Nnamdi Azikiwe University Journals
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HOW DO FEE INCOME AND TRADING & INVESTMENT INCOME TRIGGER THE PERFORMANCE OF LISTED COMMERCIAL BANKS IN NIGERIA?
No full text The study focused on understanding how the performance of commercial banks listed in Nigeria is triggered by non-interest income sources such as fee income and the trading & investment income. Specifically, the study ascertained the effect extent of fee income and the Trading and Investment Income on Return on Equity of commercial Banks in Nigeria. A total of 7 licensed listed commercial banks with international capacity was sampled over a period of 12 years ranging from 2012-2023. Data were descriptively and inferentially analysed using the panel estimated generalised least square. The findings revealed that the effect of Fee Income on Return on Equity of commercial Banks in Nigeria is positive and statistically significant (p-value = 0.0000). It was further discovered that Trading and Investment Income does significantly and positively affect the Return on Equity of commercial Banks in Nigeria (p-value = 0.00000). The study therefore concluded that the significant positive relationships found between certain non-interest income streams and financial performance, especially with Return on Equity (ROE), underscore the potential for value creation through diversified revenue strategies. It was recommended that the Executive Management and Investment Strategy Teams should expand trading and investment activities with a focus on high-growth sectors and innovative financial products. Implement robust risk management practices to safeguard investments and maximize profitability. Establish a continuous feedback loop to refine strategies based on market performance data, ensuring sustained contribution to equity growth
CASH CONTROL AND THE FINANCIAL PERFORMANCE OF LISTED AGRICULTURAL FIRMS IN NIGERIA
No full textThis study examined the effect of cash control on the financial performance of listed agricultural firms in Nigeria over the period 2015 to 2024. Specifically, it investigated the effect of cash and cash equivalents, cash to book value, and cash conversion cycle, on return on assets (ROA) of five agricultural firms listed on the Nigerian Exchange Group. The study adopted a census sampling technique and employed an ex-post facto research design using secondary data obtained from the firms’ audited financial statements. Descriptive statistics were used to summarize the data, while panel regression analysis was conducted to test the hypotheses. The study found the following: cash to book value has a significant negative effect on ROA of listed agricultural firms in Nigeria (β = -0.2483; p = 0.0162); cash conversion cycle has a positive but insignificant effect on ROA of listed agricultural firms in Nigeria (β = 0.000030; p = 0.6133); cash and cash equivalents have a significant positive effect on ROA of listed agricultural firms in Nigeria (β = 0.0903; p = 0.0000). In conclusion, holding excessive cash relative to equity may be counterproductive, possibly signaling inefficient use of shareholder capital or a reluctance to reinvest idle funds into productive operations. The study recommends that Chief Financial Officers (CFOs) of agricultural firms should minimize idle cash relative to book value by channeling excess liquidity into value-adding investments that can improve asset productivity and enhance shareholder returns.
 
HYDROCARBON EMISSIONS DISCLOSURE: A STRATEGIC PREDICTOR OF RETURN ON INVESTMENT IN THE NIGERIAN ENERGY INDUSTRY
No full textThe study examined the effect of hydrocarbon disclosure on the return on investment of listed energy firms in Nigeria. Ex-post facto research design was deployed. The population comprised eight (8) listed oil and gas firms in Nigeria. A sample size of seven (7) firms were selected using purposive sampling technique. Secondary data were collected from firms’ annual reports over a ten year period (2015-2024). The data were analysed using descriptive test while hypotheses were tested using panel estimated generalised least square regression. The findings revealed that hydrocarbon emission disclosure has a positive and significant effect on return on investment of listed energy firms in Nigeria (β = 0.073859, p = 0.0056). This indicates that environmental accountability is gaining economic weight in the investment calculus, suggesting a broader market recognition that sustainability and profitability are not mutually exclusive but potentially reinforcing dimensions of firm performance in resource-dependent economies. It is therefore recommended that the management boards of listed energy firms in Nigeria institutionalize standardized hydrocarbon emission disclosure frameworks within their annual reporting processes to strengthen market confidence, enhance investor trust, and leverage transparency as a driver of sustained financial returns
ASSET MANAGEMENT AND OPERATING EFFICIENCY OF LISTED ICT FIRMS IN NIGERIA
