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The earlier developed disinfection channels do not operate automatically because it required human contact and without motion sensors for access control system. There is a need for automated systems to ensure compliance with the WHO guidelines in order to reduce the spreading rate of the virus. Hence, this study developed a contact-free automated disinfecting channel to mitigate the spread of COVID-19 in Public Places.

The design of a contact-free automated disinfecting channel was achieved using The ATMEGA328P Microcontroller, which controls the logic state of the peripheral devices connected as an input or output, MLX90614 is a contactless temperature sensor that measures human body temperature wirelessly through infrared sensor radiations for achieving contactless measurement. The designed system was implemented using C programming language, which allowed the users to gain easy access to the system. The implementation of this research will curb the spread of COVID-19 by performing a contact-free temperature screening to allow access and spray disinfecting solution mist on the person when the temperature ranges between 36.5°C and 37.5°C and unlock the automatic door for access or denied access if the temperature is beyond 37.5°C in public places.

The performance evaluation of the developed channel was evaluated based on using System Technology Acceptance (STA), System Attitude Towards Use (SATU), Behavioral Intention (BI), System Perceived Usefulness (SPU), and System Ease of Use (SEU) on a Likert rating scale of 1 to 5. The response target of the STA, SATU, BI, SPU and SEU were 1,2,3,4 and 5, respectively and response mean of the STA, SATU, BI, SPU, and SEU were 4.18, 4.30, 4.30, 4.31 and 4.23 respectively. This showed that the response targets are greater than 3.5.

A contact-free automated disinfecting channel that mitigates the spread of COVID-19 was developed. This research finds application in public places

Wireless communication is a fundamental part of our everyday lives, powering everything from mobile phones to smart home devices. However, one of the ongoing challenges in this field is ensuring that networks provide consistent and reliable coverage, especially in busy or complex environments like cities. Traditional methods of network planning often fall short in addressing the ever-changing demands and obstacles, such as varying user densities, interference, and signal loss in urban areas. This thesis delves into how machine learning (ML) can be used to tackle these challenges and improve wireless network coverage. By employing ML algorithms, we can monitor real-time network conditions, predict how the network will perform under different scenarios, and adjust optimize coverage dynamically. The literature review highlights cutting-edge ML techniques that are currently being used to make networks more efficient, with a particular focus on improving how devices switch between network towers (handover processes), how resources are allocated, and where antennas are best placed. Our approach uses simulations to model an urban environment, considering the varying population densities and infrastructure complexities that one might find in a typical city. We randomly position base stations and users within this simulated grid and use signal propagation models to measure important performance indicators, such as Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR). These measurements are then used to train ML models using optimization techniques like reinforcement learning. To ensure the reliability of our results, we rigorously test the performance of these models through robust validation methods. The results of our study suggest that ML-based strategies can significantly improve how wireless networks are optimized, leading to more adaptive and efficient coverage. This not only enhances the user experience but also opens the door to smarter, more responsive network management in the wireless technologies of the future.

Drying, one of the oldest methods of preservation involves the reduction of moisture in agricultural produce to a level that deactivates enzymes and prevents the growth of microorganisms. Since the quality of the final product can be impacted by the drying process if not properly managed, pretreatments before drying have been introduced to help maintain the product’s quality. This study investigated the drying kinetics of African star apple fruit with respect to different pretreatment techniques, drying methods and drying temperatures adopted. Samples were pretreated using hot water blanching (80 ℃ for 3 min), steam blanching (100 ℃ for 1 min), salt (1:25 w/v for 5 min) and sugar (60 ºBrix for 5 min). Drying was carried out at 45, 55, 65 and 75 ℃ using an oven dryer and an electric dehydrator. Equilibrium moisture content was attained within 540 min. There was a noticeable drop in moisture from an average value of 79.96% to 3.09%, 2.38%, 1.33% and 1.05 % for oven drying at temperatures of 45, 55, 65 and 75 ºC, respectively and 2.39%, 1.28%, 2.21% and 1.77% for electrical dehydration at temperatures of 45, 55, 65 and 75 ºC, respectively. To accurately describe the drying behavior of African star apple pulp under various drying conditions, ten thin-layer drying models were employed. The Logarithmic and Henderson models best represented the drying behavior of the fruit, with R² values of 0.997 and 0.995 for oven and electric drying, respectively, at 55 ℃.

