Coal City University Journal of Science (CCUJoS) is an international journal published by the Faculty of Natural and Applied Sciences, Coal City University, Nigeria. The journal covers all issues related to Sciences. The journal provides a platform for researchers to publish wide range of original articles, Reviews, Case Reports, technical notes and book reviews, and Short Communication. All papers must be checked for plagiarism before submission to the journal. Consequently, all submitted manuscripts will also be subjected to double blind peer review process, and must meet the required standard before it can be accepted for publication in the journal. AIMS & SCOPE The Coal City University Science Journal is an open access, peer-reviewed and refereed journal. The main objective of CCUSJ is to provide an international platform for scholar to publish their researches. The Journal is published online and in printed versions.
AI-DRIVEN THREAT DETECTION: ENHANCING CYBERSECURITY WITH DEEP LEARNING
With the increasing complexity and volume of cyber threats, traditional security mechanisms are becoming inadequate in detecting sophisticated attacks. Artificial Intelligence (AI), particularly Deep Learning (DL), has emerged as a powerful tool in cybersecurity for identifying and mitigating threats in real time. This paper explores AI-driven threat detection techniques, leveraging deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders to enhance cybersecurity. The study examines real-world cybersecurity datasets, highlighting the effectiveness of deep learning algorithms in anomaly detection, malware classification, and intrusion detection systems (IDS). Experimental results demonstrate that AI-powered threat detection systems significantly outperform conventional approaches in terms of accuracy, adaptability, and threat prediction capabilities. The findings underscore the potential of deep learning in building robust and proactive cybersecurity defenses.
AI-Driven Cybersecurity, Deep Learning for Threat Detection, Intrusion Detection Systems (IDS), Anomaly Detection, Malware Classification.
Ugwuja, Nnenna Esther1 and Omankwu, Obinnaya Chinecherem Beloved2
Copyright (c) 2025 COAL CITY UNIVERSITY JOURNAL OF SCIENCE
A MACHINE LEARNING CLASSIFICATION MODEL FOR MOVIE REVIEWS USING N-GRAM FEATURES SELECTION
This study addresses the increasing reliance of consumers on online platforms for informed decision-making, particularly in the context of movie reviews. Using a dataset of 50,000 reviews from Kaggle’s IMDb dataset, the research develops a sentiment classification model by combining N-Gram feature extraction with diverse machine learning algorithms. The study emerges from limitations identified in prior methodologies, such as small dataset sizes, rule-based approaches, and an overreliance on TF-IDF feature extraction. Employing Python for Exploratory Data Analysis (EDA), the research undertakes key preprocessing steps, including stemming, lowercase conversion, stop-word removal, and tokenization. Central to the methodology is N-Gram feature selection, designed to capture nuanced contextual relationships between words. The machine learning algorithms applied include Linear Support Vector Classifier (Linear SVC), Logistic Regression, Decision Trees, Bernoulli Naïve Bayes, and Multinomial Naïve Bayes. Comparative analysis reveals that the N-Gram methodology—especially when combined with the Linear Support Vector Classifier—outperforms the traditional TF-IDF approach, yielding superior accuracy and more balanced confusion matrices. While Multinomial Naïve Bayes and Logistic Regression demonstrate strong effectiveness, Decision Trees show limitations in precision. The findings underscore the superiority of the N-Gram approach, particularly alongside the Linear Support Vector Classifier, as a robust framework for sentiment analysis in movie reviews. These insights carry practical implications for businesses that rely on user-generated content for decision-making, highlighting the necessity of incorporating N-Gram feature extraction into sentiment analysis models.
Sentiment Analysis, N-Gram Features, Machine Learning, Movie Reviews, IMDb Dataset, Exploratory Data Analysis.
