A fibre actuator that generates a large strain with high specific power represents a promising strategy to develop novel wearable devices and robotics. We propose a new coiled-fibre actuator based on highly drawn, hard linear low-density polyethylene (LLDPE) fibres. Driven by resistance heating, the actuator can be operated at temperatures as low as 60 °C and uses only 20% of the power consumed by previously coiled fibre actuators when generating 20 MPa of stress at 10% strain. In this temperature range, 1600 W kg−1 of specific work (8 times that of a skeletal muscle) at 69 MPa of tensile stress (230 times that of a skeletal muscle) with a work efficiency of 2% is achieved. The actuator generates strain as high as 23% at 90 °C. Given the low driving temperature, the actuator can be combined with common fabrics or stretchable conductive elastomers without thermal degradation, allowing for easy use in wearable systems. Nanostructural analysis implies that the lamellar crystals in drawn LLDPE fibres are weakly bridged with each other, which allows for easy deformation into compact helical shapes via twisting and the generation of large strain with high work efficiency.

Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

Pragmatic awareness in the field of interlanguage pragmatics has been investigated using various factors: linguistic environment, overall second language proficiency, and length of residence in the target language community. In this study, on the basis of a replication of a study on pragmatic and grammatical awareness by Bardovi-Harlig and Dörnyei (1998), learners’ motivational factors were incorporated to investigate the relationship between motivation and pragmatic awareness. Through cluster analysis, the data were analyzed from the perspective of learners’ motivational profiles in order to see how the profiles affect pragmatic awareness. The results revealed that learners’ motivational profiles influence not only their perception of error identification, but also their severity ratings of errors, suggesting that noticing and understanding of the pragmatic information (Schmidt, 1995) are important aspects in the future study of interlanguage pragmatics. 動機づけ要因から見る日本人英語学習者の語用論的意識 中間言語語用論の分野において，言語環境，熟達度，目標言語環境への滞在期間等の要因と語用論的意識の関係について調査がなされてきた。本研究では，Bardovi-Harlig and Dörnyei (1998) の研究を基に，学習者の動機づけ要因が語用論的意識に及ぼす影響を，学習者の動機づけプロファイリングから考察した。動機づけを連続体と捉える自己決定理論に基づき，クラスタ分析を用いて学習者を4つのクラスタに分類した。その結果，文法的誤りへの気づきはクラスタ間に違いはなかったが、より自律的である学習者ほど，語用論的誤りへの気づき度が高いことが明らかとなった。このことより，より自律的な学習者であるほど，形式へのnoticingから，語用論的内容を含めたunderstandingへの意識の移行（Schmidt, 1995）がなされていることが示唆された。

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