Enhancing Rehabilitation And Maintenance Of Workshop Using Fuzzy Controller Application. a Case Study Of Lathe Machine At Caritas University Amorji Nike Enugu

This study explores the enhancement of rehabilitation and maintenance practices for workshop equipment using a fuzzy controller application, with a focus on the lathe machine at Caritas University, Amorji Nike, Enugu. The research aims to improve the operational efficiency and reliability of the lathe machine by implementing an intelligent fuzzy logic-based system for fault detection and predictive maintenance. The study evaluates the system\u27s ability to detect faults, reduce machine downtime, and improve rehabilitation efficiency. Results indicate that the fuzzy controller significantly enhanced fault detection accuracy, reducing machine downtime by 40%, and providing cost-effective maintenance solutions. The system was also user-friendly, allowing workshop operators with minimal technical expertise to operate it efficiently. However, limitations such as occasional false positives under extreme environmental conditions were identified, suggesting the need for further optimization. This research contributes to the growing body of knowledge on intelligent maintenance systems, providing a framework for applying fuzzy logic to improve workshop operations, particularly in resource-constrained educational settings. The findings highlight the potential for adopting fuzzy controller applications to increase equipment reliability, reduce maintenance costs, and enhance the quality of technical education

Early Prediction of Dementia Using Machine Learning

This study explores the application of machine learning algorithms for the early prediction of dementia, aiming to improve diagnostic accuracy and reliability. Utilizing a comprehensive dataset from Kaggle, which includes both continuous and categorical variables, four machine learning models—Random Forest, Decision Tree, Logistic Regression, and Support Vector Machine (SVM)—were implemented and evaluated. The study identifies cognitive test scores, the APOE ε4 allele, and depression status as key predictors of dementia. Tree-based models demonstrated superior performance, achieving perfect scores across metrics such as accuracy, recall, precision, and F1. Despite these promising results, the study acknowledges limitations such as the reliance on a single dataset, limited predictors, and challenges in real-world validation. Future research should incorporate larger, more diverse datasets, longitudinal data, and additional predictors to improve model robustness and applicability. These findings highlight the potential of machine learning as a transformative tool in clinical settings for timely dementia diagnosis and intervention

Challenges Encountered By Residents Of Enugu North Lga In Using Facebook For Public Health Messaging During Covid-19

The study investigated the challenges encountered by residents of Enugu North LGA in using Facebook for public health messaging during Covid-19 Pandemic.  The survey research design was used.  The sample size of 273 was drawn from the population of 347,500. Multi-stage cluster sampling procedure was the technique used to carry out the study. The findings in the study shows that 77.9% of the respondents have been exposed to campaigns against Covid-19. Also, 91.5% are aware that Covid-19 can be transmitted from person to person. Also, 72.3% of the respondents believe that using Facebook for public health messaging is beneficial. However, Facebook was not the major source of information on Covid-19 for 54.6% of the respondents. This implies that respondents merely use information gotten here to augment the one from their major source of information on Covid-19. Based on the findings, the following recommendations were made;  Enhance digital literacy programmes for the residents of Enugu-North: the outcome of this study shows that misinformation and unfiltered information from non-governmental or public health sources was a major threat to the adoption of Facebook for information on the Covid-19, the research recommends enlightenment of the target population on how to discern credible information from misinformation on Facebook. Hence, the local government authority LGA, should partner with local health organizations and educational institutions to conduct workshops and seminars on digital literacy and critical thinking. This will go to a great extent in increasing knowledge of residents to make informed decisions regarding COVID-19 and other health issues based on reliable information. Enhance Health Communication Strategies on Facebook: Create targeted health communication campaigns specifically tailored for residents of Enugu-North, focusing on local languages and cultural context to improve understanding and engagement. Use interactive and engaging content formats like live Questions and Answer (Q&A) sessions, info graphics, and short videos to convey critical information about COVID-19 prevention, symptoms, and treatment options on Facebook

Role of Public Relations Practitioners in the Management of Federal Government Agencies in Nigeria (A Study of Southeast)

