Arid Zone Journal of Engineering, Technology and Environment (AZOJETE)
Not a member yet
627 research outputs found
Sort by
Development and performance evaluation of fluted pumpkin seed dehulling machine
No full textA machine for dehulling fluted pumpkin seed (Telfairia occidentalis) was developed. The main objective of
developing the machine was to provide a better substitute to traditional methods of dehulling the seed which
contains edible oil of high medicinal and nutritional values. Traditional methods are full of drudgery, slow, injury
prone and would lead to low and poor outputs in terms of quantity and quality of dehulled products. The machine is
made of five major parts: the feed hopper (for holding the seeds to be dehulled before getting into the dehulling
chamber), dehulling chamber (the part of the machine that impacts forces on seeds thereby causing fractures and
opening of seeds coats for the delivery of the oily kernels), discharge unit (exit for oily kernels and seed coats after
dehulling), the frame (for structural support and stability of all parts of the machine) and electric motor (power
source of the machine).The development process involved design of major components (shaft diameter (20 mm),
machine velocity (7.59 m/s), power requirement (3hp single phase electric motor) and structural support of 45???????? ×
45???????? × 3????????mild steel angle iron), selection of construction materials and fabrication. ANSYS R14.5 machine
design computer software was used to design the shaft and structural support; while other components were
designed with conventional design method of using design equations. The machine works on the principle of
centrifugal and impact forces. Performance evaluation was carried out after fabrication and 87.26%, 2.83g/s, 8.9%
and 3.84%were obtained for dehulling efficiency, throughput capacity, percentage partially dehulled and percentage
undehulled respectivel
Effects of Pathloss on Capacity of Wireless LTE Network in Fupre, Effurun, Nigeria
Get PDFThe impact of path loss has significant implications for the pricing strategies adopted by mobile communication companies. Accurate determination of path loss is crucial for effectively planning and operating high-capacity networks that deliver reliable services. Although previous researchers have devised various system designs, these models cannot be universally applied across all environments. The ongoing signal deterioration due to urbanization and industrialization presents a challenge to network capacity. This study aims to develop an Average path loss system specifically tailored for the planning of Worldwide System for Mobile Communication networks in the vicinity of the Federal University of Petroleum Resources Effurun (FUPRE) and its surrounding areas. The methodology employed involves instrumentation and measurement techniques. Distances in meters are measured using a digital wheel meter, while a handheld Android device equipped with G-MoN Pro software captures data including reference signal strength indicator (RSSI), latitude, and longitude during driving tests conducted along predefined routes. The collected data were subjected to regression analysis. The results indicated that the exponent path loss value (n) is 3.07, suggesting a cellular radio network environment typical of a metropolitan area. The root mean square error (RMSE) of 4.91 dB falls within an acceptable range for the reference measurement environment in the region. The average network models demonstrated greater precision than all other models, producing outputs with a smaller margin of error. Therefore, providers of GSM network infrastructure can leverage this practical model, based on the Log-Normal shadowing principle, to enhance and optimize their services in the FUPRE area and its surrounding
Investigation of the Properties of Concrete Made with Fly-Ash and Dry Waste Okra Powder
Get PDFConcrete is the most utilized construction material globally, with the issues of global warming, overburden and over-dependence on cement leads to the research into alternative materials in the production of concrete, which would not only reduce the usage of cement but also improve the performance of concrete product. The research investigates the effect of fly ash (FA) and dry waste okra powder (DWOP) on the strength performance of concrete. Concrete mix of 1:2.43:2.85 with water cement ratio of 0.5 was used. 10% by weight of cement was replaced by fly-ash, and dry waste okra powder was added at 0.25% to 1% at 0.25% interval as admixture. The parameters investigated are, chemical composition, setting time, workability, chemical composition of fly-ash and dry waste okra powder, density, compressive strength, flexural strength and split tensile strength. The results showed that fly-ash is a good pozzolan with combined SiO2, Al2O3 and Fe2O3 to equal 89.04%. The use of dry waste okra powder increases the setting time of cement/FA-DWOP paste and also resulted in the increase in workability of the concrete as the percentage admixture increases. The result showed that fly-ash ash has a pozzolanic effect and DWOP has an admixture effect on concrete properties by considering the strength activity index, higher compressive strength, higher flexural and splitting strength than control concrete for fly-ash blended admixture concrete at 0.5% and 0.75% up-to 1% admixture content. From this result, it can be concluded that FA as partial replacement material is a good pozzolanic material and DWOP is suitable as admixture material in concrete production.  
