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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.

Air pollution poses a significant global threat to both human health and environmental stability, acknowledged by the World Health Organization as a leading cause of non-communicable diseases (NCDs) and a notable contributor to climate change. This chapter offers a comprehensive review of the impacts of air pollution on health, highlighting the complex interactions with genetic predispositions and epigenetic mechanisms. The consequences of air pollution to health are extensive, spanning respiratory diseases, cardiovascular disorders, adverse pregnancy outcomes, neurodevelopmental disorders, and heightened mortality rates. Genetic factors play a pivotal role in shaping individual responses to air pollution, influencing susceptibility to respiratory illnesses and the severity of symptoms. Additionally, epigenetic changes triggered by exposure to pollutants have been linked to respiratory health issues, cancer development and progression, and even transgenerational effects spanning multiple generations. As countries, including the UK, pursue ambitious targets for reducing emissions, ongoing research into the complex interplay of air pollution, genetics, and epigenetics is essential. By unravelling the underlying mechanisms and advancing preventive and therapeutic strategies, we can protect public health and promote sustainable environmental practices in the face of this pervasive global challenge.

Exploring open value, cultivating digital capability (DC) and driving business model innovation (BMI) have become an inevitable choice for enterprises to meet market demand and adapt to environmental changes. However, as one of the situational variables of BMI, the positive or negative influence of openness has not been proved and the path mechanism between DC and BMI is not clear. Based on the dynamic capability theory, this paper takes manufacturing enterprises as an example to explore the internal impact mechanisms of organizational openness on BMI. It extends the analysis by introducing DC as a mediating variable and introducing manufacturing enterprise type (high-tech and non-high-tech) as a moderating variable.

A questionnaire survey was conducted using data collected in China, data from 355 manufacturing enterprises were collected to carry out empirical research. Participants were mainly middle and senior managers with a comprehensive grasp of their firms’ information. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to test the reliability and validity of the sample data, and negative binomial regression analysis was used to test hypothesis relationships.

The authors find an inverted U-shaped relationship between openness and BMI, and explain that excessive openness may lead to low resource utilization, organizational inertia, cooperation distrust, which will have a negative impact on BMI. DC includes digital resource capability (DRC), digital management capability (DMC) and digital collaboration capability (DCC), which promote BMI and play a mediating role between openness and BMI. Enterprise type has a moderating effect on the relationship between DC and BMI.

The results of this paper summarize the opportunities and threats of open innovation, help enterprises fully understand the double-edged sword impact of openness, guide manufacturing enterprises to be sensitive to openness and achieve sustainable innovation. By analyzing the path of DRC, DMC and DCC to BMI, managers can improve their understanding of digital-driven value creation process and improve the competitive advantage of enterprises.

This paper presents the relationships among openness, DC and BMI. We find the non-linear effects of openness on DC and BMI, bridging the inconsistent view of positive or negative relationship between openness and organizational change in previous studies. The introduction of DC extends the theory of dynamic capability in the digital age, and opens the “black box” from opening to BMI from the process perspective of DRC, DMC and DCC. From the perspective of enterprise type, this paper provides different choices of capability upgrading and strategic innovation based on openness for high-tech and non-high-tech manufacturing enterprises.

Artificial intelligence (AI) is increasingly involved in idea generation and production processes. To understand AI’s pivotal roles in the back-of-house operations of restaurants, this study aims to examine the effects of AI involvement in recipe creation and food production on consumers’ willingness to order food.

We conduct three experiments in the context of casual dining restaurants. The authors examine the main effect of AI involvement in recipe creation and food production on the willingness to order food in a hypothetical restaurant (Study 1) and a real restaurant (Study 2). In addition, the authors also investigate the mediating role of uniqueness neglect. The authors explore whether the negative effect of AI involvement in recipe creation is attenuated in the presence of cues of uniqueness consideration (Study 3).

