Purpose The purpose of this paper is, first, to examine student perspectives of their university experience in terms of the soft employability skills they develop; second, how prepared those students feel for the future employment market and finally investigate whether there are differences in perceptions between Chinese and Malaysian students given their different educational experience. Design/methodology/approach In this study, 361 predominantly Chinese undergraduate students at two universities, one in China and the other in Malaysia completed the 15-item Goldsmiths soft skills inventory using an online survey. Findings The results, analysed using factor analysis and confirmatory factor analysis, indicated that the university curriculum develops student soft skills, particularly in the Malaysian university and supports the relationship between soft skill and student preparedness for employment. The results also indicate that compared with the respondents from the Chinese university, the Malaysian university respondents were more likely to be positive to statements concerning their respective university’s ability to develop their soft skills. Research limitations/implications Such findings have implications for education providers and business in that it is important for universities to embed soft skills into the curriculum in order to develop graduate work readiness. Originality/value What this research contributes is not only consolidation of existing research in the contemporary context of a disruptive jobs market, it takes research forward through analysing student perceptions from two universities, one in Malaysia and the other in China, of the skills they develop at university and the importance of soft skills to them and their perceptions of future employment and employability. Such research will provide insight, in particular, into the role of education providers, the phenomena of underemployment among graduates in China, and be of practical significance to employers and their perception that graduates lack the necessary soft skills for the workplace (Anonymous, 2017a; Stapleton, 2017; British Council, 2015; Chan, 2015).
Conventional IMRT dose verification using film and ion chamber measurements is useful but limited with respect to the actual dose distribution received by the patient. The Monte Carlo simulation has been introduced as an independent dose verification tool for IMRT using the patient CT data and MLC leaf sequence files, which validates the dose calculation accuracy but not the plan delivery accuracy. In this work, we propose a Monte Carlo based IMRT dose verification method that reconstructs the patient dose distribution using the patient CT, actual beam data based on the information from the record and verify system (R/V), and the MLC log files obtained during dose delivery that record the MLC leaf positions and MUs delivered. Comparing the Monte Carlo dose calculation with the original IMRT plan using these data simultaneously validates the accuracy of both the IMRT dose calculation and beam delivery. Such log file based Monte Carlo simulations are expected to be employed as a useful and efficient IMRT QA modality to validate the dose delivered to the patient. We have run Monte Carlo simulations for eight IMRT prostate plans using this method and the results for the target dose were consistent with the original CORVUS treatment plans to within 3.0% and 2.0% with and without heterogeneity corrections in the dose calculation. However, significant dose deviations in nearby critical structures have been observed. The results showed that up to 9.0% of the bladder dose and up to 38.0% of the rectum dose, to which leaf position errors were found to contribute <2%, were underestimated by the CORVUS treatment planning system. The concept of average leaf position error has been defined to analyze MLC leaf position errors for an IMRT plan. A linear correlation between the target dose error and the average position error has been found based on log file based Monte Carlo simulations, showing that an average position error of 0.2 mm can result in a target dose error of about 1.0%.
In this investigation, three-class classification models of aqueous solubility (logS) and lipophilicity (logP) have been developed by using a support vector machine (SVM) method combined with a genetic algorithm (GA) for feature selection and a conjugate gradient method (CG) for parameter optimization. A 5-fold cross-validation and an independent test set method were used to evaluate the SVM classification models. For logS, the overall prediction accuracy is 87.1% for training set and 90.0% for test set. For logP, the overall prediction accuracy is 81.0% for training set and 82.0% for test set. In general, for both logS and logP, the prediction accuracies of three-class models are slightly lower by several percent than those of two-class models. A comparison between the performance of GA-CG-SVM models and that of GA-SVM models shows that the SVM parameter optimization has a significant impact on the quality of SVM classification model.
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