Caritas Journal of Physical and Life Science
Caritas Journal of Physical and Life Sciences (CJPLS) is a publication of the Faculty of Natural Sciences, made up of Microbiology, Chemistry, Biology, Industrial Chemistry, Industrial Microbiology, Biotechnology, Health Sciences, and Sciences of the Caritas University Amorji-Nike Enugu. It accepts and publishes (in English language) high-quality articles, original research papers, review articles and short communications in all disciplines of Crop Sciences, Zoology, Agricultural Science, Health and Behavioural Sciences, Physical, Computational, Biological and Environmental Sciences. It also accepts and publishes articles in cross-disciplinary fields, interfacing with science-based disciplines.
Impact Of Information Management On The Performance Of Telecommunication Industry In Ebonyi State Nigeria
This study investigated the effect of information management on the performance of telecommunication industry by examining and ascertaining the adoption on new Information Management and quality of service delivery. A survey designed was adopted on new Information Management on quality of service delivery. A total of 189 were studied. The entire sample size wasn’t much, so the researcher decided to use the entire population as the sample size. Data collected was through questionnaire structured on Likert’s point scale. While the supervisor and two (2) research experts carried a face to face and content. The reliability coefficient of the instrument was 0.89 using the text pretext method and validation of the instrument. Table and percentages were employed for data presentation and analysis. Hypotheses were tested using Pearson product moment correlation coefficient with 0.05% level of significant to determine the strength of the relationship between the variables. The findings are the following; ICT has a significant positive effect on organization performance: by reducing cost, improving skills essentials for the survival and growth of the organization in today’s turbulent and competitive business environment. Organization should build the human capacity and infrastructure needed for the utilization of the acquired ICT.
Copyright (c) 2025 Caritas Journal of Physical and Life Science
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's 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.
Chemical Constituents Of Allium Sativum (Garlic) And Curcuma Longa (Tumeric) Essential Oil
Curcuma longa (turmeric) and Allium sativum (garlic) are spices that have been indicated to have several pharmacological potentials. The current study assessed the chemical constituents of turmeric and garlic. The essential oils were hydro distilled from the rhizomes of turmeric and bulbs of garlic in a yield of 0.80% (w/w) and 0.75% (w/w) respectively. The oils were analyzed by gas chromatography (GC) and gas chromatography and mass spectrometry (GC-MS). A total of fifty-four constituents were identified for both oils representing 96.3% (A. sativum) and 96.5% (C. longa). The main constituents of the oil of C. longa were ar-tumerone (28.6%), atlantone (21.9%) and curlone (18.8%). The main constituents in A. sativum were sulfur derivative (94.1%), diallyldisulphide (15.7%), diallytrisulfied (53.9%), diallyltetrasulfide (11.7%), methylallytrisulphide (9.2%). This research shows that oil from turmeric and garlic comprises a total of 54 constituents and has 4 components in common.
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's 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.
Copyright (c) 2024 Caritas Journal of Physical and Life Science
Improving The Rehabilitation And Maintenance Of Workshop Equipments Using Artificial Neural Network Based System. A Case Study Of Caritas University Enugu
The efficient operation of workshop equipment is crucial for academic institutions, particularly in technical and vocational education. However, frequent breakdowns, inadequate maintenance schedules, and delayed rehabilitation processes often hinder the performance of such facilities. This study investigates the application of an Artificial Neural Network (ANN)-based system to improve the rehabilitation and maintenance of workshop equipment at Caritas University, Enugu. The proposed system leverages the predictive capabilities of ANN to monitor equipment usage, diagnose faults, and recommend optimal maintenance schedules. By analyzing historical maintenance data, operational parameters, and real-time feedback, the system aims to minimize downtime, reduce maintenance costs, and extend the equipment's lifespan. A case study approach was employed, integrating the ANN-based system into the university’s existing maintenance framework. Preliminary results indicate significant improvements in fault detection accuracy, response time, and resource allocation. This research highlights the potential of intelligent systems to revolutionize workshop maintenance practices, ensuring the sustainability and reliability of educational infrastructure.
