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Passive chipless RFID (radio frequency identification) sensors, devoid of batteries or wires for data transmission to a signal reader, demonstrate stability in severe conditions. Consequently, employing these sensors for metal crack detection ensures ease of deployment, longevity and reusability. This study aims to introduce a chipless RFID sensor design tailored for detecting metal cracks, emphasizing tag reusability and prolonged service life.

The passive RFID sensor is affixed to the surface of the aluminum plate under examination, positioned over the metal cracks. These cracks alter the electrical length of the sensor, thereby influencing its amplitude-frequency characteristics. Hence, the amplitude-frequency profile generated by various metal cracks can effectively ascertain the occurrence and orientation of the cracks.

Simulation and experimental results show that the proposed crack sensing tag produces different frequency amplitude changes for four directions of cracks and can recognize the crack direction. The sensor has a small size and simple structure, which makes it easy to deploy.

This research aims to deploy crack detection on metallic surfaces using passive chipless RFID sensors, analyze the amplitude-frequency characteristics of crack formation and distinguish cracks of varying widths and orientations. The designed sensor boasts a straightforward structural design, facilitating ease of deployment, and offers a degree of reusability.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

The aim of this study is to discern the role of digital finance in driving rural industrial integration and revitalization. Specifically, it intends to shed light on how the deep development of digital finance can contribute to the optimization and transformation of the rural industrial structure. The research further explores the particular effects of this financial transformation in the central and western regions of China.

This research studies the influence of digital finance on rural industrial integration across 30 Chinese provinces from 2011 to 2020. Utilizing the entropy weight method, a comprehensive evaluation index system is established to gauge the level of rural industrial integration. A two-way fixed effects model, intermediary effect model, and threshold effect model are employed to decipher the relationship between digital finance and rural industrial integration.

Findings reveal a positive relationship between digital finance and rural industrial integration. A single threshold feature was identified: beyond a traditional finance development level, the marginal effect of digital finance on rural industrial integration increases. These effects are more noticeable in central and western regions.

Empirical outcomes contribute to policy discourse on rural digital finance, assisting policymakers in crafting effective strategies. Understanding the threshold of traditional finance development provides a new perspective on the potential of digital finance to drive rural industrial integration.

Community detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic space. To address this challenge, a hyperbolic space-based dynamic graph neural network community detection model (HSDCDM) is proposed.

HSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincaré and Lorentz models to realize feature fusion and information transfer. In addition, the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended periods. Finally, the community clustering module divides the community structure by combining the node characteristics of the space domain and the time domain. To evaluate the performance of HSDCDM, experiments are conducted on both artificial and real datasets.

Experimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical networks. It shows an average improvement of 7.29% in NMI and a 9.07% increase in ARI across datasets compared to traditional methods. For complex networks with non-Euclidean geometric structures, the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space, provides a more compact embedding that preserves the data structure, and offers advantages over methods based on Euclidean geometry methods.

This model aggregates the potential information of nodes in space through manifold-preserving distribution mapping and hyperbolic graph topology modules. Moreover, it optimizes the Simple Recurrent Unit (SRU) on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space, thereby enhancing computing efficiency by eliminating the reliance on tangent space.

The sustainable development of the platform economy has been hindered by the absence and alienation of platform corporate social responsibility. Previous studies have mainly focused on the contents and governance models for platform corporate social responsibility. This study seeks to explore which strategy participants choose in the governance of platform corporate social responsibility and their influencing factors.

Using a platform ecosystem approach, a quadrilateral evolutionary game model was developed, and the stabilities of subjects’ behavioral strategies and their combinations in various scenarios were analyzed. Additionally, the effects of key parameters on the system’s evolutionary path were simulated.

The ideal steady state system is achieved when platform enterprises, complementors and consumers adopt positive strategies while the government adopts lax regulation. Moreover, the evolutionary strategies of the subjects are influenced by several factors, including the participation costs of governance, the rewards and punishments imposed by platform enterprises, as well as the reputational losses of platform enterprises and complementors due to media coverage.

