University of the Gambia

This study examines the determinants of institutional readiness for sustainable start-up incubation within a public technical training institute in The Gambia. While existing literature has largely focused on incubation outcomes, limited attention has been given to the internal institutional capacities that enable effective and sustainable incubation systems, particularly in developing economies. Drawing on the Resource-Based View (RBV), this study conceptualizes institutional readiness as a multidimensional construct and investigates how different domains of institutional support influence corresponding adequacy outcomes.The study employs a quantitative cross-sectional design using primary data collected from 117 staff members of a public technical training institution. A series of Ordinary Least Squares (OLS) regression models are estimated to examine the relationship between institutional support dimensions capital, resources, technical machinery, infrastructure, facilities and utilities, services, and space/land and institutional adequacy across corresponding domains.The findings provide strong empirical support for the proposed hypotheses, indicating that institutional support significantly enhances readiness for sustainable incubation across all domains. However, the results reveal important variations in the strength of these relationships. Space and land support and service support emerge as the most influential determinants, highlighting the critical role of physical infrastructure and service delivery systems in incubation effectiveness. In contrast, capital support, although significant, exhibits relatively weaker explanatory power, suggesting that financial resources alone are insufficient to ensure institutional readiness.The study contributes to the literature by introducing an institutional readiness perspective to business incubation and provides new empirical evidence from a low-income African context. The findings offer important policy implications for strengthening incubation systems within technical training institutions and promoting sustainable entrepreneurship development.

Abstract Understanding past interactions between humans and the ecosystems in which they lived is vital to coping with the ongoing climate change. The integration of palaeoecological proxies with archaeological and archaeobotanical records to reconstruct the evolution of past civilisations in West Africa is not very common. This limits our understanding of how the Tichitt culture of West Africa, alongside ecologies, evolved through time. Here, we re-analysed six published pollen datasets from the Sahel region and compared the results with four independent palaeoenvironmental proxies, as well as archaeological and archaeobotanical records, to observe whether the social evolution of the Tichitt culture was influenced by past climate variability. Our results show that changes involved a complex socio-ecological system, not just climate or society. The migration and arrival of the Tichitt agro-pastoral system in the Aoukar basin of Mauritania were closely connected to the final phases of the African Humid Period. The shifts in cultural tool production from early to late Tichitt phases (4.2–2.5 ka BP) reflect changes in regional environmental conditions. The fact that the culture withstood the environmental shock that occurred between 3.4 and 3.0 ka BP reveals that the Tichitt culture was complex, sophisticated, and characterised by a durable, regionally integrated system of production, exchange, and settlement planning. Severe and prolonged aridification towards 2.5 ka BP weakened and led to the dispersal of populations across the subregion. Our results provide an understanding of how climate variability and the trajectory of human technological innovations are closely related: the former served as the dominant structural constraint, while the latter served as a necessary adaptive strategy that enabled societies to outlive sudden and brief environmental shocks.

Sexual violence persists in Nigeria despite ratification of major human rights treaties and enactment of domestic legislation such as the Violence Against Persons (Prohibition) Act. This study assesses whether Nigeria’s legal and institutional frameworks provide effective protection for women, focusing on constitutional dualism, uneven state implementation, socio-cultural resistance, and under-resourced enforcement bodies. Using a qualitative normative legal analysis, it examines treaty domestication, limitations in enforcement of the VAPP Act, and structural barriers faced by survivors, drawing comparative insight from South Africa’s monist constitutional model. The study finds that fragmented criminal regimes and institutional weaknesses undermine compliance with international obligations and argues for a monist constitutional approach, harmonized national standards, and strengthened institutional capacity to align domestic practice with human rights commitments.

This paper empirically tests the Environmental Kuznets Curve (EKC) hypothesis for sub-Saharan Africa, using CO₂ emission as a proxy for environmental quality. The analysis employs panel pooled data for 39 countries from 1990 to 2019. We find evidence of an inverted U-shaped relationship between income and emissions, as GDP per capita has a positive effect and its square has a negative effect on CO₂ emissions, thereby supporting the EKC hypothesis. Our results reveal a dual role for renewable energy. When CO₂ emissions are the dependent variable, renewable energy consumption significantly reduces emissions, while non-renewable energy increases them. Additionally, population size and literacy rate are found to positively affect CO₂ emissions. In contrast, when economic growth is the dependent variable, renewable energy has a positive and significant influence. These findings indicate that expanding renewable energy production in sub-Saharan Africa offers a dual advantage, enhancing environmental quality while also contributing to stronger economic performance. By demonstrating that renewable energy reduces CO₂ emissions and supports growth, the results provide important guidance for policymakers seeking to advance sustainable development pathways that are compatible with long‑term environmental conservation across the region.