2Adedeji Oladimeji Adebare., 3Olayinka Olufunmilayo Olusanya., 4Omotayo Joseph Adeyemi
DEEP LEARNING PREDICTING MODEL FOR COURT VERDICTS
This paper presents a deep learning-based prediction model for court verdicts, developed using historical datasets of court cases from multiple countries. The datasets were subjected to standard pre-processing techniques, after which the cleaned data was partitioned into training and testing sets for model development and validation, respectively. The model was implemented using Bidirectional Long Short-Term Memory Networks (Bi-LSTM) and N-gram models. During training, the Mean Square Error (MSE) function was employed as the loss function to monitor the variation between the predicted and actual outputs, optimized through the backpropagation algorithm. To handle complex nonlinear relationships and enhance convergence speed and stability, the ReLU activation function was applied. Furthermore, the Adam optimizer was integrated into the model to improve learning efficiency. The results achieved were highly promising, with the model attaining 99.07% accuracy on the training set and 98.01% accuracy on the validation set, alongside very low error losses of 0.002% and 0.003%, respectively. The findings demonstrate the model’s ability to accurately predict court verdicts and crime types, showcasing its potential to serve as a valuable tool for both legal practitioners and individuals in anticipating case outcomes prior to trial.
Deep Learning, Loss Function, Activation Function Optimization Function
AUTOMATED EXAMINATION MISCONDUCT MONITORING USING IMAGETRAINING AMONG COMPUTER SCIENCE STUDENTS
Examination misconduct refers to any immoral or unethical behavior occurring before, during, or after an examination. It constitutes a disorganized practice that undermines the quality of education and devalues the credibility of certificates awarded to students across different levels of learning. The rising incidence of examination malpractice poses a serious challenge to educational systems and demands innovative solutions for effective management. The objective of this research is to automate examination misconduct monitoring using image training techniques capable of detecting and tracking students engaged in malpractice, with a focus on Computer Science students. The system was developed using OpenCV and PyAudio in Python, integrated with a web-based interface, and supported by a MySQL database. The Fisher Linear Discriminant algorithm was employed for face recognition, alongside dataflow and flowchart modeling to guide system implementation. The system provides proof-based monitoring, enabling institutions to capture and recognize the faces of students involved in examination misconduct. Experimental results demonstrated that the approach is effective in identifying suspicious activities and can serve as a technological framework for universities to adopt in strengthening examination integrity.
Automated, examination misconduct, monitoring, image training, students
Asogwa, Samuel Chibuzor2 and Omankwu, Obinnaya Chinecherem Beloved3
FIRST PRINCIPLES STUDY OF THE STRUCTURAL AND ELECTRONIC PROPERTIES OF POLYMORPHS OF CUI
The structural and electronic properties of zinc blende, rock-salt, and wurtzite polymorphs of Copper (I) Iodide (CuI) were investigated using first-principles calculations within the framework of Density Functional Theory (DFT). The calculated lattice constants were found to be in good agreement with experimental values. Energy–volume calculations revealed that the zinc blende and wurtzite structures are energetically more stable and thus preferred over the rock-salt structure at ambient pressure. Both zinc blende and wurtzite polymorphs exhibited a direct band gap at zero and finite pressures, whereas the rock-salt structure displayed a metallic band structure. Additionally, the cohesive and formation energies of each structure were computed to further evaluate their stability. The findings of this study are consistent with previously reported experimental and theoretical results, thereby validating the accuracy of the adopted computational approach.
CuI, Density functional theory, Cohesive energy, Formation energy, Electronic structure.
DESIGN AND CONSTRUCTION OF VARIABLE SPEED DC FAN USING TEMPERATURE SENSOR
This paper presents the design and construction of a Variable Direct Current (DC) Fan Speed Control System utilizing a Temperature Sensor. The objective of the system is to develop an efficient and responsive method for regulating the speed of a DC fan based on real-time temperature conditions. The system employs a temperature sensor to detect environmental changes, while a microcontroller processes the sensor data and adjusts the fan speed accordingly. The flexibility of the system in responding to varying temperature levels enhances its applicability in multiple scenarios, including climate control in rooms, electronics cooling, and energy conservation. The circuit was simulated, constructed, and tested by varying the surrounding temperature, and the corresponding fan speeds were recorded. Results indicated a linear relationship between temperature and fan speed, demonstrating that the constructed device performed according to the design specifications. The system automatically increased fan speed with rising temperature and reduced fan speed as the temperature decreased, exhibiting smooth switching action. The output recordings of the temperature sensor against the tachometer confirmed the sensitivity of the device, as the results consistently showed linearity. This speed control mechanism provides comfort for both domestic and industrial users, particularly in high-temperature environments and during nighttime use when the user is asleep. Furthermore, the inclusion of time and date monitoring in the circuit distinguishes this system from previous works, making it a more advanced and unique solution.
