| 1 |
Author(s):
Madu A. N., Joseph E. E., Okereke M. I., Mbakwe I. E., Anyaorie C. N ., Madu J. N..
Page No : 1-7
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UV-Vis Determination of Total Cholesterol in Various Tropical Poultry Meat Parts.
Abstract
The cholesterol contents of the different parts of tropical Gallus species were analyzed using UV-Vis spectrophotometer and results shows that in all the three species of Gallus studied, Gallus sonnratii’s intestine had the highest cholesterol content of 226.96mg/100g compared to the Gallus gallus and Gallus domesticus intestine with lower values of 215.65 mg/100g and 177.39 mg/100g respectively. Gallus gallus liver had the highest cholesterol content of 218.26 mg/100g.However, Gallus sonnratii had the highest cholesterol content of 163.48 mg/100g.In general, raw poultry meat has approximately 27 to 90 mg cholesterol/100 g and cooked poultry meat contains around 59 to 154 mg/100 A significant factor affecting cholesterol content of poultry is type of retail cut because of the difference between dark and white chicken meat and the presence of skin in many retail cuts. Most importantly too, the extent of cholesterol is indicated in the dietary.
| 2 |
Author(s):
Abdulazeez Rotimi.
Page No : 8-23
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A Review of Road Pavement Failure: A Case Study of Nigerian Road.
Abstract
The magnitude of failure/damages for several Nigerian roadways has prompted several scholars/analysts to look into the causes of these failures. In this paper, the underlying factors for roadway degradation on Nigerian highways are examined, along with potential solutions. An essential infrastructure in any settlement is recognised as the roadway. Solid pathways makes it easier to move people and things around, which boosts the economy. High accident rates, longer travel times, higher costs for vehicle maintenance, and high crime rates are just a few consequences of road failure that have been observed. Geology plays a significant role in the construction of roads because they are built on geologic earth materials and are significantly influenced by them. The main reasons why Nigerian roads fail are further identified in this paper as insufficient preliminary geological investigation, poor road design and construction, poor monitoring and quality workmanship, inadequate routine and regular maintenance, poor drainage, employing poor and low-quality materials, traffic congestion, overuse, incorrect use, and inadequate punishment for roadways failures. The majority of experts agree that Nigeria's road failure rate has recently grown despite the lack of concrete statistics. The solutions suggested include providing acceptable and adequate designs, oversight and quality control, clearing traffic on Nigerian roadways, prompt repair of such roads, constructing highway facilities, deploying skilled staff, and supportive government policies.
| 3 |
Author(s):
Adakole Sunday, Alabi Oladunni Oyelola, Francis Gitau.
Page No : 24-35
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Beneficiation of Ovbiomu Low Grade Coal Using Froth Flotation for Industrial Applications.
Abstract
Coal flotation is a highly effective separation method utilizing the differences in surface properties of coal and gangue minerals. This research entailed the beneficiation by froth flotation of low-grade coal from Obviomu Coal Mines, Edo State, Nigeria for improvement of the fixed carbon content and calorific value for industrial application. 50 kg of coal samples were collected from five different pits and was reduced using jaw crusher to a size of 1400 μm, pulverized to a size of 1100μm. A 500-gram sample of coal was weighed and ground in a ball mill until 100% of it passed through a 250-micrometer sieve; these steps were then repeated for sieve sizes of 180, 125, 90, and 63 micrometers, respectively. A DENVER flotation cell was charged with 500 grams of well-ground coal sample of 250 µm sieve size. The process was then repeated with sieve sizes of 180 μm, 125 μm, 90 μm, and 63 μm, in that order, the results of the froth flotation beneficiation method revealed that, the weight in mass of the concentrate at sieve sizes of 250 μm, 180 μm, 125 μm, 90 μm, and 63 μm was found to be 319.209 grams, 119.227 grams, 113.028 grams, 237.657 grams, and 141.343 grams, respectively, while the weight in mass of the tailings was found to be 180.791 grams, 380.773 grams, 386.972 grams, 262.343 grams, and 358.657 grams, respectively. The calorific values of the concentrate and tailings were measured using a CalK2 bomb calorimeter. At sieve sizes of 250 μm, 180 μm, 125 μm, 90 μm, and 63 μm, the concentrate's values were found to be 44.45 MJ/Kg, 43.84 MJ/Kg, 43.66 MJ/Kg, 46.02 MJ/Kg, and 43.48 MJ/Kg, respectively, while the calorific values of the tailings were found to be 3.13 MJ/Kg, 3.31 MJ/Kg, 0.79 MJ/Kg, 3.25 MJ/Kg, and 0.18 MJ/Kg respectively. The fixed carbon content at sieve sizes of 250 μm, 180 μm, 125 μm, 90 μm, and 63 μm was found to be 74.00%, 73.000%, 72.70%, 76.60%, and 72.40%, respectively, in the concentrate, while 5.20%, 5.50 %, 1.30%, 5.40%, and 0.30 % were found in the tailings. This information was obtained through proximate analysis of the concentrate and tailings at different sieve sizes. At 90 μm, the optimum sieve size, a rise in the fixed carbon content of the coal samples was recorded, accounting for 88.53% increase of the calorific value. The study concludes that direct froth flotation is a viable and efficient method for improving the quality of low-grade coal for industrial use.
| 4 |
Author(s):
Uchenna Chinyere Onyemauche, Jonathan Ugwu Okwor , Charity Elochukwu Mbanusi, Blessing Ulunma Osondu.
