Nnamdi Azikiwe University Journals
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Geospatial Analysis of Land Cover Change Impact on Flood Risk in Anambra East Local Government Area of Anambra State, Nigeria
No full textThis study was driven by the need to determine the effect of vegetation loss, topography modification and structural development to persistent flooding in Anambra East Local Government Area of Anambra State. The research employed Geographic Information System and Remote Sensing methodology for its data gathering, visualization and spatial analysis. Multi-temporal Landsat TM, ETM+ and OLI of 2001, 2012 and 2023 imageries were obtained at 11years interval. The LULC analysis revealed the dominance of five (5) classes - Bare Land, Built Up, Dense Vegetation, Farmland/Sparse Vegetation, and Water Body. The Built-up and water Body classes were observed to consistently increase over time in the study area. Builtup increased by 22.1sq km from 2001 to 2023, and water bodies increased by 1.2 sq.km. Dense vegetation though appreciated to 26.6sq.km within the period but was confined mainly in southern part and along Oyi River valley. However, Farmland/sparse vegetation lost 17.6sqkm of its spatial extent within the same time, and Bare land lost its 32.4sqkm spatial extent too. It was observed that built-up class and paved surfaces increased both in spatial extent and density over the period, and extended heavily along Anambra River floodplains. It was further observed that continuous extension of built up into flood prone areas posed dangers of increasing overland flow, constricting the natural river flow, braiding river channels, aiding river depositional processes and increasing chances of severe flooding in the area during torrential rainfalls. The study therefore recommended among others that government should enforce extant city and building regulations and initiate intensive agro forestry and green belt developments along Anambra River and areas experiencing massive urbanization to regulate surface runoff and annual inundation of the river bank
Self-motivation as a Predictor of Secondary School Students’ Academic Success in Biology: A Study of Secondary School Students in Anambra State
No full textThe purpose of the study was to ascertain if self-motivation is a predictor of secondary school students’ academic achievement in Biology in Anambra State. One research question gave direction to the study while one hypothesis was tested at the 0.05 level of significance. A correlational research design was employed for the study. The population of this study was made up of 20, 138 biology senior secondary SS2 students in the 266 public secondary schools in Anambra State. The sample for the study comprised 840 SS 2 students for the 2024/2025 academic session drawn through a multi-stage sampling procedure. Face-validated Self-Motivation Questionnaire (SMQ) was used to collect data. The data collected were analyzed using multiple regression. The findings of the study revealed that self-motivation positively and insignificantly predicted secondary school students’ academic achievement in biology. Based on the findings of the study, it was recommended that Biology teachers should encourage students to be selfmotivated to improve their academic achievement in Biology
INVESTIGATING PERCEPTIONS OF UNDERGRADUATE SCIENCE EDUCATION STUDENTS ON THE USE OF ARTIFICIAL INTELLIGENCE (AI) IN THE TEACHING AND LEARNING OF MATHEMATICS
No full textThis study investigated the perceptions of undergraduate science students regarding the use of artificial intelligence in the teaching of mathematics. The targeted population comprised 137 students( 42 male students and 95 female students) in the school of sciences,Nwafor Orizu College of Education, Nsugbe, Anambra State in 2024/2025 academic year .The sample represents the entire poulation of 137 students(comprises of 46 Biology ,18 Chemistry,10Mathematics ,4 Physics ,59 Computer Science) ,who enrolled for the Elementary Mathematics 1( MTH101)lesson using census sampling method because .This is becauseThe instrument for data collection was a 30 item 4 -point scale questionnaire classified into five thematic category named as Artificial Intelligence Perception Questionnaire(AIPQ). The data obtained was analyzed using mean, standard deviation and t-test statistics. From the findings of the study, it was determined that artificial intelligence perceptions of the students of the Computer Science , Mathematics and Physics students perception were higher than the students of the Biology and chemistry departments. Also the negative perceptions of all sample groups about artificial intelligence concept are more significant than positive perceptions.Based on the findings,it was recommended among others that the Nigeria curriculum should incorporate AI literacy, helping students understand the strengths, limitations, and proper applications of AI in mathematical problem-solving. 
