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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 paper aims to describe the academic community’s perspectives about climate change learning and engagement opportunities by means of a case study at the University of Passo Fundo, Brazil.

A set of interviews and focus groups were conducted, and data collection focused on three main groups, namely, university students, professors and managers. The analysis was developed through content analysis of the individual interviews and focus groups.

The results showed that the academic community is trying to change their attitudes and behaviours, and students would like to learn more about climate change.

This investigation combines the views of different academic groups and indicates initiatives that could boost the university initiatives towards climate action and learning.

Data-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.

Drawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.

Based on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.

The proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

The purpose of this paper is to investigate the research landscape of LSS 4.0 papers published in two well-known repositories, Scopus and Web of Science (WoS), in terms of publication trends, article distribution by author, journal, affiliations and country, and article clustering based on keywords, authors and countries. In addition, a literature review was carried out to build a conceptual framework of integrated Lean Six Sigma and Industry 4.0 (LSS 4.0) that encompasses operational, sustainability and human factors or ergonomics aspects.

The literature review of integrated Lean Six Sigma and I4.0 publications published in Scopus and WoS databases in the current decade was conducted for the present study. This study categorizes LSS, I4.0 and related research articles based on publication patterns, journals, authors and affiliations, country and continental-wise distribution and clustering the articles based on keywords and authors from the Scopus and WoS databases from 2011 to 2022 using the search strings “Lean”, “Six Sigma”, “Lean Six Sigma” and “Industry 4.0” in the Title, Abstract and Keywords using Biblioshiny, VOS viewer and Microsoft Excel.

In the recent three years, from 2020 to 2022, LSS 4.0 has been substantially increasing and is seen as an emerging and trending area. This research identifies the most influential authors, most relevant affiliations, most prolific countries and most productive journals and clusters based on keywords, authors and countries. Further, a conceptual framework was developed that includes the impact of operational, sustainability and ergonomic or human factors in LSS 4.0.

This article assists in comprehending the trends and patterns of LSS 4.0. Further, the conceptual framework helps professionals and researchers understand the significance and impact of integrating LSS and Industry 4.0 in the aspects of human factors/ergonomic, sustainability and operations. Also, the research induce professionals to incorporate all these factors while designing and implementing LSS 4.0 in their organization.

This conceptual framework and bibliometric analysis would aid in identifying potential areas of research and providing future directions in the domain of LSS 4.0. It will be beneficial for academicians, professionals and researchers who are planning to apply and integrate techniques of LSS and technologies of I4.0 in their organizations and research.

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.

This study develops and validates an innovative approach for extracting knowledge from online user reviews by integrating textual content and emojis. Recognizing the pivotal role emojis play in enhancing the expressiveness and emotional depth of digital communication, this study aims to address the significant gap in existing sentiment analysis models, which have largely overlooked the contribution of emojis in interpreting user preferences and sentiments. By constructing a comprehensive model that synergizes emotional and semantic information conveyed through emojis and text, this study seeks to provide a more nuanced understanding of user preferences, thereby enhancing the accuracy and depth of knowledge extraction from online reviews. The goal is to offer a robust framework that enables more effective and empathetic engagement with user-generated content on digital platforms, paving the way for improved service delivery, product development and customer satisfaction through informed insights into consumer behavior and sentiments.

