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The purpose of this study is to empirically examine the effects of the numerical cues (NC) used in online review comments on perceived review helpfulness and the underlying psychological mechanisms.

An experimental design approach was employed to investigate the proposed research questions. Two experiments were conducted to test the hypotheses. Mplus 7 and Stata 14.0 were used for data analysis.

Empirical findings support the positive correlation between the presence of NC in online review comments and perceived review helpfulness across different product categories. This relationship is mediated by two psychological responses of consumers: cognitive elaboration and credibility perception.

This research adds to the existing literature by focusing on the value of NC in online review comments and how they can enhance perceived review helpfulness. Some practical implications are also addressed. For example, marketers can encourage consumers to post reviews that contain quantitative information to facilitate their target markets' comprehension of a product or brand.

Building on the previous literature, the work adds incremental knowledge on the role NC in online review comments play in affecting consumers' perceptions. In addition, the research uncovers the underlying psychological responses that mediate the relationship between NC in review comments and perceived review helpfulness.

The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images.

First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data are used to fine-tune the first two convolutional layers of the pre-trained network to achieve the adaptation to the local features of the cross-modal data. For the cross-modal data of different band, an autoencoder is designed to achieve data conversion and local feature extraction.

The experimental results show the effectiveness and practicability of the proposed method. Compared with the ordinary method, the proposed method gets much better metrics.

The originality is the transfer learning strategy that fine-tunes the low-level layers for the cross-modal data application. The proposed method can achieve satisfied road segmentation with a small amount of cross-modal training data, so that is has a good application value. Still, for the similar application of cross-modal data, the idea provided by this paper is helpful.

Nowadays, the use of forged e-passport is increasing, which is threatening national security. It is important to improve the national security against international crime or terrorism. There is a weak verification process caused by lack of identification processes such as a physical check, biometric check and electronic check. The e-passport can prevent the passport cloning or forging resulting from the illegal immigration. The paper aims to discuss these issues.

This paper focuses on face recognition to improve the biometric authentication for an e-passport, and it also introduces facial permanent mark detection from the makeup or cosmetic-applied faces, twins and similar faces. An algorithm is proposed to detect the cosmetic-applied facial permanent marks such as mole, freckle, birthmark and pockmark. Active Shape Model into Active Appearance Model using Principal Component Analysis is applied to detect the facial landmarks. Facial permanent marks are detected by applying the Canny edge detector and Gradient Field Histogram of Oriented Gradient.

This paper demonstrated an algorithm and proposed facial marks detection from cosmetic or makeup-applied faces for a secure biometric passport in the field of personal identification for national security. It also presented to detect and identify identical twins and similar faces. This paper presented facial marks detection from the cosmetic-applied face, which can be mixed with traditional methods. However, the use of the proposed technique faced some challenges due to the use of cosmetic. The combinations of the algorithm for facial mark recognition matching with classical methods were able to attain lower errors in this proposed experiment.

The proposed method will enhance the national security and it will improve the biometric authentication for the e-passport. The proposed algorithm is capable of identifying facial marks from cosmetic-applied faces accurately, with less false positives. The proposed technique shows the best results.

The purpose of this paper is to conceptualize the use of Twitter hashtag as a strategy to enhance the visibility and symbolic power of social movement-related information. It examined how characteristics of hashtag drove information virality during a networked social movement.

Twitter data from two days during the Occupy Wall Street Movement in 2011 were collected. With network analysis, the authors identified popular hashtag types and examined hashtag co-occurrence patterns during the two contrasting movement days. It also provides a comparative analysis of how major types of viral hashtag may play different roles depending on different movement cycles.

The authors found that the role of hashtag influencing information virality may vary based on the context of the tweets. For example, movement participants applied more strategic hashtag combinations during the unexpected event day to reach different social circles. Consistent patterns were identified in mobilizing influential actors such as public figures. Different use patterns of media outlet hashtag were found across the two days.

Implications on how hashtag type and event dynamics may shape hashtag co-occurrence patterns were discussed.

Both geometric and non-geometric parameters have noticeable influence on the absolute positional accuracy of 6-dof articulated industrial robot. This paper aims to enhance it and improve the applicability in the field of flexible assembling processing and parts fabrication by developing a more practical parameter identification model.

The model is developed by considering both geometric parameters and joint stiffness; geometric parameters contain 27 parameters and the parallelism problem between axes 2 and 3 is involved by introducing a new parameter. The joint stiffness, as the non-geometric parameter considered in this paper, is considered by regarding the industrial robot as a rigid linkage and flexible joint model and adds six parameters. The model is formulated as the form of error via linearization.

The performance of the proposed model is validated by an experiment which is developed on KUKA KR500-3 robot. An experiment is implemented by measuring 20 positions in the work space of this robot, obtaining least-square solution of measured positions by the software MATLAB and comparing the result with the solution without considering joint stiffness. It illustrates that the identification model considering both joint stiffness and geometric parameters can modify the theoretical position of robots more accurately, where the error is within 0.5 mm in this case, and the volatility is also reduced.

