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This study examined the influence of adult education programmes on women’s performance in Ovia North-East Local Government Area of Edo State. To guide this study five (5) research questions were raised. Descriptive survey design was used in conducting the study. The population of the study consist of 244 respondents from four communities in Ovia North-East local government area of Edo state. The study adopted multi-stage sampling technique and the instrument for data collection was a structured questionnaire. It was subjected to face validity by the researcher’s project supervisor and two other lecturers in the Department of Adult and Non-formal Education, University of Benin. The findings from the study revealed among others that the adult education programmes available to women in Ovia North-East Local Government Area of Edo State includes fashion designing, hair dressing, catering and interior designing and basic literacy. The findings further revealed that through adult education programmes women are socially empowered to engage in social activities, have better information about their health, and experience an upward movement in their social status. Based on the findings, the study recommends the active mobilization of women so that they can be part of adult education programmes provided for them, adequate funds should be provided by the government to run the programme and the programme planners should tailor the programmes to meet the everyday need of the female learners in order to promote the holistic development of women in our country

This is carried out to assess the concentrations of Nitrogen dioxide (NO2) and Particulate matter (PM2.5), in Etsako East Local Government Area. Secondary data collection method was employed for the assessment. Levels of Nitrogen dioxide (NO2) and Particulate matter (PM2.5) were extracted biannually from Google Earth Engine using information from Sentinel-5-P satellite data (COPERNISCUS/5SP/NRT/L3_). A comparison of the levels of Nitrogen dioxide (NO2) and Particulate matter (PM2.5) was done biannually from the year 2019 to the year 2024 in Etsako East LGA. Results showed that the annual mean concentrations of Nitrogen dioxide (NO2) ranged from 0.000048mol/m2
to 0.000062mol/m2, the highest and lowest concentrations were found in the first half of 2021, and the first half of 2022 respectively. Particulate matter (PM2.5) ranged from -0.2341 to 0.4357. The highest and lowest concentrations
were found in the first half of 2021, and the second half of 2022 respectively. This
condition as an implication of irritating airways and exacerbating respiratory conditions on the residents. Government should play their role in policy making soas
to enforce a cleaner production process by the cement factory located there. GIS applications should also be employed and used to foster air quality and check for any deviations.

Corrosive damage remains a critical issue across various industries, especially in remote oil and gas pipeline infrastructures.This study presents the design and implementation of anIoT-based wireless sensor network (WSN) integrated with machine learning Model (SVM) for corrosion monitoring and prediction. The system architecture involved deploying sensor nodes utilizing electromagnetic techniques for real-time corrosion data acquisition. These nodes communicated with an ESP32 microcontroller equipped with wireless transmission capabilities to relay data to the Thing Speak cloud platform for storage and visualization. Subsequently, MATLAB was used to preprocess the acquired data, enabling the training and validation of a supervised machine learning model for corrosion classification and prediction. With the help of the SVM model, corroded pipeline samples could be easily dif erentiated from a corrosion-free pipeline. 80% of the recorded data was used to train the algorithm, and the rest 20% was kept for testing the data without corrosion. The first graph displayed by the model shows that the resistance values from the corroded sample fluctuate only slightly over time Additionally, the chlorine level ranged between (1000–1500)ppm, showing emission of chlorine gas from the sample. There was a significant drop in resistance in the corrosion- free sample for the second graph, with values falling below 1000ohms and No chlorine data was indicated When the model was tested and validated, the model correctly classified 59 out of 60 test samples whileone incorrectly indicating an accuracy of 98.33%.. When unseen samples were used, the model was still able to predict the presence of corrosion with almost the same amount of precision and gave results showing the state of the pipelines with a 50% chance of them being either corroded or not from a 40 sample prediction.. The results obtained af irm the ef ectiveness of both processes for corrosion monitoringinremote pipeline networks. The system’s autonomous operation, real-time data handling, and intelligent decision-making capabilities highlight its potential as a cost-ef ective and ef icient
alternative to traditional, labor-intensive methods. Moreover, its predictive capabilities enable proactive maintenance scheduling and safer operational planning, significantly reducing the risk of pipeline failure. This research thus lays a strong foundation for scalable, field-deployable corrosion monitoring systems leveraging modern IoT and AI tools