Online Patient Information Management System (OPIMS)
Online Patient Information Management System (OPIMS) is an extensive, coordinated information system designed to manage patient’s data in hospitals with the administrative process. Health care centers depend on patient information, for the efficiency of administrative process and for better management. The principle objective of OPIMS is to streamline the stream of data from the hospital towards decision making for patient planning and management in an enhanced and effective way. A Computerized PIMS will be important in light of the fact that, there are a considerable measure of challenges in keeping up a lot of data on paper, particularly as there is usually no backup for the data, access to data can be tedious in the event that requires for the search of a file, and accuracy is required in the recording of key data, and the administrator cannot manage all that is composed on the tremendous measure of paper to be utilized. Hence, it is very important for health organizations like hospitals and clinics to have a computerized patient information management system. The system was implemented via the following technologies; PHP, HTML, MySQL, CSS, JavaScript, etc.
Copyright (c) 2023 Caritas Journal of Physical and Life Science
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.
Improving Signal Reception In 4g Network Using Channel Equalizer
The efficiency of signal reception in 4G networks is critical for ensuring reliable and high-quality communication. However, challenges such as signal fading, multipath interference, and noise significantly impact network performance. This research explores the potential of channel equalizers to improve signal reception in 4G networks. Channel equalizers mitigate distortions introduced by the communication channel by adapting to changing channel conditions and restoring the transmitted signal to its original form. This study analyzes the effectiveness of equalization techniques such as linear equalizers, decision feedback equalizers, and adaptive equalizers in combating issues like inter-symbol interference (ISI) and multipath fading. Using MATLAB-based simulations and real-world signal analysis, the performance of various equalization algorithms is evaluated in terms of metrics such as bit error rate (BER), signal-to-noise ratio (SNR), and throughput. Results demonstrate that the implementation of adaptive equalizers, particularly those using least mean squares (LMS) or recursive least squares (RLS) algorithms, significantly enhances the robustness of 4G signal reception in diverse network conditions. This research concludes by recommending the integration of advanced channel equalizers into 4G network infrastructures to optimize performance and pave the way for smoother transitions to next-generation technologies like 5G.
Development And Implemention Of Modified Sinewave 3.5kva Inverter
The instability of Nigeria’s power supply has driven research into alternative solutions within the power sector. This paper focuses on the design and construction of a 3.5 kVA single-phase modified sine wave inverter, capable of efficiently converting DC voltage to AC voltage at a reduced cost. The system allows for adjustable output voltage and frequency through an integrated oscillator circuit (SG3524N), transformers, and precise switching and control circuits. Key components, including MOSFETs, diodes, resistors, and deep-cycle batteries, work collectively to meet the functional requirements of a reliable inverter. Challenges encountered during design were addressed, resulting in an inverter that delivers a stable output current of up to 15.9A and a consistent 220V AC output from a 24V DC input during performance testing.
Solar Wireless Electric Vehicle Charging System
This paper controller and LCD to develop the system, The system demonstrates how electric vehicles can any wires, No need to stop for charging, the vehicle charges while moving, Solar power a solar panel, battery, transformer, regular circuitry copper coils, AC to DC converter, at mega integrated is the road. The solar panel be charge the electric vehicle while moving on the road, eliminating the need to stop charging. Thus the system demonstrates a solar- powered wireless charging system for electric vehicles that can be described the design of solar-powered charging station for charging of electric vehicle describes the design of solar-powered charging station for charging electric vehicles that solves the key downside of fuel and pollution. Electric vehicles have now hit the road worldwide and are slowly growing in numbers. Apart from environmental benefits electric vehicles have also proven helpful in reducing the cost of travel by replacing fuel with way cheaper electricity. Well, here we develop an EV charging system that solves a unique innovative solution. This EV charging of vehicles without any wires, No need to stop for charging, the vehicle charges while moving, Solar power for keeping the charging system going, and No external power supply is needed. The system makes use of a solar panel, battery, transformer, regulator circuitry, copper coils, AC to DC converter, ATMEGA 328pu controller and LCD to develop the system. The system demonstrates how electric vehicles can be charged while moving on the road, eliminating the need to stop charging. Thus the system demonstrates a solar-powered wireless charging system for electric vehicles that can be integrated with the road. Furthermore, it is clear that solar wireless charging systems have the potential to improve energy efficiency, reduce carbon emissions and provide convenient charging experience for users.