No full textThis study examined the effect of asset management on the operating efficiency of listed ICT firms in Nigeria. The specific objective was to determine how current asset turnover and noncurrent asset turnover influence net profit margin. An ex post facto research design was adopted, using panel data covering the period 2015 to 2024. The population consisted of eight listed ICT firms, from which five were purposively selected based on data availability. Secondary data were sourced from audited annual reports, and the hypotheses were tested using fixed effect regression following the Hausman specification result. The findings showed that: current asset turnover has a positive and significant effect on net profit margin (β = 0.000274, p = 0.0000); noncurrent asset turnover has a positive and significant effect on net profit margin (β = 0.010657, p = 0.0012). In conclusion, efficient utilisation of both current and noncurrent assets plays an important role in improving the profitability of listed ICT firms in Nigeria. Hence, financial managers and accountants in listed ICT firms should regularly monitor and optimise the utilisation of current assets, such as cash, receivables, and inventory, to ensure that these resources are generating maximum revenue within the shortest possible time
ETHICAL ISSUES IN HUMAN AND ANIMAL RESEARCH: A Review
No full textResearch involving human and animal subjects is integral to advancing scientific knowledge, improving healthcare, and informing policy. In human research, ethical issues revolve around safeguarding autonomy, obtaining informed consent, ensuring beneficence, and protecting vulnerable populations such as children, prisoners, and individuals with diminished decision-making capacity. The 4Rs (Replacement, Reduction, Refinement, and Responsibility) in animal research offer a framework for minimizing animal suffering while ensuring the scientific validity of experiments. Ethical issues in human and animal research are not peripheral concerns but central pillars of responsible scientific practice. Addressing them ensures the protection of subjects, upholds human dignity and animal welfare, promotes credible science, and fosters societal benefit. A collaborative effort of researchers, ethicists, policymakers, and the broader community is paramount in handling the ethical challenges in research. Therefore, by maintaining ethical principles and practices, the scientific community can continue to push the boundaries of knowledge while upholding the fundamental rights and wellbeing of both human and animal participants. This study therefore critically examines the ethical issues surrounding both human and animal research, analysing historical contexts by examining case studies and the importance of addressing these ethical issues in research, with the ultimate goal of promoting ethically sound, socially responsible, and scientifically rigorous research practices
Development of Biology module based on SETS (Science, Environmental, Technology and Society) in ecosystem material
No full textThe development of biology modules for learning Ecosystem Material based on SETS (Science, Environmental, Technology, and Society) is expected to be able to develop students\u27 abilities in connecting science with the environment, technology, and society in everyday life, so as to improve students\u27 understanding of Ecosystem Materials. The facts obtained from the field are that the teaching materials used by teachers in the learning process are still inadequate and the learning that takes place is still monotonous, so that students find it difficult to understand the concept of Ecosystem Material. The purpose of this study is to design a SETS (Science, Environmental, Technology and Society) based module on Ecosystem Material and to determine the feasibility of a SETS (Science, Environmental, Technology and Society) based module on Ecosystem Material. The SETS-based module refers to the development model developed by Alessi and Trollip, which consists of 3 stages including (1) Planning Stage, (2) Design Stage, and (3) Development Stage. The product produced from this study is a SETS (Science, Environmental, Technology and Society) based module on Ecosystem Material. The number of validation results from material experts was 87.25% and the validation results from media experts were 86.75%, with an average percentage score of 87% so that it was declared very feasible to be used as a learning medium
Bacterial species associated with houseflies (Musca domestica) and blowflies (Lucilia cuprina and L. sericata) at a market dumpsite and possible disease risk in Benin City, Nigeria
No full textBlowflies and houseflies are common fly species in Africa. Their lifestyle and external features allow them to do more than just be a nuisance in the environment; they also act as vectors for major diseases including bacterial. This study aimed to identify bacterial species associated with common flies—specifically, Musca domestica, Lucilia cuprina, and L. sericata—sampled from the dumpsite at Uselu Market in Benin City, Edo State, Nigeria. Flies were collected from the market dumpsite using sterilized equipment, sorted, and identified. Microbial analysis of the fly samples included bacterial isolation, identification, evaluation of phenotypic virulence properties, antimicrobial susceptibility, and calculation of the multiple antibiotic resistance (MAR) index. Statistical analyses were performed using Microsoft Excel and SPSS. Identified bacterial isolates include Escherichia coli, Bacillus subtilis, Enterobacter cloacae, Pseudomonas aeruginosa, , Proteus vulgaris and Serratia marcescens. Heterotrophic Bacterial Count (HBC) varied significantly among fly species (P < 0.05). Lucilia cuprina had the highest HBC (12,200 ± 1,555.64 cfu/g), while variation in the Coliform Count was significant (P < 0.05) only between L. cuprina and L. sericata. Determinants of phenotypic virulence in bacterial isolates differed across species. Enterobacter, Serratia and Proteus exhibited a multiple antibiotic resistance (MAR) index of 0.44, while other bacteria had MAR index of 0.33. The detection of antibiotic restistant bacteria from the flies is worrisome considering the risk it pose