The focus of this study was to examine the role of public relations practitioners in the management of Nigeria Federal Agencies with particular reference to South-East. Major aim was to ascertain whether federal agencies have functional, and qualified public relations staff that contribute positively in the management of such agencies businesses. To achieve the desired aim of the study, the researchers opted for survey research method in order to drive the study. Study area was from South-East where population and sample size of 1,500 respondents were raised. Investigation showed that with over sixty federal agencies in Nigeria, effective operations of public relations practitioners are not noticed as chief executives of such agencies sideline PR units in the discharge of the agencies mandate. The study concludes that some Nigerian federal agencies are operating without functional public relations departments or units. Therefore, it recommends the intervention of the NIPR to make government at all levels to recognize the vital roles public relations plays in governance and fully incorporate qualified public relations staff in government business especially federal agencies in south-east

Mineral Processing: Production of Calcium Oxide from Nkalagu Limestone, Effect of Particle Size and Temperature

Nkalagu limestone is a commercial raw material for cement production in south eastern Nigeria. The limestone was evaluated for quicklime production using oxidation kiln. The response of particles size was evaluated using five (5) particle size variations of 180µm, 355µm, 710µm, 850µm and 1,400µm. at temperatures(T) such that 800≥T≤1200 and ΔT = 1000C. The composition of the limestone was evaluated using the Atomic Absorption Spectroscopy (AAS), Simple Electron Microscope (SEM) and Fourier Transform Infra-Red spectroscopy (FTIR).   Results obtained confirmed the importance of surface area as conversion was higher same as loss on ignition with smaller particle sizes. SEM analysis reveals the product above 9500C as cement clinker and not CaO. Higher temperatures cause the thermal interaction of associated metallic oxide impurities which normally act as fluxes, decomposing the CaO to cement clinker. Above 9000C, product obtained had a higher bulk density, lower apparent porosity, cake-like and dark. To obtain CaO from Nkalagu Limestone, temperature in excess of 9500C is not advised

Rehabilitation And Upgrade For The Configuration Techniques Of a Spark Ignition Engine Test Bed. a Case Study Of Caritas University Amorji Nike Emene Enugu

This paper presents the configuration of a spark ignition engine test bed, a device which allow measurement on engines by making them run in a static way, where there is no need to use the vehicle to which the motor was made, used specifically for research and development department of motor manufacture, in order to ensure the design and the operation of prototypes, and to evaluate the performance of spark ignition engines. The testing bed incorporates a dynamometer, fuel measurement system, emissions measurement system, and data acquisition system to measure engine performance parameters, including power output, torque, fuel consumption, and emissions. The configuration of the testing bed is discussed in detail, including the selection of components which thousands of different component where manufactured in different factories with high degree of accuracy and interchangeability, integrated to the systems. A case study is also presented at the Caritas University, a configured internal combustion engine testing bed to demonstrate the effectiveness of the testing bed in evaluating the performance of a spark ignition engine. The results show that the testing bed is capable of accurately measuring engine performance parameters, making it a valuable tool for engine development, testing and research. &nbsp

Rehabilitation And Maintenance Of Workshop Equipments Using Convolutional Neural Network (Cnn). a Case Study Of Safety In Caritas University Workshop Enugu

The effective rehabilitation and maintenance of workshop equipment are crucial for ensuring operational efficiency and safety in academic and industrial environments. This study explores the application of Convolution Neural Networks (CNN) in the rehabilitation and maintenance of workshop equipment at Caritas University Workshop, Enugu, with a focus on enhancing safety protocols. The primary aim of the research is to develop a predictive maintenance system that can detect potential equipment failures and prevent safety hazards through real-time data analysis. A CNN model was trained using sensor data and images from the workshop equipment to identify anomalies such as unusual vibrations, temperature fluctuations, and wear and tear that may signal impending failure. The results demonstrated the model\u27s ability to accurately predict equipment failures, allowing for timely maintenance interventions and reducing downtime. Additionally, the system significantly contributed to improving safety by detecting unsafe operating conditions before they led to accidents. The study found that integrating AI-driven predictive maintenance and safety protocols could optimize workshop operations, increase equipment lifespan, and enhance the overall safety of workshop environments. This research provides valuable insights into the potential of AI technologies, particularly CNNs, to revolutionize maintenance practices and safety management in educational and industrial settings. Future work should focus on expanding the dataset, optimizing computational resources, and exploring the scalability of the model for broader industrial applications