A new parameter identification model is proposed and verified. According to the experimental result, the absolute positional accuracy can be remarkably enhanced and the stability of the results can be improved, which provide more accurate parameter identification for calibration and further application.

Electrochromic window (ECW) has been gradually applied to building engineering in recent years. However, empirical study of this technology used in green building design is still lacking. This study aims to verify the lighting and energy-saving performance of ECW under a specific geographical environment.

The meditation pavilion of Jinwan new materials research institute in Zhuhai is taken for research object. Three kinds of sun-shading techniques, namely, ECW, Low-E window and ordinary glass window, with same specifications are selected as the building facade structure for simulation. Day lighting analysis, sun-shading performance and annual energy consumption are separately simulated in the same environment by the Autodesk Ecotect Analysis software. The energy-saving performance of ECW is obtained by comparisons.

Result shows that the shading performance of ECW is much better than ordinary window and Low-E window. When ECW is used in the east, west or top lighting interfaces of a building, about 40% of the total solar radiation can be reduced during daytime in summer. Taking the ordinary glass window as a basic reference, ECW can save about 90% of the annual energy consumption of the glass house. ECW can effectively reduce the annual refrigeration energy consumption of buildings in the subtropical region.

Reasonable use of ECW in the subtropical region can effectively reduce the annual energy consumption of buildings.

It is a precedent study to analyze the lighting performance and energy consumption of a glass house with ECW. The energy-saving characteristics and beautiful appearance of ECW shall make it a future green building technology.

The purpose of this paper is to explore the relationship between different mechanisms for integrating national archival resources, leading to the development of a knowledge management-based framework for facilitating the optimal utilization of national archives resources in China.

The study adopts a mixed-methods approach consisting of a review of existing mechanisms for integrating national archival resources and site visits and interviews for investigating the effectiveness of these mechanisms in real world practices for managing and utilizing national archives resources in China.

The knowledge management-based framework is shown to be useful for building interdependence, interconnections and interactions between diversified integration mechanisms to meet national, societal and public needs in China. The framework is significant for rearranging the role of knowledge management in collaboration to protect the value of national archives resources as the knowledge asset of a state. It can reengineer the knowledge management activities in communication to share the value of national archives resources as the knowledge asset of a society. Furthermore, the framework can reconfigure knowledge management artifacts in connectivity to increase the value of national archives resources as the knowledge asset of the public.

The review of the related literature is selective rather than comprehensive, and the interviews are based on a self-selected convenience sample, which is rather skewed demographically.

The proposed framework can be used to guide the development of a national strategy for the effective integration of national archives resources to promote a better delivery of public-centric knowledge services in a specific society.

This study provides a knowledge management framework for the effective integration of national archives resources toward their optimal utilization. It contributes to the examination of the role of different integration mechanisms in interactive knowledge management processes and the advancement of academic debate on the effectiveness of these mechanisms for the integration of national archives resources to facilitate their optimal utilization in China.

Big data is a key component to realise the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The purpose of this paper is to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers.

The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP consultants and project managers. The qualitative data collected were then systematically analysed by using a thematic analysis approach.

A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers.

This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision making.

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.

This research examines the impact of the National Rural E-commerce Comprehensive Demonstration Project (NRECDP) on poverty reduction and income growth in rural China.

The study develops a theoretical framework, which considers the role of geographical, technological, institutional and cultural factors for the e-commerce poverty alleviation (e-CPA) model. Empirically, this study applies the difference-in-differences (DID) model and the event study approach to evaluate the effectiveness of NRECDP on the basis of large-scale county-level and household-level panel data spanning 2010 to 2020.

The study found that the NRECDP, as a government-led, information and communication technology (ICT)-enabled, market-based program, has led to a significant increase in per capita output of primary industry employees, as well as in the disposable income of rural residents, especially those in national-level poverty-stricken (NP) counties. The interventions of the NRECDP achieved these positive outcomes through transportation and Internet infrastructure improvement, ICT adoption and human capital accumulation in impoverished towns and villages in remote rural areas. These effects are larger in the eastern region of China, followed by the central region, whereas the weakest effects were found in the western region. However, we found little evidence of the NRECDP increasing household developmental expenditure.

The study findings have important practical and policy implications for rural e-commerce development and self-sustained poverty alleviation solutions. The research revealed the significance of government NRECDP interventions for increasing rural income, reducing living costs, and empowering the rural population in its multiple social roles, namely, as consumers, producers, employees and microentrepreneurs. The local cultural context may also play a role in ICT adoption and entrepreneurship cultivation with a downstream effect on the effectiveness of e-CPA practices. Policymakers would need to ensure a supportive entrepreneur-friendly environment for rural e-commerce development and continue implementing progressive policies for poverty alleviation.

This study explores poverty alleviation issues in China by developing for the first time a multi-faceted framework that is subsequently tested by both county-level and household-level large-scale observations. Also, it is the first study to provide nationwide empirical evidence on the effectiveness of e-CPA in narrowing down the spatial and digital divides in China. In addition to the impact of geography, technology and governmental support, this study also sheds light on the role of culture in the adoption and diffusion of digital technologies and as a source of local entrepreneurial opportunities.