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This paper aims to study acoustic emission (AE) propagation characteristics by a crack under a moving heat source, which mainly provides theoretical basis and method for the actual crack detection during welding process.

The paper studied the AE characteristics in welding using thermoelastic theory, which investigates the dynamical displacement field caused by a crack and the welding heating effect. In the calculation model, the crack initiation and extension are represented by moment tensor as the AE source, and the welding heat source is the Gauss heat flux distribution. The extended finite element method (XFEM) is implemented to calculate and solve the AE response of a thermoelastic plate with a crack during the welding heating effect. The wavelet transform is applied to the time–frequency analysis of the AE signals.

The paper provides insights about the changing rule of the acoustic radiation patterns influenced by the heating effect of the moving heat source and the AE signal characteristics in thermoelastic plate by different crack lengths and depths. It reveals that the time–frequency characteristics of the AE signals from the simulation are in good agreement with the theoretical ones. The energy ratio of the antisymmetric mode A0 to symmetric mode S0 is a valuable quantitative inductor to estimate the crack depth with a certain regularity.

This paper mainly discusses the application of XFEM to calculate and analyze thermoelastic problems, and has presented few cases based on a specified configuration. Further work will focus on the calculation and analysis under different plate configurations and conditions, which is to obtain more interesting and general conclusions for guiding practice.

The paper is a successful application of XFEM to solve the problem of AE response of a crack in the dynamic welding inhomogeneous heating effect. The paper provides an effective way to obtain the AE signal characteristics in monitoring the welding crack.

The purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.

The generalized Stockwell transform (GST) and the singular value ratio spectrum (SVRS) methods are combined. A time-frequency distribution measurement criterion named the energy concentration measurement (ECM) is initially used to determine the parameter of the optimal GST method. Then, the optimal GST is applied to conduct a time-frequency transformation for a raw signal. Subsequently, the two-dimensional time-frequency matrix is obtained. Finally, the improved singular value decomposition (SVD) analysis is used to conduct a noise reduction of the time-frequency matrix. The SVRS is proposed to select the effective singular values. Furthermore, the time-domain feature of the impact signal is obtained by taking the inverse GST transform.

The simulated and experimental signals are used to verify the superiority of the proposed method over conventional methods. The obtained results show that the proposed method can effectively extract fault features of the rolling element bearing.

This paper mainly discusses the application of GST and SVRS methods to analyze the weak fault feature extraction problem. The next research direction is to explore the application of the Hilbert Huang transform (HHT) and variational modal decomposition (VMD) in the impact feature extraction of rolling bearing.

In the present study, a new SVRS method is proposed to select the number of effective singular values. This paper proposed an effective way to obtain the fault feature in monitoring of rotating machinery.

Corporate social responsibility (CSR) and innovation are the two main approaches firms utilize to promote sustainable development. However, as yet, scholars have reached no consensus regarding their precise impact on construction firm performance (CFP), hindering efforts to implement effective sustainable development strategies that improve CFP. In view that a simple linear relationship may not be sufficient to capture their precise pattern, this study aims to unveil the nonlinear impact of CSR and innovation on CFP, especially when construction firms take up a distinct competitive position.

This study first proposed four hypotheses to establish a new theoretical model by incorporating CSR, innovation, CFP and construction firms' competitive position (CFCP). Then the model was tested by using 292 annual observations collected from 75 construction firms in China. A multiple regression model analysis was carried out to analyze the survey data and validate the hypotheses.

The results reveal that both CSR and innovation have a U-shaped impact on the price-to-book ratio of a construction firm, a specific CFP measure. CFCP negatively moderates the U-shaped relationship between CSR and CFP, but positively moderates the U-shaped relationship between innovation and CFP.

This study goes beyond a simple linear view, instead of unveiling the nonlinear U-shaped effects of CSR and innovation on CFP that deepen the understanding of their complex relationships in the construction industry and makes construction firms aware that CSR and innovation can only improve performance if they reach a certain level. The moderating role of CFCP provides important implications for construction firms seeking to adopt appropriate competitive strategies related to social responsibility and innovation that both promote CFP and achieve sustainable development.

This study aims to investigate the effects of environmental risk perception on the evaluation of socioeconomic development and subjective well-being in China.

A questionnaire survey was conducted, and data were collected from 408 participants of four major universities and government organizations in Changsha, China. Structural equation modeling (SEM) was used to analyze the collected data and to test the hypotheses with regard to the relationship among latent variables. Bayesian method was used as the statistical inference procedure.

Results show that environmental risk perception has a direct negative influence on perceived socioeconomic development and on subjective total well-being (i.e. both hedonic and eudaimonic well-being). The perceived socioeconomic development has a direct positive effect on the subjective total well-being; thus, it plays a partial mediation role between environmental risk perception and subjective total well-being.

Findings suggest that the environment protection act of the government and their contribution to socioeconomic development will significantly affect the total well-being of people. The empirical analysis is based on survey data; thus, this study may suffer the common problems of questionnaire survey.

Promoting pro-environment and health behaviors is important in developing a happy society.

This study demonstrates a significant negative effect of environmental risk perception on the total well-being of people according to SEM approach. This study determines two independent paths for government and policy makers to increase the total well-being of people in China, namely, increasing socioeconomic value to people and controlling and reducing environmental threats.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

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.

The purpose of this study is to test the concept of a relatively low cost but biocompatible customized surgical guide printing method using a new composite material for the FDM process to support accurate virtual model reconstruction in CT.

Current additive manufacturing printed surgical guides have problems of scanning artifacts or low computed tomography (CT) values for virtual model reconstruction in CT-assisted surgical operations. These tools always face difficulties in precise positioning due to the effect of human soft tissues and manually made unstable landmarks. To solve this problem, this paper proposes a modified material, polyetheretherketone powder mixed with barium sulfate powder, for printing customized surgical guides with relatively low cost to support a synchronized scanning strategy, for the accurate reconstruction of human tissues and in vitro models.

A set of benchmarking experiments and clinical simulation cases were conducted. The results showed that the proposed solution can be used to print surgical guides to form stable and clear CT graphs for three-dimensional digital model reconstruction. Human tissues and in vitro models can be accurately reconstructed using clear CT graphs without any scanning artifacts or difficulties in image segmentation for virtual model reconstruction, thus facilitating accurate operation guidance and positioning.

This method has wide application potential for printing modular or customized surgical guides with low cost and reusability, especially for surgical operations using CT-assisted navigation systems in underdeveloped regions where medical device costs are a critical issue.

The purpose of this paper is to examine the relationship between logistics park platforms, logistics location and operational performance. Relationship analysis helps managers respond to the changes that today’s logistics companies face, which include limited resources and facilities, market uncertainties and financial instabilities.

A structured questionnaire is developed based on the resource-based view. Qualitative data collected from 273 companies in China are used in a structural equation model to test the conceptual model.

In logistics parks, logistics park platforms and location positively influence companies’ resource integration, which is positively related to operational performance. While logistics park platforms can improve operational performance directly, logistics location cannot. Resource integration is thus a full mediator between logistics location and operational performance, while it is a partial mediator between logistics park platforms and operational performance.

This study fills a gap in the literature on logistics parks, particularly on the relationships among operational performance, resource integration, logistics park platforms and the location of logistics parks in China. The authors provide guidelines for logistics park managers to organize their resources and develop capabilities for improving the operational performance of companies in logistics parks.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.