University of Cross River State also known as UNICROSS is a state-owned tertiary institution with four campuses spread across four Local Government Areas of the state. The Office of the Vice-Chancellor, Deputy Vice-Chancellor and the Bursar are all located in the Calabar Campus, considered the main campus of the Institution, which is located in Calabar South Local Government Area of Cross River State in Southern Nigeria. The University was established in 2002 by the then Governor Mr. Donald duke after the merging of three higher institutions: The Polytechnic of Calabar, The College of Education and Ibrahim Babangida College of Agriculture. It offers degree courses at undergraduate and post graduate levels.The university currently has campuses in Calabar, Obubra, Ogoja and Okuku.
Despite privatisation, the power sector in Nigeria remains underperforming, with power outages and frequent grid failures in 2024. This research evaluates the performance of the eleven electricity distribution companies (DISCOs) of Nigeria between 2015 and 2024 with respect to electricity supply and customers' access. Quantitative analysis was used to determine the key indicators of a utility company, such as energy supplied, number of customers, energy/customer, customer to energy ratio, customer growth rates, and energy supplied growth rates, all average quarterly and annual.The study reveals poor energy supply stability, with 2024 showing a reduced energy supply for most DISCOS when compared with 2023. The 2024 average energy supplied was 1,897.008 GWh and puts total energy supplied at 20,867.088 GWh. For 2023, the average energy and total energy supplied are 2,003.782 GWh and 22,041.602 GWh, respectively. While most DISCOS showed steady and increasing growth rates, AEDC and IEDC showed a double-digit decline in customer growth rate for 2022. This highlights the poor energy distributed on an individual basis, and the excessive customer-to-energy ratio exceeding 5000 customers/GWh signifies the extreme energy poverty and poor electrification plaguing most of the country.The study shows that privatisation of the nation’s power sector has been unable to achieve its intended objectives of robust electrification, consistent power supply and improved customer access to energy. Hence, emphasising an immediate reassessment of policy and current operations to minimise technical losses and boost energy distribution supply. The study’s value is in its holistic examination of the nation’s distribution sector performance in energy supply and customer reach. The findings aim to catalyse a deliberate collection of DISCOS’ data, a more in-depth analysis of DISCOS’ performance, and a critical identification and examination of energy distribution policies.
This study investigated the effects of aluminium on the blood characteristics of fingerling Clarias albopunctatus, for which information is limited. A total of two hundred and fifty (250) fingerlings of Clarias albopunctatus were exposed to aluminium ions at concentrations of 0.01 mg/l, 0.02 mg/l, 0.03 mg/l, 0.04 mg/l, 0.05 mg/l and 0.06 mg/l, with a control experiment (0.00), for a period of 96 hours in a static bioassay. The 96-hour LC50 of aluminium ions for Clarias albopunctatus was 0.05 mg/l, and the Maximum Admissible Toxicant Concentration (MATC) ranged between 0.005 mg/l and 0.0005 mg/l. The haematological parameters examined after 21 days were red blood cell count, white blood cell count, packed cell volume, haematocrit and haemoglobin. The white blood cell count increased rapidly with increasing concentrations of aluminium ions. Differential cell counts showed a marked increase in lymphocytes and eosinophils, with decreases in neutrophils, monocytes and basophils. The increases in lymphocytes, eosinophils and total leucocytes indicated infections that developed after exposure to aluminium ions. Sub-acute exposure to aluminium ions altered the normal functioning of the haematopoietic system in African catfish, leading to changes in red and white blood cell profiles. This study on Clarias albopunctatus indicated that aluminium toxicity induced hypochromic macrocytic anaemia, with reductions in red blood cell (RBC) counts, haemoglobin (Hb) content and packed cell volume (PCV), as well as changes in immune cells.
Despite long-standing improvements in drinking water treatment and public health regulation, Legionnaires' disease is emerging as one of the leading causes of waterborne disease-associated hospitalizations and mortality in the United States (US). Legionella spp. proliferates in engineered water systems, and accumulating evidence indicates that climate-driven environmental changes are accelerating conditions favorable for their persistence, transmission, and human exposure. This review evaluated the public health burden of Legionnaires' disease and other major waterborne pathogens in the United States and examined how climate-related factors are reshaping their epidemiology and transmission dynamics. Using a narrative review of 75 peer-reviewed studies and government reports published between 2015 and 2025, the analysis identified a sustained increase in reported Legionnaires' disease incidence since the early 2000s, with outbreaks accounting for approximately 38% of cases, 97% of hospitalizations, and nearly all reported waterborne disease-related deaths. Climate-sensitive drivers, including rising ambient and water temperatures, increased precipitation and flooding, drought-induced stagnation, and aging infrastructure, were linked to Legionella growth in premise plumbing and cooling systems, earlier seasonal peaks, and expanded geographic risk. In addition, the review documented increasing disease burden associated with Vibrio spp., Pseudomonas aeruginosa, Naegleria fowleri, Cryptosporidium, and Giardia, collectively accounting for millions of infections, over 120,000 hospitalizations, and approximately 6,600 deaths annually in the US. Findings further revealed underdiagnosis and underreporting due to reliance on passive surveillance systems and limited diagnostic testing beyond L. pneumophila serogroup 1. The reviewed evidence indicate that climate change is not only intensifying existing waterborne disease risks but also exposing critical gaps in surveillance, infrastructure resilience, and outbreak preparedness. This review highlights priority areas for climate-informed surveillance, water system management, and interdisciplinary research to reduce the risk of waterborne disease re-emergence in a changing climate.
