Facile Synthesis of Silane-Modified Mixed Metal Oxide as Catalyst in Transesterification Processes.

The fast depletion of fossil fuels has attracted researchers worldwide to explore alternative biofuels, such as biodiesel. In general, the production of biodiesel is carried out via transesterification processes of vegetable oil with the presence of a suitable catalyst. A mixed metal oxide has shown to be a very attractive heterogeneous catalyst with a high performance. Most of the mixed metal oxide is made by using the general wetness impregnation method. A simple route to synthesize silane-modified mixed metal oxide (CaO-CuO/C6) catalysts has been successfully developed. A fluorocarbon surfactant and triblock copolymers (EO)106(PO)70(EO)106 were used to prevent the crystal agglomeration of carbonate salts (CaCO3-CuCO3) as the precursor to form CaO-CuO with a definite size and morphology. The materials show high potency as a catalyst in the transesterification process to produce biodiesel. The calcined co-precipitation product has a high crystallinity form, as confirmed by the XRD analysis. The synthesized catalyst was characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX). The mechanism of surface modification and the effects of the catalytic activity were also discussed. The biodiesel purity of the final product was analyzed by gas chromatography. The optimum biodiesel yield was 90.17% using the modified mixed metal oxide CaO-CuO/C6.

Automatic distractor generation for multiple-choice English vocabulary questions.

The use of automated systems in second-language learning could substantially reduce the workload of human teachers and test creators. This study proposes a novel method for automatically generating distractors for multiple-choice English vocabulary questions. The proposed method introduces new sources for collecting distractor candidates and utilises semantic similarity and collocation information when ranking the collected candidates. We evaluated the proposed method by administering the questions to real English learners. We further asked an expert to judge the quality of the distractors generated by the proposed method, a baseline method and humans. The results show that the proposed method produces fewer problematic distractors than the baseline method. Furthermore, the generated distractors have a quality that is comparable with that of human-made distractors.

Controlling item difficulty for automatic vocabulary question generation.

The present study investigates the best factor for controlling the item difficulty of multiple-choice English vocabulary questions generated by an automatic question generation system. Three factors are considered for controlling item difficulty: (1) reading passage difficulty, (2) semantic similarity between the correct answer and distractors, and (3) the distractor word difficulty level. An experiment was conducted by administering machine-generated items to three groups of English learners. The groups were determined based on their standardised English test scores. In total, 120 items, generated using combinations of the above three factors, were tested. The results reveal that the distractor word difficulty level had the greatest impact on item difficulty, but this tendency changed depending on the proficiency of the test takers. These results will be of use when implementing a fully automatic system for administrating tests.

Evaluation of automatically generated English vocabulary questions.

This paper describes details of the evaluation experiments for questions created by an automatic question generation system. Given a target word and one of its word senses, the system generates a multiple-choice English vocabulary question asking for the closest in meaning to the target word in the reading passage. Two kinds of evaluation were conducted considering two aspects: (1) measuring English learners' proficiency and (2) their similarity to the human-made questions. The first evaluation is based on the responses from English learners obtained through administering the machine-generated and human-made questions to them, and the second is based on the subjective judgement by English teachers. Both evaluations showed that the machine-generated questions were able to achieve a comparable level with the human-made questions in both measuring English proficiency and similarity.