Cassava stands as a pivotal food crop in Sub-Saharan Africa. It is susceptible to several diseases such as bacterial blight, brown spot, green mite, and mosaic disease. There is need to identify and detect these diseases. Deep learning algorithms, particularly Convolutional Neural Networks (CNNs) are employed by numerous researchers for disease detection. However, overfitting is a major factor that hinders the overall performance of Convolutional Neural Networks (CNNs). Therefore, in this study, an optimized Convolutional Neural Network (CNN) using Artificial Bee Colony (ABC) algorithm was developed to enhance the accurate and timely detection of some cassava diseases. The model was developed by employing Artificial Bee Colony (ABC) algorithm to optimize the parameters of Convolutional Neural Network (CNN). The dataset used was downloaded from data.medeley.com, comprising healthy and infected cassava leaf images of four distinct diseases (bacterial blight, brown spot, green mite, and mosaic disease). It was split into training and testing set of 70% and 30% respectively. The images were pre-processed, Principal Component Analysis (PCA) was employed to extract relevant features and implemented using python 3.11.4. The model was evaluated in terms of Precision, Sensitivity, Specificity, False Positive Rate (FPR), Accuracy, and Average Recognition Time (ART). The CNN model recorded 85%, 85%, 85%, 4.5%, 84%, 0.161007sec for Precision, Sensitivity, Specificity, FPR, Accuracy, and ART respectively while the ABC-CNN model recorded 95%, 89%, 97%, 2.5%, 90%, 0.157266sec for Precision, Sensitivity, Specificity, FPR, Accuracy, and ART respectively, underscoring the performance of the models. Based on the results, the developed ABC-CNN model performed better across all the performance metrics compare to the CNN model. The developed ABC-CNN model is recommended for the agricultural field to ensure accurate and timely detection of cassava diseases.

Hypertension is one of the major risk factors affecting the global burden of disease and is one of the most important risk factors for cardiovascular diseases. This was a comparative assessment study on the prevalence of hypertension in relation to the food consumption patterns between teaching and non-teaching staff through a convenient sample at the Anambra State Polytechnic Mgbakwu. These variables were recorded with a mini structured questionnaire. Results indicated that the prevalence of hypertension among both groups was greatly dependent on the increase in their ages and food consumption and a higher percentage of both groups had the knowledge of hypertension. This study recommends that routine assessment could be of help to detect people that are at high risk of developing hypertension and approaches to reduce the risk of hypertension should be encouraged.

The earlier developed disinfection channels do not operate automatically because it required human contact and without motion sensors for access control system. There is a need for automated systems to ensure compliance with the WHO guidelines in order to reduce the spreading rate of the virus. Hence, this study developed a contact-free automated disinfecting channel to mitigate the spread of COVID-19 in Public Places. The design of a contact-free automated disinfecting channel was achieved using The ATMEGA328P Microcontroller, which controls the logic state of the peripheral devices connected as an input or output, MLX90614 is a contactless temperature sensor that measures human body temperature wirelessly through infrared sensor radiations for achieving contactless measurement. The designed system was implemented using C programming language, which allowed the users to gain easy access to the system. The implementation of this research will curb the spread of COVID-19 by performing a contact-free temperature screening to allow access and spray disinfecting solution mist on the person when the temperature ranges between 36.5°C and 37.5°C and unlock the automatic door for access or denied access if the temperature is beyond 37.5°C in public places. The performance evaluation of the developed channel was evaluated based on using System Technology Acceptance (STA), System Attitude Towards Use (SATU), Behavioral Intention (BI), System Perceived Usefulness (SPU), and System Ease of Use (SEU) on a Likert rating scale of 1 to 5. The response target of the STA, SATU, BI, SPU and SEU were 1,2,3,4 and 5, respectively and response mean of the STA, SATU, BI, SPU, and SEU were 4.18, 4.30, 4.30, 4.31 and 4.23 respectively. This showed that the response targets are greater than 3.5. A contact-free automated disinfecting channel that mitigates the spread of COVID-19 was developed. This research finds application in public places.

