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The purpose of this paper is to investigate the impact of buyer-seller interpersonal interactions on the purchase intention of buyers, incorporating swift guanxi as a mediator.

Based on survey data obtained from 336 Taobao Live users, PLS techniques were used to test hypotheses.

Swift guanxi exists in buyer-seller interactions and matters, as it drives buyers' purchase intention in live stream shopping. Perceived expertise, perceived similarity and perceived likeability are found to be the three essential interpersonal interaction factors promoting the formation of swift guanxi. Perceived familiarity is also found to be significant but to a lesser extent. In addition, all these interpersonal interaction factors are found to significantly affect purchase intention through the mediation of swift guanxi.

Swift guanxi has been less explored in live stream shopping. This study takes the lead in empirically examining the mediating role of swift guanxi in the relationship between interpersonal interaction factors and purchase intention and offers a description of key buyer-seller interpersonal interaction factors (perceived expertise, perceived similarity and perceived likeability), thereby helping to extend the swift guanxi literature in social commerce.

At present the energy generation and distribution landscape is changing rapidly. The energy grid is becoming increasingly smart, relying on an information network for the purposes of monitoring and optimization. However, because of the particularly stringent regulatory and technical constraints posed by smart grids, it is not possible to use ordinary communication protocols. The purpose of this paper is to revisit such constraints, reviewing the various options available today to realize smart‐metering networks.

After describing the regulatory, technological and stakeholders' constraints, the authors provide a taxonomy of network technologies, discussing their suitability and weaknesses in the context of smart‐metering systems. The authors also give a snapshot of the current standardization panorama, identifying key differences among various geographical regions.

It is found that the field of smart‐metering networks still consists of a fragmented set of standards and solutions, leaving open a number of issues relating to the design and deployment of suitable systems.

This paper addresses the need to better understand state‐of‐the‐art and open issues in the fast‐evolving area of smart energy grids, with particular attention to the challenges faced by communication engineers.

Additive manufacturing (AM) has shown its capability in producing complex geometries. Due to the additive nature, the in situ layer-wise inspection of geometric accuracy is essential to making AM reach its full potential. This paper aims to propose a novel automated in-plane alignment and error quantification framework to distinguish the fabrication, measurement and alignment errors in AM.

In this work, a multi-resolution framework based on wavelet decomposition is proposed to automatically align two-dimensional point clouds via a polar coordinate representation and then to differentiate errors from different sources based on a randomized complete block design approach. In addition, a two-stage optimization model is proposed to find the best configuration of the multi-resolution framework.

The proposed framework can not only distinguish errors attributed to different sources but also evaluate the performance and consistency of alignment results under different levels of details.

A sample part with different featured layers, including a simple free-form layer, a defective layer and a layer with internal features, is used to illustrate the effectiveness and efficiency of the proposed framework. The proposed alignment method outperforms the widely used iterative closest point algorithm.

This work fills a research gap of state-of-the-art studies by automatically quantifying different types of error inherent in manufacturing, measuring and part alignment.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

This study examines the heterogeneous correlations between rural farmers' land renting behavior and their grain production when they experienced a significant price decline.

We used well-timed panel data obtained from a two-round survey held in 2013 and 2017 among 621 households in the North China Plain. The empirical analyses were conducted by using the pooled ordinary least squares (OLS) and fixed effects models.

Rural tenants were having heterogeneous responses in land renting behavior and agricultural production when there was a price decline. A group of optimistic tenants (as professional farmers) were more likely to enlarge the farm scale for grain production through land rental markets but decrease variable investment levels (and subsequently decreased productivity) to cope with price decline. In contrast, nonprofessional farmers (the other rural tenants) were rather pessimistic about market performance, and they significantly decreased their grain production area to cope the price decline, but there was no decrease in grain productivity through reducing variable inputs.

This study contributes to the extant literature on the relationship between farmers' land renting-in behavior and agricultural production. By dividing the tenants into professional and nonprofessional farmers, we argue that there is a significant heterogeneous correlation between rural tenants' land renting behavior and grain production when farmers experience a price decline.

The purpose of this paper is to address the role of social robots in the education industry, specifically within special developmental schools, as a part of an innovation technology portfolio. It identifies critical success factors (CSFs) arising from the development, adoption and implementation of social robots to educate students with special needs and assist their teachers.

The study engaged in longitudinal research over 12 months, tracking the role of the Matilda robot in providing educational services to students with special needs.

The results propose a three-faceted framework for social robot application in special education: development, adoption and implementation.

The study has shown the willingness of students and teachers to embrace social robot technology, and the CSF that arise from this adoption. It has also found that social robots achieve the greatest success within the development, adoption and implementation framework when championed by executive management, and peer teacher support.