Variable Direct Current, Speed control, Fan, Temperature Sensor, Automatic
APPLICATION OF SELF-POTENTIAL GEOPHYSICAL METHOD IN INVESTIGATION OF PB-ZN SULPHIDE DEPOSITS IN ENYIGBA AND ENVIRONS, SOUTH EASTERN NIGERIA
The self-potential (SP) method was employed to investigate lead-zinc sulphide ore deposit veins in the Enyigba area of Ebonyi State, southeastern Nigeria. The study area lies between latitudes 6°09′00″N to 6°14′00″N and longitudes 8°05′00″E to 8°10′00″E, approximately 13 km south of Abakaliki. The lithological units of the formation consist mainly of shales, underlain by the Abakaliki Shale Formation of the Asu-River Group. Field measurements were carried out using the ABEM SAS 1000 to obtain SP values, which were subsequently plotted and analyzed using Surfer 11.0 software. Data interpretation was conducted qualitatively. The recorded SP values consisted of both positive and negative anomalies. The positive anomalies ranged from +0.15 mV to +70.38 mV, while the negative anomalies ranged from −0.1 mV to −110.25 mV. The iso-potential maps generated from the data revealed the distribution of Pb-Zn sulphide veins within the surveyed area. Results indicate a fairly good comparison across datasets. Notably, the negative SP anomalies were found to be the most diagnostic, signifying the presence of sulphide ore deposits. The survey further suggests that the mineralized veins exhibit varying orientations, with the majority trending in the NW–SE direction. In conclusion, the SP survey results strongly suggest the possible existence of lead-zinc sulphide ore deposits in the Enyigba area. However, for greater accuracy, it is recommended that the SP method be integrated with other geophysical techniques such as gravity, magnetics, electromagnetic surveys, or resistivity methods. Such integration would provide a more comprehensive understanding of the ore deposit distribution and the pattern of mineralization.
Oyebanjo Oluwakemi Abosede2 , Egunjobi Kolawole Abiodun2 , Chime Ifeanyichukwu Princewell3
DESIGN AND CONSTRUCTION OF AN ADVANCED 40WATTS WIRELESS PUBLIC ADDRESS SYSTEM
This paper presents the design, development, and implementation of an advanced wireless public address (PA) system, emphasizing its components and operational functionalities. The system is composed of several integral units, including a power supply unit, an FM transmitter, an FM receiver, a preamplifier stage, and a power amplifier stage. The process begins with an audio signal captured by a microphone and converted into an electrical signal. This signal is wirelessly transmitted through the FM transmitter and subsequently received by the FM receiver. Upon reception, the signal undergoes demodulation, after which it is passed through the preamplifier stage for initial amplification and conditioning. It is then further processed in the power amplifier stage, which delivers a robust 40-watt output to an 8-ohm loudspeaker, ensuring clear and powerful audio delivery. To expand functionality, the system incorporates an additional FM transmitter operating at 87.5 MHz, enabling enhanced flexibility and improved performance. Overall, this design provides an effective and reliable solution for modern wireless public address applications, offering efficient audio transmission and high-quality sound reproduction.