RELATIONSHIPS BETWEEN PRINCIPALS’ COMMUNICATION STYLES AND ADMINISTRATIVE EFFECTIVENESS IN PUBLIC SECONDARY SCHOOLS IN ANAMBRA STATE
No full textThe need for administrative effectiveness in secondary school systems in Anambra State to ensure quality and prompt delivery of educational services cannot be over-emphasized. This prompted the researchers to determine the relationship between principals’ communication styles and administrative effectiveness in public secondary schools in Anambra State. Two research questions guided the study and two hypotheses were tested at. 05 level of significance. Correlation research design was adopted for the study. The population was 5,214 participants comprising 264 principals and 4,950 teachers in public secondary schools in Anambra State. Proportionate stratified random sampling technique was used to draw the sample for study. Structured questionnaires were used for data collection. The instruments were face and construct validated. The researcher administered 285 copies of the instruments were administered to the respondents with the help of two research assistants using direct method and retrieval and 280 copies were retrieved. Data were analyzed using Pearson Product Moment Correlation Coefficient while the p-value was used to determine the significance of relationship at 0.05 significant levels for all hypotheses. The results among others revealed that principals agreed that there is very high positive relationship between result-driven communication style and administrative effectiveness in public secondary schools in Anambra. Accordingly, it was recommended among others that efforts should be made to encourage principals to adopt result-driven communication style so as to improve on their administrative effectiveness
BRIDGING KNOWLEDGE AND INNOVATION: THE ROLE OF ARTIFICIAL INTELLIGENCE ON RESOURCE ALLOCATION MANAGEMENT IN PUBLIC UNIVERSITIES IN ANAMBRA STATE
No full textThis study was guided by three research questions and three hypotheses. It adopted a descriptive survey design. The population of the study comprised of 7,064 employees in two public universities in Anambra state. Stratified random sampling was used in selecting 232 staff (male 109, female 123) as sample. The instrument for data collection was a structured questionnaire titled “Role of Artificial Intelligence on Resource Allocation Management in Public Universities Questionnaire (RAIRAMPUQ)”. It was drafted from the three research questions. The instrument was validated by 3 experts. The reliability was established using 20 university staff who were not selected as sample for this study. The test-retest technique was used for the process. Cronbach Alpha technique was used to compute the result which yielded reliability coefficient values of 0.83, 0.80 and 0.75 with a general index of 0.79 and thus, confirmed the instrument reliable. The questionnaires were distributed and collected at the spot. A total of 255 questionnaires that were distributed, only 232 were fully filled and retrieved. The data collected were analyzed using mean and standard deviation while t-test was used to test the hypothesis at .05 level of significance. Findings show that Artificial Intelligence play significant role in human, material and financial resources of public universities. In human resource management, AI helps in the enhancement of staff engagement, aids in analysing employee data to identify potential individuals, helps students carry out their academic activities, recruitment of employees and staff development. The study recommends among all that human resource in Nigerian universities be annually trained to the mastery of computer so that they can effectively utilize opportunities in AI to perform their academic duties professionally
Women-on-Women Oppression in Female Migrant Experiences: A Study of selected Nollywood films
No full textThe narratives of female migrant experiences in Nigeria have never been told without enumerating its attendant vulnerabilities and prejudices that are unavoidably linked to irregular migration. This has manifested in various forms such as physical, mental, and emotional abuse, trafficking, exploitation, and domestic servitude. In all of these, patriarchy has mostly been blamed as the major source of women\u27s subjugation. With the proliferation of gender discourses in search of equality in the relationship between men and women, little or no result has been recorded. Considering the multiple streams of women\u27s oppression, there is a need to look inward to dig out the roles women play in the sustenance of patriarchal structures that form the bedrock of women’s subjugation. This research work interrogates the intra-gender relations in two Nollywood films, Kenneth Gyang’s Oloture (2019), and Lonzo Nzekwe’s Anchor Baby (2010). Employing the purposive sampling technique of the qualitative research methodology, these six migration-themed films were carefully selected and interrogated through content analysis. The research argument is framed around Crenshaw Kimberley’s theory of Intersectional Feminism and Tracie Utoh-Ezeajugh’s theory of Intra-genderism. Findings reveal that there are hidden rivalries that constantly exist among women which inhibit the actualization of female bonding. This study thus suggests that the female gender should break down all internal structures that are detrimental to their peaceful co-existence and focus more on genuine love and support for one another
Development of Innovative Waste Plastic-Sand Mixer for Sustainable Paver Blocks Production
