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This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.

In this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.

In this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.

The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

This study aims to propose a cooperative adhesion control method for trains with multiple motors electric locomotives. The method is intended to optimize the output torque of each motor, maximize the utilization of train adhesion within the total torque command, reduce the train skidding/sliding phenomenon and achieve optimal adhesion utilization for each axle, thus realizing the optimal allocation of the multi-motor electric locomotives.

In this study, a model predictive control (MPC)-based cooperative maximum adhesion tracking control method for multi-motor electric locomotives is presented. Firstly, train traction system with multiple motors is constructed in accordance with Newton’s second law. These equations include the train dynamics equations, the axle dynamics equations, and the wheel-rail adhesion coefficient equations. Then, a new MPC-based multi-axle adhesion co-optimization method is put forward. This method calculates the optimal output torque through real-time iteration based on the known reference slip speed to achieve multi-axle co-optimization under different circumstances.

This paper presents a MPC system designed for the cooperative control of multi-axle adhesion. The results indicate that the proposed control system is able to optimize the adhesion of multiple axles under numerous different conditions and achieve the optimal power distribution based on the reduction of train skidding/sliding.

This study presents a novel cooperative adhesion tracking control scheme. It is designed for multi-motor electric locomotives, which has rarely been studied before. And simulations are carried out in different conditions, including variable surfaces and motor failing.

Intuitionistic linguistic fuzzy information (ILFI), characterized by linguistic terms and intuitionistic fuzzy sets (IFSs), can easily express the fuzzy information in the process of muticriteria decision making (MCDM) and muticriteria group decision making (MCGDM) problems. The purpose of this paper is to provide an overview of aggregation operators (AOs) and applications of ILFI.

First, some meaningful AOs for ILFI are summarized, and some extended MCDM approaches for intuitionistic uncertain linguistic variables (IULVs), such as extended TOPSIS, extended TODIM, extended VIKOR, are discussed. Then, the authors summarize and analyze the applications about the AOs of IULVs.

IULVs, characterized by linguistic terms and IFSs, can more detailed and comprehensively express the criteria values in the process of MCDM and MCGDM. Therefore, lots of researchers pay more and more attention to the MCDM or MCGDM methods with IULVs.

The authors summarize and analyze the applications about the AOs of IULVs Finally, the authors point out some possible directions for future research.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

This study investigates the applications of computer vision (CV) technology in the tourism sector to predict visitors' facial and emotion detection, augmented reality (AR) visitor engagements, destination crowd management and sustainable tourism practices.

This study employed a systematic literature review, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology and bibliometric study on research articles related to the tourism sector. In total, 407 articles from the year, 2013 to 2024, all indexed in Scopus, were screened. However, only 150 relevant ones on CV in Tourism were selected based on the following criteria: academic journal publication, English language, empirical evidence provision and publication up to 2024.

The findings reveal a burgeoning interest in utilizing CV in tourism, highlighting its potential for crowd management and personalized experience. However, ethical concerns surrounding facial recognition and integration challenges need addressing. AR enhances engagement, but ethical and accessibility issues persist. Image processing aids sustainability efforts but requires precision and integration for effectiveness.

The study’s originality lies in its thorough examination of CV’s role in tourism, covering facial recognition, crowd insights, AR and image processing for sustainability. It addresses ethical concerns and proposes advancements for a more responsible and sustainable tourist experience, offering novel insights for industry development.

The aim of this paper is to examine the effect of information disclosure by unlisted bond issuers on the stock price informativeness of listed firms in the same industry.

This paper takes advantage of information disclosure during the bond issuance and examines the spillover effect of unlisted bond issuers' information disclosure on listed firms in the stock market. The sample is composed of A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2007 to 2018. All the data are obtained from the China Stock Market and Accounting Research and WIND databases. The impact of bond market information disclosure on price informativeness of listed firms in the same industry is identified through multivariate regression analyses.

Empirical results show that price informativeness of listed firms has a significantly positive association with the information disclosure of same-industry unlisted bond issuers. Further analyses show that the above finding is more significant when information disclosure of bond issuers is a more important channel for acquiring industry information (i.e. when industry is more concentrated, when economic uncertainty is high, and when industry information is less transparent) and understanding the industry competitive landscape (i.e. when bond issuers are relatively large, when bond issuers and listed firms have more direct product competition, when bond issuance firms are large-scale state-owned business groups), and when there are more cross-market information intermediaries (i.e. more cross-market institutional investors and more sell-side analysts). This paper indicates that information disclosure of bond issuers has a positive spillover effect on the stock market.

