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To enhance the detection efficiency of printed circuit board (PCB) soldered dot, this study aims to detect PCB soldered dot with image processing method.

Through a series of image processing algorithms, threshold segmentation and feature extraction of the solder joint images were completed; then, sphericity was confirmed based on the area and perimeter, as well as the shape parameter and eccentricity ratio of the calculated region, paving the way for defect pattern recognition.

It proves that the method with high efficiency and precision can satisfy the requirements of PCB soldered dot detection.

This paper is original in presenting a method for PCB welding spot detection.

This research explores the complex interplay of multiple social factors with regard to what might encourage or inhibit users to interact with social commerce (SC).

To investigate the phenomenon, we developed a model based on goal-directed behaviour and pluralistic ignorance theory (typically generated by universal behavioural adherence to social norms). Based on the 394 valid responses collected from a survey, partial least squares structural equation modelling (PLS-SEM), PROCESS and ANOVA were employed to examine the research hypotheses.

The results show that pluralistic ignorance and commercial desire positively influence SC intention. More importantly, our results show that the moderating effect of pluralistic ignorance dampens the positive relationship between social subjective norms and commercial desire. The findings also suggest that pluralistic ignorance mediates the relationships between: (1) social identity and SC intentions and (2) fear of isolation and SC intentions.

Consequently, this study reveals that SC intentions result from complex interactions between an individual’s psychology and social phenomena. Theoretical and managerial implications are also discussed to provide for the successful development of strategies regarding SC for researchers and SNSs operators.

This paper aims to investigate the influence of the contact status between the seal ring and its support on the seal performance in hydrostatic mechanical face seal.

A thermal fluid-solid interaction (TFSI) model of hydrostatic mechanical face seal is further developed, in which the multi-body contacts between components are particularly given more attention. The numerical models of the flow field and complete seal assemblies are developed. A specific energy equation is obtained to simplify the calculation of film temperature. Based on the mechanism for the continuity condition of the physical quantities at the fluid–solid interface, the TFSI model uses an on-line iterative coupling method.

The contact status between seal ring and its support affects the seal performance significantly. The rotating ring and the stationary ring contribute differently because of the contact status difference.

The contact status between the seal ring and the ring seat is key to gain an insight into the performance of the hydrostatic mechanical face seal thus provides guidance for mechanical seal design.

It is aiming at finding tribology performance laws and mechanism of sintering materials, including new materials, which are popular or potential materials of mechanical seals working under water condition with different working parameters, involving load and sliding speed.

Falex-1506 tribo-machine has been used. The upper sample is rotated against the stationary sample below. They are all rings. The samples are WNV2, sintered pressureless bonded; CHV1, graphite-added PLSiC; R, sintered reaction bonded; R2, graphite-added RBSiC, cemented carbide YN6 and graphite MSMG. Twenty kinds of hard/hard and soft/hard pairs were tested under water condition. Then, SEM was used to observe the sliding surface to explain their rubbing mechanism.

Friction coefficients decrease with the load increasing under water lubrication due to water holding by small holes on their surface. But the friction coefficients have no change with the varying of velocity. The hard alloy/sintered silicon carbon pairs may be better choice, where the lowest friction coefficient is only about 0.02, than soft/hard or silicon carbon/silicon carbon pairs under water lubrication, especially under heavy load.

The test was carried under load from 89 N (0.7 MPa) to 356 N (2.80 MPa) and sliding velocity from 0.746 to 5.074 m/s where the lubrication is in mixed.

The detailed values of frictional coefficients of popular and potential sintered material of mechanical seals working under water lubrication were given by the experimental research which may be helpful for the choice of mechanical seal materials.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.