The power system is a complex network of generation, transmission, distribution components that work together to provide electrical energy to consumers. The characteristics of power systems, such as voltage, frequency, and stability, play a critical role in ensuring reliable and efficient operation of the system. This paper provides an over review of power system characteristics, including their definitions, measurement, and significance in power system operation. In particular, the paper discusses the importance of maintaining voltage and frequency within acceptable ranges, as well as the impact of power system stability on the overall reliability of the system. Additionally, the paper explores some of the challenges associated with power system characteristics, such as voltage collapse and frequency fluctuations, and highlights some of the current research efforts aimed at addressing these challenges. Overall, this paper serves as a useful resource for power system engineers and researchers seeking to better understand the key characteristics of power system operation and reliability.
Automatic generation control (AGC) Voltage fluctutation
Design Of a Mini-Conveyor Using Pulley-To-Pulley v-Belt Transmission System
A conveyor system is a common piece of mechanical handling equipment that aids in the movement of materials from one another. Conveyors are especially useful in applications involving the transportation of heavy or bulky materials, which made them very popular in the material handling and packaging industries. This project described the current research on belt driven conveyor systems.
Experimental Determination of Thermal Conductivities of Cast Iron and Copper using Linear Heat Conduction Principle
This research work deals with the determination of thermal conductivities of aluminum, cast iron and copper using linear heat conduction principle. The experiment was carried out within a distance of holes 15mm for copper and 12mm for aluminum and cast iron drilled on the plastic rod enable the sample material fit in perfectly between the heat region and the cold region. From the analysis, the results show that the thermal conductivity of copper, aluminum and cast iron is 289.023W/m. K,133.983W/make and 55.046W/make respectively. Comparing the results obtained with previous experiment, the thermal conductivity values obtained from the sample fall within the general range for materials.
Web-Based Guide for Newly Admitted Students in Caritas University, Amorji-Nike, Enugu
Web-Based Guide for Newly Admitted Students in the Caritas University Amorji Nike, Enugu is designed to support newly admitted students at Caritas University, providing comprehensive resources to facilitate a smooth transition to University life. The guide features a clickable map for easy navigation, PDF department guides for in-depth information on academic programs, and a PDF registration guide to streamline the process of obtaining a registration number. The system was implemented using Windows 10 platform and the following technologies were employed; MySQL for database; Python programming language; and HTML, CSS & JavaScript for the frontend.
The Potential of Machine Learning Techniques in Predicting Malaria Outbreaks in Nigeria
Malaria, an infectious disease transmitted by mosquitoes and caused by protists of the Plasmodium genus, poses a significant global health threat, contributing substantially to morbidity and mortality rates. World Health Organization (WHO) estimated approximately 229 million cases worldwide, with children under five years old comprising 67% (274,000) of those affected, representing the most vulnerable demographic group. Despite the prevalence of malaria, existing research has not extensively explored the utilization of machine learning techniques to predict malaria outbreaks, in Nigeria. This study aims to fill this gap by employing five supervised machine learning methods: Naive Bayes, Support Vector Machines (SVM), Linear Regression, Logistic Regression, and K-Nearest Neighbor. Utilizing meteorological and malaria incidence data spanning from 2010 to 2020, the research employed the Scikit-learn library within the Anaconda IDE, utilizing the Python programming language. Results indicate that Naive Bayes achieved the highest accuracy, with an average accuracy of 79.1% for both testing and training datasets, making it the optimal model for predicting malaria incidence outbreaks based on the dataset utilized. Following closely is Support Vector Machine (SVM) with an average accuracy of 75.45% for both testing and training data, followed by K-Nearest Neighbor with an average accuracy of 70.8%. Logistic Regression exhibited an average accuracy of 68%. However, Linear Regression, with an average accuracy of 26.05%, is not recommended for predicting malaria incidence outbreaks based on the findings of this research.
