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High temperature gradient induces high residual stress, producing an important effect on the part manufacturing during laser powder bed fusion (LPBF). The purpose of this study is to investigate the effect of the molten pool mode on the thermal stress of Ti-6Al-4V alloy during different deposition processes.

A coupled thermal-mechanical finite element model was built. The developed model was validated by comparing the numerical results with the experimental data in the maximum molten pool temperature, the molten pool dimension and the residual stress described in the previous work.

For the single-track process, the keyhole mode caused an increase in both the maximum stress and the high-stress area compared with the conduction mode. For the multitrack process, a lower tensile stress around the scanning track and a higher compressive stress below the scanning track were found in the keyhole mode. For the multilayer process, the stress along the scanning direction at the middle of the part changed from tensile stress to compressive stress with the increase in the deposition layer number. As the powder layer number increased, the stress along the scanning direction near the top surface of the part decreased while the stress along the deposition direction obviously increased, indicating that the stress along the deposition direction became the dominant stress. The keyhole mode can reduce the residual stress near the top of the part, and the conduction mode was more likely to produce a low residual stress near the bottom of the part.

The results provide a systematic understanding of thermal stress during the LPBF process.

The purpose of this paper is to examine the important application value of extending the concept of classification rule, so that it can describe and measure the uncertainty of classification knowledge.

The rough concept lattice (RCL), which is an effective tool for uncertain data analysis and knowledge discovery, reflects a kind of unification of concept intent and upper/lower approximation extent, as well as the certain and uncertain relations between objects and attributes.

A classification rules extraction algorithm, extraction algorithm of classification rule (EACR), based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule. The algorithm EACR is experimentally validated by taking the star spectrum data as the decision context.

An efficient way for classification rule extraction is provided.

The algorithm EACR based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule.

The purpose of this paper is to investigate the impact of corporate social responsibility (CSR) on the performance of service innovation (PSI), and the mediating effect of knowledge acquisition.

Drawing on social exchange and knowledge management theories, this paper establishes a relevant conceptual model and adopts a hierarchical regression analysis to examine the model with a data set of 298 firms from China.

CSR positively affects the PSI; however, the effects vary when firms take responsibility for different stakeholders. CSR for the same group of stakeholders influences differently the short-term financial and long-term non-financial PSI, whereas knowledge acquisition mediates the impact of employee and customer CSR on PSI, but not the impact of community CSR on PSI.

Managers could improve the PSI of the firm by strategically assuming CSR and by managing corporate knowledge acquisition activities.

This study contributes to the service innovation literature by identifying the influence of particular types of CSR on PSI, and by highlighting the influencing mechanism of knowledge acquisition. It extends scholarly understanding of the antecedents of PSI as well as the business returns to CSR.

The purpose of this paper is to investigate the impact of online service quality on swift guanxi that influences customer repurchase intention and the moderating role of gender in the relationship between online service quality and swift guanxi.

Based on survey data obtained from 274 college students, structural equation modeling was used to test the research hypotheses.

The results show that the dimensions of online service quality (i.e. perceived control, convenience and customer service) are positively related to buyer-seller swift guanxi, which influences repurchase intention. Additionally, men are driven mainly by perceived control and service convenience when establishing swift guanxi, whereas women care more about customer service.

Online service quality is the key factor underlying customer repurchase intention. In addition, gender differences exist in many aspects of online shopping including information processing, perceived service, and perceived risk. This is one of the first empirical studies that empirically examine the effects of three dimensions of online service quality on swift guanxi and take gender differences into consideration. Based on the conceptual and empirical evidence, this study provides the practical and theoretical implications of these findings.

The paper attempts to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a two-part tariff coordination mechanism.

To deal with this pricing conflict problems of dual-channel supply chain consisting of dominant manufacturer and a retailer, considering the fact that online reviews and in-sale service are important factors on consumers’ purchase decisions, the authors establish some basic models and exploit them to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a profit-sharing coordination mechanism.

The results show that the optimal online direct selling price is positively correlated with product perceived quality obtained from online reviews and negatively correlated with the in-sale service. The traditional retail price is positively correlated with the in-sale service and weakly correlated with online reviews. For the manufacturer and retailer, whether decentralized decision or coordination contract, their profits increase with the increase of the in-sale service in a certain range and quality perceived from spontaneous online reviews. Online reviews and in-sale service are important factors on consumers’ purchase decisions. Positive in-sale services and online reviews can provide consumers with a better shopping experience, thereby promoting their enthusiasm for shopping and improving their quality of life. The two-part tariff coordination mechanism improves the profits of the manufacturer and the traditional retailer, respectively, through the transfer fee.

The proposed approach can well analyze the channel conflicts and pricing problems between retailers and manufacturers with respect to product offline price and online price. The analysis and results can inform decision-making for manufacturers and retailers.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

Periodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.

In this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.

To improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.

This paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.

Digital humanities database is one of the essential tools in digital humanities research area. Therefore, examining the usage of digital humanities database in academic papers is conducive to assessing the value of digital humanities database for scientific research activities and improving the construction of digital humanities infrastructure.

This paper constructs an evaluation system of digital humanities database from the perspective of academic influence and social influence, with mention frequency, usage motivation, platform access data, usage region and usage discipline as indicators and takes China Biographical Database Project as the empirical object to explore the usage of digital humanities database in China.

The data analysis result demonstrates that digital humanities databases are widely used and recognized in China. However, the problem of low actual usage remains.

This paper constructs the digital humanistic database's evaluation system and discusses applying the digital humanistic database in China, which provides a new perspective and method for the influence evaluation study of the digital humanistic database.

This study examines the effects of risk reduction, seller reputation, eWOM and warranties on consumer confidence and online impulse buying intentions through the lens of the stimulus-organism-response (SOR) model. It also explores the role of installment options in online impulse buying.

We performed a between-subjects experiment (installment vs. no-installment option). Data were collected from 199 consumers and analyzed using SmartPLS 3.3.9.

The results show that risk reduction, eWOM and warranty positively affect consumer confidence (CC) in online shopping and that CC positively predicts online impulse buying intentions. It was also found that the effect of CC on online impulse buying intentions was stronger when consumers were offered an installment option than a no-installment option.

Findings suggest that online sellers should invest more resources in building consumer confidence, which affects online impulse buying intentions. Moreover, online sellers should offer installment options to consumers, thereby reaping stronger effects of consumer confidence on online impulse buying intentions.

Employing the experimental approach, this study makes a novel attempt to examine the role of the installment option in fostering online impulse buying intentions among Pakistani consumers. Likewise, this is one of the early studies to explore the effects of risk reduction, seller reputation, eWOM and warranties on CC among Pakistani consumers using the SOR model.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.