This study offers insights into improving the governance effectiveness of platform corporate social responsibility for managers and practitioners.

This study contributes to existing literature by considering the rational orientation of platform ecosystem members and revealing the interaction mechanisms among members. Furthermore, this study combines collective action theory and reputation theory to clarify the influencing factors on members’ behaviors.

Supercritical CO₂ (SC–CO₂) fracturing is a potential technology that creates a complex fracturing fracture network to improve reservoir permeability. SC–CO₂-driven intersections of the fracturing fracture network are influenced by some key factors, including the disturbances generated form natural fractures, adjacent multi-wells and adjacent fractures, which increase the challenges in evaluation, control and optimization of the SC–CO₂ fracturing fracture networks. If the evaluation of the fracture network is not accurate and effective, the risk of oil and gas development will increase due to the microseismicity induced by multi-well SC–CO₂ fracturing, which makes it challenging to control the on-site engineering practices.

The numerical models considering the thermal-hydro-mechanical coupling effect in multi-well SC–CO₂ fracturing were established, and the typical cases considering naturally fracture and multi-wells were proposed to investigate the intersections and connections of fracturing fracture network, shear stress shadows and induced microseismic events. The quantitative results from the typical cases, such as fracture length, volume, fluid rate, pore pressure and the maximum and accumulated magnitudes of induced microseismic events, were derived.

In naturally fractured reservoirs, SC–CO₂ fracturing fractures will deflect and propagate along the natural fractures, eventually intersect and connect with fractures from other wells. The quantitative results indicate that SC–CO₂ fracturing in naturally fractured reservoirs produces larger fractures than the slick water as fracturing fluid, due to the ability of SC–CO₂ to connect macroscopic and microscopic fractures. Compared with slick water fracturing, SC–CO₂ fracturing can increase the length of fractures, but it will not increase microseismic events; therefore, SC–CO₂ fracturing can improve fracturing efficiency and increase productivity, but it may not simultaneously lead to additional microseismic events.

The results of this study on the multi-well SC–CO₂ fracturing may provide references for the fracturing design of deep oil and gas resource extraction, and provide some beneficial supports for the induced microseismic event disasters, promoting the next step of engineering application of multi-well SC–CO₂ fracturing.

As intelligent technology advances, practical applications often involve data with multiple labels. Therefore, multi-label feature selection methods have attracted much attention to extract valuable information. However, current methods tend to lack interpretability when evaluating the relationship between different types of variables without considering the potential causal relationship.

To address the above problems, we propose an ensemble causal feature selection method based on mutual information and group fusion strategy (CMIFS) for multi-label data. First, the causal relationship between labels and features is analyzed by local causal structure learning, respectively, to obtain a causal feature set. Second, we eliminate false positive features from the obtained feature set using mutual information to improve the feature subset reliability. Eventually, we employ a group fusion strategy to fuse the obtained feature subsets from multiple data sub-space to enhance the stability of the results.

Experimental comparisons are performed on six datasets to validate that our proposal can enhance the interpretation and robustness of the model compared with other methods in different metrics. Furthermore, the statistical analyses further validate the effectiveness of our approach.

The present study makes a noteworthy contribution to proposing a causal feature selection approach based on mutual information to obtain an approximate optimal feature subset for multi-label data. Additionally, our proposal adopts the group fusion strategy to guarantee the robustness of the obtained feature subset.

The ancient town of Lijiang is a representative place of ethnic minorities in China’s southwest border area jointly built by many ethnic groups. Its rich and diversified history, culture and architecture as well as its artistic and spiritual values need to be better retained and explored.

The protection and inheritance of Lijiang’s cultural heritage will be improved through the construction of digital memory resources. To guide Lijiang’s digital memory construction, this study explores strategies of digital memory construction by analyzing four case studies of well-known memory projects from China and America.

