| 1 |
Author(s):
Adakole Sunday, Alabi Oladunni Oyelola, Osaghae Osarenren Samuel.
Page No : 1-15
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Assessment of Ovbiomu Coal for Industrial Application
Abstract
This research is to assess the quality of Ovbiomu lignite coal deposited for optimum utilization in metallurgical industries in Ovbiomu. Samples of coal were collected for analysis and sample was collected from five different stationary lots until 10 kilograms of the sample was collected and 500g was taken for characterization. The sample was reduced using jaw crusher and ball milled to a size of 1400μm and 1 kilograms of 1400μm was further reduced to 1100μm and was classified into various sieve sizes using mechanical sieve shaker. CalK2 bomb calorimeter was used to determine the calorific value of the head sample, the economic liberation size and the actual liberation size. The proximate analysis of the head sample and all the sieve sizes was done using Furnace, oven, porcelain crucibles, analytical balance, and desiccator to ascertain the individual carbon content. Ultimate analysis was done using XRD and concentration was done using froth flotation method. Result from sieve analysis shows that at 1000 μm, 710 μm, 500 μm, 355 μm, 250 μm, 180 μm, 25 μm, 90 μm, 63 μm, and -63 μm, the following weight was retained 1.45 g 1.86 g, 3.73 g, 3.5 g, 14.61 g, 48.28 g, 0.15 g, 0.17 g and 0.20 g respectively. The economic liberation size was found out to be 180um where most of the sample is retained and the actual liberation size was found out to be 125um but with a very small quantity retained. The results of the calorific value of the head sample and each of the sieve sizes of 250μm, 180μm, and 125μm, was determined to be 24.51MJ/Kg, 25.86MJ/Kj, 18.57MJ/Kg and 38.07MJ/Kg respectively, the following are result of percentage carbon content, for the Head sample (40.65%), 1000μm (29.55%), 710μm (38.53%), 500μm (42.43%), 335μm (35.42%), 250μm(43.07%), 180μm (30.92),125μm (63.40%) 90μm (0.56%) 63μm (0.62%) and -63μm (9.91%). The result of ultimate analysis of the sample shows Nitrogen (1.15%), Hydrogen (4.80%), Sulphur (0.13%) and Oxygen (29.56%).
| 2 |
Author(s):
Edim Eka James, Glory Sunday Etim, Arikpo Nneoyi Nnana, Inyang Bassey Inyang, Obi Ikenna Celestine.
Page No : 16-34
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Influencer Marketing and Consumers’ Purchase Behaviour Towards New Smartphone Brands
Abstract
This study assessed influencer marketing and consumers’ purchase behaviour towards new smartphone brands. Its specific purpose was to determine the effect of celebrity endorsement, giveaway contest and pre-release contest on consumers’ purchase behaviour towards smartphone brands. It adopted cross-sectional survey research design, which enabled the collection of primary data from 232 smartphone users through a structured questionnaire survey. Data analysis and interpretation was done using descriptive statistics, while hypotheses testing was done using multiple linear regression in the Statistical Package for the Social Sciences (SPSS 23). The study found that celebrity endorsement, giveaway contest and pre-release campaign had significant positive effects on consumers’ purchase behaviour towards new smartphone brands. Therefore, we recommended that: smartphone marketing companies should devote more resources to celebrity endorsement by contracting famous and credible celebrities to promote their new brands prior to actual release in order to influence massive product patronage from their followers; all sorts of giveaway contests, including hashtag, photo and referral contests should be included in the introductory marketing mix for new smartphone brands in order to generate positive buzzes on the internet capable of creating mass consumer awareness and patronage; among others. We also provided empirical suggestions to guide further research efforts.
| 3 |
Author(s):
Ismail A. Mahmoud, Umar Jibrin Muhammad, Sagir Jibrin Kawu, Mohammed Mukhtar Magaji, Mahmud M. Jibril.
Page No : 35-51
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Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria
Abstract
Climate change always had a massive effect on worldwide cities. which can only be decreased through considering renewable energy sources (wind energy, solar energy). However, the need to focus on wind energy prediction will be the best solution to the world electricity petition. Wind power (WP) estimating techniques have been used for diverse literature studies for many decades. The hardest way to improve WP is its nature of differences that make it a tough undertaking to forecast. In line with the outdated ways of predicting wind speed (WS), employing machine learning methods (ML) has become an essential tool for studying such a problem. The methodology used for this study focuses on sanitizing efficient models to precisely predict WP regimens. Two ML models were employed “Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN)” for WS estimation. The experimental methods were used to focus the WS prediction. The prophecy models were trained using a 24-hour’ time-series data driven from Kano state Region, one of the biggest cities in Nigeria. Thus, investigating the (ML) forecast performance was done in terms of coefficient of determination (R²), linear correlation coefficient (R), Mean Square Error (MSE), and Root Mean square error (RMSE). Were. The predicted result shows that the FFNN produces superior outcomes compared to GPR. With R²= 1, R = 1, MSE = 6.62E-20, and RMSE = 2.57E-10
| 4 |
Author(s):
Kalu Uchenna.
Page No : 52-66
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Solution to Fourier Transforms Method to Complex Variable of Non-homogeneous Fractional Differential Equations
Abstract
This work is devoted to the study of fractional differential equations involving Caputo non-homogeneous fractional differential equations. Using Fourier transform method, a complex variable explicit solution to non-homogeneous second-order fractional differential equation was obtained.
| 5 |
Author(s):
Musa Hamza, Adamu Mohammed Babayo, Usman Aliyu Jalam, Abbas Sa'id El-nafaty.
Page No : 67-79
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Occupant’s Thermal Perception in Mixed-mode Office Buildings of the Tropical Climate
Abstract
Thermal comfort and energy consumption in office buildings is a global critical concern. This study investigated this challenge in the Faculty of Environmental Technology, Abubakar Tafawa Balewa University, Bauchi-Nigeria. Through a field survey and physical environment measurements. The study explored occupant perceptions of thermal comfort and satisfaction. It evaluated the thermal comfort and satisfaction of occupants in a mixed-mode office building, focusing on potential discrepancies between perceived comfort and internationally recommended standards. Despite air temperatures falling outside the PMV model's comfort range, high thermal comfort, and satisfaction levels were reported by the respondents. These findings align with other studies in Nigeria, suggesting adaptation and acclimatization to local conditions. The study further examined the relationship between thermal comfort and occupant satisfaction. The result revealed a moderate positive association, suggesting increased thermal comfort leads to higher satisfaction among occupants. While thermal comfort explained 25% of the variance in satisfaction scores. Finally, the study suggests the localization of comfort standards, improved mixed-mode system performance, and encouraging evidence-based design interventions that will ultimately benefit both occupants and the environment.
