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Measurement uncertainty calculation is an important and complicated problem in digitised components inspection. In such inspections, a coordinate measuring machine (CMM) and laser scanner are usually used to get the surface point clouds of the component in different postures. Then, the point clouds are registered to construct fully connected point clouds of the component’s surfaces. However, in most cases, the measurement uncertainty is difficult to estimate after the scanned point cloud has been registered. This paper aims to propose a simplified method for calculating the uncertainty of point cloud measurements based on spatial feature registration.

In the proposed method, algorithmic models are used to calculate the point cloud measurement uncertainty based on noncontact measurements of the planes, lines and points of the component and spatial feature registration.

The measurement uncertainty based on spatial feature registration is related to the mutual position of registration features and the number of sensor commutation in the scanning process, but not to the spatial distribution of the measured feature. The results of experiments conducted verify the efficacy of the proposed method.

The proposed method provides an efficient algorithm for calculating the measurement uncertainty of registration point clouds based on part features, and therefore has important theoretical and practical significance in digitised components inspection.

This study aims to explore the degree of Chinese consumers' trust and confidence in the Chinese dairy products supply chain and the relationships between trust and overall confidence in dairy products safety and quality.

This study collected data from 1,278 respondents by field survey from five provinces of China. The data were analyzed using ordered logit model.

This study shows the following results: (1) Chinese consumer confidence in domestic dairy products and trust in actors of the dairy chain are at a moderate-to-low level. (2) Government regulators are considered to take the most responsibility, with both an optimism-enhancing and a pessimism-reducing effect (the former effect is greater), while perceived trust in dairy farmers and retailers has little effect. (3) Perceived care has both an optimism-enhancing and a pessimism-reducing effect, and the former effect is stronger. Competence and openness have an optimism-enhancing effect and a pessimism-reducing effect, respectively. (4) The importance of the three dimensions of trust related to optimism-increasing and pessimism-reduction is limited, except in the case of government regulators.

This study contributes to a better understanding of consumer trust in food safety and also help demonstrate to the actors and institutions involved in the dairy supply chain the best way to improve the performance of their duties to meet the consumers' needs for safe and quality dairy products.

China's increasing income inequality might cause a series of problems, such as the slowdown of economic growth, social and economic tension, the decline of the ecological environment quality and the threat to citizens' health. Consequently, income inequality will inevitably affect the ecological well-being performance (EWP) level of China's provinces through the above aspects. Analyzing the impact of income inequality on EWP and its spatial spillover effects are conducive to improving the level of EWP in China. Therefore, the research purpose of this paper is to use China's provincial data from 2001 to 2017 to analyze the impact of income inequality on EWP and the spatial spillover effect based on the evaluation of the EWP value of each province.

At first, this study utilizes the super efficiency slacks-based measure model (Super-SBM model) to calculate the EWP values of 30 provinces in China, which can evaluate and rank the effective decision units in the SBM model and make up for the defect that the effective decision units cannot be distinguished. Then this study applies the spatial Durbin model and Tobit regression model (SDM-Tobit model) to explore the impact of income inequality and other influencing factors on EWP and the spatial spillover effects in adjacent areas.

Firstly, the average EWP in China fluctuated slightly and showed a downward trend from 2001 to 2017. In addition, the EWP values of the provinces in the western region are usually weaker than those in the eastern and central regions. Moreover, income inequality is negatively correlated with EWP, and the EWP has a spatial spillover effect, which means the EWP level in a region is affected by EWP values in the adjacent regions. Furthermore, the industrial structure and urbanization level are both negatively related to EWP, while technology level, investment openness, trade openness and education level are positively related to EWP.

Compared with the existing research, the possible contribution of this research is that it takes income inequality as one of the important influencing factors of EWP and adopts the SDM-Tobit model to analyze the impact mechanism of income inequality on EWP from the perspective of time and space, providing new ideas for improving the EWP of various provinces in China.

The purpose of this paper is to gain an in-depth understanding of the research progress, hotspots and future trends in the field of functional clothing.

The records of 4,153 pieces of literature related to functional clothing were retrieved from Web of Science by using a comprehensive retrieval strategy. A piece of software, CiteSpace was used as a tool to visualize the results of specific terms, such as author, institution and keyword. By analyzing the knowledge maps with several indicators, the intellectual basis and research fronts for the functional clothing domain could then be demonstrated.

