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Research Article
Growth Response and Nutrient Utilization of Heterobranchus Bidorsalis Juveniles Fed Graded Levels of Melon Shell Meal
Danwali Ismail Musa,
Lawee Aliyu Yusuf
Issue:
Volume 9, Issue 1, April 2024
Pages:
1-5
Received:
14 August 2023
Accepted:
4 September 2023
Published:
8 January 2024
Abstract: The study evaluated the effect of including maize offal with graded levels of melon shell in diets of Heterobranchus bidorsalis juveniles (15.30±1.20g). This study was conducted for 8 weeks. The diet constitute 42% crude protein content with melon shell at different inclusion levels of 0%, 25%, 50%, 75% and 100% respectively. One hundred juveniles were randomly stocked into ten tanks for the five treatments in duplicate. Fish in each tank was fed 3% body weight of diet twice daily. Weights of fish were taken weekly. Data collected were analyzed using one way Analysis of Variance (ANOVA). The result showed that the ash content ranged from 2.80% in fish fed diet one to 4.03% in fish fed diet four. Crude fibre content ranged from 8.52% of fish fed diet one to 12.99% in fish fed diet five. Also, Ether extract ranged from 7.90% in fish fed diet one to 17.45% in fish fed diet five while the crude protein content values ranged from 39.66% in fish fed diet one to 40.05% in fish fed in diet five. The proximate values of the experimental diets showed a significant differences (P<0.05) among treatments. Mean Weight Gain (MWG) ranged between 97.01g in diet one and 107.82g in fish fed diet five. The Specific Growth Rate (SGR) ranged between 11.00%/day in fish fed diet five and 12.98%/day in fish fed diet three. Feed Conversion Ratio (FCR) ranged between 2.01 in fish fed diet three and 2.68 in fish fed diet one. Survival Rate (SR) ranged between 95.00% in fish fed diet one, three and 100.00% in fish fed diet two, four and five respectively. Feed Intake (FI) ranged between 3.68g in fish fed diet one and 5.74g in fish fed diet five. Result of the present study demonstrated that growth and nutrient utilization of Heterobranchus bidorsalis juveniles was significantly (P<0.05) affected by the graded level of melon shell in diet fed. The nutritionist should look more inward in using non-conventional fish feed such as melon shell so as to reduce the cost of fish feed and also reduce the level of environmental pollution. Melon shell meal can be included in the diet of Heterobranchus bidorsalis up to 50% inclusion level without any adverse effects on the growth. Therefore, fish farmers can have significant save in the inclusion rate of maize offal in the diets of catfish production.
Abstract: The study evaluated the effect of including maize offal with graded levels of melon shell in diets of Heterobranchus bidorsalis juveniles (15.30±1.20g). This study was conducted for 8 weeks. The diet constitute 42% crude protein content with melon shell at different inclusion levels of 0%, 25%, 50%, 75% and 100% respectively. One hundred juveniles ...
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Research Article
Optimization of Some Selected Processing Parameters on Oil Yield from Developed Oil Expeller for Groundnut (Arachishypogaea) Seeds
Abdullateef Balogun,
Kamaru Alaba Iyalabani,
Faoiyyah Uthman,
Segun Bamidele Olarinoye
Issue:
Volume 9, Issue 1, April 2024
Pages:
6-11
Received:
29 August 2023
Accepted:
14 September 2023
Published:
8 January 2024
Abstract: The use of mechanical pressing is thought to be a suitable method for small and medium-sized farmers in developing nations, due to its reduced initial and ongoing expenses when compared to the use of a screw press and a solvent expression procedure. A combined roaster-expelling machine was used in the study to examine the effects of applied pressure, moisture content, and roasting temperature on the oil output of groundnut seeds. A 33 box – benken design was used for the experiment. Moisture content, roasting temperature, and pressure applied were experimental variables that affected oil yield. Using the response surface analysis method, the experiment's parameters were optimized. Data analysis shows that all the variables significantly affected the oil yield at 95% confidence level. The optimum conditions of the independent variables for the oil yield were determined at, moisture content 6%, roasting temperature 110°C and applied pressure 25 Mpa at corresponding oil yield of 24.5%. Also, the R2 and R2 adj. value of 0.9561 and 0.3432 respectively indicated that the regression model was a good one and verification experiment confirmed the validity of the predicted model. The experimental values were not significantly distinct from the expected values at p0.05, although they were extremely close to them. The developed regression model has served as a foundation for choosing the best process variables for the recovery of oil while using a combination roaster and melon seed expression machine.
Abstract: The use of mechanical pressing is thought to be a suitable method for small and medium-sized farmers in developing nations, due to its reduced initial and ongoing expenses when compared to the use of a screw press and a solvent expression procedure. A combined roaster-expelling machine was used in the study to examine the effects of applied pressur...
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Research Article
Improving the Control of Autonomous Navigation of a Robot with Artificial Neural Network for Optimum Performance
Obasi Emmanuel Chukwubueze,
Eneh Innocent Ifeanyichukwu,
Ene Princewill Chigozie*
Issue:
Volume 9, Issue 1, April 2024
Pages:
12-20
Received:
4 July 2023
Accepted:
25 July 2023
Published:
8 January 2024
Abstract: In the field of autonomous robotics, enhancing the navigation system of robots is a crucial aspect that directly impacts their performance. This study presents a novel approach to addressing this challenge with an Artificial Neural Network (ANN) model. The research focuses on improving the navigation capabilities of a differential drive robot using the line-following method for route tracking and the dead reckoning technique for localization. It investigates a differential drive robot model controlled with a PID controller and derives the transfer function of the PID model. Through simulations, it becomes apparent that the PID model exhibits a continuous overshoot in its response, which negatively affects the behaviour of the robot's wheels. Ordinarily, continuous manual tuning will be required to correctly tune the PID controller to a value where the overshoots will be negligible, and this could be onerous. To overcome this limitation, an ANN controller is proposed, leveraging the learning capabilities of the neural network. Data from the PID controller transfer function is utilized to train the ANN model, enabling it to understand patterns and relationships. The ANN controller is then substituted in place of the PID controller in the simulation. The results showcase a remarkable 13.1% improvement in the robot's wheel response, highlighting the transformative potential of this approach for revolutionizing autonomous robot navigation in industrial applications. By using the transfer function of the PID model to train an ANN model, this study offers a powerful framework for enhancing the navigation performance of a differential drive autonomous robot and shows performance improvements in control, flexibility, and adaptation to changing conditions. These discoveries have significant ramifications for the industry and will pave the way for intelligent and effective autonomous robot navigation systems. The research provides a comprehensive understanding of the challenges associated with the differential drive robot model controlled with a PID controller and offers a robust approach to how this can be alleviated. The significance of this study lies in its ability to address the continuous overshoot issue observed in the PID controller's response by training an ANN controller with data from the PID controller. The proposed approach minimizes overshoot and improves the robot's wheel response, ultimately enhancing its navigation capabilities. Overall, this study demonstrates the potential of an ANN model to revolutionize autonomous robot navigation in industrial applications. The notable improvement achieved in the robot's wheel response validates the effectiveness of this approach. Future research can further optimize this integrated approach in real-world scenarios, leading to intelligent and efficient autonomous robot navigation systems across diverse industrial settings.
Abstract: In the field of autonomous robotics, enhancing the navigation system of robots is a crucial aspect that directly impacts their performance. This study presents a novel approach to addressing this challenge with an Artificial Neural Network (ANN) model. The research focuses on improving the navigation capabilities of a differential drive robot using...
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