Hate Speech Identification in West Africa, using Machine-Learning Techniques
Get PDFThe tremendous rise in social media usage over the past ten years has resulted in an extraordinary spike in hate speech activities in West Africa. Because of this, her unity is constantly in peril. This study combines relevant natural language processing techniques and machine learning classifiers to create a hate speech detection model using hate speech from West African countries, including Pidgin English, on Twitter, now ‘X’. The data was pre-processed using word embedding, CountVectorizer, and Term Frequency-Inverse Document Frequency (Tf-Idf) to extract useful characteristics from the cleaned dataset. Five machine learning classifiers were used to train the dataset, these include Logistic Regression (LR), Naïve Bayes (NB), Extreme Gradient Boost (XGBoost), Deep Neural Network (DNN), and Bidirectional Long and Short-Term Memory (Bi-LSTM). The Bi-LSTM fitted on Global Vectors (GloVe) embedding produced the best experiment results, with an accuracy of 92% and an F1-Score of 83% when assessed on a test set. The machine learning models generally demonstrated strong performance on test data, suggesting that they had internalised the knowledge from the training set and could use it to analyse new data
Student Perceptions of ICT-Based Lecture Delivery During COVID-19 Pandemic
Get PDFThe COVID-19 pandemic forced schools to adopt online learning methods. This study investigates the impact of Information and Communication Technology (ICT) usage on student performance in Adamawa State, Nigeria, focusing on Yola North Local Government. A quantitative approach employed a survey questionnaire distributed to 170 participants (students, teachers, and principals). Descriptive statistics analysed demographic profiles, ICT access, online learning satisfaction, and perceived barriers. While many students and teachers agreed they had sufficient IT skills, access to equipment and internet connectivity was limited. Zoom was the preferred online learning tool, but challenges like inconsistent communication, difficulty participating, and limited practical sessions were reported. Barriers included lack of consistent power supply, poor learning environment, and limited access to materials. ICT played a crucial role in continuing education during the pandemic, but significant challenges remain. Improved access to resources, better connectivity, and enhanced online learning practices are essential for optimizing student performance in future disruptions
Influence of Agroforestry Technology Adoption on Poverty Reduction Among Farming Households in Okunland of Kogi State, Nigeria
Get PDFThe significance of Agroforestry to the enhancement of livelihoods of farming households, through its contribution to food and income security as well as the improvement of crop productivity of farmers, cannot be overemphasized. Consequent upon this, the study assessed the impact of agroforestry practices on poverty reduction among small farming families in Okunland, Kogi State, Nigeria. In selecting respondents for the study, a multi-stage sampling approach. This study used descriptive statistics and inferential statistics for analysis. The descriptive statistics used are frequencies and percentages, while the inferential statistics used is Foster Greer Thorbecke (FGT) technique. Findings showed that about 35% of those who adopted the practice of agroforestry did not measure up to the poverty line (N54, 520.13k). Therefore, since they were found below the poverty line, they were regarded as poor. For those who did not adopt the practice of agroforestry, sixty seven percent of them were found below the poverty line (N31, 654.19k). By implication, this category of people can be regarded as poor. In order to reveal the extent of poverty among the respondents, FGT poverty index was used. Results from this therefore showed that the farming households who adopted and practiced agroforestry technology had a better livelihood when compared to those that failed to adopt the technology. In addition, it was discovered from the study that several constraints were militating against the adoption of agroforestry technologies by the farming households in the study area. Some of the constraints were lack of knowledge and required skills on agroforestry, long gestation period of trees, insufficient land for tree planting, lack of planting materials among others. Consequent upon this, this study recommends that necessary efforts should be made by government and concerned stakeholders to increase the adoption of agroforestry technology by creating awareness and sensitizing farmers on significance of adopting agroforestry technology and the associated benefits derivable from agroforestry practices. To accomplish this, extension agents and subject matter specialists on agroforestry should train and enlighten farmers on how best to use their land so as to accommodate both arable and tree crops so as to ensure improved productivity. 
The Implication of Tillage Practices and Decapitation Techniques on the Growth and Biomass Yield of Fluted Pumpkin
Get PDFThe implication of tillage practices and decapitation techniques on the growth and biomass yield of fluted pumpkin (Telfairia occidentalis) cultivated in an inland swamp land in the dry season was studied. The treatments considered on decapitation were: (A) the control (intact/un-decapitated plants), (B) removal of half the total number of branches on the plant at time of decapitation, (C) removal of all lateral branches and leaves at time of decapitation, (D) removal of all the leaves on the plant at time of decapitation and (E) cutting off half of the entire shoot system. While the treatments considered on tillage practices were: (i) Bed tillage (ii) Ridge tillage (iii) Flat (No tillage). Therefore, the total treatment combinations were fifteen. After the imposition of decapitation, data were collected on growth and yield parameters of the newly developed foliage. The parameters were number of leaves, plant height, total number of new branches, leaf length and breadth, the fresh and dry weight of root, leaves, and shoot biomass. The decapitation (harvesting) techniques and tillage practices adopted in this study affected biomass re-growth and development of fluted pumpkin (Telfaira occidentalis). Removal of half number of branches on bed tillage practice gives the best performance, when compared with the intact (control) plant and other treatments.  