Prediction of Heart Disease using Autoencoder with LightGMB and Gradient Boosting
No full textCardiovascular disease (CVD) refers to heart disease. CVD is seen to cause majority of death in world. Because of this problem many researchers have been drawn to this area to develop models and systems to predict occurrence of heart disease for early treatment. This study developed a Heart disease predicative model using Autoencoder with LightGBM and Gradient Boosting. The Dataset used was gotten from kaggle.com. One Hot Encoding, SMOTE were used to pre-processed the dataset. Feature extraction was done using Autoencoder. Two classification methods: LightGBM and Gradient Boosting were employed to build the predictive model. The result shows that Autoencoder perform very well with low values of MSE, RMSE, MAE and High Value of F2 score. The result of LightGBM shows a specificity of 95.7%, precision value of 95.7%, recall value of 94.7% ,F1 value of 95.2% , AUC value of 0.99 and Accuracy value of 95 While the results of Gradient Boosting shows Specificity of 91.7%, Precision value of 91.5%, Recall value of 90.0% ,F1 value of 91.0% , AUC value of 0.96 and Accuracy value of 90. The study concluded that LightGBM perform better that Gradient Boosting. The Model is recommended to the health sector management to guide their decision making. Its potential integration with predictive model and clinical validation will assist greatly in improving the heart disease diagnosis and prevention. Further research could be done with more validating metrics, more deep learning techniques
Investigation of Hydrocarbon Associated Gas Separation in a Vertical Vessel: A Steady-State and Dynamic Simulation Using Aspen Hysys
No full textThe global push for decarbonization has amplified the need for the sustainable utilization of conventional fossil fuels. Associated gas, a low-value byproduct of petroleum production frequently flared or vented, has gained attention as a viable solution for reducing the carbon footprint of petroleum operations. When converted into valuable products, it offers the potential to enhance the sustainability and efficiency of production systems. This study presents the separation of associated gas (methane, ethane, propane and n-butane) into various proportions in liquid and gas product streams using Aspen HYSYS® software 11 under both steady state and dynamic conditions. The flowsheet was developed using a Pen-Robinson thermodynamic fluid package with a stream flow rate of 1000 kgmole/h charged into a vertical separator. The effect of pressure drop within the column on the product stream separation was investigated in the steady state. The system responses for step changes in molar flow, vessel liquid level, vessel pressure and methane fraction in the vapour product stream were examined in the dynamic simulation using a proportional-integral-derivative (PID) controller. The steady-state result showed that the pressure drop within the column significantly affects separation efficiency, with methane showing the steepest decline in molar flow within the liquid stream product. The n-butane exhibited a non-linear optimal range, while the ethane and propane decreased steadily. In the dynamic state, the system can handle flow rates between 900–1100 kgmole/h, liquid levels of 20–30%, and pressures of 47–49 bar, while maintaining methane composition between 55–65%. The system attained stability by decreasing the rise time and reducing the overshoot and settling time of the flow controller. The Pressure controller has the highest proportional gain (1.00) for a strong response to changes, while methane composition control has the smallest integral time (0.07), indicating rapid adjustments for deviations. This study effectively describes the steady state and the dynamic behaviours of the associated gas separation system, providing critical information for process optimization and control
Production of Zinc Chloride Modified Avocado Pear Seed Activated Carbon: Optimization of Preparation Conditions using ANN Modeling and RSM-Aided Box Behnken Design
No full textThe exploration of the Zinc chloride activated avocado pear seed (ZAPS) production process employing Response surface methodology aided Box Benhken design and Artificial neural network algorithms was the main focus of this research. During the study, ANN and RSM models were deployed to assess the effect of process settings such as impregnation ratio, activation time and temperature on the measured BET surface of ZAPS. The RSM and ANN BET neural models were comparatively analyzed to ascertain the optimal process settings to produce the best response (BET surface area). The Analysis of Variance (ANOVA) outcome unveiled that the key independent variable(s) were activation temperature and impregnation ratio for ZnCl2 modified avocado pear seed (APS) fabrication. The optimum preparation conditions for developing maximal BET surface area of 457.16 m2.g-1 were impregnation ratio (0.84), activation time (67.64) and activation temperature (813.94oC). The ANN neural model was ascertained to be the better model with respective root-mean-square-error (RMSE) and overall regression coefficient (R) of 31.93 and 0.9838. Sensitivity analysis outcome revealed that temperature of activation had the predominant effect on ANN model performance with sensitivity (S) value of 88.9%. This study demonstrated that ANN neural network and RSM can be applied as effective tools for optimization of the avocado pear seed (APS) alkali activation process