Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model

Introductory Engineering Mathematics (a skill builder for engineers) involves developing problem-solving attributes throughout the teaching period. Therefore, the prediction of students’ final course grades with continuous assessment marks is a useful toolkit for degree program educators. Predictive models are practical tools used to evaluate the effectiveness of teaching as well as assessing the students’ progression and implementing interventions for the best learning outcomes. This study develops a novel multivariate adaptive regression spline (MARS) model to predict the weighted score WS (i.e., the course grade). To construct the proposed MARS model, Introductory Engineering Mathematics performance data over five years from the University of Southern Queensland, Australia, were used to design predictive models using input predictors of online quizzes, written assignments, and examination scores. About 60% of randomised predictor grade data were applied to train the model (with 25% of the training set used for validation) and 40% to test the model. Based on the cross-correlation of inputs vs. the WS, 12 distinct combinations with single (i.e., M1–M5) and multiple (M6–M12) features were created to assess the influence of each on the WS with results bench-marked via a decision tree regression (DTR), kernel ridge regression (KRR), and a k-nearest neighbour (KNN) model. The influence of each predictor on WS clearly showed that online quizzes provide the least contribution. However, the MARS model improved dramatically by including written assignments and examination scores. The research demonstrates the merits of the proposed MARS model in uncovering relationships among continuous learning variables, which also provides a distinct advantage to educators in developing early intervention and moderating their teaching by predicting the performance of students ahead of final outcome for a course. The findings and future application have significant practical implications in teaching and learning interventions or planning aimed to improve graduate outcomes in undergraduate engineering program cohorts

Special Section on Condition Monitoring and Fault Accommodation in Electric and Hybrid Propulsion Systems

LGEP 2014 ID = 1675International audienc

Future vision for reduction of range anxiety by using an improved state of charge estimation algorithm for electric vehicle batteries implemented with low-cost microcontrollers

The fear of being stranded by a depleted electric vehicle (EV) battery is commonly referred to as \u27range anxiety\u27. This study explores a future vision for a comprehensive driver alerting algorithm to reduce the range anxiety by increasing the accuracy of the EV range estimation method. The key piece of information required to achieve this is an accurate battery state of charge (SoC) estimation. This study proposes an improved SoC estimation algorithm for implementation using low-cost microcontrollers. A method by which this improved algorithm can be implemented in a distributed battery management system is presented. The improved SoC estimation can be used to provide an enhanced range estimation method that can take into consideration a variety of environmental and behavioural factors. The proposed range estimate is more accurate than when only SoC is considered and can be implemented as part of a comprehensive driver alerting system to alert the EV driver of the expected energy required to reach the destination, the expected range with current SoC, an advisory if charging will be required prior to reaching the destination and the suggested duration of the charging required. By reducing the uncertainty surrounding EV range, it is hoped that the uptake of EVs can be improved

Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction

Global energy consumption has increased significantly in recent decades due to changes in the industrial and economic sectors. Accurate demand estimates are critical for decision-makers to save operation and maintenance costs, improve energy reliability, and make informed decisions for future development. This study evaluates a newly proposed soft technique called Variational Mode Decomposition (VMD) to improve the accuracy of power consumption forecasts. To validate the experimental results, we compared the predicted energy consumption values with measured values from five geographically diverse countries, including developed and developing countries. The study examined different time horizons and performed seasonal evaluations. The VMD-BiGRU and VMD-LSTM models show consistent and superior prediction accuracy, outperforming other models by 20% to 50% on all evaluation measures. In addition, we evaluated the efficiency of VMD-based models over different forecast horizons and find that they are most effective for short- to medium-term forecasts (1 to 12 months). For longer-term forecasts, we recommend combining VMD with specialized techniques. Overall, this study recommends using VMD to forecast electricity consumption in different regions, emphasizing carefully considering forecast horizons for optimal results