Urban biodiversity in Glenfield, Leicester reflects a rich mosaic of woodlands, grasslands, parks, and riparian habitats that sustain diverse plant and bird communities. These interconnected green spaces support ecological resilience, species dispersal, and essential ecosystem services such as climate regulation, air purification, and flood mitigation. The River Soar acts as a key ecological corridor, enhancing habitat connectivity. Recorded flora contain bioactive compounds with medicinal value, while avifauna represent varied ecological guilds, all listed as Least Concern. Overall, Glenfield demonstrates how urban landscapes can maintain significant biodiversity and contribute to human well-being.
Lemi Abiodun Ghariokwu is one of the most prominent and distinctive visual artists in contemporary Nigerian art, widely recognized for his politically charged, culturally assertive, and technically eclectic paintings and album cover illustrations. Despite his significant contribution to Nigerian and African visual culture over five decades, scholarly documentation of his work remains sparse. This study examines twelve selected paintings by Ghariokwu, analyzing them thematically and stylistically to document his artistic philosophy, technique, and contribution to contemporary Nigerian art. The study employed a qualitative approach, combining primary data gathered through oral interviews and studio visits with secondary data from libraries, exhibition catalogues, and internet sources. The selected works are classified under socio-political commentary, Pan-African iconicity, cultural identity, and liberation themes. The findings reveal that Ghariokwu's paintings are characterized by vigorous expressionist illustration, vibrant colour symbolism, and a consistent Pan-Africanist ideological orientation. His work serves both aesthetic and didactic functions, bridging music and visual art through album cover design and communicating social critique to broad audiences. The study concludes that Ghariokwu represents an essential voice in the documentation of African cultural identity and resistance, and that systematic scholarly attention to his work is urgently needed.
Toothpastes are agents produced in the form of pastes, gels, powders, or liquid usually in conjunction with a toothbrush, for cleaning dental plaque and improving of oral health. These toothpastes are now produced in the herbal and non-herbal forms to meet the demands of the teaming population in search of a healthy oral cavity, so as to improve oral health. This research was carried out to compare the effects of herbal toothpastes and their non-herbal on human dental flora. Five oral swab samples were procured from early morning mouth rinses of volunteer subjects using swab sticks to determine the effect of herbal and non-herbal toothpaste on bacterial isolates from the human dental cavity. Blood Agar, Nutrient Agar, and Sabouraud Dextrose Agar were used in culturing at 37 for 24 hours using the streak plate method. The isolates were identified using standard microbiological techniques, which included colonial examination, Gram staining technique and Biochemical tests such as citrate, catalase, sugar fermentation, motility, and haemolysis test. The results showed different bacterial isolates: Staphylococcus spp, Streptococcus spp and Lactobacillus spp, as well as fungal isolates: Candida albicans and Aspergillus spp. Understanding the relationship between toothpaste ingredients and the oral microbiota can aid in formulating toothpaste products that promote optimal oral health and maintain a balanced oral microbial ecosystem.
Increasing adoption of artificial intelligence (AI) in healthcare raises energy-demand questions that are especially acute in resource-limited regions. Because operational, application-level energy-metering data for medical AI systems in Sub-Saharan African hospitals are not yet publicly available, this study develops a climate-calibrated simulation framework that couples real facility profiles (bed capacity for six Nigerian teaching hospitals, from institutional and published sources) with real long-term climate normals (temperature and humidity for each hospital city) and a transparently parameterized medical-AI workload overlay (diurnal, weekly, and holiday patterns informed by prior literature). Statistical models (ARIMA, SARIMA, Holt-Winters exponential smoothing) and machine-learning models (a purpose-built LSTM and a hybrid LSTM-ARIMA) were evaluated on the resulting 58-month (January 2020-October 2024) simulated series using a consistent walk-forward, one-step-ahead protocol. Seasonal statistical models achieved the lowest forecast error (Holt-Winters MAPE=0.36%, SARIMA MAPE=0.37%), outperforming the hybrid LSTM-ARIMA (MAPE=0.51%) and a standalone LSTM (MAPE=0.61%), while the hybrid was most accurate at very short (1-day) horizons. Diurnal peak-hour (0800-1400) demand exceeded off-peak demand by 35%, and dry/harmattan-season demand exceeded the annual mean by 1.4-5.3% depending on institution and local climate. Rather than asserting a single best algorithm, this study's central contribution is the framework itself: a reusable, real-data-calibrated method for generating defensible medical-AI energy-demand scenarios where metered data do not yet exist, together with comparative evidence that the relative advantage of statistical versus machine-learning forecasting depends on how strongly deterministic the underlying seasonal structure is. The framework and comparative findings offer a starting point for energy planning and policy-making for AI-enabled healthcare across Sub-Saharan Africa (SSA), pending validation against real operational meter data.
The ever-increasing energy demands, rising costs, and alarming depletion of fossil fuels, combined with the environmental risks associated with their use for energy generation, have been driving forces behind the use of renewable energy sources such as solar energy. The conversion and utilization of solar energy as an alternative source of energy, has increasingly aroused the interest of many researchers. In meeting the needs of some Africa countries, especially Nigeria, with unstable and erratic supplies of electricity, a solar-powered refrigerator using electricity from the national grid and the power generated by photovoltaic was designed, developed, and then modelled. The system works on power from the grid system when there is supply of electricity and automatically switches to be powered by the PV whenever there is power failure from the grid system. Moreover, a dry cell battery was built into the system to store charges and run the system at night or when there are no solar insolation and supply of electricity. Due to the variation in the insolation received by a particular surface with respect to time, a solar tracking system was incorporated. From the field performance test results, the cooling rates of the system when being powered by PV and AC are approximately 0.4oC/min. Therefore, there was a significant improvement recorded in the avoidance of the spoilage of perishable products as compared to when the system was only being powered by the erratic electricity supplies from the grid system. The applicability of the system to different climatic regions in most Africa countries is quite enormous and as it could be used for perishable food storage, it will also contribute to improvement in the health services and living conditions in most regions in Africa countries where there are irregular supplies of electricity from the grid. Since the system requires very little routine maintenance, it is essentially beneficial in developing countries where maintenance facilities are usually poor.