The 70s and 80s economic boom led to the upsurge in the volume of automobiles in the country and eventually rapid increase in traffic or vehicular movements on the Nigerian roads. This growth cum development regrettably snowballed into an alarming and endemic increase in road traffic crashes with its attendant high rate of fatality. The loss of precious lives of many Nigerians and their valuable properties that followed these road crashes, prompted and motivated the need to create a safe motoring road culture in Nigeria. Thus, the establishment of the Federal Road Safety Commission (FRSC) on the 18th February, 1988 with its operative arm known as “corps”; uniform personnel. As a lead agency in road traffic administration and safety management, the corps is primarily saddled with the mandate to create a safe-motoring environment in Nigeria. Records therefore has it that the activities of the corps led to a drastic reduction of road related crashes by 3.8% as early as 1990, and 66% twenty years later. It is against this backdrop that this study-paper attempted to investigate the challenges confronting the corps in its bid to live up to the mandate which is primarily aimed at reducing road traffic crashes in the country’s roads-highways to the barest minimum. This paper equally examined the historical background of road safety (FRSC) campaign in today’s Nigeria as well as the development of road transportation system and its concomitant emergence of public transport system in the country. At the introductory part of the work the study also highlighted the statutory responsibility of FRSC. The causes, nature and impact of road traffic crashes were looked at while some relevant concepts as well as literature were explained and reviewed. The paper employed qualitative historical methodology in the analysis and discussions of the information procured from the secondary sources on one hand, and primary data or oral interviews of relevant stakeholders on the other hand. In view of the observed challenges facing the corps in actualizing its mandates, recommendations were however made on how to combat them and as well as reduce the menace of road traffic crashes and its associated injuries, fatalities or carnage

This study shows the simplification of brand switching by a given population to one or more other brands or alternatives. It was observed that over time consumers’ preferences changed from one brand of a product to another. These changes were as a result of several factors ranging from taste to cost, availability of product, availability of substitute or similar products, durability, branding, etc. Stochastic matrix, as a common tool in consumer preference analysis, was used. This class of matrices has as its entries the probabilities that a consumer would transit from one state, that is, switch from one brand of product to another. This matrix was obtained from data on past consumer behaviour. The population of consumers was categorized to see the pattern of brand switching and its effect of production. Seven products labeled A-G were used to study the switching patterns and were generalized by means of factorials. The obtained model was then used to predict the volume of consumption of a particular product over a period of ten months.

Design and implementation of computerized based system for the diagnosis and treatment of sexually transmitted diseases, will be designed to reduce the problems such as; bulkiness of record, inaccurate diagnosis of the type of sexually transmitted diseases (STDs) which will result in wrong prescription of drugs, repetitive or monotonous computation and misplacement or damage of patients record files. The method of data collection adopted will be interview and questionnaires. The design tools that will be applied will be data flow diagram, flow charts, and algorithms. The methodologies used were Structured System Analysis and Design Methodology (SSADM) and Object Oriented Analysis and Design Methodology (OOADM). MySQL Server might be used as Database Software and ASP.Net as scripting language for the implementation for this research. Moreover this work will be able to eradicate and ensure automatic diagnostic approach to such matters as Sexually Transmitted Diseases (STDs) and to minimize time wasted in storing and retrieving patient’s record. This research has succeeded in acquainting the workers in the hospital with the knowledge of database and use of the computer system for problem solving

The study investigated the nutritional of soya milk (soya bean) in Mgbakwu, Anambra State. The samples were analyzed in the laboratory to ascertain the nutritional composition of soya milk (soya bean produced in Mgbakwu, Anambra State). From the analysis, result shows that shows that carbohydrate content of soya milk 38.74 percent, fat 16.690 percent, Ash 13.153 percent, protein 9.80 percent, moisture content 10.039percent. Therefore, the study recommended that soya milk should be consumed in other for one to get adequate nutritional diet.