Level of Knowledge Awareness and Use of Planning as Writing Strategy by EFL International Students in UUM English Intensive Course

Malaysian English Language Curriculum makes it compulsory for every newly intake student to master and pass the English Writing Tasks (EWT) as among the basic skills in the language learning processes. However, most of the English Foreign Language (EFL) international students face difficulties with the EWT during the English Intensive Course (EIC) leading to consistent mass failures. The possible reasons of these failures could be due to the neglect of the writing strategies. Hence, the central focus of this paper is to identify and determine the EFL international students’ level of awareness and the use of planning as writing strategy before writing English essays. To this end, convenient purposive sampling strategy was used where 50 EFL (postgraduate and undergraduate) international students drawn from Universiti Utara Malaysian EIC program were selected and administered Writing Strategy Questionnaires (WSQ). The participants hailed from various countries who used and learned English as a foreign language, namely; Jordan, Iraq, Saudi Arabia, Libya, Palestine among others. The data were analyzed using SPSS. The findings revealed proportionate disparity between the EFL students that use planning strategy before starting writing English essays (usually true = 28%) with those that do not (usually not true = 28%). In terms of Revising Requirement for writing process before one start writing an essay in English, the findings revealed validity (40%) of participants’ responses at 82% cumulative. This is followed by “somewhat true” responses at 24% and 42% cumulative. These imply the EFL international students’ reasonable use of planning and having knowledge awareness of writing strateg

Modeling Thermal Conductivity, Thermal Diffusivity and Specific Heat of Asphalt Concrete Using Beta Regression and Mixture Volumetrics

The main objective of this paper is to develop predictive models using Beta regression for laboratory-prepared hot mix asphalt (HMA) specimens' thermal properties, including thermal conductivity (TC), thermal diffusivity (TD) and specific heat (SH). Thirty such specimens were prepared while varying the mixture's nominal maximum aggregate sizes (NMAS) and gradation coarseness. The widely used Transient Plane Source (TPS) method was employed to determine the thermal properties of the asphalt concrete. Only one type of asphalt binder was used for preparing all specimens. The air void volume (Va) and the effective binder volume (Vbe) were calculated for each mixture. To this end, the multiple linear regressions and the non-linear beta regressions were employed. Laboratory work resulted in hundred and fifty (150) data points. Three nominal maximum aggregate sizes, two gradation coarseness levels, five replicates and five different locations of measurements to ensure accuracy and repeatability in the obtained results. In conclusion, using Va and Vbe as predictors provided reliable predictive models for the thermal properties of different asphalt mixtures. The distribution of Va and Vbe was identified, and synthetic data was created to evaluate the accuracy of the models. Apart from R2 values, beta regression was more reliable to predict thermal properties of asphalt mixtures than multiple linear regression

Modeling Asphalt Pavement Frictional Properties using Different Machine Learning Algorithms

The objective of this work is to use some machine learning algorithms and test its efficiency in developing models to predict Locked Wheel Skid Trailer (LWST) values from Dynamic Friction Tester (DFT) and Circular Texture Meter (CTM) measurements conducted on asphalt pavement surfaces. For this prediction, three models were developed using DFT measurements at different speeds starting from 20km/h (12.5 mph) up to 64 km/h (40 mph) and then same DFT measurements as combination with Mean Profile Depth (MPD) and the last model used the International Friction Index (IFI) parameters (F60 and SP). The machine learning techniques includes two supervised learning algorithms: the Multi-Layer Perceptron (MLP) type of Artificial Neural Networks (ANN) and M5P tree model. In addition to one lazy algorithm called the K Nearest Neighbor (KNN) or Instance-Based Learner (IBL). The results showed that MLP models are the best in terms of the correlation coefficient that resulted in 81% prediction power using DFT parameters. Additionally, it was shown that the result of tree models was close to ANN but with much simpler regression. However, KNN models were recommended for LWST prediction of similar data characteristics and it is expected that this algorithm will be more efficient as the training data set becomes larger

