MARKETING OF FERMENTED CASSAVA FLOUR IN AKINYELE LOCAL GOVERNMENT AREA, OYO STATE

The study was carried out in Akinyele Local Government Area of Oyo State on marketing of fermented cassava flour (lafun) with the objective of examining the socio-economic characteristics of cassava marketers, determining the profit margin, ascertaining effect of some variables influencing the supply of cassava flour and identifying associated constraints. Primary data used for the study was collected from 80 respondents using simple random sampling technique. The empirical evidence from the analy- sis shows that the traders are predominantly female (70%) and mostly married (62.5%). The study further reveals that the marketing of lafun is a profitable venture at both the wholesale and retail levels with a profit of N6,890.32 per month. The quantity of cassava supplied was significantly determined by transportation cost (P0.01), marketing experience (P0.05), years of education (P0.10) and cost of storage (P0.05).The markets were observed to face transportation, storage and packaging problems. As trans- portation cost increased, the quantity of lafun supplied to market was also found to increase. The study recommends the assistance of government in the provision of infrastructural facilities

A robust energy policy review of selected African countries: An impetus for energy sustainability in Nigeria

Power rationing has become the bane of the Nigerian power sector, plunging the nation into prolonged periods of darkness and costing about 2.5 percent of her GDP annually. Although, installed generating capacity is almost 13 GW, the situation worsened by an overdependence on thermal and hydro generation, high losses, and a poor tariff structure. In the face of these challenges, Nigeria seeks to achieve universal access by 2030 with sustainable power having a share of 30 per cent in her energy mix. Despite the existence of frameworks supporting this target, Nigeria's policies are still weak; indicated by her low Regulatory Indicator for Sustainable Energy (RISE) score of 30. To reach universal access by 2030 and fulfil SDG 7; Nigeria needs the right mix of policies. This study aims to review, draw lessons from the successful and unsuccessful implementation of similar policies in five countries and give recommendations

Development of an Autonomous Vehicle for Smart Irrigation

Creating a reliable means of supplying water for irrigation in regions with little or no rainfall across a year is a form of precision agriculture. The compounding problem, however, is the inability to efficiently and conservatively manage the external water source to irrigate the soil. Most times, the amount of water supplied exceeds what crop roots need. To this end, An Autonomous Vehicle for Smart Irrigation System was developed to provide water, herbicide, pesticide, and water to agricultural cultivation to meet the demand for root crops crop roots. This project captures the design, simulation, development, and performance evaluation of the application of Autonomous Vehicles for Smart Irrigation using an Intelligent reprogrammable controller. The soil moisture sensors measure and transmit in real-time, the value of specific soil nutritional requirements to a receiver the autonomous vehicle dispense based on the requirement of soil nutrients to a specific location. With the use of transceivers, these moisture levels are then transmitted to an autonomous vehicle which is set in action when the moisture values are lower than what is required for the growth of the crops. The stress analysis of the Autonomous Vehicle was also carried out to optimize the working operation of the Autonomous Vehicle

An IoT-Based Multimodal Real-Time Home Control System for the Physically Challenged: Design and Implementation

Physical impairments affect a significant proportion of the global populace, emphasizing the need for assistive technologies to increase the ability of these individuals to perform daily activities autonomously. This study discusses the development and implementation of a multimodal home control system, designed to afford physically challenged individuals greater control over their home environments. This system utilizes the Internet of Things (IoT) for its functionality. The system is primarily based on the utilization of the Amazon Alexa Echo Dot, which facilitates speech-based control, and a sequential clap recognition system, both made possible through an internet connection. These methods are further supplemented by an additional manual switching option, thereby ensuring a diverse range of control methods. The processing core of this system consists of an Arduino Uno and an ESP32 Devkit module. In conjunction with these, a sound detector is employed to discern and process a variety of clap patterns, which is set to function at a predefined threshold. The Amazon Alexa Echo Dot serves as the primary interface for voice commands and real-time information retrieval. Furthermore, an Android smartphone, equipped with the Alexa application, provides alternate interfaces for appliance control, through both soft buttons and voice commands. Based on an analysis of this system, it is suggested that it is not only viable but also effective. Key attributes of the system include rapid response times, aesthetic appeal, secure operation, low energy consumption, and most importantly, increased accessibility for physically disabled individuals