Corrosive damage remains a critical issue across various industries, especially in remote oil and gas pipeline infrastructures.This study presents the design and implementation of anIoT-based wireless sensor network (WSN) integrated with machine learning Model (SVM) for corrosion monitoring and prediction. The system architecture involved deploying sensor nodes utilizing electromagnetic techniques for real-time corrosion data acquisition. These nodes communicated with an ESP32 microcontroller equipped with wireless transmission capabilities to relay data to the Thing Speak cloud platform for storage and visualization. Subsequently, MATLAB was used to preprocess the acquired data, enabling the training and validation of a supervised machine learning model for corrosion classification and prediction. With the help of the SVM model, corroded pipeline samples could be easily dif erentiated from a corrosion-free pipeline. 80% of the recorded data was used to train the algorithm, and the rest 20% was kept for testing the data without corrosion. The first graph displayed by the model shows that the resistance values from the corroded sample fluctuate only slightly over time Additionally, the chlorine level ranged between (1000–1500)ppm, showing emission of chlorine gas from the sample. There was a significant drop in resistance in the corrosion- free sample for the second graph, with values falling below 1000ohms and No chlorine data was indicated When the model was tested and validated, the model correctly classified 59 out of 60 test samples whileone incorrectly indicating an accuracy of 98.33%.. When unseen samples were used, the model was still able to predict the presence of corrosion with almost the same amount of precision and gave results showing the state of the pipelines with a 50% chance of them being either corroded or not from a 40 sample prediction.. The results obtained af irm the ef ectiveness of both processes for corrosion monitoringinremote pipeline networks. The system’s autonomous operation, real-time data handling, and intelligent decision-making capabilities highlight its potential as a cost-ef ective and ef icient
alternative to traditional, labor-intensive methods. Moreover, its predictive capabilities enable proactive maintenance scheduling and safer operational planning, significantly reducing the risk of pipeline failure. This research thus lays a strong foundation for scalable, field-deployable corrosion monitoring systems leveraging modern IoT and AI tools

The study was designed to assess the concentrations of zinc and chromiumin some commercially available green and Black sold teas they include Lipton, Top tea, Richmond tea, Cinnamon Tea and Natural green tea within Benin City, Nigeria. Five of the most popular brands among consumers were purchased in the open market. They were digested, infused (coldandhot) and analyzed for their heavy metal content using the atomic absorption spectrophotometer. The heavy metal concentration varied among the different brands of tea in the study. In the tea samples zinc concentration ranged between 35mg/ kg to 70mg/kg while chromium gave the lowest value of 0.65mg/kg and maximum concentration of 22mg/kg. The cold and hot infusion samples revealed very low concentrations of both zinc and chromium (most of them below detectable limits) ranging between 0.03 mg/l to 0.10 mg/l. In conclusion, the risk of heavy metal exposure via the consumption of these tea is low, with no significant health implications to consumers and thus does not pose a threat to food safety.

This study evaluated the operational and health risk factors associated with municipal solid waste scavenging in two privately-operated municipal Solid waste dumpsites in Benin City, Edo State. Awell- structured questionnaire was administered to 100 randomly selected scavengers; the questionnaire covered demographics, personal details, and information on the types and frequencies of health hazards encountered by waste scavengers, providing insights into the challenges they face. The findings revealed significant health risks including injuries, respiratory problems, and gastrointestinal illnesses, highlighting the need for interventions to improve the safety and well-being of the scavenging operations. Furthermore, they also face several operational risks due largely to the lack of use of personal protection equipment, resulting in cuts, respiratory issues, and gastrointestinal illnesses, with a notable proportion experiencing frequent headaches and a high prevalence of malaria. We recommend that the operators of the dumpsites should be regulated by the Edo State Waste Management Board, with strict guidelines and punitive measures put in place for lack of compliance. On the part of the dumpsite operators, they should provide all necessary personal protection equipment and enforce their use as a standard operating procedure. Furthermore, first aid equipment for staff, drinking water and bathrooms with soap and water for their sanitation should be provided.

