real-time can make predictions more accurate, helping risk assessment models stay useful in changing market conditions.

Botswana’s semi-arid climate makes it vulnerable to climate change, particularly drought, which threatens agricultural productivity. This study assesses drought impact on Botswana’s agricultural sector using Climate Hazards Center Infrared Precipitation with Station (CHIRPS) rainfall data and Climate Hazards Center Infrared Temperature with Station (CHIRTS) temperature data (25 km) to compute the Standardized Precipitation Index (SPI), Standardized Temperature Condition Index (STCI) and Standardized Precipitation Evapotranspiration Index (SPEI) at seasonal/annual time scales (1, 3, 6 and 12 months). The indices are used to assess their ability to predict crop yields using national data during Botswana’s rainy season, while employing univariate and multivariate statistical models. Statistical models also linked historical drought patterns to yield variability with the Percentage Area Affected (PAA) by drought, identifying key predictors. A majority of the crops (sunflower, maize, sorghum and pulses) showed variability which was best explained by SPEI 6 more particularly under the PAA multivariate models, with the highest and moderate explanatory power (R2) found in sunflower (0.48) and maize (0.43). However, variability in millet was best explained by SPI-3, although the R2 was low (0.26). Other crops displayed positive coefficients within the models, which may be attributed to the varieties grown being drought tolerant. Nevertheless, the impacts from drought, which resulted in low yields, were shown by the negative coefficients across most crops. For a more holistic approach, the study also employed questionnaire data to capture first-hand local knowledge. The results showed drought to be among the indicators of climate change that were mostly perceived as well as its effects, in which yield decline, crop damage and crop pests and diseases were among the most perceived effects. Overall, this highlighted the sector’s vulnerability to the changes in climate. The study therefore underscores the need for integrated drought early warning systems, adaptive agricultural/water management and insights for policymakers to enhance drought resilience in Botswana, aligning with global sustainability goals.

Attaching and effacing pathogens, including enterohemorrhagic Escherichia coli (EHEC), colonize their preferred intestinal niche by sensing diverse host-, diet-, and microbiota-derived signals and coordinating the expression of virulence factors. D-serine, a host metabolite abundant in urine but scarce in the intestine, restricts EHEC colonization by transcriptionally repressing the type 3 secretion system (T3SS) while activating the SOS stress response. However, the mechanism underlying virulence regulation by D-serine remains unestablished. Here, we show that multiple amino acids, including L-serine converge on this pathway, repressing the T3SS without inducing the SOS response. Transcriptomic analyses showed a common response to D- and L-serine dominated by repression of nitrogen stress response genes. Mutational analysis identified the response regulators NtrC and Nac as essential mediators of T3SS repression by both serine enantiomers. Disruption of L-serine deaminase enzymes crucially revealed that T3SS repression depends on cytoplasmic ammonia/ammonium release rather than sensing of intact serine. While EHEC lacks canonical D-serine catabolic capacity, through metabolomics we provide evidence of oxidative deamination activity, capable of producing this regulatory signal. Together, these findings establish a mechanistic link between amino acid catabolism, nitrogen stress signaling, and virulence regulation in EHEC, highlighting how metabolic flux fine-tunes pathogen adaptation to intestinal niches.

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and heads, alignment-regularized fine-tuning, and row-level inference via sliding-window overlap averaging. To address the dominant deployment failure mode, a Safe Calibration stage robustly filters calibration pairs and selects among candidate calibrators under a strict do-no-harm criterion. On an unseen deployment stream (2154 labeled rows), overlap-averaged raw inference achieves MAE = 0.0439, RMSE = 0.0501, and R2 = 0.7451, consistent with mid-to-high SoH range compression, while Safe Calibration (Isotonic-Balanced selected) corrects nonlinear scaling without violating monotonic structure, improving to MAE = 0.0188, RMSE = 0.0252, and R2 = 0.9357 to obtain a complete understanding of the challenges due to domain shifts, evaluation is extended to include other architecture baselines such as TCN-only, Transformer-only, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and a Ridge regression baseline. Also added is explicit alignment and calibration ablations that include CORAL off/on, that is, none vs. Safe-Global vs. Context-Aware under identical leakage-safe splits and the same overlap-averaged deployment inference operator. This work goes beyond peak-score reporting and looks at the robustness of a pipeline under domain shift, which is quantified across four random seeds and multiple deployment streams, with uncertainty summarized via mean ± std and bootstrap confidence intervals for Mean of Absolute value of Errors (MAE)/Root of the Mean of the Square of Errors (RMSE) computed from per-example absolute errors.

Abstract While Botswana has stable and strong institutions, the external trade balance was negative between 1960 and 1984 and between 2010 and 2014, which reflects poor export performance. Minor recoveries were experienced between 2016 and 2018. The growth rates of per capita gross domestic product (GDP) and GDP were less than 5%, on average, from 1990 until 2024. Evidence is required on whether the quality of institutions provides a robust and strong foundation for improving export flows and hence economic growth. Institutions were captured by the economic freedom of the World index and the polity score, which collectively measure a country’s institutional quality. The study considered the need for policy response to strengthen institutional quality and pick up export performance to support the country’s aspirations to attain higher income status by 2036. Using a trade openness framework, the study examined the contribution of institutional quality to export performance. A modified gravity model is employed, using data from the World Bank covering the period 1997–2022. The study confirmed both long run and joint causality between export performance and its covariates. The absence of short-run causality demonstrated the need for dynamic adjustments to correct disequilibrium. Institutional quality improved export performance, and its effect varied according to its measurement. Botswana’s export performance and hence flows to trading partners depended on remoteness, population, factor endowment and the country’s level of development. It is expected that Botswana strengthens democratic institutions to improve the trade balance.

Soil moisture influences the composition and diversity of weed species, as well as the competitive dynamics between crops and weeds. The purpose of this study was to determine how soil moisture shape weed community structure in flood recession farming systems. Vegetation sampling was conducted in 17 fields using 1 m² quadrats along gradient-oriented transects, and soil samples were collected at the upper and lower slopes of each field across the 2 cropping seasons at depth of 15 cm, 30 cm, 45 cm and 60 cm, each field had 4 composite samples per cropping season giving a total of 136 composite samples. Statistical analyses, including Kruskal–Wallis, hierarchical cluster analysis, indicator species analysis, multi-response permutation procedures, and Canonical Correspondence Analysis, were employed to evaluate weed distribution and their relationship with environmental variables. The findings showed statistically significant differences (p < 0.05) in soil moisture distribution across the various zones, with distinct weed communities corresponding to specific moisture levels. Species such as Cenchrus biflorus and Tricholaena monachne were more abundant in well-irrigated areas, while Boerhavia coccinea and Portulaca oleracea thrived by accessing moisture near the surface at a depth of 15 cm. In contrast, deep-rooted weeds like Sida cordifolia and drought-resistant species such as Panicum repens favored deeper soil moisture found at 45 cm and beyond. These results emphasise the critical role of soil moisture in shaping weed community composition, affecting both species competition and ecological adaptation within cropping systems. The study highlights the need for effective moisture management to enhance weed control and support sustainable agricultural productivity.