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This study aims to examine the effects of technology-, organisation- and environment-readiness, smart economic development, change valence, social cohesion and quality of life on citizenship in the context of smart cities.

The study employed a customized questionnaire which was completed by 280 residents of China’s first-tier cities. This study tested the framework using the partial least squares structural equation modelling (PLS-SEM) technique.

The results indicated that smart economy development, social cohesion, change valence, technological readiness, organizational readiness and environmental readiness have a significant impact on the quality of life. Quality of life has a positive impact on citizenship.

This study adds new insights to smart city academic discussions. The study addresses a critical gap identified in existing literature which urges the need for a balance between user-centric, organization-centric and technology-centric approaches. It offers a fresh perspective on how the smart economy, social cohesion and readiness factors are interlinked. These elements together shape urban living experiences. For policymakers and urban planners, our findings provide clear guidance. They highlight the complex dynamics that must be considered to build more unified, inclusive and sustainable smart cities.

Existing research suggests a positive relationship between firms’ research and development investments (RDIs) and their patenting performance (PP) according to assumptions of linear productivity and homogeneous behavior. This study unravels the RDI–PP relationship by taking a strategic view to reveal its underlying mechanisms.

We study the effects of firms’ RDI on PP in the context of China’s listed firms in 16 patent-intensive industries, including the pharmaceutical, computer communication, electronic equipment and electrical machinery and equipment manufacturing industries. To test our hypotheses, we use panel data from 2010 to 2017. We apply generalized estimating equations to estimate our models.

The study finds an inverted U-shaped relationship between RDI and PP that arises from the transition of innovation portfolios and the strategic balancing of patenting costs and benefits. The study further examines two contingencies: (1) top management team (TMT) education level and (2) TMT compensation. It shows the turning point of the inverted U-shape shifts to the right when TMT education level is high; the curve flattens when TMT education level and TMT compensation are high.

We contribute to literature on innovation and appropriability strategy in three ways: First, we reveal the underlying mechanisms of the inverted U-shaped relationship between RDI and PP. Second, because previous research on appropriability strategies pays little attention to how innovation portfolios influence patenting decisions at the firm level, we provide evidence and insights on how the tension between exploitative and explorative innovations affects appropriability strategies. Third, we connect appropriability strategy literature with two streams of literature: corporate governance and upper-echelon theory.

To optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. However, delay predictions often involve imprecise shifts based on known delay times. Real-time and accurate train delay predictions, facilitated by data-driven neural network models, can significantly reduce dispatcher stress and improve adjustment plans. Leveraging current train operation data, these models enable swift and precise predictions, addressing challenges posed by train delays in high-speed rail networks during unforeseen events.

This paper proposes CBLA-net, a neural network architecture for predicting late arrival times. It combines CNN, Bi-LSTM, and attention mechanisms to extract features, handle time series data, and enhance information utilization. Trained on operational data from the Beijing-Tianjin line, it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.

This study evaluates our model's predictive performance using two data approaches: one considering full data and another focusing only on late arrivals. Results show precise and rapid predictions. Training with full data achieves a MAE of approximately 0.54 minutes and a RMSE of 0.65 minutes, surpassing the model trained solely on delay data (MAE: is about 1.02 min, RMSE: is about 1.52 min). Despite superior overall performance with full data, the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals. For enhanced adaptability to real-world train operations, training with full data is recommended.

This paper introduces a novel neural network model, CBLA-net, for predicting train delay times. It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.

We are the first to investigate the relationship between seasoned equity offerings (SEOs) and anchoring on historical high prices in China.

We use the ratio of the recent closing price to its historical high in the previous 12–60 months (anchoring-high-price ratio) to study its impact on the market timing of SEOs.

Empirical results show that the anchoring-high-price ratio significantly and positively affects the probability of additional stock issuances. Contrary to the USA market, the Chinese stock market reacts negatively to the SEOs at historical highs. Moreover, the anchoring-high-price ratio exacerbates the negative effect of announcements and leads to long-term underperformance. Finally, we investigate the impact of the anchoring-high-price ratio on a company’s capital structure, showing that the additional issuance anchoring on historical highs reduces the company’s leverage ratio in the long run. Overall, our findings support the anchoring theory and can help understand better the anchoring behavior of managers and the company’s decision on additional stock issuances.

We are the first to use the anchoring-high-price ratio to study the timing of SEOs. We find that the anchoring-high-price ratio positively affects the probability of SEOs. Unlike the USA, the Chinese stock market reacts negatively to SEOs at high prices. SEOs anchoring on historical highs reduce a firm’s leverage ratio in the long run. Finally, our results support the anchoring theory.

The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.

This paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.

The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.

This paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

This paper aims to propose a diverless weld bead maintenance welding technology to prevent the leakage of subsea oil and gas pipeline and solve the key problems in the maintenance of subsea pipeline.

Based on the analysis of the cross-section of the fillet weld, the multi-layer and multi-pass welding path planning of the submarine pipeline sleeve fillet weld is studied, and thus a multi-layer and multi-pass welding path planning strategy is proposed. A welding seam filling method is designed, and the end position of the welding gun is planned, which provides a theoretical basis for the motion control of the maintenance system.

The trajectory planning and adjustment of multi-layer and multi-pass fillet welding and the motion stability control of the rotating mechanism are realized.

It provides the basis for the prototype design of the submarine pipeline maintenance and welding robot system, and also lays the foundation for the in-depth research on the intelligent maintenance system of submarine pipeline.

The maintenance of diverless subsea pipeline is a new type of maintenance method, which can solve the problem of large amount of subsea maintenance work with high efficiency.

Climate change has been universally recognized as a major threat to human well-being, necessitating a comprehensive transformation of people's activities. Various measures have been proposed to contain climate change among which the green transformation grabs special attention, thanks to its desirable properties. Within the green transformation process, green tourism comes to prominence with huge potential. As one of the largest carbon emitters, the transition towards green tourism may offer substantial benefits not only for tourism companies but also for the whole economy. Yet, most studies tend to focus on the adverse effects of tourism on climate change while overlooking the potential impact of climate change on tourism. This chapter clarifies the feedback relationship between climate change and tourism and makes some recommendations.

This paper aims to compare the Greek and Chinese rhetorical traditions and explore their influences on today’s business and marketing communication across relevant cultures. In particular, it uses the Aristotelian persuasive orientations as reference points to introduce the Chinese rhetoric, and interpret cultural differences in persuasion from a historical and sociocultural perspective. It has been found that Greek and Chinese rhetoric and persuasion were developed to meet the needs of the social and cultural environments and this rule still applies to today’s business communication. The logical approach has been emphasised in the English rhetorical tradition while both qing (emotional approach) and li (logical approach) are the focus of persuasion in the Chinese tradition. This difference is also the root of cultural differences in modern business communication. Findings from both English and Chinese texts and data are examined to substantiate our focal argument.

This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample.

To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed.

The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices.

With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.