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This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

The vulnerability of multinational corporation (MNC) knowledge network is one of the major causes for the failure and even the death of MNCs in the fierce global market competition. Employee turnover and knowledge loss are the triggers for the MNC knowledge network vulnerability and a matter of serious concern in the evolution and development of MNC knowledge network. The purpose of this work is to propose a valid and quantitative measurement method to investigate the influence of employee loss and knowledge loss on the vulnerability of MNC knowledge network.

MNC knowledge network is inherently a heterogeneous network where there are mainly two types of units: employees and their knowledge. Therefore, this paper establishes a weighted super-network model for MNC knowledge network to depict its heterogeneous composition. On the basis of the weighted MNC knowledge super-network, the static and dynamic vulnerability measurement methods are further proposed to investigate and evaluate MNC knowledge network vulnerability.

A real case is given to illustrate the applicability of the proposed weighted MNC knowledge super-network model and the network vulnerability measurement methods. The results show the super-network model proposed in this paper can effectively embody the complex features of MNC knowledge network, and the vulnerability measurement methods can effectively investigate the influence of employee loss and knowledge loss on network vulnerability.

From the perspective of super-network, researchers and practitioners can get a more systematic and deeper understanding of the MNC knowledge network and its human and knowledge resource constitute which are vital for the evolution and development of MNC. Moreover, the MNC knowledge network vulnerability measurement methods can effectively measure and analyze the influence of resource loss on network vulnerability, which can provide a helpful decision support for monitoring and managing of MNC knowledge network vulnerability to reduce its adverse effects.

This study aims to achieve long-term prediction on a specific monotonic data series of atmospheric corrosion rate vs time.

This paper presents a new method, used to the collected corrosion data of carbon steel provided by the China Gateway to Corrosion and Protection, that combines non-linear gray Bernoulli model (NGBM(1,1) with genetic algorithm to attain the purpose of this study.

Results of the experiments showed that the present study’s method is more accurate than other algorithms. In particular, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method in data sets are 9.15 per cent and 1.23 µm/a, respectively. Furthermore, this study illustrates that model parameter can be used to evaluate the similarity of curve tendency between two carbon steel data sets.

Corrosion data are part of a typical small-sample data set, and these also belong to a gray system because corrosion has a clear outcome and an uncertainly occurrence mechanism. In this work, a new gray forecast model was proposed to achieve the goal of long-term prediction of carbon steel in China.

The purpose of this paper is to illustrate that some enterprise unions in South China, as strategic labor actors, made local progress in collective bargaining, but further elaborates on why gainful bargaining would require a more systematic understanding of the prevailing industrial structure.

This paper is mainly drawn from intensive site visits and 51 in-depth interviews in 2013 and 2014, and several follow-ups up to 2018. Three cases of collective bargaining, featuring different union strategies of assertive negotiation, informal cooperation and direct confrontation, are discussed in detail.

The study illustrates that viable collective bargaining with worker-supported unions is possible in China. However, the effectiveness of bargaining does not count on this alone; the supply chain structure also imposes significant constraints, mainly by narrowing the bargaining scope of each supplier and differentiating the structural power of their unions. In these cases, institutionalized union coordination beyond individual suppliers is proposed.

These cases began as post-strike bargaining in Japanese auto supply chains and became the frontier of industrial relations in China. The impact of the supply chain in different sectors or regions requires further study.

This paper draws attention to the effect of an “invisible” but increasingly significant factor, industrial structure, on enterprise-level collective bargaining in China, unlike many previous criticisms of unwillingness or incompetence among labor actors.

The purpose of this paper is to artificially construct a functional surface with self-propulsion flow characteristics for the directional transportation of propellant in surface tension tanks.

In this study, a method to enhance the propulsion efficiency by using functional surfaces of self-propulsion performance was proposed. Superhydrophilic wedged-groove with the superhydrophobic background was fabricated and the self-propulsion capacity was verified.

It is found that the self-propulsion capacity is related to the divergence angle of the wedged-groove in the hydrophilic area, and the velocity of the droplets on the deflector plate is the largest with the divergence angle of 4°; the temperature gradient field formed by the condensing device at the nozzle can accelerate the droplet outflow from the tank.

Realization of this idea provides an accurate control strategy for the complex flow process of propellant in plate surface tension tanks, which could enhance the efficiency of the tension tank significantly.

This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.

The authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.

The authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.

The authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.

The purpose of this paper is to look at the journey and experience of the University of Hong Kong (HKU) Research Data Management (RDM) practice to respond to the needs of researchers in an academic library.

The research data services (RDS) practice is based on the FAIR data principle. And the authors designed the RDM Stewardship framework to implement the RDS step by step.

The HKU Libraries developed and implemented a set of RDS under a research data stewardship framework in response to the recent evolving research needs for RDM amongst the academic communities. The services cover policy and procedure settings for research data planning, research data infrastructure establishment, data curation services and provision of online resources and instructional guidelines.

This study provides an example of an approach to respond to the needs of the academic libraries about how to start the RDS including the data policy, data repository, data librarianship and data curation.

This chapter aims to analyze how digital entrepreneurship is developed in the food industry of the European Union, comparing digital skills and big data indicators in all enterprises and the food industry. Using Eurostat Digital Economy and Society database, the authors obtained data between 2016 and 2020 – including the indicators: information and communications technology (ICT) specialists and ICT training to digital skills, and smart devices, geolocation, and social media to big data assessment. Furthermore, we compared all enterprises with those that manufacture beverages, food, and tobacco products. The authors identified that the food sector is still behind the other sectors regarding digitalization. Consequently, this research contributes to understanding entrepreneurs’ digital skills and how them relate to the use of big data in the food industry. Moreover, it also allows identifying the digital indicators of the food industry as less innovative than other industry digital indicators.

In this chapter, the author explores and discusses the transformation of management from the perspective of complex systems, systems change and the Taoist yin-yang theory. Modern complex organizations require innovative management practices that move beyond traditional approaches. Consequently, many organizations are experiencing a management shift towards more holistic and sustainable methods. This shift encompasses changes in organizational structures and modes of inquiry. Organizations are transitioning from rigid hierarchies to more collaborative and agile configurations while adopting a systemic approach to management that acknowledges the interconnectedness and interdependence of all organizational aspects. Simultaneously, there is a paradigm shift in management thinking, replacing the conventional focus on ego-action with eco-action, which emphasizes the integration of diverse perspectives and the cultivation of self-awareness and introspection. This shift in mindset is embodied in the yin-yang paradigm, highlighting the importance of balance and harmony between contrasting forces.

A number of theoretical paradigms provide a networking space for the trio and complementary fields of comparative, international, and development educational (CIDE) research. Critics periodically attribute the field's lack of a sound theoretical base or commitment to one area of scientific research or another as a primary weakness in the field.¹ Espoused theoretical paradigms often provide the knowledge debate arena in which academic fields interact and build together. In an alternative perspective from this criticism, we argue that the strength of the CIDE field resides in its ability to combine multiple theoretical perspectives that offer researchers a variety of potentially fruitful metatheoretical analyses. Thus, we do not view this lack of theoretical specification as a weakness; it is the very fabric that enables CIDE educationists to study and represent increasingly complex global and local education systems.