The Federal University of Technology Owerri (FUTO) is a federal government university in Owerri West, the capital of Imo State, Nigeria. The university is bounded by the communities of Eziobodo, Umuchima, Ihiagwa and Obinze. It is the premier federal university of technology in the South-East and South-South parts of Nigeria.The university is the oldest university of technology in Nigeria and was established in 1980 by executive fiat with the composition and appointment of the first provisional council by Nigeria's first Executive President, Shehu Shagari. It became the first of three such universities set up by the Federal Government of Nigeria which sought to establish a University of Technology in each geo-political region and particularly in a state which did not have a conventional university.FUTO began with 225 students and 60 staff (28 academic and 32 non-academic). Like other federal universities in Nigeria, FUTO is headed by a Chancellor who is usually a royal father, and followed by a Vice Chancellor who oversees the daily activities of the university.The University Senate is the highest decision making arm of the university. FUTO students are fondly called Futoites and currently has over 25,000 students. FUTO has over 50 professors. The current vice-chancellor is Professor Nnenna Nnannaya Oti.
The economic and environmental problems linked with cement manufacture are critical challenges faced by the construction industry. Therefore, there is a need to develop and apply approaches that will promote the sustainability of cement-based materials. This study evaluated the partial substitution of ordinary Portland cement with blended corn cob ash (CCA) and coconut shell ash (CSA) for sandcrete block production. Cement replacement levels of 0
Abstract As competition in the business world is getting tougher, many businesses are confronted with the need to effectively manage increasingly large-scale datasets for predictive decision making for enterprise systems. With the increased sophistication in data variation, collection and analysis due to the emergence of new technologies, there is need for a hybrid predictive decision-making model that will handle the massive amounts of large-scale data from enterprise information systems and applications across various industries to guarantee better decision-making. This research work is aimed at developing a hybrid predictive decision-making model for enterprise data analytics, leveraging on the synergies of Prophet, ARIMA, and Random Forest to improve the prediction accuracy of seasonal and non-linear data in enterprise systems. Decision-making processes could be enhanced via the provision of more reliable forecasts. Obviously, single model predictive approaches lack the robustness and flexibility needed to capture patterns accurately; hence it has become crucial to implement a hybrid model that will leverage the synergies of different forecasting techniques to provide accurate and actionable insights, as well as empower organizations to make informed decisions based on comprehensive data analytics. For this study, a historical supply chain dataset of a retail outlet was used for data analysis. The data was split using the train_test_split while RandomizedSearchCV was used for hyperparameter tuning. Also, the Apache Spark open-source technology was adopted to assess the feasibility of our model for real-time enterprise application, with the Star schema adopted to organize data into measurable facts and descriptive dimensions to simplify data analysis and optimized for SQL data querying. To capture seasonality and linear components of data, the Prophet and ARIMA time series models were used while Random Forest was used for non-linear relationships. The models were trained and evaluated individually after which they were combined and blended using an ensemble approach of a weighted mean average. Evaluation of the hybrid predictive decision-making model was made by comparing the model’s predictions against actual values using RMSE, MAE, MAPE and R2 evaluation metrics. The result revealed that combining the forecasting models gave a lower error RMSE value of 4.83, MAE value of 3.32, lower MAPE of 1.92% and, R2 of 99.2% as against the individual model evaluation result of Prophet (high error RMSE value of 43991.04, MAE value of 38754.39, MAPE of 27.4%, and R2 of − 6.5); ARIMA (RMSE value of 68.35, MAE value of 60.36, MAPE of 49.0%, and R2 of − 0.54), Random Forest (RMSE value of 3.66, MAE value of 1.90, MAPE of 1.54, and R2 of 0.99). Our hybrid model was also compared with other state-of-the-art ensemble models which equally showed improve sales forecasting performance with a prediction accuracy of 0.40% more than the second-best performing model.
Air pollution from open dumpsites poses a severe but under-quantified threat to children in urban Nigeria. This paper combined high-resolution spatio-temporal air monitoring and community survey of health in order to evaluate the effects of dumpsite emissions on Acute Respiratory Infection (ARI) among children below the age of five years in Owerri, Nigeria. We implemented automated systems to check the criteria pollutants of NO2, SO2, CO as well as O3 in seven dumpsites and one control site, and surveyed 546 mothers and 2355 children. Findings indicated that SO2, CO, and O3 were 2-4 times higher than the National Ambient Air Quality Standards over a range of 90-137km2. The exposure based on multi-pollutant (Health-Adjusted Air Quality Index or HAQI) showed constantly poor to dangerous levels (147 423) in the air, with the major risk factors being SO2 and CO. The prevalence of ARI in exposed children was 1.65, and the mortality rate due to the proximity was 0.38. The CO and O3 were found to be the main statistical predictors of ARI in high-risk sites as a result of single-pollutant regression. Recalibrated cost-benefit analysis, adjusted to a total exposed population, showed that the Net Present Value of dumpsite relocation is 3.13 million plus an 11.99 Benefit-Cost Ratio, and is economically viable to parameter uncertainty with sensitivity analysis. This research gives strong reasons to believe that dumpsite gaseous pollutants are a significant risk of respiratory disease in children and that engineered relocation is a cost-efficient intervention by the government in the community because of its health benefits. Our policy recommendations are urgent intervention to cover dumpsite placement assistance 2km away, ≥ 2km away residences, and real-time air quality alerts as solutions to maintain child health in Nigerian cities.
A binder-free composite hybrid electrode material was deposited through electrodeposition by combining carbon-based materials (graphene oxide) and battery like materials (NiO, ZnO and Al2O3) in a single electrode. The structural, morphological and electrochemical properties of CaAl2O4–ZnO/rGO composite thin film were studied to characterize the hybrid composites. The average crystallite size D of the hybrid composites is 23 nm with an average inter planar spacing of 1.90 Å and lattice constants (a = b = c = 1.456 Å) that reveal a rhombohedral structure. The introduction of Ni increases the cracks and nullifies the rod-like morphology of the undoped thin film while the presence of Ag reveals the nonexistence of cracks at lower concentrations of Ag dopant but densely packed homogenous noodle-like grains with cracks can be seen at a 0.03 M sample. EDX analysis confirmed the presence of deposited elements with the exception of Ag, and this could possibly be due to the low intensity of the Ag dopant or the low deposition voltage. Cyclic voltammetry curves show typical electrochemical behavior with clear oxidation and reduction peaks for most samples. The Nix = 0.06 doped CaAl2O4–ZnO/rGO composite thin films have the maximum specific capacitance of 673 F/g at a scan rate of 100 mV/s, while the Agx = 0.03 doped thin film has the minimum specific capacitance of 205 F/g at the same scan rate. indicating that NCZr3 has a good electrochemical behavior at 0.1 mA/g with an energy density and a power density of 59.83 W · h/Kg and 26.926 W · h/Kg, respectively. These results reveal the suitability of the Ni doped material as an electrode material for supercapacitor applications and confirm the model of roughening. Further studies should be explored by lowering scan rates (5–50 mV/s), adding binder materials, and properly varying Ag concentration and deposition time in order to explore more desirable properties for advanced applications.
This study investigated the effect of financial technology (FinTech) on financial inclusion in Nigeria over the period 2000–2024. Nigeria has seen rapid advancement in FinTech-driven innovations, including mobile banking, internet banking, agent banking, and digital payment systems, which are widely embraced as fundamental instruments for mitigating financial exclusion, particularly among rural and low-income communities. The study utilized yearly secondary data sourced from the Central Bank of Nigeria (CBN) Statistical Bulletin and the World Bank. An Autoregressive Distributed Lag (ARDL) model was used to assess the effect of selected FinTech, namely the volume of Automated Teller Machine (ATM) transactions, point-of-sale (POS) transactions, and mobile banking transactions on the financial inclusion index. The ARDL results confirmed existence of a long-run association between FinTech indicators and financial inclusion. Empirical findings showed that ATM and POS transaction volumes exerted a significant negative effect on the financial inclusion, while mobile banking transactions had a positive but weak long-run effect. Notwithstanding the rising adoption of FinTech, drawbacks such as insufficient digital infrastructure, cyber insecurity, low levels of financial literacy, and regulatory inconsistencies continue to undermine effectiveness of FinTech in deepening financial inclusion. Consequently, the study recommended that policymakers strengthen regulatory frameworks, invest in digital infrastructure, and intensify financial literacy initiatives to promote inclusive and sustainable financial inclusion through FinTech innovations. Finally, this study is anchored on the financial intermediation and technology diffusion theories because core intermediation functions are performed through FinTech innovations thereby giving financial services to populations that were previously excluded.