From the case studies analysis, factors of digital memory construction were identified and compared. Factors led to the discussion of strategies for constructing the digital memory of Lijiang within its design, construction and service phases.

The ancient town of Lijiang is a famous historical and cultural city in China, and it is also a representative place of ethnic minorities in the border area jointly built by many ethnic groups. The rich culture should be preserved and digitalized to offer better use for the whole nation.

The purpose of this study is to examine how the implementation of edge computing can enhance the progress of the circular economy within supply chains and to address the challenges and best practices associated with this emerging technology.

This study utilized a streamlined evaluation technique that employed Latent Dirichlet Allocation modeling for thorough content analysis. Extensive searches were conducted among prominent publishers, including IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi, utilizing pertinent keywords associated with edge computing, circular economy, sustainability and supply chain. The search process yielded a total of 103 articles, with the keywords being searched specifically within the titles or abstracts of these articles.

There has been a notable rise in the volume of scholarly articles dedicated to edge computing in the circular economy and supply chain management. After conducting a thorough examination of the published papers, three main research themes were identified, focused on technology, optimization and circular economy and sustainability. Edge computing adoption in supply chains results in a more responsive, efficient and agile supply chain, leading to enhanced decision-making capabilities and improved customer satisfaction. However, the adoption also poses challenges, such as data integration, security concerns, device management, connectivity and cost.

This paper offers valuable insights into the research trends of edge computing in the circular economy and supply chains, highlighting its significant role in optimizing supply chain operations and advancing the circular economy by processing and analyzing real time data generated by the internet of Things, sensors and other state-of-the-art tools and devices.

This paper investigates the distinctive role of the US stock exchanges in the process of international consolidation. Besides the USA's leading role in financial markets, the focus on the country is motivated by its uniqueness within the stock exchange consolidation landscape, since, on the one hand, it has been involved in two different stock exchange mergers – with Nasdaq and NYSE – and, on the other hand, it has experienced a “reversal”, having joined and then left the Euronext-NYSE platform.

To investigate the effect of the NYSE-Euronext split on cross-border holdings and the role of the US as a member of the consolidated platform, we adopt a feasible Generalized Least Squares specification correcting for both heteroskedasticity and general correlation of observations across destination-countries, with standard errors adjusted for two-way clustering at the investing-country and year levels.

Differently from other mergers, we find a weak sensitivity of US inward and outward cross-border investments to stock exchange consolidation, and, consequently, to its reversal. The data suggest that the larger, the more liquid and the more visible the involved stock exchanges are, the less sensitive cross-border investment is to consolidation. Drawing on the cross-listing and cross-delisting literature, we formulate the conjecture that this evidence can be explained by decreasing returns of foreign investment to consolidation: the extraordinary large size, liquidity and visibility of the US stock exchanges diminishes the value of the role played by stock exchange consolidation in reducing cross-border barriers among member countries, so that it makes also the effects of its retreat non-significant.

This paper is the first, to best of our knowledge, to investigate the mirror phenomenon, that is, the “consolidation reversal” process of the NYSE stock exchange, the purpose being to understand its consequences for cross-border holdings. In the first part of this paper, we document no significant effect of the 2014 reversal on cross-border investments. The apparent absence of this effect could be due either to a level of cross-border investments remaining equally high (denoting persistence in investors' behavior) or to an equally non-significant effect of consolidation and reversal of the US stock exchanges on cross-border equity investments. The evidence supports the latter hypothesis and reveals an overall weak sensitivity of US cross-border investments (inward or outward) to stock exchange consolidation and, consequently, to its reversal. We formulate the conjecture, tested in the second part of the paper, that this evidence is due to the presence of diminishing returns of exchange consolidation's scale for foreign investors: the extraordinary large size, liquidity and visibility of the US stock exchanges makes the role of stock exchange consolidation less valuable in dampening cross-border barriers; consequently, also the reversal phenomenon presents no sizeable effects.