Child abuse is a pervasive issue affecting children worldwide, with severe consequencesfor their physical, emotional, and psychological well-being. This study investigates themanifestation of child abuse among primary school pupils in Benin City, Nigeria. Adescriptive survey design was employed, with a sample of 120 pupils from public and private schools. The findings corroborate existing literature on emotional and physical abuse, highlighting the need for urgent attention to prevent and address child abuse in educational settings. The study recommends awareness programme, teacher training and policy reforms to ensure a safe and supportive environment for children.

This study investigates the role of money supply in leveraging financial deepeningtoenhance economic growth in Nigeria. Using the Engle-Granger two-step cointegrationmethod, both long-run and short-run relationships between financial deepening indicators, money supply, and economic growth were analyzed. The study aims to determinewhether financial deepening, when complemented by an adequate money supply, significantly contributes to economic expansion. The findings reveal that financial deepening (measured as CPS/GDP) alone does not significantly impact economic growth in Nigeria, suggesting that credit to the private sector has not been effectively translated into productive investments. Similarly, money supply (M2) does not independently drive economic growth, indicating that liquidity expansion without efficient financial intermediation may not yield substantial economic benefits. The interaction between financial deepening and money supply was also found to be statistically insignificant, implying that Nigeria’s financial system has not fully integrated these financial indicators to stimulate long-term economic growth. In the short run, neither financial deepening normoney supply independently contributes to economic growth. However, the error correction term (ECT) is negative and highly significant, confirming the presence of astable long-run relationship and a relatively fast speed of adjustment toward equilibrium. This suggests that while financial deepening and money supply may not immediately impact growth, their effects become more pronounced over time as structural adjustment stake place. The study concludes that financial deepening and money supply alone are insufficient to drive economic growth in Nigeria. Instead, stronger financial sectorre forms, improved credit allocation mechanisms, enhanced financial intermediation, and macroeconomic stability are needed to ensure that financial deepening translates into sustainable economic growth. The study recommends that policymakers strengthen financial regulations, promote financial inclusion, align monetary policies with real-sector growth, and ensure that credit expansion effectively supports productive activities. These measures will enhance the contribution of financial deepening and money supply to Nigeria’s long-term economic development. This research provides valuable insights for policymakers, financial institutions, and economic stakeholders, emphasizing the need for a more integrated and strategic approach to financial sector development to drive sustainable economic growth in Nigeria.

The purpose of this study is to take a psychoanalytical reading of selected plays by examining the impact of truama such as Anger, depression, low self-esteem and coping mechanism of Truama such as begging, violence and reliance on hope in StellaDiaOyedepo See and the Wife’s Fury.

This study was designed to assess the methods of teaching integrated science courses to integrated science students in Nigerian Institutions. Three research questions was raised. The study was delimited to integrated science students in Nigerian institutions. The survey research design was used in conducting the study. The population of the study consisted of integrated science students across the four levels of Integrated Science Education course area in the department of CIT, University of Benin of which a sample size of 50studentswere used for the study. The main instrument for the study was a self-structured questionnaire. The validity of the instrument was established by giving it to the supervisor of the research work and two other experts in the field of this research study. The reliability of the instrument was established using the test-retest method of estimating reliability. After computation, the reliability yielded a Coefficient of0.783. The questionnaire was distributed directly to the respondents by the researcher. After the collection and collation of the instrument, the data obtained were subjected to analysis using frequency counts and percentages. Results from the study showed among other things that, there is an average use of adequate methods of teaching integrated science education by the lecturers teaching integrated science courses and also there is an average use of the adequate and necessary instructional/teaching aids to guide teaching and learning. Consequent upon the findings of this study, it was recommended that; state government through the state ministry of education should improve the knowledge of teachers on the methods of teaching that can enhance teaching and learning, government should ensure that they make provision for adequate learning facilities and environment in order for teachers to be able to adopt these methods effectively, the deans in the faculties where integrated science courses are being taught and the Head of Departments of Integrated science should ensure they monitor the teaching and learning in order to monitor the adoption and use of effective teaching methods, students should be enlightened properly on the techniques of the various teaching methods and how to adjust properly