The novelty of the research is that the authors examine industry information spillover from unlisted firms to listed firms leveraging on unlisted firms' information disclosure in bond markets.

In this chapter, rephrasing Spivak's question into ‘can subaltern children speak?’, I reorient the research on China's gigantic population of children and youths in rural migrant families towards a critical interpretative approach. Based on life history and longitudinal ethnographic interview gathered with three cases, I unpack the multiple meanings migrants' children attach to mobility in their childhood experiences. First, despite emotional difficulties, children see their parents' out-migration more as a ‘mobility imperative’ than their abandonment of parental responsibilities, which should be contextualized in China's long-term urban-biased social policies and the resultant development gaps in rural and urban societies. Second, the seemingly ‘unstable’ and ‘flexible’ mobility patterns observed in migrant families should be understood in relation to a long-term family social mobility strategy to promote children's educational achievement and future attainment. The combination of absent class politics in an illiberal society with an enduring ideology of education-based meritocracy in Confucianism makes this strategy a culturally legitimate channel of social struggle for recognition and respect for the subaltern. Last, children in migrant families are active contributors to their families' everyday organization amidst mobilities through sharing care and household responsibilities, and developing temporal and mobility strategies to keep alive intergenerational exchanges and family togetherness. The study uncovers coexisting resilience and vulnerabilities of migrants' children in their ‘doing class’ in contemporary China. It also contributes insights into our understanding of the diversity of childhoods in Asian societies at the intersection of familyhood, class dynamics and cultural politics.

The interconnections between climate change and health are well studied. However, there is a perceived need for studies that examine how responses to health hazards (e.g. cardiovascular diseases, ozone layer effects, allergens, mental health and vector-borne diseases) may assist in reducing their impacts. The purpose of this paper is to review the evidence on health responses to climate hazards and list some measures to address them.

A mixed literature review, bibliometric analysis and an original online survey were undertaken on 140 participants from 55 countries spread across all geographical regions.

The bibliometric analysis identified that most climate-related health hazards are associated with extreme weather events. However, only one-third of the investigated papers specifically analysed the connections between climate change and health hazards, revealing a thematic gap. Also, although Africa is highly affected by climate change, only 5% of the assessed studies focused on this continent. Many respondents to the survey indicated “heat distress” as a significant vulnerability. The survey also identified social determinants relevant to climate-induced health vulnerabilities, such as socioeconomic and environmental factors, infrastructure and pre-existing health conditions. Most respondents agree that policies and regulations are the most effective adaptation tools to address the public health hazards triggered by climate change. This paper presents some suggestions for optimising public health responses to health hazards associated with climate change, such as the inclusion of climate-related components in public health policies, setting up monitoring systems to assess the extent to which specific climate events may pose a health threat, establishing plans to cope with the health implications of heatwaves, increased measures to protect vulnerable groups and education and awareness-raising initiatives to reduce the overall vulnerability of the population to climate-related health hazards. These measures may assist the ongoing global efforts to understand better – and cope with – the impacts of climate change on health.

The combination of a literature review, bibliometric analysis and an original world survey identified and presented a wide range of responses.

This article aims to map the expectations of manufacturing companies towards suppliers in terms of implementing improvement activities. The article poses two research questions: RQ1: What kind of improvement of activities do the surveyed producers expect from their suppliers? RQ2: Do factors such as size, capital or implemented systems influence different assessments of the analyzed requirements toward suppliers?

The Computer Assisted Telephone Interview (CATI) technique was used to collect data. The sample consists of 150 producers (employing over 50 people) who were suppliers for enterprises from the automotive, electromechanical and chemical sectors operating in the Polish business-to-business (B2B) market. We analyzed 11 improvement activities, while their correlation structure was examined by exploratory factor analysis.

We have identified three latent factors – risk reduction, product innovation and increasing efficiency – which summarize the main expectations of manufacturing companies towards suppliers. Expectations for these factors are independent of the implemented management system, although the analysis showed higher expectations for product innovation in organizations with the implementation of Kaizen.

The article fills the research gap in the literature. The research results presented in the literature so far have focused on the expectations of enterprises towards suppliers in terms of meeting the criteria for their initial and periodic assessment. The research gap in the article is the result of empirical research presenting the expectations of manufacturers towards suppliers in terms of improving their processes. Based on the findings of the presented study, development trends and implications for managers responsible for purchasing processes and relationships with suppliers can be determined.