Access to specialty surgical care is growing in many low-income countries, but it remains unclear how hospital workforces can leverage technology to manage large numbers of increasingly complex patients. Large language models (LLMs) may be helpful for this type of clinical decision support, but their real-world performance and safety remain uncertain. The objective of this study was to evaluate feasibility, usability, and potential benefits and risks of an LLM-based assistant for postoperative neurosurgical care in the Gambia. A prospective, single-arm implementation study was conducted at the Edward Francis Small Teaching Hospital. A convenience sample of 4 medical officers (MOs) and 5 nurses assigned to the neurosurgical service participated. A prompted GPT-4o Turbo was deployed on OpenAI Pro accounts to support performance. Usability, helpfulness, and safety were the primary outcomes. Cost-effectiveness was a secondary outcome. Participants completed 75 LLM-assisted interactions on 9 postoperative neurosurgery patients. Usability metrics indicated a moderately high cognitive workload, marginally acceptable usability of the LLM system, and high perceived ease of use. Management plan quality improved in 45 of 75 mock rounds interactions (60%), with a mean improvement of 8.5% (P < .001) on mock rounds scoring rubrics. The improvement was greater for MOs (21.0% change) than nurses (6.5% change). In hypothetical case dilemmas, MO plan accuracy improved by 22.7% (P = .001), and critical errors declined from 33.3% to 0%. Fourteen care changes for 9 patients were attributed to LLM suggestions, including 6 that potentially prevented major morbidity. No unsafe outputs were detected. Exploratory cost analysis suggested potential savings from clinical care changes exceeded the labor costs involved in LLM use. LLM use was associated with improved plan quality without observed safety concerns, while also prompting clinically meaningful care changes. Larger, controlled studies are needed to determine generalizability, durability of benefit, and patient-centered outcomes.

Air pollution levels vary over time depending on pollution source location, emission dynamics, and meteorological situation. Thus, Realistic pollutant concentration predictions for a certain period of time remain challenging. This study x-rayed criteria pollutant levels emitted from two prominent industries in Kano Using a HAT 200 PMX detector and an Altair 5X multi-gas detector on a pilot scale. Mean pollutants monitored are from 8 to 24 µg m−3 for PM2.5, 17–50 µg m−3 for PM10, 4–16 µg m−3 for CO, 3–15 µg m−3 for SO2 and 31–52 µg m−3 for NO2. These results are promising except CO levels that exceeded 11.4 µg m−3 limit. With a coefficient of determination (r2) ranging from 0.6 to 0.8, there is a substantial positive association between the monthly mean concentration (MMA) and monthly maximum average concentrations (MMC) during 24 h in the two sites. A fair evaluation of the probabilities of distributed hazardous concentrations in all the locations under study was indicated by the excellent association between the MMA and MMC values displayed throughout the 24 h. The multiple regression coefficients of the different meteorological variables’ effect on the pollutants concentrations clearly show that higher PM2.5 and PM10 values are as a result of dust resuspension due to strong winds, seasonal dust transport, relative humidity and temperature. In contrast, the concentration of the gaseous pollutants was observed to increase when the temperature and relative humidity were high. However, it was observed that high wind speed and direction will reduce the concentrations of gaseous pollutants in the air as revealed by the multiple regression equations. The high levels of CO concentrations found from the sources can be regulated by industrial gas absorbers or complete combustion.

In the contemporary landscape, environmental degradation and climate change pose formidable challenges for developing economies like India, which has undergone rapid economic expansion over recent decades. This growth has contributed to elevated global greenhouse gas emissions and significant ecological concerns. The present study investigates the intricate interconnections among India's fiscal deficit, oil prices, technological innovations, human capital, urbanization, foreign direct investment (FDI), and carbon dioxide emissions. Utilizing time-series data from 1991 to 2023, the analysis employs advanced econometric approaches, including the Autoregressive Distributed Lag (ARDL), Non-linear Autoregressive Distributed Lag (NARDL), and Dynamic Autoregressive Distributed Lag (DARDL) models, to assess both short- and long-term dynamics among these variables. Recognizing that outcomes can vary across methodologies, this research integrates multiple techniques to comprehensively explore the economy-environment nexus in India, filling a critical void in existing literature. Evidence of cointegration confirms enduring relationships among the factors. Although the models yield varying effects for some variables, they uniformly underscore the roles of fiscal policies, technological progress, and urbanization in curbing environmental harm while fostering sustainable economic development. Robustness is ensured through diagnostic and stability assessments. These findings offer valuable guidance for Indian policymakers in crafting targeted interventions to improve environmental sustainability and bolster economic advancement.