This study uses a structured methodology to integrate and analyze text and emojis from online reviews for effective knowledge extraction, focusing on user preferences and sentiments. This methodology consists of four key stages. First, this study leverages high-frequency noun analysis to identify and extract product attributes mentioned in online user reviews. By focusing on nouns that appear frequently, the authors can systematically discern the primary features or aspects of products that users discuss, thereby providing a foundation for a more detailed sentiment and preference analysis. Second, a foundational sentiment dictionary is established that incorporates sentiment-bearing words, intensifiers and negation terms to analyze the textual part of the reviews. This dictionary is used to assign sentiment scores to phrases and sentences within reviews, allowing the quantification of textual sentiments based on the presence and combination of these predefined lexical items. Third, an emoticon sentiment dictionary is developed to address the emotional content conveyed through emojis. This dictionary categorizes emojis based on their associated sentiments, thus enabling the quantification of emotional expressions in reviews. The sentiment scores derived from the emojis are then integrated with those from the textual analysis. This integration considers the weights of text- and emoji-based emotions to compute a comprehensive attribute sentiment score that reflects a nuanced understanding of user sentiments and preferences. Finally, the authors conduct an empirical study to validate the effectiveness of the proposed methodology in mining user preferences from online reviews by applying the approach to a data set of online reviews and evaluating its ability to accurately identify product attributes and user sentiments. The validation process assessed the reliability and accuracy of the methodology in extracting meaningful insights from the complex interplay between text and emojis. This study offers a holistic and nuanced framework for knowledge extraction from online reviews, capturing both explicit and implicit sentiments expressed by users through text and emojis. By integrating these elements, this study seeks to provide a comprehensive understanding of user preferences, contributing to improved consumer insight and strategic decision-making for businesses and researchers.

The application of the proposed methodology for integrating emojis with text in online reviews yields significant findings that underscore the feasibility and value of extracting realistic user knowledge to gain insights from user-generated content. The analysis successfully captured consumer preferences, which are instrumental in informing service decisions and driving innovation. This achievement is largely attributed to the development and utilization of a comprehensive emotion-sentiment dictionary tailored to interpret the complex interplay between textual and emoji-based expressions in online reviews. By implementing a sentiment calculation model that intricately combines textual sentiment analysis with emoji sentiment analysis, this study was able to accurately determine the final attribute emotion for various product features discussed in the reviews. This model effectively characterized the emotional knowledge of online users and provided a nuanced understanding of their sentiments and preferences. The emotional knowledge extracted is not only quantifiable but also rich in context, offering deeper insights into consumer behavior and attitudes. Furthermore, a case analysis is conducted to rigorously test the validity of the proposed model in a real-world scenario. This practical examination revealed that the model is not only capable of accurately extracting and analyzing user preferences but is also adaptable to different contexts and product categories. The case analysis highlights the robustness and flexibility of the model, demonstrating its potential to enhance the precision of knowledge extraction processes significantly. Overall, the results confirm the effectiveness of the proposed approach in integrating text and emojis for comprehensive knowledge extraction from online reviews. The findings validate the model’s capability to offer actionable insights into consumer preferences, thereby supporting more informed and strategic decision-making by businesses. This study contributes to the broader field of sentiment analysis by showcasing the untapped potential of emojis as valuable indicators of user sentiments, opening new avenues for research and applications in digital marketing and consumer behavior analysis.

This study introduces a pioneering approach to extract knowledge from Web user interactions, notably through the integration of online reviews that incorporate both textual content and emoticons. This innovative methodology stands out because it holistically considers the dual channels of communication, text and emojis, to comprehensively mine Web user preferences. The key contribution of this study lies in its novel insights into the extraction of consumer preferences, advancing beyond traditional text-based analysis to embrace nuanced expressions conveyed through emoticons. The originality of this study is underpinned by its acknowledgment of emoticons as a significant and untapped source of sentiment and preference indicators in online reviews. By effectively merging emoticon analysis and emoji emotion scoring with textual sentiment analysis, this study enriches the understanding of Web user preferences and enhances the accuracy and depth of consumer preference insights. This dual-analysis approach represents a significant leap forward in sentiment analysis, setting a new standard for how digital communication can be leveraged to derive meaningful insights into consumer behavior. Furthermore, the results have practical implications to businesses and marketers. The insights gained from this integrated analytical approach offer a more granular and emotionally nuanced view of customer feedback, which can inform more effective marketing strategies, product development and customer service practices. By pioneering this comprehensive method of knowledge extraction, this study paves the way for future research and practice to interpret and respond more accurately to the complex landscape of online consumer expressions. This study’s originality and value lie in its innovative method of capturing and analyzing the rich tapestry of Web user communication, offering a ground-breaking perspective on consumer preference extraction that promises to enhance both academic research and practical applications in the digital era.