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Recent archiving and curatorial practices took advantage of the advancement in digital technologies, creating immersive and interactive experiences to emphasize the plurality of memory materials, encourage personalized sense-making and extract, manage and share the ever-growing surrounding knowledge. Audiovisual (AV) content, with its growing importance and popularity, is less explored on that end than texts and images. This paper examines the trend of datafication in AV archives and answers the critical question, “What to extract from AV materials and why?”.

This study roots in a comprehensive state-of-the-art review of digital methods and curatorial practices in AV archives. The thinking model for mapping AV archive data to purposes is based on pre-existing models for understanding multimedia content and metadata standards.

The thinking model connects AV content descriptors (data perspective) and purposes (curatorial perspective) and provides a theoretical map of how information extracted from AV archives should be fused and embedded for memory institutions. The model is constructed by looking into the three broad dimensions of audiovisual content – archival, affective and aesthetic, social and historical.

This paper contributes uniquely to the intersection of computational archives, audiovisual content and public sense-making experiences. It provides updates and insights to work towards datafied AV archives and cope with the increasing needs in the sense-making end using AV archives.

This paper aims to present a novel robotic system for airport pavement inspection tasks.

The cloud-edge-terminal-based distributed architecture is designed for the proposed robotic system. Then, the following three major parts are designed and deployed, respectively: Terminal: the wheeled-robot-based data collection system. Edge: remote monitoring and data analysis system. Cloud: shared database center of the inspection data and knowledge.

Validation and application results show that the proposed system satisfies the demands of automated airport pavement inspection tasks and saves the cost of manpower and time.

The proposed system provides a novel solution for the full process of airport pavement inspection. Compared with the traditional manual method, the robotic system can guarantee complete coverage and provide high-precision pavement inspection results with less time and labor costs.

The purpose of this paper is to develop high-performance Au-coated Ag alloy wires (ACAA wires) and demonstrate the effect of Au coating layers on the bonding performance and oxidation resistance for stable and reliable electronic packaging applications.

ACAA wire with a diameter of approximately 25 µm and Au layer thickness of approximately 100 nm were prepared by the continuous casting, plating and wire drawing method. The bonding performance of the ACAA wires were studied through bonding on 3,535 chips. The oxidation resistance of ACAA wires and Ag alloy wires (AA wires) were comparatively studied by means of chemical oxidation tests, accelerated life tests and electrochemical tests systematically.

ACAA wires could form axi-symmetrical spherical free air balls with controllable diameter of 1.5∼2.5 times of the wire diameter after electric flame-off process. The ball shear strength of ACAA wire was higher than that of AA wires. Most importantly, because of the surface Au coating layer, the oxidation resistance of ACAA wires was much enhanced.

ACAA wires with different lengths of heat affected zone were not developed in this study, which limited their application with different loop height requirements.

With higher bonding strength and oxidation resistance, ACAA wires would be a better choice than previous reported AA wire in chip packaging which require high stability and reliability.

This paper provides a kind of novel ACAA wire, which possess the merits of high bonding strength and reliability, and show great potential in electronic packaging applications.

The purpose of this study is to investigate the crevice corrosion behavior and mechanism of laser additive manufacturing (LAM) nickel-based alloy under wedge-shaped crevice.

First, the opening size of the wedge-shaped crevice was designed to 0.1, 0.3 and 0.5 mm by controlling the thickness of silicon rubber and the double-side adhesive tape. Then, one side of the glass sheet was stuck on the silicon strip and keep the electrodes of Rows 1 and 2 outside the crevice as a reference, and the opposite side was stuck to the wire beam electrode by silica gel.

The current density with a maximum value of 5.7 × 10⁻⁶ A/cm² was observed at the crevice opening of 0.5 mm, while the lowest value of 9.2 × 10⁻⁷ A/cm² was found at the crevice opening of 0.1 mm. In addition, the corrosion resistance at the inside of the crevice is higher than that at the outside and the middle of the crevice. It means that the internal width of the wedge-shaped crevice tends toward 0, which hinders the migration of ions in the corrosive medium. The generation of corrosive products further reduce the crevice size to cause the inhibition of corrosion at the inside of the crevice as well.

The multilayer and multipath LAM component is prepared to show the complex microstructure, which made the corrosion behavior and mechanism at wedge-shaped crevice nondeterminacy.

Digital transformation is essential for commercial banks to maintain long-term competitiveness in the digital economy era. This study aims to investigate the relationship between inside debt and the bank's digital transformation.

This study set up a quasi-natural experiment based on implementing the executive compensation deferral system in the Chinese banking industry. Using the annual panel data of 180 commercial banks in China from 2007 to 2021, this study employed the difference-in-differences (DID) method to conduct an empirical analysis.

This study confirms a significant statistical relationship between inside debt and the bank's digital transformation, and managerial myopia is the transmission channel of inside debt affecting the bank's digital transformation. Furthermore, the development of Internet finance and the enhancement of bankers' confidence will improve the contributions of inside debt to the bank's digital transformation.

This study contributes to the literature on inside debt and the bank's digital transformation. It has specific policy value for the scientific design of the banking compensation mechanism and accelerating banks' digital transformation.

Based on social cognitive theory, this study aims to explore the psychological mechanism behind consumer verification behavior following tourism e-commerce live-streaming.

Based on grounded theory, data were collected through 20 semi-structured in-depth interviews and analyzed.

This study identified that companies commonly use reminder messages and secondary promotions to facilitate the verification of tourism live-streaming products. Throughout this process, consumers undergo various psychologies related to verification. Specifically, they experience four positive verification psychologies: fear of missing out, anticipated emotions, status self-esteem and promotional perception. They also encounter two negative verification psychologies: psychological reactance and invasiveness. In addition, environmental factors such as the type of tourism live-streaming products and tourism destinations, along with individual trait factors like cognitive miserliness, tourism experience, autonomy, regulatory mode and impulsiveness, play significant roles in shaping verification behavior. These factors collectively influence the formation of verification behavior.

This study can provide recommendations for tourism companies to conduct marketing events following live-streaming. It is one of the earlier comprehensive studies discussing how to promote verification behavior following tourism e-commerce live-streaming. It helps to understand the psychological mechanism underlying the formation of verification behavior.

Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.

Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.

In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.

The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.

Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.

Given the potential benefits of gamification in running apps, it is necessary to explore the impact of users’ gameful experience on their intention to continue using running apps. This study aims to empirically investigate this relationship along with the roles of brand attitude as a mediator and negative online reviews as a moderator.

The study surveyed 332 running app users in China. The reliability and validity of measures were confirmed by confirmatory factor analysis (CFA). The proposed hypotheses were verified by Process Macro.

The results show that (1) gameful experience positively impacts intention to continue using running apps, (2) brand attitude mediates the relationship between gameful experience and intention to continue using running apps and (3) negative online reviews moderate the relationship between gameful experience and brand attitude but not the relationship between brand attitude and intention to continue using running apps. Specifically, the effect of gameful experience on brand attitude decreases as users’ perception of negative online reviews increases.

These findings have both theoretical and practical implications for understanding the relationship among users’ gameful experience, brand attitude and intention to continue using running apps, as well as for developing effective gamification strategies to enhance user engagement and retention in running apps.

Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.