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

The advent of smart technology in urban environments has often been hailed as the solution to a plethora of contemporary urban challenges, ranging from environmental conservation to waste management and transportation. However, the critical aspect of security, encompassing crime detection and prevention, is frequently overlooked. Moreover, there is a dearth of research exploring the potential disruption of conventional face detection and recognition systems by new smart city surveillance security cameras, particularly those which autonomously update their databases. This paper addresses this gap by proposing the enhancement of security in smart cities through the development of an adaptive Age-Invariant Face Recognition (AIFR) model. A non-intrusive AIFR model was constructed using a convolutional neural network and transfer learning techniques, and was then integrated into surveillance cameras. These cameras, designed to capture the faces of city residents at regular intervals, consequently updated their databases autonomously. Upon testing, the developed model demonstrated its potential to substantially improve security by effectively detecting and identifying the residents and visitors of smart cities, and updating their database profiles. Remarkably, the model retained its effectiveness even with significant age intra-class variation, with the capability to alert relevant authorities about potential criminals or missing individuals. This research underscores the potential of adaptive face recognition systems in bolstering security measures within smart urban environments

Development of a Malicious Network Traffic Intrusion Detection System Using Deep Learning

With the exponential surge in the number of internet-connected devices, the attack surface for potential cyber threats has correspondingly expanded. Such a landscape necessitates the evolution of intrusion detection systems to counter the increasingly sophisticated mechanisms employed by cyber attackers. Traditional machine learning methods, coupled with existing deep learning implementations, are observed to exhibit limited proficiency due to their reliance on outdated datasets. Their performance is further compromised by elevated false positive rates, decreased detection rates, and an inability to efficiently detect novel attacks. In an attempt to address these challenges, this study proposes a deep learning-based system specifically designed for the detection of malicious network traffic. Three distinct deep learning models were employed: Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). These models were trained using two contemporary benchmark intrusion detection datasets: the CICIDS 2017 and the Coburg Intrusion Detection Data Sets (CIDDS). A robust preprocessing procedure was conducted to merge these datasets based on common and essential features, creating a comprehensive dataset for model training. Two separate experimental setups were utilized to configure these models. Among the three models, the LSTM displayed superior performance in both experimental configurations. It achieved an accuracy of 98.09%, a precision of 98.14%, an F1-Score of 98.09%, a True Positive Rate (TPR) of 98.05%, a True Negative Rate (TNR) of 99.69%, a False Positive Rate (FPR) of 0.31%, and a False Negative Rate (FNR) of 1.95%

Development of a Sustainable Internet of Things-Based System for Monitoring Cattle Health and Location with Web and Mobile Application Feedback

Cattle farming is undoubtedly one of the most lucrative subsectors of agriculture globally, but faces significant challenges such as the inability to monitor cattle health and location, and cattle rustling. This research aimed to develop a system to resolve these issues using sensors to monitor ambient/body temperature, magnetometer, and GPS. The proposed system comprises these components in a head strap. Data were transmitted via a long-range (LoRa) module to a base station, then to a website and mobile app using General Packet Radio Service (GPRS)/satellite. Information was received and monitored in real-time. Testing showed the system could be deployed in vast farmland to monitor cattle health and location satisfactorily in real-time. Unlike other systems, this system monitors cattle health and location with/without mobile network coverage due to satellite communication. In conclusion, the proposed system monitors cattle health and location status with or without mobile network coverage due to an alternative communication channel (satellite) compared to other related systems

Gender differences in the perception and handling of occupational stress among workers in commercial banks in Ibadan, Nigeria

Occupational stress is common among workers in many organizations, especially individuals that work in extremely rigorous and demanding institutions like the bank. Thus, the study was aimed at investigating gender differences in the perception and handling of Occupational stress among bank workers in Ibadan metropolis. The theory of job demands-control (support) was used as the theoretical framework for this study. Data was obtained through quantitative research methods, in detecting the levels of stress within the organization, the perceptions of workers concerning their jobs and working environments, stress management techniques and job control. Purposive sampling method was used to select 300 respondents from 15 commercial banks within Ibadan metropolis. Findings from the study showed that there is gender difference in the way bank workers handle occupational stress and the coping mechanisms adopted. In testing the hypotheses, with a t-value 3.205, it was identified that male and female workers perceived stress similarly while the t-test results showed that respondents did not have adequate control over their jobs although in comparing the both genders, the males had more control over their jobs than female workers. In view of these findings, the paper recommends thatbanking institutions should create and encourage their workers to participate in recreational activities and events. The paper also recommends that bank workers should seek social support from friends and family members as coping mechanism rather than succumbing to negative coping mechanisms like smoking and consuming alcohol