RADIOLOGICAL ASSESSMENT OF SOIL SAMPLES FROM SELECTED DUMPSITES IN MOWE, OGUN STATE NIGERIA
The activity concentration of soil samples from selected dumpsites within Mowe, Ogun State, Nigeria, was determined. These dumpsites serve as recipients of municipal waste from diverse sources. Two soil samples each were collected from five selected dumpsites, and Gamma Spectrometry analysis was performed to determine the specific activity of radionuclides using a NaI (Tl) scintillation detector. The results showed that the activity concentrations of radionuclides in the soil samples ranged from 4.94 ± 0.47 Bq/kg to 28.10 ± 2.68 Bq/kg for ²³²Th, 1.81 ± 0.28 Bq/kg to 125.27 ± 21.36 Bq/kg for ²³⁸U, and 779.07 ± 63.93 Bq/kg to 1746.61 ± 11.82 Bq/kg for ⁴⁰K. Furthermore, the calculated mean values for the external hazard index (H_{ex}) and radium equivalent activity (Ra_{eq}) were 0.485 and 165.74 Bq/kg, respectively, both of which are below the permissible safety limits of 1.0 and 370 Bq/kg. However, the average values of the absorbed dose rate and the gamma representative index (I_γ), recorded as 82.55 nGy/h and 1.164 Bq/kg, respectively, were higher than the worldwide average values of 55 nGy/h and 1.0 Bq/kg. Despite this, the external hazard index remained less than unity, and the annual effective dose rate (outdoor) was lower than the recommended limit of 1.0 mSv/yr. It is therefore concluded that the soil samples from the studied dumpsites do not pose any significant radiological hazard to the surrounding environment and human population.
Dose, Pollution, Contamination, Municipal, Radioactivity, Radiation, and Radionuclide.
DESIGN AND IMPLEMENTATION OF AN ONLINE STUDENT HOSTEL ALLOCATION MANAGEMENT SYSTEM
The issue of student accommodation is a critical concern that requires serious attention from the management of tertiary institutions. As student enrollment increases, reliance on manual hostel allocation results in more paperwork, inefficiency, and administrative delays. To address this challenge, this study presents the design and implementation of an automated hostel allocation management system for Tai Solarin University, Ogun State. The study identified both the functional and non-functional requirements of the proposed system and modeled its design using Unified Modeling Language (UML) tools, including use case diagrams, activity diagrams, and flowcharts. The system was implemented with Web 2.0 technologies such as HTML, PHP, and JavaScript, while Structured Query Language (SQL) was used for database management. The results indicate that the developed system significantly enhances the efficiency of hostel allocation by reducing the stress associated with the existing manual process. Furthermore, the system improves service delivery to students by providing a faster, more reliable, and transparent allocation process.
COMPUTER-BASED ACADEMIC MONITORING AND CONTROL OF LECTURE ATTENDANCE AT MICHAEL OKPARA UNIVERSITY OF AGRICULTURE, UMUDIKE
Academic monitoring in Nigerian tertiary institutions largely depends on students’ and lecturers’ punctual attendance at lectures as well as the outcomes reflected in their examination reports. However, ensuring consistent attendance remains a major challenge, and the traditional manual method of taking attendance is often cumbersome and ineffective. To address these limitations, attempts have been made using electronic cards and clock-in systems. Nevertheless, such methods present issues such as the possibility of recording attendance for another individual. In light of these shortcomings, this research successfully applied computer-based Face Recognition Technology (FRT) to monitor and control lecture attendance. The objective of the study was to develop an automated academic monitoring system capable of capturing students’ and lecturers’ lecture attendance and relating these records to examination performance. The system was designed using the Object-Oriented Analysis and Design (OOAD) methodology. Data on students and lecturers were stored in a system database created with MySQL Database Management System (MySQL-DBMS), with each user assigned a unique identification number. The platform integrated the Fisherface algorithm, available in the Python library and implemented through OpenCV, to perform face detection and recognition for accurate time monitoring of lecture attendance. The results showed that the system efficiently recognized faces, recorded lecture sessions in real time, and captured still images for monitoring purposes. Furthermore, the system demonstrated the ability to generate students’ results that established a relationship between lecture attendance and examination performance.
MOBILE TECHNOLOGY USE AMONG COMPUTER SCIENCE EDUCATORS DURING CORONAVIRUS SCHOOL CLOSURE
The study examined mobile technology usage among Computer Science educators during the Coronavirus school closure in Nigeria. A total of 200 questionnaires were administered online to Computer Science educators, selected through a purposive random sampling technique from various tertiary institutions across the country. The collected data were analyzed using frequencies, percentages, regression, and ANOVA. The results revealed that the use of mobile technologies had a statistically significant effect on the educational activities of Computer Science educators who participated in the study. Findings also indicated a significant relationship between participants’ perceptions of mobile technology and their extent of usage for educational purposes during the school closure. Mobile technologies such as smartphones, laptops, tablets, e-readers, notebooks, smartwatches, PDAs, and videoconferencing tools (e.g., Zoom, Google Meet) were commonly used by the participants to support their educational activities. However, several factors were found to inhibit usage. These included small screen sizes, poor network connectivity, frequent power outages, high data costs, ignorance, transition challenges, and functionality issues.
