Background: Students’ academic achievement is regarded as the scholastic standing of students at the end of a given study period that is expressed in terms of grades. The key to bridging the attainment gap at the end of their study period is through their cumulative grade points over the duration of the study. Predictive validity study on students first-year GPA as a predictor of their final-year CGPA was carried out to predict the students’ academic performance in Chemical, Civil, Electrical, and Mechanical Engineering. Purpose/Hypothesis: This study examined the relationship between first-year GPA and final-year CGPA, as well as the relationship between Age, Gender and Geopolitical zones on first-year GPA and CGPA of Engineering students in the Faculty of Engineering students University of Abuja, Nigeria. The data obtained from the four Departments; Chemical, Civil, Electrical and Mechanical were analyzed. Two hypotheses were formulated to guide the study. Design/Method: An ex-post factor research approach was adopted, and Pearson’s correlation and Regression Analysis were fitted with the data using Minitab software. Results: The results of the study highlighted that first-year GPA had a strong positive relationship with final-year CGPA. Age, Gender and Geopolitical zones have no correlation with students’ final-year CGPA. The regression equations can be used to predict students’ CGPA to bridge the attainment gap at the end of their studies. Conclusions: Finally, the study emphasized the need to admit more female students in Engineering studies as they constitute 12.9% of the population.
Manufacturing facilities are systems that require adequate designing, maintenance and reservations for improvement in the future. Layouts need to be effectively designed to reduce operating to the minimum. Computer simulation is a process of investigating and analyzing the behavior of production processes for effective decision-making using computers to generate solutions that will positively impact short, and long-term planning of the Plants and save costs of real-life implementation. This study investigated a 500Kg capacity shea nut processing plant using FlexSim©. The findings from the initial model were not effective and experienced bottlenecks in workstations (Roaster and Milling) sections, poor cycle and lead times coupled with manual labor, Plant efficiency was 35.7%. However, the Improvement Layout Model was able to address these bottlenecks, the results showed the Plant efficiency increased to 83.3%, shorter lead and cycle times, improved machine utilization, and throughput capacity of the Plant. The results were an indication of conformance to the layout design developed to aid in enhancing the traditional shea nut processing that is largely dominated by traditional processing practices.
In this study, a prototype robotic arm yam heap-maker using Arduino was developed. Nigeria produces around 75% of global yam production which is widely consumed as staple foods in Africa and Asia and as raw materials for processing into other finished goods. The production of this economic commodity is largely crude and labor-intensive as such the need to adopt a modern approach to farming. The prototype was designed to perform heapmaking activities in the cultivation process of yam and utilizes two Degrees of Freedom (2 DOF), it has an overall weight of 2.39 Kg, 350 mm length, 250 mm width, and 240 mm height. A systematic design method of the product design process was adopted in the prototype development. The heap maker was controlled remotely using an android phone. The trial experiments were performed on sandy, loamy, and clay soils. The average effective heap height and depth were best observed on loamy soil with 5 cm height and 7 cm depth.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.