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The smoothness of the high-speed railway (HSR) on the bridge may exceed the allowable standard when an earthquake causes vibrations for HSR bridges, which may threaten the safety of running trains. Indeed, few studies have evaluated the exceeding probability of rail displacement exceeding the allowable standard. The purposes of this article are to provide a method for investigating the exceeding probability of the rail displacement of HSRs under seismic excitation and to calculate the exceeding probability.

In order to investigate the exceeding probability of the rail displacement under different seismic excitations, the workflow of analyzing the smoothness of the rail based on incremental dynamic analysis (IDA) is proposed, and the intensity measure and limit state for the exceeding probability analysis of HSRs are defined. Then a finite element model (FEM) of an assumed HSR track-bridge system is constructed, which comprises a five-span simply-supported girder bridge supporting a finite length CRTS II ballastless track. Under different seismic excitations, the seismic displacement response of the rail is calculated; the character of the rail displacement is analyzed; and the exceeding probability of the rail vertical displacement exceeding the allowable standard (2mm) is investigated.

The results show that: (1) The bridge-abutment joint position may form a step-like under seismic excitation, threatening the running safety of high-speed trains under seismic excitations, and the rail displacements at mid-span positions are bigger than that at other positions on the bridge. (2) The exceeding probability of rail displacement is up to about 44% when PGA = 0.01g, which is the level-five risk probability and can be described as 'very likely to happen'. (3) The exceeding probability of the rail at the mid-span positions is bigger than that above other positions of the bridge, and the mid-span positions of the track-bridge system above the bridge may be the most hazardous area for the running safety of trains under seismic excitation when high-speed trains run on bridges.

The work extends the seismic hazardous analysis of HSRs and would lead to a better understanding of the exceeding probability for the rail of HSRs under seismic excitations and better references for the alert of the HSR operation.

This study aims to present a vision-guided robotic system design for application in vat photopolymerization additive manufacturing (AM), enabling vat photopolymerization AM hybrid with injection molding process.

In the system, a robot equipped with a camera and a custom-made gripper as well as driven by a visual servoing (VS) controller is expected to perceive objective, handle variation, connect multi-process steps in soft tooling process and realize automation of vat photopolymerization AM. Meanwhile, the vat photopolymerization AM printer is customized in both hardware and software to interact with the robotic system.

By ArUco marker-based vision-guided robotic system, the printing platform can be manipulated in arbitrary initial position quickly and robustly, which constitutes the first step in exploring automation of vat photopolymerization AM hybrid with soft tooling process.

The vision-guided robotic system monitors and controls vat photopolymerization AM process, which has potential for vat photopolymerization AM hybrid with other mass production methods, for instance, injection molding.

Studies have shown that negative publicity adversely affects brand trust, but exactly how brand trust can be damaged remains poorly understood. This study aims to explore how negative publicity influences image congruity and, subsequently, brand trust. In addition, the study also examined the effectiveness of two corporate strategies to repair both congruity and trust.

Based on a valid sample of 522 Chinese consumers between the ages 20 and 50, this study adopted a quasi-experimental design involving two types of negative publicity (performance- and value-related) and two initial corporate repair strategies (compensation and public apology) intended to repair brand trust.

Negative publicity shaped brand trust through both functional congruity and self-congruity. Moreover, the type of negative publicity affected the role of image congruity in brand trust. The effectiveness of repair strategies further depended on the type of negative publicity.

Mobile phones were an appropriate focal product for this research, but examining only one product category may limit findings’ generalizability. Negative emotions such as frustration or anger and their relationships with congruity can also be addressed in future work. Subsequent research can additionally consider more conditions to explore alternative routes of processing related to brand trust.

Brand trust is a vulnerable brand asset on which negative publicity can have seriously negative consequences. Marketers and brand managers should assess the extent to which negative publicity can damage image congruity and brand trust and come up with different repair strategies subsequently.

This study contributes to the limited and fragmented literature on consumers’ evaluations of negative information. Findings offer fresh insight into the impacts of negative publicity on image congruity and brand trust. The implications extend beyond negative publicity to other forms of negative information, such as rumors, fake news and negative word of mouth. Results also highlight the importance of adopting appropriate repair strategies to restore consumers’ trust in the event of negative publicity.

This research aims to grasp hotel guests' motives and potential benefits sought when interacting with other guests, service personnel and residents and examines how these benefits can contribute to the total guest experience.

Mixed methods are adopted for the purpose of this study comprising individual interviews and a questionnaire survey.

Five groups of advantages emerge from individual interviews, including friendliness in interaction, social benefits, information acquisition, curiosity gratification and hospitality services. In the survey, which gathers 326 questionnaires, this study reveals that the five types of benefits derived from hotel guests' interactions could be further categorized into two dimensions: civility (e.g. friendliness and social) and utility (e.g. information, curiosity and service). The study confirms that four out of five potential or expected benefits from this personal interaction is significantly associated with the total hotel experience.

Respondents of this study are culturally homogenous; as a result, multi-cultural settings should be considered for future research.