MATLAB SimEvent for Traffic Queue Model
Get PDFThe use of MATLAB (SimEvent) for network performance measure determination is encouraged by the novel precision that comes with artificial intelligence techniques. However, most efforts in this direction have been undervalued due to distribution assumptions. Therefore, a comparative analysis with an analytical method is studied in this work, with service rate distribution determined, using a distributive StatAssist tool. Kendall’s model adopted was M/M/1 for a single server queueing system and was confirmed by the service rate distributive pattern as 1.0 second per customer. Estimating the network measures showed analytical and simulation models hourly average queue length of 3,751 vehicles, with 1hr and 11minutes waiting time. The total daily queueing length is 8419vehicles, with 8819secs total waiting time. The utilization factor of 0.984 and the model is found to be 0.9825/1.025/1. From the optimum determination of the analytical model (AM) and simulation model (SM) weekly performance measures carried out, the AM produced the following average waiting time 3751, 125, 56, 35, 110, 19, and 17, from Monday through Sunday. In contrast, the SM produced an average waiting time of 3681, 3007, 2789, 2567, 2854, 2467, and 1976 respectively. Due to the continuous waiting line output, the SM has proven realistic. Therefore, the Simulation model coupled with distributive StatAssist tool is recommended for queueing studies. A comparative study of simulators is recommended for further studies
Development of a Generic On-Line Departmental E-Library using the Iterative Design Approach
Get PDFThis work was based on the development of a departmental digital library that is generic in nature as can be adopted by departments of any specialisation. It aimed at providing easy access to textbooks, lecture materials, past questions, project reports, research theses, published articles, current innovations in the field of study of the department, and various online resources to facilitate academic excellence, efficient delivery of information and advance research activities. A generic iterative and incremental software design approach deployed and a well-structured database directory for managing e-resources was designed. The implementation took the two dimensions of the front-end client-side which involved designing both the user interface and user experience while considering factors such as accessibility, readability and usability and the back-end server-side operations which involved creating a connection between the front-end and the server and managing the database. HTML, CSS and Java Script were employed in programming the front-end while the back-end server side was programmed with PHP, MySQL, and a Content Management System. With e-resources uploaded into the cloud and results of tests conducted on the website showed average time to complete an e-resource search and download is 16.2 seconds, result of the readability test is 8.2 and the result of the accessibility test is 79 aggregated from the results of four different accessibility testers. The generic departmental e-library was developed with website designed to provide easy of online accessibility to students and staff of the department.
 
Exploring the Influence of Noise on Voice Recognition Systems: A Case Study of Supervised Learning Algorithms
Get PDFSpeech recognition systems have become increasingly prevalent in various applications, ranging from virtual assistants to voice-controlled devices and dictation software. The desire to design human-computer interfaces that are more accessible, intuitive, and efficient cannot be overemphasized. Such advancements hold significant potential to enhance accessibility, productivity, safety, and user experience across various domains and industries. Despite significant advancements in speech recognition technology, several challenges persist. One primary challenge is achieving high accuracy and robustness across diverse speaking styles, accents, and environmental conditions. Background noise, variations in pronunciation, and overlapping speech can introduce errors and degrade performance, especially in real-world scenarios. This study explores the pursuit of accurate and lightweight algorithms in biometric applications, focusing on voice recognition. Using a dataset featuring renowned leaders' voices, the research compares K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) techniques. Employing Mel Frequency Cepstral Coefficients (MFCC) and considering noisy and noiseless environments, the study reveals that ANN outperforms KNN. In noisy conditions, ANN achieves 62.4% accuracy, while KNN reaches 33.4%. In noiseless settings, ANN's accuracy rises to 95%, surpassing KNN's 88%. Assessment metrics like False Acceptance Rate, False Rejection Rate, F1 Score, Recall, Precision, and Receiver Operator Characteristics Curve are analyzed. The study emphasizes the detrimental impact of noise on recognition accuracy and underscores ANN's consistent superiority over KNN. Despite challenges posed by noisy environments, the research highlights the potential benefits of these approaches, emphasizing ANN's superior performance across scenarios. This research showcases the significance of accurate biometric systems, emphasizing ANN's advantages in enhancing usability and precision, especially in real-world noisy conditions.