The result indicated that functional clothing was a popular research field, with approximately 500 papers published worldwide in 2020. Its main research area was material science and involved public environmental and occupational health, engineering, etc. showing the characteristic of multi-interdisciplinary. Textile Research Journal and International Journal of Clothing Science and Technology were the top two journals in this field. The USA, China, Australia, England and Germany have been active and frequently cooperating with each other. Donghua University, the Hong Kong Polytechnic University and NASA, with the largest number of publications, were identified as the main research drivers. According to the co-citation analysis, thermal stress, nanogenerator and electrospinning were the topics of most cited articles during the past 20 years.

The findings identified smart clothing and protective clothing to be the research frontiers in the field of functional clothing, which deserved further study in the future.

The outcomes offered an overview of the research status and future trends of the functional clothing field. It could not only provide scholars with convenience in identifying research hotspots and building potential cooperation in the follow-up research, but also assist beginners in searching core scholars and literature of great significance.

Presently, a wide range of polyurethane adhesives can be obtained using different kinds of polyols and isocyanates. However, the applied temperature of the polyurethane adhesive is not more than 80°C. The film of polyurethane adhesive will be softened and deformed when its applied temperature is more than 100°C. Thus, the mechanical property of the polyurethane adhesive is decreased clearly, which limits its further application. The purpose of the study is to improve the heat resistance of polyols, especially polyester polyols and its resultant polyurethane adhesives.

The more rigid benzene ring is introduced into the polyester polyols to improve the heat resistance of its resultant polyurethane adhesive.

The more rigid benzene ring has ben introduced into the polyester polyols and the heat resistance of its resultant polyurethane adhesive is improved.

The polyester polyols with more rigid benzene ring have been prepared successfully by the vacuum melting method when diethylene glycol, neopentyl glycol, 1,6-hexanediol, ethanediol, isophthalic acid, terephthalic acid, sebacic acid and adipic acid are used as raw materials and tetra-isopropyl titanate is adopted as the catalyst.

The aim of this study is to present a novel approach based on semantic fingerprinting and a clustering algorithm called density-based spatial clustering of applications with noise (DBSCAN), which can be used to convert investor records into 128-bit semantic fingerprints. Inventor disambiguation is a method used to discover a unique set of underlying inventors and map a set of patents to their corresponding inventors. Resolving the ambiguities between inventors is necessary to improve the quality of the patent database and to ensure accurate entity-level analysis. Most existing methods are based on machine learning and, while they often show good performance, this comes at the cost of time, computational power and storage space.

Using DBSCAN, the meta and textual data in inventor records are converted into 128-bit semantic fingerprints. However, rather than using a string comparison or cosine similarity to calculate the distance between pair-wise fingerprint records, a binary number comparison function was used in DBSCAN. DBSCAN then clusters the inventor records based on this distance to disambiguate inventor names.

Experiments conducted on the PatentsView campaign database of the United States Patent and Trademark Office show that this method disambiguates inventor names with recall greater than 99 per cent in less time and with substantially smaller storage requirement.

A better semantic fingerprint algorithm and a better distance function may improve precision. Setting of different clustering parameters for each block or other clustering algorithms will be considered to improve the accuracy of the disambiguation results even further.

Compared with the existing methods, the proposed method does not rely on feature selection and complex feature comparison computation. Most importantly, running time and storage requirements are drastically reduced.

This study aims to explore how transformational leadership influences employees' creativity by considering the role of the environment and psychology. The study aims to provide insights into the impact of transformational leadership on team innovation climate, team reflexivity, psychological capital and employee creativity while also examining the moderating effect of environmental dynamism on these relationships.

This study employed a multi-source, multi-wave approach, utilizing data from 618 participants in 118 teams. It constructed a multilevel structural equation model and estimated the confidence intervals of the mediated and moderated effects using the Markov chain Monte Carlo method.

The results of the multilevel analyses indicated that transformational leadership positively influenced the team innovation climate, team reflexivity, psychological capital and employee creativity. Moreover, the study found that environmental dynamism positively moderates the relationships among transformational leadership, team reflexivity, psychological capital and employee creativity.

Drawing on social cognitive theory and the motivated information processing in groups model, this study offers new insights into the interplay between transformational leadership and creativity. It examines the moderating role of cross-level process linkages and environmental dynamism, thereby validating and extending relevant theories.