Modeling Dust Generation on Low-Volume Roads Based on Vehicle Speed and Surface Fines Content

This study analyzes the role of vehicle speed and surface fines content on dust emission. Accordingly, fifty unpaved road sections in Iowa were evaluated; surface loose-aggregate samples were collected, and dust was collected using a Colorado State Dustometer at three speeds: 25 mph, 40 mph, and 55 mph. The data were analyzed using analysis of variance (ANOVA) test. Several dust-prediction models were developed utilizing multiple linear regression (ML), nonlinear regression with an interaction term (NLI), nonlinear beta regression (NLB), nonlinear curve-fitting regression (NLCF), and a multilayer neural network (MNN). The model predictors included vehicle speed and surface fines content. When models were evaluated using synthetic data and compared using post-hoc analysis, it was found that dust increases exponentially as vehicle speed increases and increases linearly as surface fines content increases. Also, at higher speeds, dust values will converge independently of the fines content in the surface materials. The ANOVA test results revealed that vehicle speed, surface fines content, and their interaction significantly affected dust emissions. The accuracy of models ranged from acceptable to good. The coefficients of determination (R2) for ML, NLI, NLB, NLCF, and MNNTraining models were 0.703, 0.718, 0.689, 0.696, and 0.776, respectively. Evaluation of the models showed that independent of the R2 value, the MNN model was the most accurate in predicting dust emissions, followed by the NLCF model, the ML model, the NLB model, and lastly the NLI model. The post-hoc test showed that MNNTraining, NLCF, and ML models produced comparable results.This is a manuscript of an article published as Alsheyab, Mohammad Ahmad, Bo Yang, Halil Ceylan, and Sunghwan Kim. "Modeling Dust Generation on Low-Volume Roads Based on Vehicle Speed and Surface Fines Content." Transportation Research Record (2023): 03611981231158339. DOI: 10.1177/03611981231158339. Copyright 2023 National Academy of Sciences: Transportation Research Board. Posted with permission

Effect of aggregate gradation and asphalt mix volumetrics on the thermal properties of asphalt concrete

The objective of this study is to investigate the impact of aggregate gradation and Superpave volumetrics on thermal properties of asphalt mixtures. Thirty asphalt concrete specimens with three different Nominal Maximum Aggregate Sizes (NMAS) of 19.0, 12.5 and 9.5 mm and two levels of gradation coarseness: fine gradation (FG) and coarse gradation (CG) were prepared. The Transient Plane Source (TPS) method was used to determine thermal properties. Based on the analyses performed, it was concluded visually that heat transfer is highly dependent on the contact area which, in turn, is related to air void volume. Unlike NMAS, the gradation coarseness (G) has some effect on thermal conductivity (TC) (p-value = 0.008 < 0.05). On the contrary, the NMAS, unlike gradation coarseness, has a significant effect on thermal diffusivity (TD) with a p-value of 0.025. Both gradation coarseness and NMAS have a significant effect on thermal diffusivity (p-value = 0.029 < 0.05) and do not have much effect on specific heat (SH) where p-value is greater than 0.05. Nevertheless, it was noticed that the thermal diffusivity increased linearly with an increase in aggregate size from 85.64 mm2/s to 125.79 mm2/s at 9.5 NMAS FG and 19 mm NMAS FG, respectively. Whereas the specific heat and thermal conductivity increase as the asphalt content (AC) increases. Consequently, the highest average values of thermal conductivity and specific heat are 2 W/m.K and 0.025 M.J/m3. K, respectively, at the highest asphalt content of 6.1%. Multiple linear regression, non-linear regression and deep learning (MLR, NLR and DL) equations were developed. The non-linear regression resulted relatively in the best predictive power of all equations. Finally, larger datasets are needed to predict thermal properties with higher confidence