Coronavirus, Mobile technology, Smartphone, ZOOM,Computer Science Educators, School closure, Online education.
Ugwuja Nnenna Esther2 , Nwosu Kanayo Chike3 ,
CRITERIA RELATED TECHNIQUES FOR SOFTWARE EVALUATION
This paper presents a review of techniques and criteria for software evaluation. It highlights software evaluation as a critical aspect of the software engineering process, involving the selection of one software product from several alternatives. Various techniques for software evaluation are discussed, including the Analytic Hierarchy Process (AHP) Software Evaluation Technique, the Weighted Average Sum Technique, and the Fuzzy-Based Approach Software Evaluation Technique. The strengths and weaknesses of each technique are outlined to provide a broad understanding of their application in software evaluation. In addition, the paper identifies key criteria to be considered during software evaluation, which are commonly classified into groups such as vendor-related factors, hardware/software compatibility, cost-benefit analysis, security, and reliability. The study concludes that software evaluation plays an essential role in enabling software companies, individuals, and organizations to ensure that the software solutions they adopt are reliable, efficient, and capable of meeting user needs.
INTEGRATION OF 21ST CENTURY COMPETENCIES IN TEACHING AND LEARNING OF BIOLOGY
Education in the 21st century requires the adoption and integration of modern competencies to effectively achieve teaching and learning objectives. These competencies include skills that foster critical thinking, learner-centered approaches, problem solving, creativity, and productivity in learning. This study examined the extent of integration of 21st-century competencies in the teaching and learning of Biology at the tertiary level. Data were collected through structured questionnaires administered to fifty Biology lecturers from selected tertiary institutions in Nigeria. However, only thirty-two questionnaires were retrieved and found valid for analysis. The results revealed that the respondents possessed good knowledge of 21st-century competencies, and the extent of their integration into the teaching and learning process was high, with a mean score of 24.59±2.05. The findings further indicated no significant difference in respondents’ knowledge of 21st-century skills based on gender, qualifications, or teaching experience. The study concluded that the integration of 21st-century competencies should be strongly encouraged among educators to adequately prepare learners for a digital-driven future.
NEURAL NETWORKS VERSUS CONVENTIONAL COMPUTERS
The first artificial neuron was developed in 1943 by neurophysiologist Warren McCulloch and logician Walter Pitts. However, the limited technology available at that time restricted its practical application. Neural networks, with their remarkable ability to extract meaning from complex and imprecise data, are now widely used to identify patterns and detect trends that may be too intricate for humans or traditional computer techniques to recognize. A trained neural network can be regarded as an “expert” within the specific category of information it has been exposed to during training. In contrast, conventional computers operate using an algorithmic approach, whereby the system follows a predetermined set of instructions to solve a problem. If the exact steps required are not known, the computer cannot execute the task. Neural networks, on the other hand, process information in a manner similar to the human brain. They consist of a large number of highly interconnected processing units (neurons) that work in parallel to solve problems. Unlike conventional programming, neural networks learn by example and cannot be explicitly programmed for a particular task. Importantly, neural networks and conventional computers are not competitors but complements. While algorithmic approaches are more suitable for tasks such as arithmetic operations, neural networks excel in tasks that require pattern recognition or adaptive learning. In many applications, the most effective systems combine both approaches, with conventional computers supervising neural networks to achieve maximum efficiency.