We demonstrate that AI involvement in food production does not elicit negative responses to a menu but that consumers show unfavorable responses when AI is involved in recipe creation. The authors also identify the mediating role of uniqueness neglect. Furthermore, the authors reveal a way to mitigate the negative perceptions of AI involvement in tasks requiring intuition and instinctive decision-making (i.e. recipe creation) by incorporating cues that emphasize uniqueness considerations.

We deliver causal evidence for the significant impacts of AI involvement in recipe creation and food production, using multiple experimental designs involving both hypothetical and real restaurants. The findings, thus, can tackle an ongoing challenge in the tourism and hospitality industry – the deficit of human resources in back-of-house operations.

This study aims to investigate the application of red cabbage extract in biodegradable composite-based edible film and evaluate its physicochemical characteristics and ability to be used in steamed chicken packaging.

Cassava starch, gelatin and glycerol were used as basic materials for edible film. Red cabbage extract was infused, and eggshell powder was also incorporated. The smart edible film formulation consists of six treatments which are C (control: cassava starch + gelatin + glycerol), CE (control + 0.1% eggshell powder), CRA (control + red cabbage A ratio), CERA (control + 0.1% eggshell powder + red cabbage A ratio), CERB (control + 0.1% eggshell powder + red cabbage B ratio) and CERC (control + 0.1% eggshell powder + red cabbage C ratio).

The different ratios of red cabbage extract in the formulation of the edible film affected its physicochemical properties (p < 0.05). The range of anthocyanin content were 0.39–11.53 mg cy-3-glu-eq/100 g and phenolic content were 19.87–369.68 mg GAE/100 g. Meanwhile, the antioxidant activity was 12.35%–51.09%. The tensile strength in all treatments was lower than control and adding red cabbage extract decreased the tensile strength. On the other hand, the elongation increased. The water vapor transmission rate was ranged from 158.09 to 191.19 g/m²/24 h. Morphological changes can be observed from scanning electron microscopy and optical data. Furthermore, using steamed chicken as a model, the edible film can show a response through the changes in edible film color, as confirmed by the pH value, total plate count and sensory quality of the stored steamed chicken.

There is no report available on the infusion of red cabbage extract on the bio composite edible film materials. The result shows a promising packaging material that can be used as an alternative to plastic packaging.

This study aims to investigate the impact of firms’ digital orientation (FDO) on corporate green innovation (CGI) among Chinese firms, examining the effects of financial constraint as the mediator and exploring heterogeneous effects across different firm contexts.

Using a sample of 28,697 firm-year observations from Chinese A-share listed companies (2008–2021), we employ a novel multidimensional measure of FDO derived from textual analysis of corporate annual reports. CGI is quantified using patent-based metrics. We utilize fixed-effects panel data models as benchmark regression to quantify FDO’s impact on CGI. Later, we utilize two-stage least squares, alternate measure for core explanatory variable, alternate as well as lead measures for explained variable and propensity score matching to tackle concerns for potential endogeneity.

Our results unveil a substantial positive connection between FDO and CGI. This connection is facilitated through the alleviation of financial constraints. Furthermore, heterogeneity analysis shows that the impact of FDO on CGI is more pronounced for state-owned enterprises, firms in areas with lower financial technology development and politically connected firms.

Our findings suggest that managers should view FDO as a strategic posture that can drive sustainable innovation, not just as a technological imperative. Policymakers should consider the role of FDO when designing policies to promote CGI, particularly in less-developed regions.

This study extends current understanding by: (1) Employing a comprehensive multidimensional measure of FDO that goes beyond the existing technologically focused digital transformation matrices. (2) Identifying financial constraints as a key mediating mechanism in the FDO–CGI relationship. (3) Revealing heterogeneous effects across different firm contexts, providing nuanced insights into how institutional and environmental factors moderate this relationship.

Laser fading, commonly used in the denim industry, is a computer-controlled, dry, ecological finishing method whereas conventional methods include high water, energy and time consumption. Resolution and pixel time are crucial parameters of laser source influencing the effect of laser treatment. The purpose of this study is to determine the optimum laser parameters of CO₂ laser followed by enzyme washing and to compare the tensile strength and color values of laser-treated denim fabric with that of conventional enzyme-faded.

