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The purpose of this study is to examine the themes of trauma and resistance as depicted in Abubakar Ibrahim's Season of Crimson Blossoms and Nawal El Saadawi's Woman at Point Zero. Both novels offer profound insights into the experiences of women navigating oppressive societal structures and patriarchal systems, highlighting the resilience and agency of individuals in the face of adversity.

With the increasing global demand for sustainable and renewable energy, biodiesel has becomean essential alternative to traditional fossil fuels. This study looks into producing biodiesel fromwaste cooking oil (WCO) by using a unique catalyst made from calcined periwinkle shells. The WCO was characterized to uncover its main properties using ASTMD6751 standardmethodand the catalyst was produced through calcination at 900°C. The transesterification process wasoptimized using Response Surface Methodology (RSM) with a Box-Behnken design, usingfactors like catalyst loading (1–10 wt.%), reaction time (30–150 minutes), temperature(40–80°C), and the molar ratio of alcohol to oil (3:1–10:1). The result obtained from the characterization of the WCO are acid value of 6.17 mg KOH/g, a free fatty acid (FFA) content of 3.09%, a viscosity of 9.2 mPa.s at 30.08°C, a saponification value of 244.14 mg KOH/g, and a density of 956 kg/m³. The analysis of the calcined periwinkle shell show that it contains a high amount of calcium oxide (CaO) of about 97.08%, as revealed by Energy Dispersive X-ray (EDX) analysis. Additionally, Fourier Transform Infrared Spectroscopy (FTIR) confirmed the existence of functional groups necessary for biodiesel production, while Scanning Electron Microscopy (SEM) showed a highly porous structure, which significantly improved its catalytic efficiency. With the optimized conditions, a biodiesel
yield of over 90% was achieved. The final biodiesel product met industry standards and exhibited enhanced physicochemical properties.

African salad popularly called “Abacha, "Abacha Ncha" or "Abacha and Ugba” contains lots of fresh raw vegetables and other ingredients which can be consumed without cooking. It is widely accessed for it is composed of food ingredients known to be rich in protein, carbohydrate, vitamins and minerals. The aim of this study was to determine the bacteriological quality of ready-to-eat African salads sold around the University of Benin, Ugbowo campus area, Edo state. Ready-to-eat African salad samples were purchased with and without "ugba" condiment from sellers in the Ugbowo campus area. All samples were serially diluted and inoculated on Nutrient agar, Eosin Methylene blue (EMB) agar and Simmons citrate agar. Bacterial counts from this study ranged from 5.15±0.50 to 9.75 ± 1.06 x 10
4.cfu/g. Pure cultures were obtained and seven different isolates were identified using cultural, morphological and biochemical characteristics. The isolates include Bacillus sp., Salmonella so., Staphylococcus aureus, Citrobacter sp., Streptococcus sp., Staphylococcus sp. and Micrococcus sp. Bacillus sp. had the highest percentage frequency of 29.41% while Staphylococcus sp. had the lowest percentage frequency of 5.88%. The presence of Salmonella sp. in the ready to eat African salad samples indicates poor hygienic practice during the processing of these foods which can pose a great health risk to consumers.

The Internet has simplified information services provided in this ICT era. One can easily acquire the required knowledge and information on a single click, thus, making information generation and dissemination timely. Regrettably, this may not be so for categories of individuals who seemed to have deviated from the original plan of the Internet for academics. This study examined the impact of internet on the student’s reading culture, the impact of internet on student’s academic achievement and the positive and negative impact of the internet on students’ reading academic performance. Convenient sampling technique with proportionate representation was used to determine sample size (100) students of selected secondary schools in Edo State. Structured questionnaire was used for data collection and this was supplemented with interview of key informants. Data was analyze using descriptive statistics which include measures of central tendency. Chi-square was use to compare observed data with data we would expect to obtain according to a specific hypothesis. The data collected was analyzed using statistical package for social scientist (SPSS). Results showed that majority of the respondents were of the opinion that internet usage is essential for educational development and that there is a significant relationship between internet usage and students reading culture. The study recommended that Management of secondary schools should organize seminars aimed at teaching the students the right use of the Internet to improve their reading culture, among other things.

The stucy investigated challenges of teaching English language in rural areas using Ikpoba-OkhaLocal Government Area of Edo State as a case study. The design adopted for this study is the descriptive survey method. Four (4) research questions were raised for the study. The population of this study consisted of all the secondary school teachers inlkpoba-Okha Local Government Area of Edo State. A total of sixty (60) teachers were prposively selected as the sample size from eight (8) secondary schools in the LGA. A structured questionnaire was used to collect data from respondents. The research instrument used was validated/ by experts in the Department of Educational Foundations. Data collected from the survey were statistically analyzed. Afier analysis, results were display edontables showing frequencies, percentages, mean scores and standard deviations of responses, including the decisions made. Based on the fndings, it was discovered mother tongue interference affects rural students' pronunciation of certain English words and comprehension of English language; there is not enough provision of school facilities for the/ adequate teaching and learning of English Language in rural areas, etc. Some recommendations were made afterwards. It was reconmended that students should be made aware of the differences in the phonological, orthographical, vocabulary and grammatical patterns in English and their mother tongue; government should provide adequate, spacious classes with adequate equipment and school facilities in order to meet the needs of the learners and their aspiration, etc

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.