Rice (Oryza sativa), a staple food worldwide, can accumulate heavy metals from contaminated soil and irrigation water, displacing essential nutrients. Chronic dietary exposure to these metals has been linked to severe health risks in man and other animals. This study investigates the physicochemical properties and concentrations of arsenic (As) and lead (Pb) in prevalent rice varieties and associated irrigation water sources across four major agro-ecological rice-producing zones in Nigeria. Water and rice grain samples were collected from three farming communities in each of four states: Ekiti (Southwest), Cross River (Southsouth), Nasarawa (Northcentral), and Taraba (Northeast). Prominent local varieties (FARO 43, FARO 44, NERICA 2, and NERICA 4) were analyzed using standard methods for key nutrients and heavy metals. Results showed that Arsenic (As) concentrations in all rice samples were below the Codex Alimentarius limit (0.2 mg/kg), with a maximum of 0.016 ± 0.003 mg/kg found in Awe (Nasarawa). Lead concentrations ranged from 0.009 ± 0.008 mg/kg (Ire-Ekiti) to 0.077 ± 0.005 mg/kg (Iyin-Ekiti). Overall, the organic matter content (OC) of rice samples were in the order Igbemo > Awe > Obubra > Ekureku > Iyin-Ekiti > Akamkpa > Ire-Ekiti > Wukari > Donga > Obi > Azara > Ibi. Nutrient levels varied, with nitrogen (a proxy for protein) ranging from 0.85% to 1.51%. It was also found out that Arsenic levels in all irrigation water samples significantly exceeded the WHO drinking water guideline (0.01 mg/L) and the FAO irrigation threshold (0.10 mg/L). As concentrations ranged from 2.45 - 2.61 ppm in Donga (Taraba), and 6.82 – 7.14 ppm in Ibi (Taraba). Lead in water reached 0.87 ± 0.06 ppm (Iyin-Ekiti), far above safe limits. A weak positive correlation was observed between lead in water and lead in rice (r = 0.23). Stronger positive correlations were found between water lead and rice nitrogen (r = 0.63), calcium (r = 0.54), and magnesium (r = 0.42) content, suggesting a potential synergistic interaction affecting nutrient uptake. This study provides region-specific data revealing severe arsenic and lead contamination in Nigerian irrigation water, posing a direct risk to agricultural safety and food security. While rice grains currently show metal levels within regulatory limits of Codex Alimentarius, this does not preclude potential chronic exposure risks for local populations. Therefore, persistent contamination of water sources necessitate immediate remediation strategies to prevent future accumulation and safeguard public health. Outputs of this research will serve as guide
Machine learning (ML) is increasingly recognized as a valuable tool for promoting inclusive education through the early identification of learning difficulties and targeted support for students with intellectual disabilities. This study examined lecturers’ use of ML-informed tools and their perceptions of the effectiveness of ML-based educational interventions in Nigerian universities. A descriptive survey design was adopted involving 600 lecturers from federal and state universities in South-South Nigeria. Data were analysed using mean, standard deviation, independent-samples t-test, and multiple regression. Findings revealed a low level of lecturers’ use of ML-informed tools, reflecting limited training, infrastructure, and classroom integration. Despite this, lecturers perceived ML-based interventions as effective for improving early identification and enhancing students’ academic engagement, particularly in attention, motivation, communication, and task completion. Regression results showed that lecturers’ use of ML was the strongest predictor of academic engagement, whereas gender, academic rank, teaching experience, and institutional type were not significant predictors. The study highlights a gap between the perceived value of ML and its practical implementation in higher education. It concludes that strengthening lecturer capacity, digital infrastructure, and institutional support is essential for effective adoption of AI-driven inclusive practices. The findings contribute to emerging scholarship on AI-enabled inclusive education and provide evidence for advancing equitable and quality higher education.
