This study presents learning vector quantization neural network modelling to predict injury severity of driver as well as riders, which applies to the backbone of traffic networks for London's central business district. The potential associations between injury severity classes and crash related factors that contribute to their generation are discovered. Accordingly, the model is addressed as an identification technique for contributory factors and range of interventions for road safety. Unsurprisingly, approaching a T/staggered junction is detected as an accident hotspot. Injuries caused by going ahead on a bend and turning manoeuvres are ranked as the next most important contributory factors. Likewise, the affect of most junction actions were almost triple compared to the other indexes. All other sensitive predictors approximately were held near as equal; injuries involving a stationary or parked vehicle, factors related to junction control, crossing facilities, alcohol involvement, rush hours, and vehicle type. Following this implication, with the purpose of maximising the likelihood of injury accuracy, the model is predicted through the most sensitive predictors.

No abstract

Learning Management Systems (LMS) have played a significant role in education. The purpose of this study is to investigate the acceptance level of LMS amongst students of two Universities in Tehran, Payamnoor and Farhangian. The total number of participants was 200. This study was directed based on a quantitative research method and data collection from a questionnaire which was then interpreted according to accurate statistical procedures through SPSS software. Results show that most students, regardless their gender, age, and department were satisfied with the usage of Payamnoor and Farhangian LMSs. However, a student’s grades seem to play a significant role regarding his or hers level of satisfaction from the LMS.

The use of technology in instruction has brought about different perceptions. The need to know how teachers integrate technology in instruction has brought about different views. Therefore, this study mainly seeks to understand these views on lecturers' technological pedagogical content knowledge (TPACK) perceptions, as it examines how their views differ according to gender, employment status, department and the state of in-service training oriented towards the use of technology. In order to achieve the above stated aim, the researcher statistically examined Eastern Mediterranean University (EMU) Faculty of Education lecturers' TPACK perceptions. In this research, a TPACK survey instrument was administered to 53 lecturers, and a questionnaire was used to ascertain their perception levels across the seven TPACK dimensions. Mean, standard deviation, percentage, frequency and non-parametric tests (Mann-Whitney U test and Kruskal-Wallis test) were used for data analysis. The study reveals that lecturers' perceptions of TPACK were significantly high across all knowledge dimensions and there were statistically significant differences on how lecturers viewed TPACK according to the above listed variables. These differences occurred in Technological Knowledge (TK) and Pedagogical Content Knowledge (PCK) according to gender; Technological Knowledge (TK) and Technological Pedagogical Knowledge (TPK) according to employment status; Technological Knowledge (TK), Technological Pedagogical Knowledge (TPK), and Technological Pedagogical Content Knowledge (TPACK) according to department, and Pedagogical Content Knowledge (PCK) according to in-service training.

This paper focuses on predicting injury severity of a driver or rider by applying multi-layer perceptron (MLP), support vector machine (SVM), and a hybrid MLP-SVM method. By correlating the injury severity results and the influences that support their creation, this study was able to determine the key influences affecting the injury severity. The result indicated that the vehicle type, vehicle manoeuvre, lack of necessary crossing facilities for cyclists, 1st point of impact, and junction actions had a greater effect on the likelihood of injury severity. Following this indication, by maximising the prediction accuracies, a comparison between the models was made through exerting the most sensitive predictors in order to evaluate the models' performance against each other. The outcomes specified that the proposed hybrid model achieved a significant improvement in terms of prediction accuracy compared with other models.