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This paper investigates the impact of investor attention on the COVID-19 concept stocks in China's stock market from the perspectives of the macroeconomy, the stock market and the COVID-19 pandemic.

On the basis of controlling the time effects and individual fixed effects, this paper studies the impact of investor attention on the COVID-19 concept stocks in China's stock market through a set of fixed effect panel data models. Among them, investor attention focuses on macroeconomy, stock market and the COVID-19 pandemic, respectively, while stock indicators cover return, volatility and turnover. In addition, this paper also examines the heterogeneity influence of investor attention on the COVID-19 concept stocks from the perspective of time and stock classification.

Findings indicate that the attention to macroeconomy does not have a statistically significant effect on the return, unlike the attention to stock market and COVID-19 incident. Three types of investor attention have significant positive effects on the volatility and turnover rate. During the outbreak of the domestic epidemic, the impact of investor attention was significantly higher than that during the outbreak of the epidemic overseas. A finer-grained analysis shows that the attention to stock market has significantly increased the return of preventive type and treatment type stocks, while diagnostic-related stocks have been most affected by the attention to COVID-19 incident.

The major limitation of this work is the construction of investor attention. Although Baidu index is widely used, investor attention can be assessed more accurately based on more unstructured data. In addition, the effect of the COVID-19 can also be investigated in a longer time domain. Further research can be combined with the dynamics of the COVID-19 pandemic to more comprehensively evaluate its impact on the stock market.

The research proves that investor attention plays an important role in stock pricing and provides empirical evidence on the behavioral foundations of the conceptual sector of the stock market under uncertainty. It also has practical implications for regulators and investors interested in conducting accurate asset allocation and risk assessment.

Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.

In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.

In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.

The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

Sustainable urban rail transit requires noise barriers. However, these barriers’ durability varies due to the differing aerodynamic impacts they experience. The purpose of this paper is to investigate the aerodynamic discrepancies of trains when they meet within two types of rectangular noise barriers: fully enclosed (FERNB) and semi-enclosed with vertical plates (SERNBVB). The research also considers the sensitivity of the scale ratio in these scenarios.

A 1:16 scaled moving model test analyzed spatiotemporal patterns and discrepancies in aerodynamic pressures during train meetings. Three-dimensional computational fluid dynamics models, with scale ratios of 1:1, 1:8 and 1:16, used the improved delayed detached eddy simulation turbulence model and slip grid technique. Comparing scale ratios on aerodynamic pressure discrepancies between the two types of noise barriers and revealing the flow field mechanism were done. The goal is to establish the relationship between aerodynamic pressure at scale and in full scale.

The aerodynamic pressure on SERNBVB is influenced by the train’s head and tail waves, whereas for FERNB, it is affected by pressure wave and head-tail waves. Notably, SERNBVB's aerodynamic pressure is more sensitive to changes in scale ratio. As the scale ratio decreases, the aerodynamic pressure on the noise barrier gradually increases.

A train-meeting moving model test is conducted within the noise barrier. Comparison of aerodynamic discrepancies during train meets between two types of rectangular noise barriers and the relationship between the scale and the full scale are established considering the modeling scale ratio.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

The reliability of solder joints is closely related to the growth of an intermetallic compound (IMC) layer between the lead-free solder and substrate interface. This paper aims to investigate the growth behavior of the interfacial IMC layer during isothermal aging at 125°C for Sn-3Ag-3Sb-xIn/Cu (x = 0, 1, 2, 3, 4, 5 Wt.%) solder joints with different In contents and commercial Sn-3Ag-0.5Cu/Cu solder joints.

In this paper, Sn-3Ag-3Sb-xIn/Cu (x = 0, 1, 2, 3, 4, 5 Wt.%) and commercial Sn-3Ag-0.5Cu/Cu solder were prepared for bonding Cu substrate. Then these samples were subjected to isothermal aging for 0, 2, 8, 14, 25 and 45 days. Scanning electron microscopy and transmission electron microscopy were used to analyze the soldering interface reaction and the difference in IMC growth behavior during the isothermal aging process.

When the concentration of In in the Sn-3Ag-3Sb-xIn/Cu solder joints exceeded 2 Wt.%, a substantial amount of InSb particles were produced. These particles acted as a diffusion barrier, impeding the growth of the IMC layer at the interface. The growth of the Cu₃Sn layer during the aging process was strongly correlated with the presence of In. The growth rate of the Cu₃Sn layer was significantly reduced when the In concentration exceeded 3 Wt.%.

The addition of In promotes the formation of InSb particles in Sn-3Ag-3Sb-xIn/Cu solder joints. These particles limit the growth of the total IMC layer, while a higher In content also slows the growth of the Cu₃Sn layer. This study is significant for designing alloy compositions for new high-reliability solders.

The purpose of this study is the formation and growth of nanoscale intermetallic compounds (IMCs) when laser is used as a heat source to form solder joints.

This study investigates the Sn/Cu and Sn-0.1AlN/Cu structure using laser soldering under different laser power: (200, 225 and 250 W) and heating time: (2, 3 and 4 s).

The results show clearly that the formation of nano-Cu₆Sn₅ films is feasible in the laser heating (200 W and 2 s) with Sn/Cu and Sn-0.1AlN/Cu system. The nano-Cu₆Sn₅ films with thickness of 500 nm and grains with 700 nm are generally parallel to the Cu surface with Sn-0.1AlN. Both IMC films thickness of Sn/Cu and Sn-0.1AlN/Cu solder joints gradually increased from 524.2 to 2025.8 nm as the laser heating time and the laser power extended. Nevertheless, doping AlN nanoparticles can slow down the growth rate of Cu₆Sn₅ films in Sn solder joints due to its adsorption.

The formation of nano-Cu₆Sn₅ films using laser heating can provide a new method for nanofilm development to realize the metallurgical interconnection in electronic packaging.

Purpose: Human capital development strategies and components in hospitality are covered in this chapter. This study examines hospitality human capital development, including training, leadership, technology integration, staff well-being, diversity, equality, and inclusion (DEI).

Design/methodology/approach: This chapter uses secondary data from the relevant research article literature. The design shows how human capital development techniques affect hospitality. This study addresses human capital development needs in the hospitality business as technology becomes increasingly essential.

Findings: Hospitality worker adaptation and flexibility for human capital development, including wellness efforts for work–life balance, are discussed in the chapter. The results also address recognition and incentive systems to foster a healthy hospitality workplace culture that supports employee human capital development.

Research limitations: Human capital research in hospitality is minimal. Future research should examine additional human capital determinants affecting hospitality sector success and their relevance in varied settings for global hospitality and tourism growth.

Practical implications: This study can help hospitality and tourism stakeholders, policymakers, and local communities improve the symbiotic relationship for human capital development. Practical implications emphasise the necessity of inclusive development initiatives that empower hospitality communities and maintain global hospitality and tourism.

Originality/value: This study examines how human capital development affects hospitality, particularly tourism. The novelty is studying development strategies as drivers for good change, teaching sustainable global human capital development in international tourism.

The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.

In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.

Based on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.

With the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.