DEVELOPMENT OF AN ENHANCED FIREWALL SYSTEM FOR NETWORK SECURITY
The Internet and computer networks face increasing security threats on a daily basis. With new types of attacks emerging regularly, the development of flexible and adaptive security-oriented approaches, such as firewalls, has become a critical challenge. This study aimed to design and implement a multi-layered protective system capable of preventing intrusions of any type in networks connected to the Internet. The proposed enhanced firewall system integrates multiple security mechanisms, including a firewall, push notification service, malware filtering system, and intrusion detection system, to provide comprehensive protection. To achieve this, a firewall framework was proposed, with its features outlined in the introduction. A literature review was conducted to examine related works in the field of network and Internet security. Additionally, an existing system was studied, and a design phase was carried out to improve on identified gaps. The methodology adopted was the Object-Oriented Analysis and Design Methodology (OOADM), which facilitated systematic modeling of the system’s components and interactions. The enhanced firewall system—incorporating intrusion detection and push notification—was developed using the Visual Basic programming language and Microsoft Access database system. This work demonstrates that integrating multiple layers of protection enhances the ability of firewalls to mitigate evolving threats in Internet and network security environments.
EFFECT OF COLLABORATIVE LEARNING STRATEGY ON ACADEMIC ACHIEVEMENT OF SECONDARY SCHOOL STUDENTS IN CHEMISTRY
The study investigated the effect of a collaborative learning strategy on the academic achievement of secondary school students in chemistry. Six research questions and six null hypotheses tested at the 0.05 level of significance guided the study. A quasi-experimental design, specifically the pretest-posttest non-equivalent group design, was adopted. The study was conducted in the Abakaliki Education Zone of Ebonyi State. The population comprised 2,305 Senior Secondary II (SSII) chemistry students across 138 secondary schools in the zone. A sample of 161 SSII chemistry students from four selected co-educational boarding schools was used. The sample consisted of 80 males and 81 females in their intact classes. A coin toss was used to assign treatment and control groups. Two instructional packages were prepared: one for the treatment group and one for the control group. The treatment group was taught chemistry using the collaborative learning strategy, while the control group was taught using an individualized learning approach. The researcher-developed Chemistry Achievement Test (CAT) served as the instrument for data collection. Mean and standard deviation were used to answer the research questions, while Analysis of Covariance (ANCOVA) at 0.05 significance level was employed to test the hypotheses. Findings revealed a significant difference in the mean achievement scores of students taught with the collaborative learning strategy compared to those taught with the conventional method. However, no significant difference was found in the mean achievement scores of male and female students taught with the collaborative learning strategy. Similarly, no significant interaction effect between gender and learning strategy on students’ mean achievement was observed. Based on these findings, it was recommended that collaborative learning strategies should be adopted in teaching chemistry, as they enhance academic achievement for both male and female students. Curriculum planners should also incorporate collaborative learning strategies as a recommended approach in secondary school chemistry instruction. The study concluded that collaborative learning is an effective instructional approach and is suitable for both male and female students.
APPLICATION OF EMERGING TECHNOLOGIES IN MITIGATION OF E-COMMERCE SECURITY CHALLENGES
Technology penetration has significantly influenced the emergence of e-businesses and the widespread use of e-commerce platforms. In the 21st century, e-commerce platforms have become vital tools for promoting entrepreneurial activities and businesses, enhancing both visibility and accessibility to customers, goods, and services. The COVID-19 pandemic further amplified digital migration, accelerating reliance on e-commerce platforms. Despite these advantages, concerns persist regarding security issues, which stakeholders identify as a major threat to the growth and survival of online businesses. Network and system vulnerabilities make e-commerce platforms susceptible to cyberattacks, often resulting in data leakages that compromise data integrity, breach customer rights and privacy, and erode trust between e-commerce businesses and their customers. This study highlights the key sources of e-commerce security challenges and examines the potential of emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and blockchain in mitigating these threats. To strengthen e-commerce security, the study recommends measures including data encryption, secure payment gateways, multi-factor authentication (MFA), regular software updates and patches, and strict adherence to user privacy policies and compliance regulations.
