An Analysis of EFL Learners’ Needs for Student-Centered Translation Course Design

In recent years, translation learning has been a main focus of university language learners, but no studies in students’ needs have been explored for translation course design. Thus, the current research aimed to analyze EFL learners’ needs for student-centered translation course design. The subjects were 90 juniors from the Department of Applied Foreign Languages at a technological university in central Taiwan. The instrument was a 45-item questionnaire on learning goals, course planning, instructional materials, teaching and learning, and evaluation. Descriptive analysis was conducted on the Likert-scale questionnaire items to calculate frequencies, percentages, means, ranks, and standard deviations. The results showed that English-major students believed translation courses are required for both language and working skill training. The findings also implied that translation curriculum should involve more authentic materials, learning activities, and evaluation. The genres and topics selection need to take learners’ interests and small ‘c’ cultural knowledge into account. It is also suggested that classes be smaller to increase interactions between teachers and students. With explicit guidelines, group work in a translation course can lead to success in translation learning. The practical implications of the current study were also discussed

Fine-Scale Genetic Profile and Admixture History of Two Hmong-Mien-Speaking Miao Tribes from Southwest China Inferred from Genome-Wide Data

As the dominant indigenous minority in Southern China, Hmong-Mien speaking Miao people were thought to be the descendants of Neolithic Yangtze rice farmers. However, the fine-scale population structure and genetic profile of the Miao populations remains unclear due to the limited Miao samples from Southern China and Southeast Asia. Here, we genotyped 19 individuals from the two largest Miao tribes in Guizhou province (Southwest China) via SNP chips and co-analyzed with published available modern and ancient East Asians. We observed that studied Guizhou Miao displayed a closer genomic affinity with present-day and Neolithic-to-Iron Age Southern East Asians than with most Northern East Asians. The genetic substructure within Miao groups was driven by different levels of genetic interaction with other ethnolinguistic groups: Hunan Miao (central China) harbored higher proportions of Northern East Asians-related ancestry; Guizhou Miao (Southwest China) and Vietnam Miao (mainland Southeast Asia) received the additional gene flow mainly from surrounding Tai-Kadai speaking-related ancestry. Besides, there were more complex admixture events between newly studied Guizhou Xijiang Miao and surrounding populations compared with studied Guizhou Congjiang Miao. The qpAdm model further demonstrated that the primary ancestry of Hunan Miao, studied Guizhou Miao and Vietnam Miao derived from ancient Southern East Asian (SEA)-related ancestry (represented by coastal Early Neolithic SEA Liangdao2) with the additional gene flow from ancient northern East Asian-related ancestry (represented by spatiotemporally inland Yellow River farmers), with slightly different proportions. Conclusively, our genomic evidence revealed the complex and distinct demographic history of different Miao tribes

Analytical Studies on a Modified Nagel-Schreckenberg Model with the Fukui-Ishibashi Acceleration Rule

We propose and study a one-dimensional traffic flow cellular automaton model of high-speed vehicles with the Fukui-Ishibashi-type (FI) acceleration rule for all cars, and the Nagel-Schreckenberg-type (NS) stochastic delay mechanism. By using the car-oriented mean field theory, we obtain analytically the fundamental diagrams of the average speed and vehicle flux depending on the vehicle density and stochastic delay probability. Our theoretical results, which may contribute to the exact analytical theory of the NS model, are in excellent agreement with numerical simulations.Comment: 3 pages previous; now 4 pages 2 eps figure

Reaction Behaviors of Bagasse Modified with Phthalic Anhydride in 1‐Allyl‐3‐Methylimidazolium Chloride with Catalyst 4‐Dimethylaminopyridine

The modification of lignocellulose with cyclic anhydrides could confer stronger hydrophilic properties to lignocellulose, which could be used in many industrial fields. To elucidate the modification mechanism of lignocellulose, bagasse was phthalated comparatively with its three main components in 1‐allyl‐3‐methylimidazolium chloride (AmimCl) using 4‐dimethylaminopyridine as catalyst and phthalic anhydride as acylation reagent in the present study. From FT‐IR and 2D HSQC analyses, the skeleton of bagasse and the fractions were not significantly changed during phthalation in AmimCl. 2D HSQC results suggested that the reactive hydroxyls in bagasse were partially phthalated, and the reactivity of the hydroxyls in anhydroglucose units followed the order C‐6 > C‐2 > C‐3. Similarly, the reactivity order of hydroxyls in anhydroxylose units was C‐2 > C‐3. For lignin, the predominant diesterification occurred during the homogeneous modification, and both aliphatic and aromatic hydroxyls were phthalated. The reactivity order of phenolic hydroxyls was S‐OH > G‐OH > H‐OH, which was distinct from that without catalyst. In addition, it was found that the thermal stability of phthalated bagasse was affected by the disruption of cellulose crystallinity and the degradation of components. The thermal stability of the phthalated bagasse decreased upon chemical modification and regeneration

Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follow a semi-supervised learning manner, which notably limits their wide use in reality since labels are usually scarce in real applications. Recently, contrastive learning, a self-supervised method, becomes one of the most exciting learning paradigms and shows great potential when there are no labels. In this paper, we study the problem of self-supervised HGNNs and propose a novel co-contrastive learning mechanism for HGNNs, named HeCo. Different from traditional contrastive learning which only focuses on contrasting positive and negative samples, HeCo employs cross-view contrastive mechanism. Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously. Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views. This enables the two views to collaboratively supervise each other and finally learn high-level node embeddings. Moreover, to further boost the performance of HeCo, two additional methods are designed to generate harder negative samples with high quality. Besides the invariant factors, view-specific factors complementally provide the diverse structure information between different nodes, which also should be contained into the final embeddings. Therefore, we need to further explore each view independently and propose a modified model, called HeCo++. Specifically, HeCo++ conducts hierarchical contrastive learning, including cross-view and intra-view contrasts, which aims to enhance the mining of respective structures.Comment: This paper has been accepted by TKDE as a regular paper. arXiv admin note: substantial text overlap with arXiv:2105.0911