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The purpose of this paper is to propose sentiment annotation at sentence level to reduce information overloading while reading product/service reviews in the internet.

The keyword-based sentiment analysis is applied for highlighting review sentences. An experiment is conducted for demonstrating its effectiveness.

A prototype is built for highlighting tourism review sentences in Chinese with positive or negative sentiment polarity. An experiment results indicates that sentiment annotation can increase information quality and user’s intention to read tourism reviews.

This study has made two major contributions: proposing the approach of adding sentiment annotation at sentence level of review texts for assisting decision-making; validating the relationships among the information quality constructs. However, in this study, sentiment analysis was conducted on a limited corpus; future research may try a larger corpus. Besides, the annotation system was built on the tourism data. Future studies might try to apply to other areas.

If the proposed annotation systems become popular, both tourists and attraction providers would obtain benefits. In this era of smart tourism, tourists could browse through the huge amount of internet information more quickly. Attraction providers could understand what are the strengths and weaknesses of their facilities more easily. The application of this sentiment analysis is possible for other languages, especially for non-spaced languages.

Facing large amounts of data, past researchers were engaged in automatically constructing a compact yet meaningful abstraction of the texts. However, users have different positions and purposes. This study proposes an alternative approach to add sentiment annotation at sentence level for assisting users.

Branched articulated robots (BARs) are highly non-linear systems; accurate dynamic identification is critical for model-based control in high-speed and heavy-load applications. However, due to some dynamic parameters being redundant, dynamic models are singular, which increases the calculation amount and reduces the robustness of identification. This paper aims to propose a novel methodology for the dynamic analysis and redundant parameters elimination of BARs.

At first, the motion of a rigid body is divided into constraint-dependent and constraint-independent. The redundancy of inertial parameters is analyzed from physical constraints. Then, the redundant parameters are eliminated by mapping posterior links to their antecedents, which can be applied for re-deriving the Newton–Euler formulas. Finally, through parameter transformation, the presented dynamic model is non-singular and available for identification directly.

New formulas for redundant parameters elimination are explicit and computationally efficient. This unifies the redundant parameters elimination of prismatic and revolute joints for BARs, and it is also applicable to other types of joints containing constraints. The proposed approach is conducive to facilitating the modelling phase during the robot identification. Simulation studies are conducted to illustrate the effectiveness of the proposed redundant parameters elimination and non-singular dynamic model determination. Experimental studies are carried out to verify the result of the identification algorithm.

This work proposes to determine and directly identify the non-redundant dynamic model of robots, which can help to reduce the procedure of obtaining the reversible regression matrix for identification.

Currently, there is a dearth of research studies regarding macro analysis of the workforce productivity of the US construction industry. The purpose of this paper is to calculate the workforce productivity changes of the US construction industry from 2006 to 2016, with the number of laborers as input and value of construction industry as output.

The present study introduced the data envelopment analysis (DEA) based Malmquist productivity index model to measure the workforce productivity of the US construction industry from 2006 to 2016.

The results indicated that the workforce productivity of the US construction industry experienced a continuous decline, except for the increases from 2011 to 2013 and from 2014 to 2015. It was also shown that there were gaps in the workforce productivity development level among all states and nine regions in the US construction industry. Besides, the relationship between workforce productivity and four aspects, including real estate price, workforce, climate distribution and economic factors, was analyzed.

The calculation of the productivity of the US construction industry is based on the premise that the external environment is fixed and unchanged from 2006 to 2016, but the multi-level DEA model for further calculation is required for obtaining more effective conclusions.

This paper measures the workforce productivity of the US construction industry over the past 11 years, which added latest analysis and knowledge into the construction industry, providing decision-makers with advice and data support to formulate policies to improve workforce productivity.

This study provided both government decision-makers and industrial practitioners with important macro background environment information, which will facilitate the improvement of workforce productivity in the construction industry in different regions of the US.

Team cohesion and other team processes are inherently dynamic mechanisms that contribute to team effectiveness. Unfortunately, extant research has typically treated team cohesion and other processes as static, and failed to capture how these processes change over time and the implications of these changes. In this chapter, we discuss the characteristics of team process dynamics and highlight the importance of temporal considerations when measuring team cohesion. We introduce innovative research methods that can be applied to assess and monitor team cohesion and other process dynamics. Finally, we discuss future directions for the research and practical applications of these new methods to enhance our understanding of the dynamics of team cohesion and other processes.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.