Tourism and hospitality literature on people's interaction is mostly center around social aspects of interaction. The current study comprehensively explores all expected utilities of interaction, occurring in all sorts of interactions (e.g. customer-to-resident and customer-to-service personnel). Specifically, the findings of this study uncover the underlying factors which prompt the tourists to interact with other people in a lodging setting and examine the relative importance of those underlying factors to the total lodging experiences.

Today’s software engineers often work in teams to develop complex software systems. Therefore, successful software engineering in practice require team members to possess not only sound programming skills such as analysis, design, coding and testing but also soft skills such as communication, collaboration and self-management. However, existing examination-based assessments are often inadequate for quantifying students’ soft skill development. The purpose of this paper is to explore alternative ways for assessing software engineering students’ skills through a data-driven approach.

In this paper, the exploratory data analysis approach is adopted. Leveraging the proposed online agile project management tool – Human-centred Agile Software Engineering (HASE), a study was conducted involving 21 Scrum teams consisting of over 100 undergraduate software engineering students in multi-week coursework projects in 2014.

During this study, students performed close to 170,000 software engineering activities logged by HASE. By analysing the collected activity trajectory data set, the authors demonstrate the potential for this new research direction to enable software engineering educators to have a quantifiable way of understanding their students’ skill development, and take a proactive approach in helping them improve their programming and soft skills.

To the best of the authors’ knowledge, there has yet to be published previous studies using software engineering activity data to assess software engineers’ skills.

Understanding China's carbon dioxide (CO2) emission status is crucial for getting Carbon Neutrality status. The purpose of the paper is to calculate two possible scenarios for CO2 emission distribution and calculated input-output flows of CO2 emissions for every 31 China provinces for 2012, 2015 and 2017 years.

In this study using the input and output (IO) table's data for the selected years, the authors found the volume of CO2 emissions per one Yuan of revenue for the industry in 2012 and the coefficient of emission reduction compared to 2012.

Results show that in the industries with a huge volume of CO2 emissions, such as “Mining and washing of coal”, the authors cannot observe the reduction processes for years. Industries where emissions are being reduced are “Processing of petroleum, coking, nuclear fuel”, “Production and distribution of electric power and heat power”, “Agriculture, Forestry, Animal Husbandry and Fishery”. For the “construction” industry the situation with emissions did not change.

“Transport, storage, and postal services” and “Smelting and processing of metals” industries in China has the second place concerning emissions, but over the past period, emissions have been sufficiently reduced. “Construction” industry produces a lot of emissions, but this industry does not carry products characterized by large emissions from other industries. Authors can observe that Jiangsu produces a lot of CO2 emissions, but they do not take products characterized by significant emissions from other provinces. Shandong produces a lot of emissions and consumes many of products characterized by large emissions from other provinces. However, Shandong showed a reduction in CO2 emissions from 2012 to 2017.

Allocation of complex crowdsourcing tasks, which typically include heterogeneous attributes such as value, difficulty, skill required, effort required and deadline, is still a challenging open problem. In recent years, agent-based crowdsourcing approaches focusing on recommendations or incentives have emerged to dynamically match workers with diverse characteristics to tasks to achieve high collective productivity. However, existing approaches are mostly designed based on expert knowledge grounded in well-established theoretical frameworks. They often fail to leverage on user-generated data to capture the complex interaction of crowdsourcing participants’ behaviours. This paper aims to address this challenge.

The paper proposes a policy network plus reputation network (PNRN) approach which combines supervised learning and reinforcement learning to imitate human task allocation strategies which beat artificial intelligence strategies in this large-scale empirical study. The proposed approach incorporates a policy network for the selection of task allocation strategies and a reputation network for calculating the trends of worker reputation fluctuations. Then, by iteratively applying the policy network and reputation network, a multi-round allocation strategy is proposed.

PNRN has been trained and evaluated using a large-scale real human task allocation strategy data set derived from the Agile Manager game with close to 500,000 decision records from 1,144 players in over 9,000 game sessions. Extensive experiments demonstrate the validity and efficiency of computational complex crowdsourcing task allocation strategy learned from human participants.

The paper can give a better task allocation strategy in the crowdsourcing systems.

The purpose of this paper is to summarize the status and characteristics of rail technology of high-speed railway in China, and point out the development direction of rail technology of high-speed railway.

This study reviews the evolution of high-speed rail standards in China, comparing their chemical composition, mechanical attributes and geometric specifications with EN standards. It delves into the status of rail production technology, shifts in key performance indicators and the quality characteristics of rails. The analysis further examines the interplay between wheels and rails, the implementation of grinding technology and the techniques for inspecting rail service conditions. It encapsulates the salient features of rail operation and maintenance within the high-speed railway ecosystem. The paper concludes with an insightful prognosis of high-speed railway technology development in China.

The rail standards of high-speed railway in China are scientific and advanced, highly operational and in line with international standards. The quality and performance of rail in China have reached the world’s advanced level. The 60N profile guarantees the operation quality of wheel–rail interaction effectively. The rail grinding technology system scientifically guarantees the long-term good service performance of the rail. The rail service state detection technology is scientific and efficient. The rail technology will take “more intelligent” and “higher speed” as the development direction to meet the future needs of high-speed railway in China.

The development direction of rail technology for high-speed railway in China is defined, which will promote the continuous innovation and breakthrough of rail technology.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.