No full textThis research investigated the development innovative of a waste plastic-sand mixer for sustainable paver block production. The Full Factorial Design (FFD) at two factors, two levels with four experimental runs were chosen to obtain the paver blocks with high compressive strength. The low (-1) and high (+1) levels for the plastic waste were set to be 30% weight and 40% weight while that for the sand, were 40% weight and 70% weight. ANOVA was employed for the analysis to know the most significant factor for compressive strength. It was observed that plastic waste was the most significant factor responsible for good compressive strength as shown by the F-statistics. The plastic-sand paver blocks produced were subjected to compression tests in accordance with ASTM C140 specification, SEM - (Scanning Electron Microscopy) and Vickers micro-hardness test. From the FFD, it was observed that the crushing force exerted on the paver blocks increased with an increase in the weight and density of the paver. The maximum compressive strength was seen to be 0.75 N/mm2 for a paver block weight of 0.936 kg. The SEM morphology results showed a bond of a white farinaceous layer which indicates the presence of silica obtained from the sand and the black colour which indicates the plastic waste used. The waste plastic/sand mixer was successfully achieved at a cost of 1,200 to 0.73 – $2.18. The study concludes that plastic-sand paver blocks are candidate materials that could be used as substitutes for concrete paver
Evaluating Advances in Machine Learning Algorithms for Predicting and Preventing Maternal and Foetal Mortality in Nigerian Healthcare: A Systematic Approach
No full textThis study systematically analysed developments in machine learning (ML)-based prediction algorithms aimed at reducing maternal and foetal mortality in Nigerian hospitals. Key causes of maternal death in Nigeria include obstetric haemorrhage, eclampsia, sepsis, obstructed labour, and complications from unsafe abortions. The comparison of maternal mortality ratios between Nigeria and developed countries highlights significant disparities, emphasizing the need for targeted interventions. This research employed a random-effects model to synthesize effect sizes from multiple studies, accounting for variations in study populations and hospital settings. Metrics such as precision, accuracy, recall, and F1-score were used to evaluate ML algorithms including logistic regression, decision trees, random forests, support vector machines (SVMs), neural networks, and ensemble methods. The results indicate high prediction accuracy of 80-90% for these algorithms, with neural networks performing best at 90% accuracy. The implementation challenges such as data quality, limited technology access, and ethical considerations pose significant barriers. Improving data infrastructure, fostering interdisciplinary collaboration, and establishing ethical frameworks are crucial for successful ML integration in healthcare. The study emphasized on the potential of ML to transform maternal and foetal healthcare through early detection, personalized care, and optimized resource allocation, with more emphasis on the need for holistic approaches to address both technical and socio-cultural challenges in Nigeria. Future research should focus on developing robust ML algorithms, enhancing data interoperability, and promoting a data-driven culture to improve maternal health outcomes
Lean Principles Integration with Digital Technologies: A Synergistic Approach to Modern Manufacturing
No full textThe integration of lean principles with digital technologies marks a transformative shift in modern manufacturing and operations management. Lean methodologies focus on reducing waste, optimizing resources, and maximizing value, while digital tools such as IoT, AI, and Big Data Analytics enable real-time monitoring, predictive insights, and automation. This study explores how the combination of these paradigms will enhance operational efficiency, agility, and competitiveness in manufacturing environments. Through an analysis of applications like smart production systems, predictive maintenance, and digital value stream mapping, the research highlights significant benefits, including improved quality, faster decision-making, and reduced downtime. It also examines challenges such as technological complexity, data security, organizational resistance, and the need for workforce upskilling. Emerging trends like Industry 5.0 and human-centric smart factories are discussed, emphasizing the evolving landscape of digitally-driven lean manufacturing. The findings demonstrated that integrating lean principles with digital technologies is no longer optional, but essential for firms aiming to thrive in an increasingly dynamic global market. This synergy represents a strategic pathway towards sustainable operational excellence and innovation in the manufacturing sector
Biochemical Synthesis of Zinc-Graphene Nano-Composite Using Cassava Leaf Extract
No full textThis research explores eco-friendly methods for creating zinc–graphene nanocomposites through green synthesis techniques using plant materials. The study used cassava (Manihot esculenta) leaf extract as a natural substance to reduce and stabilise the nanocomposite. Energy Dispersive X-ray (EDX) spectroscopy were used to identify the elements and stability of the produced material. The EDX analysis demonstrated that the primary elements of the nanocomposite were zinc and carbon from graphene oxide, which measured 44.50% and 20.70%, respectively. The 15.00% oxygen measurement confirmed the existence of graphene oxide within the material. The analysis identified trace elements, including magnesium (3.62%), calcium (7.01%), iron (3.60%), sulphur (2.40%), sodium (2.02%), and potassium (1.15%), which were likely derived from cassava extract or reaction intermediates. These components have the potential to introduce extra catalytic properties as well as biological functions to the material. The research findings confirm the feasibility of sustainable bio-based methods for creating advanced nanomaterials. The research highlights material science\u27s progressive movement towards environmentally sustainable nanotechnology approaches