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Cold weather and the subsequent demand for lightweight apparel have prompted research work in the field of thermal insulation clothing. In addition to natural fibres, such as wool and cashmere, hollow chemical fibres, such as polyester or polypropylene fibre, have been studied and used to make thermal-insulation clothing. Primarily, modifications are made to increase the hollow ratio; however, this results in the fibre becoming brittle and prone to collapse. Yet many animals survive in harsh and cold conditions, and their hair plays a key role in keeping the body warm.

In the research under discussion, 4 types of cold-resistant animals are selected for observation of their hair fibres based on optical and scanning electronic microscopies. The characteristics of the hair fibres of cold-resistant animals are examined, including colour, scale density, scale thickness, medullary distribution and morphology of cross sections. The conclusions show that there are some common characteristics among cold-resistant animals, i.e., the cross sections are circular or ellipse or anomaly ellipse, scale density ranges from 40 to 100 scales per 1 mm, scale styles vary, and scale angles are small. The experimental results may be used to provide a modification method for functional fibres, especially for thermal-insulation fibres.

The structure of wool fibre is a key point in clarifying its properties and performances. Wool fibre can be separated into cuticle cells, cortical cells and even macrofibrils using formic acid as the medium by ultrasonication. The disintegration process of wool fibre is significantly subjected to many parameters. In order to obtain the maximum ratio of disintegration of wool fibre, the effect of these parameters on the disintegration process is studied. The separated wool fibre components are examined with a scanning electron microscopy and the disintegration rates are calculated from the weight proportion of residual and original wool fibres. The results indicate that wool fibre can be separated into cortical cells and macrofibrils by ultrasonication in formic acid. The broken ratio of wool fibre varies greatly with different conditions of ultrasonic irradiation, such as irradiation time, temperature, output power of a transducer, shape of vessel, position of probe, ratio of wool fibre to formic acid, and state of wool fibre. Additionally, ultrasonic irradiation is an efficient method for the preparation of intact cortical cells with high yield from wool fibre in appropriate conditions.

Stimuli-sensitive poly (N-isopropylacrylamide-co-sodium acrylate) [poly (NiPAAm-co-SA)] hydrogels are prepared by free radical polymerization of N-isopropylacrylamide (NiPAAm) and sodium acrylate (SA) using N, N'-methylene bisacrylamide (BIS) as a crosslinking agent. The effects of SA content, crosslinking agent concentration and the pre-gel solution concentration on the swelling behavior of poly (NiPAAm-co-SA) hydrogel are investigated systematically. The results indicate that the swelling capacities increase with an increase in the amount of hydrophilic poly (sodium acrylate) anionic units, and thermo-sensitivity is weakened simultaneously. The effect of the pre-gel solution concentration on the swellabilities of poly (NiPAAm-co-SA) is more than that of the crosslinking agent concentration. Furthermore, the pH-sensitivity of the networks is also investigated, and it is found that the swelling behavior of poly (NiPAAm-co-SA) hydrogels obviously improves when the pH value is 5.

This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business.

The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model.

According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models.

The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution.

Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers.

The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.

This paper aims to present an open-architecture kinematic controller, which was developed for articulated robots, facing the demands of various applications and low cost on robot system.

A general approach to develop this controller is described in hardware and software design. The hardware consists of embedded boards and programable multi-axes controller (PMAC), connected with ethernet, and the software is implemented on a robot operating system with MoveIt!. The authors also developed a teach pendant running as a LAN node to provide a human–machine interface (HMI).

The proposed approach was applied to several real articulated robot systems and was proved to be effective and portable. The proposed controller was compared with several similar systems to verify its integrality and flexibility. The openness of this controller was discussed and is summarized at the end of this paper.

The proposed approach provided an open and low-complex solution for experimental studies in the lab and short-run production in small workshops.

Several contributions are made by the research. The actuation model and communication were implemented to integrate the trajectory planning module and PMAC for setting up the physical interface. Method and program interface based on kinematics was provided to generate various interpolations for trajectory planning. A teach pedant with HMI was developed for controlling and programing the robot.

