This study was conducted to reveal what Memrise, an online vocabulary study tool, can offer to upper-intermediate EFL learners compared to traditional vocabulary exercises in L2 vocabulary learning. Two groups of upper-intermediate learners (N=80) were randomly assigned to the experimental group and the control group and were given the Vocabulary Knowledge Scale, VKS for short, as the pre-test and post-test. The participants in both groups were exposed to the target vocabulary items in the same reading text. While those in the experimental group created list of target vocabulary items collaboratively in Memrise and then studied the sets individually, the learners in the control group did traditional vocabulary exercises. The results of the post-tests indicated that there was a significant difference between the control group and the experimental group in favor of the experimental group. The researchers discuss possible pedagogical implications of this significant finding for EFL vocabulary instruction.

The cut quality is of great importance during the laser cutting process. The quality of laser cut mainly depends on an appropriate selection of process parameters. In this paper, the effect of process parameters was analysed on the laser cut quality of an uncommon alloy, the tungsten alloy (W ≈ 92.5 % and the remainder Fe and Ni) sheet with thickness of 1 mm. This alloy has a wide application in different industrial areas, e.g. in medical applications, the automobile sectors, and the aircraft industry. This paper introduces a developed back-propagation artificial neural network (BP- ANN) model for the analysis and prediction of cut quality during the CO2 laser cutting process. In the presented study, three input process parameters were considered such as laser power, cutting speed and assist gas type, and two output parameters such as kerf width and average surface roughness. Amongst the 42 experimental results, 34 data sets were chosen for training the network, whilst the remaining 8 results were used as test data. The average prediction error was found to be 5.5 % for kerf width and 9.5 % for surface roughness. The results of the predicted kerf width and surface roughness by the BP-ANN model were compared with experimental data. Based on the results of the study, it was shown that the proposed artificial neural network model could be a useful tool for analysing and predicting surface roughness and kerf width during CO2 laser cutting processes.

Abstract This paper defines mathematical models of value changes for surface roughness ( Ra , μm) and heat affected zone width ( HAZ , mm) during high-alloyed steel 1.4828 laser cutting using oxygen as an assistance gas. For the definition of appropriate mathematical models, multiple linear regression analysis is used, with four independent variables that were varied at five levels. Following parameters are varied: cutting speed ( V ), assist gas pressure ( p ), focus position ( fs ) and stand-off ( N d ). In comparison between the model and the experimental results, it can be concluded that the effects of specified parameters on cut quality, productivity and thus the legitimacy of this technology for cutting high-alloyed steels are well described by the obtained mathematical models.