Page No : 36-46
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Application of Machine Learning in Waste Management: An Introductory Approach.
Abstract
Waste Management is a daily task in urban areas, which needs a huge amount of labour resources and this affects natural, budgetary, efficiency and social aspect of our cities. Manual sorting of garbage is a difficult process that is expensive and that is why scientists create and study automated sorting methods that increase the efficiency of the recycling process. Most recently, there has been a drift in combining waste that is prime scheme with low cost IoT architectural design on a test board. However, the results from all these past approaches and techniques are still not clear and cannot be applied in real systems, such as in cities and campuses. This work introduces the design of a micro controller that is single, low cost, straight forward with an ultrasound sensor which can measure the filling height of a garbage Trash Bin and send information using LORA Technology. A novel IoT based Machine Learning method in combination with Genetic Algorithm to predict the probability of collecting waste in real environment based on historical data were used in this study. This is combined with a microcontroller system designed with a sensor module for measuring the height that is the fillings levels of each trash bin. The system can optimize the collection of waste with the shortest path by using genetic algorithm. Python was used for analyzing the data.Using the above Machine Learning techniques like Logical Regression cum Genetic Algorithm to compute the paths of wastes collection with different time schedules, it is cumbersome to get efficient route optimization; hence the aim of this paper was to present an IoT cloud solution combining device connection, data processing, control and ensuring route optimization. Genetic Algorithm ensures perfect route optimization.
| 5 |
Author(s):
Minyo Wisdom Emmanuel, Adewuyi Benjamin, Alabi Oladunni, Ola-Omole Omoyemi.
Page No : 47-63
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Chemical, Mineralogical, and Grindability Studies of Anka-Brabra Copper Ore.
Abstract
This study examined the chemical characteristics, mineralogy, and grindability of copper ore from Anka-Brabra, Zamfara State, Nigeria. The ore was crushed, ground, and mixed to achieve a uniform sample. The ore sample was characterized using EDX-XRF, XRD, SEM, and petrological microscope. Grindability and fractional sieve analysis techniques were used to ascertain optimal grinding size through particle size distribution and the liberation size of the mineral. Results showed the main copper mineral was malachite (Cu2(CO3)(OH)2) with traces of other minerals. Analysis indicated a copper content of up to 20.2%. The ideal grinding size for liberation was determined to be 160 microns, with 53% of the ore particles reaching an acceptable size of 250 microns. The ore sample was characterized using EDX-XRF, XRD, SEM, and petrological microscope. Grindability and fractional sieve analysis techniques were used to ascertain optimal grinding size through particle size distribution and the liberation size of the mineral.
| 6 |
Author(s):
Ashraf Ishaq, Sumayyah Sophie Nandom, Tsentob joy Samson, Maryam Suleiman.
Page No : 64-80
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Application of Gaussian Model and Deep Learning Encoder-Decoder Algorithm for Single-Image Reflection Removal.
Abstract
Images of target scenes shot through clear, reflective materials like glass are frequently interfered by unwanted reflection scenes which often overlaid on top of the targeted scenes. This, has constantly degrades the quality of the captured images and affects their subsequent analyses. While cognitively, distinguishing a recognizable object from its reflection in a picture is not difficult for humans, it is highly difficult and more complex in computer vision due to the ill-posed nature of the problem. In this research an enhanced single-image reflection removal model was developed by combining Gaussian filter and deep learning encoder-decoder for effective performance. While the Gaussian filter denoises the reflection-contaminated image, the encoder-decoder network learns the features of the image to produce reflection-free image. The proposed network is an end-to-end trained network with three losses. The experimental findings showed that the proposed model out-performed several state-of-the-art methods both qualitatively and quantitatively on five different datasets.
| 7 |
Author(s):
Anthony Edet, Abasiama Silas, Enobong Ekaetor, Ubong Etuk, Etoroabasi Isaac, Anietie Uwah.
Page No : 81-102
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Data-Driven Framework for Classification and Management of Start-Up Risk for High Investment Returns.