The purpose of this paper is to study the application ways of extended reality (XR) in teaching, the specific teaching effects and how to implement teaching integration.

The study adopts an umbrella review approach, locates and screens 20 pertinent meta-analysis studies published in international journals and conducts a systematic review of the effects of teaching applications of XR technologies in terms of subject categories, education levels and teaching cycles.

The study finds that the effects of virtual reality and augmented reality technologies on teaching effectiveness can reach up to 0.723 and 0.951, indicating that XR technologies can improve teaching.

Through the systematic analysis of 20 related element analysis studies published in international journals, the specific methods and effects of XR technology applied to teaching are finally obtained and put forward the new application trend of XR promoting teaching effect.

In the past decade, more and more research has also focused on the specific methods and effects of XR technology applied to teaching. However, there are still problems such as unclear technology application path and unclear effect. Based on this, it is necessary to further conduct an overall overview and analysis of the impact of XR technology on teaching effect when it is integrated into teaching, systematically study the teaching effect of XR technology in different education levels and subject types and provide a theoretical basis for more detailed research on XR technology integration into teaching.

Suggests that misunderstandings frequently occur when trying to understand Chinese language and culture, and so gives the implied meaning of various Chinese expressions and sayings such as greetings, thanks, respect, age, congratulations and taboo subjects.

This work aims to explore why people review their experienced online shopping in such a manner (promptness), and what is the potential relationship between the users’ review promptness and review motivation as well as reviewed contents.

To evaluate the customers’ responses regarding their shopping experiences, in this paper, the “purchase-review” promptness is studied to explore the temporal characteristics of users’ reviewing behavior online. Then, an aspect mining method was introduced for assessment of review text. Finally, a theoretical model is proposed to analyze how the customers’ reviews were formed.

First, the length of time elapsed between purchase and review was found to follow a power-law distribution, which characterizes an important number of human behaviors. Within online review behaviors, this meant that a high frequency population of reviewers tended to publish relatively quick reviews online. This showed that the customers’ reviewing behaviors on e-commerce websites may have been affected by extrinsic motivations, intrinsic motivations or both. Second, the proposed review-to-feature mapping technique is a feasible method for exploring reviewers’ opinions in both massive and sparse reviews. Finally, the customers’ reviewing behaviors were found to be mostly consistent with reviewers’ motivations.

First, the authors propose that the “promptness” of users in posting online reviews is an important external manifestation of their motivation, product experience and service experience. Second, a semi-supervised method of review-to-aspect mapping is used to solve the data quality problem in mining information from massive text data, which vary in length, detail and quality. Finally, a huge amount of e-commerce customers’ purchase-review promptness are studied and the results indicate that not all product features are responsible for the “prompt” posting of users’ reviews, and that the platform’s strategy to encourage users to post reviews will not work in the long term.

The purpose of this paper is to investigate a chromium‐free conversion coating treatment for magnesium with phytic acid solution.

Pure magnesium was selected for the tests. Phytic acid solution was used as the conversion solution of surface treatment. The samples of magnesium were immersed in the solution under certain conditions to form a conversion coating on the surface of magnesium. The formation process of phytic acid conversion coating was studied by detecting the open circuit potential (OCP) and weight change of the pure magnesium for different conversion treatment times. Scanning electron microscopy (SEM) and electron energy spectrum (EDS) were used to examine the morphologies and compositions of the coatings, respectively.

The experimental results showed that the conversion coating had a multideck structure with netlike morphology, which is similar in nature to chromate conversion coatings, and was composed mainly of Mg, P, O and C. The contents of C and P and the size of the cracks in different conversion layers decreased from the external layer to the inner layer. The thickness of the conversion coating varied from 1.0μm to 15μm according to the processing parameters. The hydroxyl groups and phosphate carboxyl groups in the coating, which have similar properties to an organic paint coat, are beneficial to the combination of substrate and organic paint coating. The formation mechanism and thickness variation of the conversion coatings also are discussed.

The paper shows that phytic acid conversion coating could improve the electrochemical properties of magnesium and provide effective protection, which can improve the corrosion resistance of magnesium.