Evaluation of Nigerian Oil Palm Frond Biomass Potential as a Feedstock for Bioenergy Generation
Global energy consumption is rising quickly, the world must turn to renewable energy sources. One such renewable energy source that has attracted a lot of interest recently is bioenergy. A key agricultural crop in Nigeria, oil palm. However, the majority of the biomass produced by the oil palm is wasted or underutilised, which has negative environmental effects. This untapped resource could be a great source of fuel for the creation of bioenergy. The purpose of this research is to assess the potential of Nigerian oil palm fronds as a feedstock for the creation of bioenergy. This study used thermogravimetric analysis to reveal the physicochemical properties of the frond sample. According to the results of the proximate analysis, the amounts of volatile matter, fixed carbon, ash content, and higher heating value were 78.78wt per cent, 15.01wt per cent, 6.21wt per cent, and 17.63MJ/kg, respectively. Aluminium, potassium, silicon, sodium, calcium, magnesium, and chlorine were the main inorganic mineral elements found in the biomass ash, whereas phosphorus, sulphur, and iron were found in trace amounts. The feedstock is a potential material for thermochemical conversion to produce bioenergy, according to the results of the thermogravimetric and FTIR analyses, which identified characteristics indicative of energy feedstock biomass sample. This study will contribute to the existing knowledge and research on bioenergy production for sustainable and effective biorefineries that would produce different bioenergy products from Nigeria's palm oil resources.
Evaluating the Efficiency of Modified Silica Gel with N, N'-Ethylenebis (acetylacetone)diamine for Copper (II) Ions Removal in Aqueous Solutions
This study evaluated the adsorption capacity of physically modified silica gel (SG), for copper (II) ion using aqueous solutions of N, N’-Ethylenebis(acetylacetone)diamine-C18H28N2O4 (EAD). Copper (II) adsorptive efficiency was studied under different experimental conditions and optimized before spectrophotometric analysis using EAD reagent. Results indicated that sorption of Cu(II) was favourable at pH 4, and sample volume of 50 mL, themin-1. The quantitative recovery of Cu(II) ion was achieved by elution with 5.0 mL of 1.0 molL-1 HNO3. Equilibrium data were tested and found to follow the Langmuir isotherm model. Kinetic data fitted well in pseudo-second-order kinetics. These results suggest that modified SG is an excellent metal extractor for the preconcentration of Cu(II) from the spiked aqueous phase. In conclusion, the physically modified silica gel showed a high potential for the removal of copper (II) ions from aqueous solutions. The adsorption capacity was influenced by the concentration of EAD in the solution, with the highest adsorption capacity observed at an EAD concentration of 2 mM. The study provides valuable insights for the development of effective and efficient adsorbents for heavy metal ion removal from wastewater.
Design and construction of a pelton wheel turbine for power generation
Design and construction of a pelton wheel turbine for power generation is under taken. The pelton wheel turbine is a type of impulse turbine which under the atmospheric pressure and on this head which lies in the range of 200m. The pelton wheel turbine is an axial flow turbine. This work includes among other things the change in design parameter such as the change in discharge of water by which the velocity of jet changes due to change on the force exerted by the jet on the bucket which is responsible for the bucket movement which means the change in the speed of the runner with respect to the change in the jet velocity. The speed of the runner changes by two methods: first, is to change the discharge as well as the jet velocity which can be done by change in head and second, is the modification in the runner. Here the method used is changing the jet velocity by running at different and various height of 2.3m, 2m and 1.45m to ensure that experimental and calculations are made and graphical analysis obtained from the designed values.
