Social media has transformed the mode of communication globally by providing an extensive system for exchanging ideas, initiating business contracts, and proposing new professional ideas. However, there are many limitations to the use of social media, such as misinformation, lack of effective content moderation, digital piracy, data breaches, identity fraud, and fake news. In order to address these limitations, several studies have introduced the application of Blockchain technology in social media. Blockchains can provides transparency, traceability, tamper-proofing, confidentiality, security, information control, and supervision. This paper is a systematic literature review of papers covering the application of Blockchain technology in social media. To the best of our knowledge, this is the first systematic literature review that elucidates the combination of Blockchain and social media. Using several electronic databases, 42 related papers were reviewed. Our findings show that previous studies on the applications of Blockchain in social media are focused mainly on blocking fake news and enhancing data privacy. Research in this domain began in 2017. This review additionally discusses several challenges in applying Blockchain technologies in social media contexts, and proposes alternative ideas for future implementation and research.

In the field of bearing fault diagnosis, it is difficult to obtain a large amount of labeled data for training, and therefore it is easy to overfit the model, which makes it less robust and less generalizable. To address this problem, this article offers a novel semi-supervised contrastive learning (SSCL) method based on a multiscale attention (MSA) mechanism and multitarget contrastive learning (MCL) for rolling bearing fault diagnosis under limited labeled samples. First, the proposed SSCL utilizes improved MCL to pretrain the model as a means of capturing potentially generic features in unlabeled data. Then, semi-supervised contrast learning is constructed and based on the pretrained model combining a limited amount of labeled data and a large amount of unlabeled data to jointly learn the feature mapping and further enhance the feature extraction capability of the model through an MSA mechanism. Case Western Reserve University, the Society for Mechanical Failure Prevention Techniques, and Paderborn University are common motor-bearing datasets that are used in this article for testing the proposed technique. As a result of the experimental findings, SSCL is demonstrated to be superior to other approaches in similar situations.

Industrial defect detection is crucial for ensuring product quality and production efficiency, playing a pivotal role in advancing smart manufacturing. This paper reviews defect detection technologies for various industrial products, including metals, textiles, and printed circuit boards, and introduces an innovative classification system. It also offers a detailed analysis of recent developments and practical applications of large models in industry defect detection. First, the basic principles of industrial defect detection are outlined. The detection methods are then categorized into three main groups: traditional image processing, machine learning, and deep learning, with their principles, case studies, limitations, and future development directions analyzed. Traditional methods consist of image preprocessing, segmentation, and feature extraction. Machine learning methods are divided into point-distance-based, hyperplane-based, tree-based, and neural network-based classification algorithms. Deep learning models are classified into two types: accuracy-oriented and efficiency-oriented. The paper organizes industrial defect datasets by type (multi-product and single-product), evaluates data quality and availability, and summarizes common evaluation metrics for accuracy, efficiency by task requirements. It also compares the latest methods on two public datasets to guide further research in defect detection. Real-world examples illustrate the end-to-end process, from data processing and hardware configuration to model training and deployment, while exploring the value and limitations of these technologies from the perspective of industry stakeholders. Finally, a systematic analysis of the key challenges and corresponding solutions is presented at the data and performance levels, and looks forward to the future direction of technological development, highlighting innovative paths and application potentials.

The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods.