Search results

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Developed countries control pandemics using smart decisions and processes based on medical standards and modern technologies. Studies on risk-reduction and humantechnology interaction are scarce. This study developed a model to examine the relationship between citizens, pandemic-related technology and official safety practices.

This study investigated the mediating role of new health regulations and moderating role of safety incentives due to COVID-19 case reduction in pandemic severity control. This study included 407 operations managers, nursing staff conducting pandemic testing and reporting, doctors and security personnel in China. An artificial neural network (ANN) was used to check nonlinear regressions and model predictability.

The results demonstrated the impact of the introduction of new technology protocols on the implementation of new health regulations and aided pandemic severity control. The safety incentive of case reductions moderated the relationship between new health regulations and pandemic severity control. New health regulations mediated the relationship between the introduction of new technology protocols and pandemic severity control.

Further research should be conducted on pandemic severity in diversely populated cities, particularly those that require safety measures and controls. Future studies should focus on cloud computing for nurses, busy campuses and communal living spaces.

Authorities should involve citizens in pandemic-related technical advances to reduce local viral transmission and infection. New health regulations improved people's interactions with new technological protocols and understanding of pandemic severity. Pandemic management authorities should work with medical and security employees.

This study is the first to demonstrate that a safety framework with technology-oriented techniques could reduce future pandemics using managerial initiatives.

The purpose of this paper is to examine the influence of clients' trust, opportunism and adaptation on contractual (non)cooperation with a mediating role of coordination in the construction industry.

A questionnaire was used to collect data from employees of the Pakistani construction industry. Smart partial least square (SmartPLS) has been used for analyzing the data of 270 respondents from construction projects.

The results of the SmartPLS indicate that (1) Trust and contract coordination positively while opportunism negatively influence contractor's contractual cooperation. (2) Contract adaptation and contract coordination positively influence the noncontractual cooperation of the contractor. (3) Moreover, contract coordination positively mediates the relationship between trust and noncontractual cooperation, but negatively mediates the relationship between opportunism and contract adaptation and noncontractual cooperation.

The findings of this research suggest several policy implications for administrative authorities, project managers and policymakers. These authorities need to focus on clients' trust, opportunism and adaptation because these factors significantly influence contract coordination and cooperation in the construction industry. Emphasizing these factors will enable project managers to gain economies of scale and mitigate project failure.

To the best of the authors’ search and knowledge, they did not find any study examining the mediating role of coordination between trust, opportunism, adaptation and cooperation in the construction industry. Hence, the present study advances their understanding in the field of project management and construction business.

This study aims to examine the role of authenticity in tourists’ destination selection, analyze the factors that influence tourists to form their initial opinions and explore how tourists construct the authenticity of traditional villages. The authors selected Chengkan village in Huizhou district, Huangshan city, as a case. In the study, the authors constructed an attribute-hardware-software research framework and analyzed tourists’ authentic emic experiences from the perspective of constructivism. The findings of this study suggest that tourists’ destination selection is influenced by authenticity. The destination culture brokers who interact with tourists play an essential role in forming authentic experiences. According to differences in how tourists construct authenticity, the study divided tourists into three types: primitive imagination, aesthetic reality and rational cognition. The results of this study provide a deeper understanding of various viewpoints about authenticity research and contribute to the academic discussion on how to understand the authenticity of unique cultural heritage sites such as traditional villages in the context of tourism development.

This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.

The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.

Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.

The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

Social media fatigue (SMF) has been widely recognized; however, previous studies have included various concepts into a single fatigue construct. Fatigue has typically been explored from the stressor-strain-outcome (SSO) or stimulus-organism-response (SOR) perspectives. To further investigate SMF, the authors split it into the two constructs of exhaustion and disinterest. Furthermore, the authors introduced the concept of emotional labor and identified rules that may affect surface and deep acting strategies.

The authors designed and conducted a survey to collect data from social networking platform users.

Results from 364 users of social networking platforms supported most of the authors' hypotheses. First, most of the display rules affect the choice of deep or surface acting. Second, both types of acting lead to exhaustion, but only surface acting leads to disinterest. Third, discontinuance intention is affected by both types of fatigue.

This study contributes to SMF research by adding more antecedents (deep and surface acting) based on the emotional labor perspective and showing the impacts of communication rules on emotional labor. In addition, this study also distinguishes disinterest-style fatigue from exhaustion.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

Integrating transaction costs economics and task-technology fit theory, this study distinguishes two categories of social media–enabled interactions, namely task-related interactions and tie-related interactions, and explores the match between these two and firms' use of contracts in achieving safeguarding and coordinating purposes in interfirm governance.

Two studies were conducted to test the hypotheses. In Study 1, this study collaborated with a professional market research firm and collected responses from Chinese manufacturing firms in a survey. In Study 2, this study designed a scenario-based experiment and collected 239 participants from the Credamo platform.

This study categorized social media–enabled interactions into task-related interactions and tie-related interactions and conducted two studies to reveal that the safeguarding purpose of contract specificity is amplified by tie-related interactions, whereas the coordinating purpose of contract specificity is strengthened by task-related interactions.

This study assumes that firms permit and encourage the use of social media. However, some firms might prohibit the use of social media due to risk issues, or their partners may be prohibited from using social media.

Given that social media–enabled interactions have joint effects with contracts in achieving safeguarding and coordinating purposes, a firm's employees should match their goals with an appropriate type of social media–enabled interactions.

This study enriches the interfirm governance literature by uncovering the roles of these two types of interactions in matching contract specificity to achieve safeguarding and coordinating purposes, which provides actionable insights for managers in governing interfirm relationships.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

The interaction between evaluators is underestimated in legitimacy literature. This study aims to examine the impact of CEO celebrity on initial public offerings (IPOs) underpricing in Strategic Emerging Industries (SEIs). Based on legitimacy and limited attention effect, this study introduces a new antecedent to the asset pricing literature under a particular sample.

This paper illustrates how CEO celebrity promotes IPO underpricing by enhancing the legitimacy and then explores how the CEO characteristics can moderate this relationship. Using 1,128 IPO companies in China SEIs from 2010 to 2019, cross-section data is used to build a multiple linear regression model to test the hypotheses.

The result indicates that CEO celebrity is positively related to IPO underpricing. Founder CEO and CEO duality amplify the relationship. Further analysis shows that the relationship between CEO celebrity and IPO underpricing is more pronounced in firms with high Baidu search and low market sentiment.

This study provides insights into how CEO celebrity as notable internal information shapes the formation of investors' preliminary impressions of firms. The evidence consists of legitimacy and limited attention perspective by showing how investors favor, follow and hype the stocks with celebrity CEOs. The results extend the knowledge about how CEO characteristics influence information frictions in asset pricing during IPO.