Design and development of an automatic braking system in automobiles using ultrasonic sensors
Application of advanced breaking system using ultrasonic sensor in automobile is undertaken. An ultrasonic setup is placed in front of the vehicle and the setups consist of an emitter and a receiver. Ultrasonic emitter always emits the ultrasonic waves, whenever an obstacle is detected then the wave gets reflected and the receiver receives the signal. Reflected wave sends the signal to the Arduino Uno from that, based on distance of object, it actuates the buzzer or brakes. Ultrasonic Braking System (UBS) car provides the glimpse into the future of automotive safety. With the Ultrasonic Braking System (UBS), we can prevent more accident and save more lives.
Neural Networks Versus Conventional Computers
The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits. But the technology available at that time did not allow them to do much. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task. Neural networks and conventional algorithmic computers are not in competition but complement each other. There are tasks that are more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to neural networks. Even more, a large number of tasks, require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to perform at maximum efficiency.
Digital Eye: The Role of Technology in the Life of the Blind and the Visually Impaired
It would have been unthinkable 30 years ago that cell phone would read, print documents and describe things to the blind. Today’s technology not only makes life easier for everyone, but it allows the visually impaired to do numerous things such as write documents, browse the internet and send and receive emails. Screen Reading software and special talking and Braille devices allow those with no vision to use computers, cell phones and other electronic devices independently. Similarly, people with low vision can use screen magnification software and devices that will allow them to see letters, pictures and other objects without having to struggle or strain their remaining vision. It is now an established fact that, with proper training and opportunity, the average blind person can do the average job in the average place of business, and do it as well as his sighted neighbor. The skills of independent mobility, communication, and the activities of daily living are known, available, and acquirable. When an individual becomes blind, he faces two major problems: First, he must learn the skills and techniques which will enable him to carry on as a normal, productive citizen; and second, he must become aware of and learn to cope with public attitudes and misconceptions about blindness. These attitudes and misconceptions go to the very roots of our culture and permeate every aspect of social behavior and thinking. The first of these problems is far easier to solve and that is the focus of this paper.
Characterization of Nutritional and Phytochemical Compositions of Locally Consumed Leafy Vegetables from Afikpo, Ebonyi State, Nigeria
Vegetables are an important part of human and animal diets worldwide. This is because veggies contain crucial protective nutrients for good health and illness prevention. Many vegetables, on the other hand, have yet to be described to determine their nutritional value. We used standard analytical methods to analyze the nutritional and phytochemical contents of four traditional leafy vegetables (P. soyansii, P. santalinoides, V. doniana, and C. esculenta) consumed in the South-East (Ebonyi), Nigeria. Data were analyzed using Graph Pad Prism 7.0 set at p<0.05 statistically significant level. We presented results in graphs accordingly. The maximum magnesium content was found in Vitex doniana while the lowest was found in P. santalinoides. Calcium concentration was highest in Colocasia esculenta and lowest in P. soyansii. Zinc was high in V. doniana but was not significant (p<0.05) in P. santalinoides. Vitamin C was the most abundant in C. esculenta, followed by vitamin B1. V. doniana has greater amounts of vitamins B3 and E than other veggies. A considerable number of bioactive compounds were found in the leaves of P. soyansii, P. santalinoides, V. doniana, and C. esculenta. The alkaloid content of P. soyansii was the greatest, while that of P. santalinoides was the lowest. C. esculenta has a high saponin content, while P. soyoansii has a low saponin content. In addition, tannins and phenol are both abundant in C. esculenta, but not phenol. Therefore, we reported that these vegetables have a significant dietary role in human nutrition, particularly in the prevention of malnutrition illnesses by micronutrients and bioactive supplementation.