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Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

This study aims to present that the chemo-responsive shape recovery of thermoplastic polyurethane (TPU) is tunable by solvents with different solubility parameters, and it is generic for chemo-responsive shape-memory polymer and its composites.

Two kinds of commercial TPU samples with different thicknesses were prepared by panel vulcanizer and injection molding (an industrial manner) to investigate their chemo-responsive shape memory properties in acetic ether and acetone.

Results showed that all of TPU films with different thicknesses can fully recover their original shapes weather they recover in acetic ether or acetone. But the recovery time of TPU films in acetone is greatly reduced, especially for the twisting samples. The residual strains of recovery TPU samples after extension reduce obviously.

The great decrement of recovery time is related to two factors. One is due to the bigger solubility parameter of acetone with higher dipole moment compared with those of acetic ether, and the other is the remained internal stress of TPU films after preparation. The internal stress is identified to have an effect on the shape-memory properties by comparing the recovery process of samples with/without annealing. The reduced residual strains of recovery TPU samples after extension is due to the increasing mobility of polymer segments after molecules of acetic ether penetrates into the polymeric chains.

This is a universal strategy to control the recovery process of shape-memory materials or composites. The underlying mechanism is generic and should be applicable to chemo-responsive shape-memory polymers or their composites.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

This paper aims to create and to study multifunctional shape memory polymer (SMP) composites having temperature-sensing and actuating capabilities by embedding thermochromic particles within the polymer matrix.

The multifunctional materials were fabricated following a process consisting of blending (of the thermochromic particles and the SMP at various ratios), mixing, degasing, moulding and thermal curing, prepared by incorporating thermochromic particles within the polymer. The effect of the thermochromic particles on the thermomechanical properties and thermally responsive shape memory effect of the resulting multifunction SMP composites were characterised and interpreted.

It was found that exposure of the composites to temperatures above 70°C led to a pronounced change of their colour that was recorded by the thermal and electrical actuation approaches and was reproducibly reversible. It was also found that the colour of the composites was independent of the mechanical state of the SMP. Such effects enabled monitoring of the onset of the set/release temperature of the SMP matrix. Furthermore, the combination of thermochromic additive and the SMP resulted in significantly improved thermomechanical strength, absorption of infrared radiation and the temperature distribution of the SMP composites.

The temperature-sensing and actuating capabilities of the polymeric shape memory composites developed through this study will help to extend the field of potential applications of such composites to fields including sensors, actuators, security labels and information dissemination, where colour indication is an advantageous feature.

The SMP composites capable of temperature sensing and actuating are novel.

This paper aims to study the synergistic effect of self-assembled carboxylic acid-functionalised carbon nanotube (CNT) and nafion/silica nanofibre nanopaper on the electro-activated shape memory effect (SME) and shape recovery behaviour of shape memory polymer (SMP) nanocomposite.

Carboxylic acid-functionalised CNT and nafion/silica nanofibre are first self-assembled onto carbon fibre by means of deposition and electrospinning approaches, respectively, to form functionally graded nanopaper. The combination of carbon fibre and CNT is introduced to enable the actuation of the SME in SMP by means of Joule heating at a low electric voltage of 3.0-5.0 V.

Nafion/silica nanofibre is used to improve the shape recovery behaviour and performance of the SMP for enhanced heat transfer and electrical actuation effectiveness. Low electrical voltage actuation and high electrical actuation effectiveness of 32.5 per cent in SMP has been achieved.

A simple way for fabricating electro-activated SMP nanocomposites has been developed by using functionally graded CNT and nafion/silica nanofibre nanopaper.

The outcome of this study will help to fabricate the SMP composite with high electrical actuation effectiveness under low electrical voltage actuation.

Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation.

This paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model.

Experiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction.

The research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.

The focus in this study is a model that predicts continuance intention of online multi-player games. In this integrated model, the social cognitive theory (SCT) lays out the foundation of two types of pre-use (pre-play) expectations, the flow theory captures the affective feeling with the game as a moderator for the effect from the two pre-use expectations, and subjective norm together with its associated antecedents cover a wide spectrum of social influences.

A questionnaire was designed and pre-tested before distributing to target respondents. The reliability and validity of the instrument both met the commonly accepted guidelines. The integrated model was assessed first by examining its measurement model and then the structural model.

The integration of cognitive, affective and social influence in this model explains a larger amount of variance compared to the competing models and existing studies.

Unlike a popular trend that studies predictors of online games from either cognitive or affect angle, the work looks at both together to study how their joint effect is related to continuance intention. This marks an important improvement as cognitive expectations derived from SCT captures the pre-use experience that may be influenced or swayed by sources including those that are inflated or incorrect. By studying flow as a moderator in conjunction with other sources of influence, the authors are able to further the understanding of how the pre-use expectations may be shaped by one's own experience.

The purpose of this paper is to prepare novel electroactive shape memory nanocomposites based on graphene and study the thermomechanical property and shape memory behavior of nanocomposites.

Graphene was dispersed in N,N-dimethylformamide, and the mixture was spooned into epoxy-cyanate ester mixtures to form graphene/epoxy-cyanate ester nanocomposites. The nanocomposites were deformed under 150°C, and shape recovery test was conducted under an electric voltage of 20-100 V.

Graphene is used to improve the shape recovery behavior and performance of shape-memory polymers (SMPs) for enhanced electrical actuation effectiveness. With increment of graphene content, the shape recovery speed of nanocomposites increases significantly.

A simple way for fabricating electro-activated SMP nanocomposites has been developed by using graphene.

The outcome of this study will help to fabricate the SMP nanocomposites with high electrical actuation effectiveness and improve the shape recovery speed of the nanocomposites.

This paper aims to propose a hybrid method based on polynomial fitting bias self-compensation, grey forward-backward linear prediction (GFBLP) and moving average filter (MAF) for error compensation in micro-electromechanical system gyroscope signal especially under motion state.

The error compensation can be divided into two processes: bias correction and noise reduction. A polynomial drift angle fitting algorithm is used to correct bias before denoising processing. For noise reduction, operation can be taken in two stages: detection and processing. First, sample variances are used to judge motion state. According to the detection results, algorithmic system switches between grey GFBLP and MAF to ensure fast convergence rate and small steady-state error.

Experimental results show that the proposed method can correct bias effectively for practical gyroscope signal, and can eliminate noise effectively for both practical gyroscope signal and synthetic signal, which indicates the effectiveness of the proposed method.

Bias correction and noise reduction are considerations. Noise contained in practical or synthetic signal can be reduced rapidly and effectively, which benefits from the new idea of combination grey GFBLP, MAF and sample variances. And most importantly, it is applicable for signal denoising under arbitrary motion state condition, which is different from other methods where the convergence performance is seldom analyzed.

This paper aims to present a phenomenological model to investigate the underlying mechanism and predict the bio-inspired performance under different thermo-temporal conditions.

Flory-Rehner free-energy functions are applied to quantitatively identify the driving forces in the viscously bio-inspired response of a dynamic polymer network. Furthermore, the permeation transition equation is adopted to couple water gradient and water sorption/desorption into the free-energy function.

The results show that the influence of potential energy on deformation can be related to a stretching ratio that uniquely determines water sorption/desorption, locomotion frequency and contractile stress. Finally, by means of combining the free-energy function and Arrhenius equation, a phenomenological thermo-temporal model is developed and verified by the experimental results.

This study focuses on exploring the theoretical mechanism and significantly enhances understanding of relevant experimental features reported previously.

The outcome of this study will provide a powerful phenomenological and quantitative tool for study on shape memory effect in bio-inspired polymers.