Abstract
This research explores the classification of startup risks to achieve high investment returns using Random Forest Regression. The study aims to identify and predict potential risks faced by startups, thereby aiding investors in making informed decisions. We analyzed a dataset comprising various features such as funding levels, market size, expenses, team experience, product development stage, customer satisfaction scores, and revenue streams. We employed a Random Forest Regression model to evaluate the predictive power of these features. The model's performance was assessed using several metrics: Mean Squared Error (MSE), R-squared, Mean Absolute Error (MAE), Mean Squared Logarithmic Error (MSLE), and Explained Variance Score.The model demonstrated robust predictive capabilities, with an MSE of 0.255, R-squared of 0.9515, MAE of 0.782, MSLE of 0.219, and an Explained Variance Score of 0.915. These results indicate that the model effectively captures the variance in startup risks and predicts them with high accuracy. Feature importance analysis revealed that expenses and funding levels were the most critical factors influencing startup risk classification. The distribution of risks identified 12.4% Strategic Risks, 12.6% Financial Risks, 13.1% Operational Risks, 13.7% Market Risks, and 48.2% of activities with no significant risks.Based on our findings, we recommend that investors focus on key features as outlined in this research when assessing startup risks. By employing the insights provided by our model, investors can better identify high-potential startups, optimize resource allocation, and improve their investment strategies.The Random Forest Regression model offers a reliable tool for predicting and classifying startup risks, providing valuable insights that can enhance investment decision-making and ultimately lead to higher returns.
| 8 |
Author(s):
Kamak Yamlach Shedrach, Etemi Joshua Garba, Ahmed Musa Iliyasu, Umar Gambo Abubakar, John Halilu Otso.
Page No : 103-126
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An Object-Oriented Analytical Approach to Modelling and Simulation of a Secured and Intelligent Home Automation System Using Internet of Things.
Abstract
Intelligent Home Automation System is one of the subjects that is gaining traction and popularity due to its numerous benefits across the globe today. Home automation is the process of remotely monitoring and controlling household appliances and devices. With the Internet's and its applications' never-ending growth, there is a lot of promise and scope for remote access, control, and monitoring of network-enabled appliances and devices. This paper developed an easy-to-use, secured and intelligent home automation system; capable of remotely monitoring and controlling appliances in the home, by integrating it with an affordable, flexible, versatile, open-sourced, single-board microprocessor and yet scalable Arduino Uno. The work was carried out in the following stages; First, the Arduino Uno was set up with Bluetooth and Wi-Fi connections, and android device interfacing. Secondly, upon successful connection and configurations, the system successfully worked with the home model developed. Lastly, a unique and yet simple user interface was created using the Android MIT App inventor, thereby completing the system design. The intelligent home automation system was fully simulated given the above steps, ensuring that everyday use of home appliances is monitored and controlled for a more secured, seamless and intuitive user experience. The paper also examined a number of intelligent home automation systems and technologies from a variety of defined perspectives. It focused on the home automation idea, in which smart devices are used to integrate home automation systems and technologies with a central single board – microprocessor (Arduino Uno); Life is becoming simpler and easier in all aspects due to advancements in automation technologies, particularly in homes and working environments.
| 9 |
Author(s):
Obi B. I..
Page No : 127-135
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Unsteady Magnetohydrodynamic Flow of Non-Newtonian Fluid In An Inclined Plane With Joule Heating.
Abstract
This present paper is on numerical study of unsteady magnetohydrodynamic flow of non-Newtonian fluid in an Inclined plane with Joule heating. The set of coupled non-linear partial differential equations are solved collocation technique, the effects of some physical parameters examined. Third grade parameter is introduced to account for the non-Newtonian fluid. Results from the investigation reveals that increase in the third grade parameter increases the flow velocity and decreases the temperature andiincrease in Eckert number leads to an increase in the temperature of the cylindrical walls. Results further show that increase in Prandtl number enhances viscous dissipation. This implies that the boundary layer thickness decreases with increase in Prandtl number, thereby reducing the temperature profiles.
| 10 |
Author(s):
Adegbite Ismaila Olawale, Asabi Oladipupo, Omisore Adedotun Olurin , Adewoye kamorudeen Sola.
Page No : 136-144
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Comparison Analysis of Methods of Estimation: A Non-Bayesian Estimation of Marshal Olkin Alpha Power Inverse Exponential Distribution.
Abstract
A non-Bayesian approach to parameter estimation, statistical inference and decision-making are discussed and compared. A pragmatic criterion, success in practice, as well as logical consistency is emphasized in comparing alternative approaches. In this study, attention is given to skew distribution for modelling lifetime data in particular, Marshal Olkin Alpha Power Inverse Exponential (MOAPIE) distribution. Parameters of the distribution were estimated using non-Bayesian estimation methods of Maximum Likelihood Estimation, Least Square Estimation and Weighted Least Square Estimation. Finally, simulated and real life data applications illustrate the performance of the estimation methods.
Keywords: Non-Bayesian estimation, Maximum Likelihood estimation, Least Square estimation,
Weighted Least Square estimation, Simulation.