Two different indigo-dyed, sulfur bottom-indigo-dyed and only indigo-dyed organic cotton denim fabrics with different unit weights, were lasetreated with different laser parameters and then subjected to 10 min enzyme washing. Tensile strength, abrasion resistance, and change in fabric unit weight were tested. CIE (L*a*b*, ΔE*, h°, C*) color values, color strength (K/S), yellowness and whiteness indexes were measured to identify the color differences. Color fastness tests including washing, rubbing, light, water and perspiration fastness were investigated.

Most effective laser fading in terms of good mechanical properties and color values was obtained at 40 dpi resolution and 300 µs pixel time.

Conventional enzyme fading of denim fabrics is a wet process and requires a long process time of 40–45 min and high temperatures, leading to high energy and water consumption. Laser fading, on the other hand, is a dry and ecological method, but causes a decrease in mechanical properties of the fabric, and an increase in yellowness. In this study, unlike the similar studies in the literature, denim fading was carried out by a combination of laser treatment followed by only 10 min enzyme washing in order to eliminate or minimize the drawbacks of the denim fading, such as high energy and water consumption for enzyme fading and decrease in mechanical properties of the fabric and increase in yellowness for laser fading. This method was applied to two different dyed denim fabrics, sulfur (bottom) and indigo (top) and laser process conditions were optimized to achieve the desired fading effects compared to conventional enzyme fading.

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.

This paper aims to investigate the interconnectedness of redenomination risk premiums across the four main European sovereign bond markets (France, Germany, Italy and the Netherlands).

The authors used the time-varying parameter vector autoregressions method to achieve the objectives.

This study reveals that the redenomination risk connectedness throughout the Euro area sovereign bond markets is dynamic and exhibits remarkable variations across various crisis episodes, such as the COVID-19 pandemic and Russia–Ukraine tensions. In addition, the analysis uncovers significant bilateral connections between countries. Furthermore, the research finds that spillovers from the US dollar redenomination premium (USDRP) are greater than those from the euro redenomination premium (ERP) and currency redenomination premium (CRP). However, during the Ukraine–Russia tensions, the connectedness between the USDRP is stronger than that between the ERP and CRP. On the other hand, the connectedness between CRP is higher than the USDRP and ERP during the COVID-19 pandemic. Importantly, this study demonstrates that the four countries play a role as both shock transmitters and receivers, switching alternatively.

This study contributes to the related literature by exploring the redenomination risk connectedness throughout the Euro area sovereign bond markets. Specifically, we rely on the USDRP, the ERP and CRP. These findings have serious implications for both portfolio risk management.

Interorganizational knowledge sharing (IKS) is an important strategy for organizations to obtain exterior knowledge resources to enhance innovation and value co-creation in megaprojects. However, little research has deeply investigated multiple attributes of organizational motivations and the combinatorial effects of these motivations on facilitating IKS. Based on resource dependency theory, this study examined the net and combinatorial effects of four types of motivations (i.e. image motivations, reactive motivations, project-based co-creative economic motivations and cross-project co-creative economic motivations).

Questionnaire surveys were administered to 244 practitioners with over 10 years of experience in construction megaprojects. The collected data were analyzed using a partial least square-structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA).

The findings of PLS-SEM support that image and project-based co-creative economic motivations positively drive IKS. The results of fsQCA reveal three types of efficacious configurations, including comprehensive benefits-integrated driven, short-term benefits-dominated driven and long-term benefits-dominated driven configurations.

This study provides insight into broadening the understanding of multidimensional organizational motivations for IKS and fostering the configurational perspective to their effects with the idea that one size does not fit all. Managers should develop effective interventions considering the combination of various motivations and use multi-layered incentive structures that reward both immediate project-specific outcomes and long-term relationship-building efforts.