PREVALENCE OF QUINOLONE RESISTANCE IN SALMONELLA ISOLATED FROM POULTRY INTESTINES
This study was designed to evaluate the prevalence of quinolone resistance in Salmonella spp. isolated from poultry intestines. A total of 25 poultry intestine samples were aseptically collected from poultry farms in Abakaliki metropolis using sterile swab sticks and properly labeled. The samples were immediately transported to the laboratory for microbiological analysis. Sample processing, bacterial isolation, and identification were carried out using standard microbiological procedures. The antibiotic susceptibility patterns of the isolates were determined using the standard Kirby-Bauer disk diffusion method and interpreted according to the guidelines of the Clinical Laboratory Standards Institute (CLSI). The results revealed the presence of Salmonella spp. in poultry intestines. Antibiotic susceptibility testing showed that the highest sensitivity rate was associated with cefepime, with 80% sensitivity recorded against the Salmonella strains. Conversely, the highest resistance rate was observed with amoxicillin and ofloxacin, both of which recorded 100% resistance, followed by levofloxacin, ciprofloxacin, and norfloxacin, each with 93.3% resistance. In conclusion, this study demonstrates that Salmonella strains isolated from poultry intestines in Abakaliki harbor quinolone-resistant genes, posing a potential public health concern.
FED-BATCH MIXOTROPHIC PRODUCTION OF EUGLENA GRACILIS BIOMASS USING NPK 15:15:15 FERTILIZER
Mixotrophic cultivation of Euglena gracilis results in high algal biomass with elevated intracellular contents. However, the cost of production remains high due to the expensive media components. In this study, the production of Euglena gracilis biomass using a cheap and easily available medium was investigated in a fed-batch culture. The growth of cells in NPK 15:15:15 fertilizer, with peptone and ethanol as nitrogen and carbon sources, was compared using varying ethanol feed rates of 0.5 mL L⁻¹, 1.0 mL L⁻¹, and 2.0 mL L⁻¹ within 24 hours, under light intensities of 2570 Lux, 1642 Lux, and 851 Lux, respectively. The growth rates of E. gracilis under the different ethanol feed rates and light intensities were ranked as 1.0 mL L⁻¹ > 0.5 mL L⁻¹ > 2.0 mL L⁻¹ and 2570 Lux = 1642 Lux > 851 Lux, respectively. Peptone did not significantly enhance the growth of E. gracilis.
IMPACT OF E-LEARNING PLATFORMS ON STUDENTS’ INTEREST AND ACADEMIC ACHIEVEMENT IN DATA STRUCTURE COURSE
The study examined the impact of e-learning platforms on students’ interest and academic achievement in a Data Structure course. A total of 50 Computer Science students enrolled in the course at the tertiary institution level participated in the study. The sample was divided into two groups: an experimental group (n = 25) and a control group (n = 25). The Experimental Group (EG) was taught using an e-learning platform, while the Control Group (CG) received instruction through the traditional classroom approach. An achievement test on Data Structure was administered as both a pre-test and a post-test to the two groups, and the hypotheses were tested using ANOVA. The post-test results revealed a statistically significant difference in academic achievement between the experimental group and the control group, in favor of the experimental group after the treatment. The mean score of the EG was statistically higher than that of the CG, indicating that the EG outperformed the CG. Furthermore, the findings showed that the use of e-learning platforms had a statistically significant effect on the learning interest of the EG after the treatment. In conclusion, the study recommends that the use of e-learning platforms should be encouraged in teaching and learning. However, the transition to full adoption should be gradual to enable effective adjustment by both students and educators.
MICROBIOLOGICAL ASSESSMENT OF FRESH BEEF FROM BWARI AND KUBWA ABATTOIRS: FOCUS ON STAPHYLOCOCCUS AUREUS ISOLATION AND IDENTIFICATION
Staphylococcus aureus is a Gram-positive bacterium widely recognised for its role in both clinical infections and foodborne illnesses. Its presence in meat products poses a considerable public health risk due to its ability to contaminate food and produce harmful enterotoxins. This study aimed to isolate and identify S. aureus from fresh meat samples obtained from slaughterhouses in Bwari and Kubwa, Abuja, Nigeria. A total of 100 meat samples were randomly and aseptically collected. The sample size was determined using the single proportion formula described by Lwanga and Lemeshow (1991), based on an assumed prevalence of 30%, a 95% confidence level, and a 10% margin of error. This number also aligns with international microbiological food safety guidelines, such as those of the International Commission on Microbiological Specifications for Foods (ICMSF), which recommend 60–100 samples for microbial quality assessments. Standard microbiological methods were employed for the preliminary identification of S. aureus, including culturing on Mannitol Salt Agar, Gram staining, and biochemical characterisation. Confirmation was performed using the coagulase test, a well-established diagnostic marker for pathogenic S. aureus. Results showed that 36% of the samples exhibited growth typical of S. aureus on selective media, while 18% were confirmed as coagulase-positive S. aureus. These findings underscore the potential public health hazards linked to meat contamination at the abattoir level and highlight the urgent need for improved hygiene practices during meat processing and handling.