Absorption and Simulation of Carbon IV Oxide Recovery Plant with Monoethanolamine Solvent using Aspen HYSYS

Carbon IV Oxide (CO2) was extracted from a natural gas (NG) stream containing 8.7% carbon dioxide, 17.8% water, 73.4% nitrogen, 0.1886% oxygen, 0.0017% sulfur dioxide, and 0.0097% nitrox using monoethanolamine (MEA) solvent. The CO2 is an acidic and greenhouse gas which may cause corrosion attacks on the pipelines, vessels and global warming when the concentration is accumulated appreciably, hence the need to free the natural gas from it. The process parameters were 500 tons per day flow rate, 1500C temperature, and 101.6 kPa pressure. Using ASPEN HYSYS, an optimization and technical parameter study was conducted for a CO2 recovery process from mixture of gas of a natural gas liquefaction plant at different percentage recoveries (75%, 80%, 85%, 90%, 95%, and 99%). The procedure was based on the use of MEA solutions in an absorption/desorption process. Recovering more CO2 from the NG than was initially present is the aim. Deviations of 3% and 10% and root mean square error of 0.5 and 1.5 from the validation of the simulation result with plant data show that, in contrast to earlier research, the simulation using Aspen HYSYS of V8.8 was able to extract 99% of the 8.7% CO2 from NG. The models showed that CO2 recovery was possible once pumps were installed inside the facility. The simulation result further showed that the overall cost of the recovery CO2 plant including the cost of utilities, was obtained to be $19.629m

Maintenance And Characterisation Of Rock Well Hardness Testing Machine

Rockwell hardness testing machines are crucial in determining material hardness. However, their accuracy and reliability can be compromised if not properly maintained. This project focuses on the rehabilitation and maintenance of the Rockwell hardness testing machine in the Mechanical Engineering Department Caritas University, Amorji Nike, Enugu. The machine’s accuracy and reliability were restored through diagnostic tests, replacement of worn-out parts, calibration, and development of a maintenance schedule.The reliability and functionality of the Rockwell Hardness Testing Machine are critical for material testing and mechanical engineering applications. However, frequent power fluctuations, component wear, and inadequate maintenance practices often compromise the performance of this essential equipment, particularly in educational institutions like Caritas University Amorji Nike Enugu. This study investigates the application of a Fuzzy Logic-Based Static Var Compensator (SVC) to improve the rehabilitation and maintenance of the Rockwell Hardness Testing Machine. The fuzzy-based SVC system is designed to enhance voltage stability, mitigate harmonic distortions, and ensure a consistent power supply to the machine. By integrating intelligent control algorithms, the system can predict and adapt to varying operational conditions, enabling proactive maintenance and reducing the likelihood of system failures. This approach not only safeguards sensitive components from power-related damages but also extends the machine’s operational lifespan and minimizes downtime. The study employs a combination of experimental analysis and simulation to evaluate the performance of the fuzzy-based SVC system. Results demonstrate significant improvements in power quality, reduced maintenance costs, and enhanced operational reliability of the Rockwell Hardness Testing Machine. The findings underscore the potential of intelligent power systems in modernizing equipment maintenance practices and ensuring sustainable engineering education

Impact Of Government Expenditure On Unemployment In Nigeria

Despite rising government expenditure over the years, unemployment remains high and persistent. This study examines the impact of government   expenditure   on unemployment in Nigeria over a period of 1981 to 2023. An autoregressive distributed lag (ARDL) model was utilized for the analysis. Major findings indicate that general government expenditure has a negative effect on unemployment in the long run while final consumption expenditure has a negative but weak impact on unemployment in Nigeria. Furthermore, the short-run results indicate that past periods of gross national expenditure decrease current unemployment while general government expenditure increase current unemployment. The study concludes that government expenditure is an effective tool for reducing unemployment in Nigeria and The government should implement targeted job creation programs in high impact sectors like infrastructure and social services, alongside strict anti-corruption measures to ensure transparent and efficient allocation of public funds. &nbsp