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Item Role of language in solving mathematical word problems among grade 9 learners.(DRS Publishers, 2024-05-30)Objectives: The study aimed to explore mathematical word problem solving abilities among Grade9 learners in Tshwane North District Schools. It highlighted how language plays a pivotal role in learning mathematics and understanding mathematical word problems. Moreover, it showed how language inadequacy and incorrect translation affect Grade 9 learners’ solutions to mathematical word problems in schools around Tshwane North District. Methods: The study used both qualitative and quantitative methods. It also made use of contextual, exploratory, and descriptive statistical data. The study involved 26 nineth-grade learners in Tshwane North District secondary schools in Gauteng Province. Data collection was based on learners’ written work (a questionnaire) and analysing the results of the administered test. Data was analysed to detect the language difficulties that learners’ face when translating and solving mathematical word problems. The analysis process involved developing initial insights, coding, interpreting, and drawing conclusions to determine whether there is a connection between language proficiency and solving mathematical word problems. Results: The study showed that learners face difficulties in mathematical processes such as inadequate language comprehension when translating words into mathematical symbols. It also revealed that there is a strong connection between vocabulary knowledge and word problem solving, resulting in learning challenges related to understanding the meaning associated with mathematical word problems. Conclusions: Evidence from the word problem test for Grade 9 learners revealed that mathematical vocabulary and syntactic features are the main factors causing difficulties in understanding and solving mathematical word problems.Item Factors influencing low intension detection rate in a non-invasive EEG-based brain computer interface system.(Institute of Advanced Engineering and Science, 2020-04-14)Motor imagery (MI) responses extracted from the brain in the form of EEG signals have been widely utilized for intention detection in brain computer interface (BCI) systems. However, due to the non-linearity and the non-stationarity of EEG signals, BCI systems suffer from low MI prediction rate with both known and unknown influncing factors. This paper investigates the impact of visual stimulus, feature dimensions and artifacts on MI task detection rate, towards improving MI prediction rate. Three EEG datasets were utilized to facilitate the investigation. Three filters (band-pass, notch and common average reference) and the independent component analysis (ICA) were applied on each datasets, to eliminate the impact of artifact. Three sets of features where extracted from artifact free ICA components, from which more relevant features were selected. Moreover, the selected feature subsets were incorporated into three classifiers, NB, Regression Tree and K-NN to predict four MI and hybrid tasks. K-NN classifier outperformed the other two classifies in each dataset. The highest classification accuracy is obtained in hybrid task EEG dataset. Moreover, accurately predicted EEG classes were applied to a robotic arm control.Item Hybrid order characteristics in car-following behavior.202(Institute of Advanced Engineering and Science, 2020-03-26)This paper addresses the hybrid order behavior in car-following processes, which was not reported in the existing literatures. The behavior is supported by both experimental data and theoretical simulations. To demonstrate this behavior, the first order and the second order car-following behaviors are defined. By comparing car-following behaviors in the existing analystic models and the real traffic context, this paper finds that a significant amount of the second order car-following processes in real traffic context do not match the models. The structural mismatches suggest the existence of unmodelled dynamics in the existing methods. In fact, the car following behavior is determined by more factors than the immediate proceeding vehicle. Therefore, the existing car-following models must be improved to accommodate these factors. This forms one of the main values of this paper. This paper then defines the hybrid order car-following behavior and prompts to associate this behavior with the concerned unmodelled dynamics. A neural network is employed to model such dynamics. The proposed hybrid order behavior matches the fact that the car-following behavior is determined by multiple vehicles instead of the immediate proceeding one only. This is valuable in providing guidance on the improvement of existing models.Item Numerical and experimental analysis of surface roughness during the milling operation of titanium alloy Ti6Al4V.(Engineering and Technology Publishing, 2020-12-30)Titanium alloy (Ti-6Al-4V) has many industrial applications due to its excellent mechanical properties. However, its low thermal conductivity often results in surface and dimensional inaccuracies during machining operations. In this study, an experimental investigation was done to characterise the influence of milling parameters on the surface roughness of Ti-6Al-4V. The numerical experimentation involves the use of the Response Surface Methodology (RSM) with three factors namely: the speed, feed and depth of cut. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine and a carbide-cutting insert (RCKT1204MO-PM S40T). The comparative analysis of the results obtained indicate that the milling parameters and cutting conditions significantly influenced the surface finish of the titanium alloy. The results obtained from the physical experiments indicate an increase in the magnitude of the surface roughness when the cutting parameters exceed their optimal values. The machining parameters which resulted in the least surface roughness (Ra: 0.035 µm, Rz: 1.12 µm and Rq: 0.277 µm) under the air cooling condition were: cutting speed (265 m/min), feed per tooth (0.05 mm) and depth of cut (0.5 mm). Information on the effect of machining parameters on surface roughness will assist manufacturers in selecting the most feasible combination of the process parameters for producing titanium alloy (Ti-6Al-4V) parts with improved surface quality.Item Energy and carbon emission efficiency prediction: Applications in future transport manufacturing.(MDPI, 2021-12-03)The long-term impact of high-energy consumption in the manufacturing sector results in adverse environmental effects. Energy consumption and carbon emission prediction in the production environment is an essential requirement to mitigate climate change. The aim of this paper is to evaluate, model, construct, and validate the electricity generated data errors of an automotive component manufacturing company in South Africa for prediction of future transport manufacturing energy consumption and carbon emissions. The energy consumption and carbon emission data of an automotive component manufacturing company were explored for decision making, using data from 2016 to 2018 for prediction of future transport manufacturing energy consumption. The result is an ARIMA model with regression-correlated error fittings in the generalized least squares estimation of future forecast values for five years. The result is validated with RSS, showing an improvement of 89.61% in AR and 99.1% in MA when combined and an RMSE value of 449.8932 at a confidence level of 95%. This paper proposes a model for efficient prediction of energy consumption and carbon emissions for better decision making and utilize appropriate precautions to improve eco-friendly operation.
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