HEPATOTOXICITY EFFECTS OF AQUEOUS EXTRACT OF MILLETTIA ABOENSIS LEAVES ON THE BIOCHEMICAL PARAMETERS OF LIVER MARKER ENZYMES IN MICE
This study evaluated the hepatotoxic effects of the aqueous extract of M. aboensis leaves on the biochemical parameters of liver marker enzymes in mice. Phytochemical screening of the extract revealed relatively high concentrations of reducing sugars, tannins, and flavonoids; moderate concentrations of alkaloids, steroids, and glycosides; and low concentrations of soluble carbohydrates, saponins, and hydrogen cyanides, while terpenoids were not detected. The median lethal dose (LD₅₀) of the extract showed no mortality at a dose of 5000 mg/kg body weight. Biochemical assays indicated that Aspartate Aminotransferase (AST) and Alanine Aminotransferase (ALT) activities in the serum of treated groups (2 and 3), administered 100 and 1000 mg/kg body weight of the extract, showed a significant increase (p < 0.05) compared to the control group (normal saline). However, Alkaline Phosphatase (ALP) activity in the serum of mice in groups 2 and 3 exhibited no significant change (p > 0.05) relative to the control. The observed increase in AST and ALT levels suggests that the extract may have some hepatotoxic effects on the experimental animals. Conversely, the non-significant change in ALP activity indicates that the extract did not adversely affect the hepatocytes in mice.
UPGRADING STUDENTS’ ACHIEVEMENT AND RETENTION IN BASIC SCIENCE USING ANALOGY AND DEMONSTRATION STRATEGIES
The study investigated the effects of analogy and demonstration teaching strategies on Basic Eight students’ achievement and retention in Basic Science in Buruku Local Government Area of Benue State, Nigeria. A quasi-experimental design, specifically the pre-test, post-test control group design, was adopted. The sample comprised 174 Basic Eight students drawn from four randomly selected schools. A validated 20-item Basic Science Achievement Test (BSAT) was used for data collection, with a reliability coefficient of 0.76 established using the Kuder-Richardson Formula 20. Out of the four schools, two were assigned to experimental groups and two to control groups. Data were analyzed using mean and standard deviation to answer the research questions, while Analysis of Covariance (ANCOVA) was employed to test four hypotheses at the 0.05 level of significance. The findings revealed that the analogy strategy was more effective in enhancing students’ achievement and retention in Basic Science [F(1,171) = 82.013, p < 0.05] and [F(1,171) = 46.200, p < 0.05], respectively. The results further indicated that gender was not a significant factor in students’ achievement and retention [F(1,89) = 0.06, p > 0.05] and [F(1,89) = 0.39, p > 0.05], respectively. Based on these findings, the study recommended that Basic Science teachers should be encouraged to adopt analogy teaching strategies for effective learning of the subject.
Analogy strategy, Demonstration method, Basic science achievement, Retention, Basic science, Gender
AWARENESS OF INNOVATIVE EDUCATIONAL SOFTWARE AMONG EDUCATORS IN TERTIARY INSTITUTIONS IN ENUGU STATE, NIGERIA
The main purpose of this study was to ascertain the extent of awareness of innovative educational software among educators in tertiary institutions in Enugu State. A total of 68 questionnaires were administered to respondents drawn from three different tertiary institutions in the state. The results were analyzed using mean and standard deviation, while the hypothesis was tested with ANOVA at a 0.05 level of significance. The findings revealed a generally low awareness of innovative educational software among the respondents. Despite the growing importance of educational software, most of the educators were unfamiliar with some of the listed applications, which may negatively influence their level of usage. The study concluded by recommending that tertiary institutions should organize regular training and capacity-building programs for their staff to equip them with the necessary digital knowledge and skills required to teach effectively and compete in the 21st century.