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The purpose of this paper is to analyze numerically the blowup in finite time of the solutions to a one-dimensional, bidirectional, nonlinear wave model equation for the propagation of small-amplitude waves in shallow water, as a function of the relaxation time, linear and nonlinear drift, power of the nonlinear advection flux, viscosity coefficient, viscous attenuation, and amplitude, smoothness and width of three types of initial conditions.

An implicit, first-order accurate in time, finite difference method valid for semipositive relaxation times has been used to solve the equation in a truncated domain for three different initial conditions, a first-order time derivative initially equal to zero and several constant wave speeds.

The numerical experiments show a very rapid transient from the initial conditions to the formation of a leading propagating wave, whose duration depends strongly on the shape, amplitude and width of the initial data as well as on the coefficients of the bidirectional equation. The blowup times for the triangular conditions have been found to be larger than those for the Gaussian ones, and the latter are larger than those for rectangular conditions, thus indicating that the blowup time decreases as the smoothness of the initial conditions decreases. The blowup time has also been found to decrease as the relaxation time, degree of nonlinearity, linear drift coefficient and amplitude of the initial conditions are increased, and as the width of the initial condition is decreased, but it increases as the viscosity coefficient is increased. No blowup has been observed for relaxation times smaller than one-hundredth, viscosity coefficients larger than ten-thousandths, quadratic and cubic nonlinearities, and initial Gaussian, triangular and rectangular conditions of unity amplitude.

The blowup of a one-dimensional, bidirectional equation that is a model for the propagation of waves in shallow water, longitudinal displacement in homogeneous viscoelastic bars, nerve conduction, nonlinear acoustics and heat transfer in very small devices and/or at very high transfer rates has been determined numerically as a function of the linear and nonlinear drift coefficients, power of the nonlinear drift, viscosity coefficient, viscous attenuation, and amplitude, smoothness and width of the initial conditions for nonzero relaxation times.

This study aims to investigate low-carbon footprint travel choices, considering both destination attributes and climate change perceptions, and examine the impacts of nudging (a communication tool to alter individuals’ choices in a predictable way) on tourists’ preferences for carbon mitigation in destinations.

A discrete choice experiment questionnaire was administered to a sample of 958 Hong Kong respondents. Hybrid choice modeling was used to examine the respondents’ preferences for destination attributes and to explain preference heterogeneity using tourists’ climate change perceptions. The respondents’ willingness to pay for the destination attributes was also calculated to measure the monetary value of the attributes.

Destination type, carbon emissions and travel cost had significant effects on tourists’ choices of destination. Nudging increased tourists’ preference for low-carbon footprint choices. Tourists with higher climate change perceptions were more likely than others to select low-carbon destinations with carbon offset projects.

The findings of this study provide an impetus for destination management organizations to support local carbon offset projects, implement policies that mitigate carbon emissions and develop sustainable tourism to fulfill tourists’ demand for low-carbon footprint travel choices. Based on the findings, policymakers could promote sustainable tourism by publishing relevant climate change information on social media.

This study addressed a gap in the literature on tourist travel choice by considering carbon emission-related attributes and climate change perceptions and by confirming the role of nudging in increasing the choice of low-carbon destinations.

This chapter delves into the impact of digital initiatives on firms and sheds light on how they can be explained through market reactions and the resource/capabilities mechanism. By providing a novel conceptual framework that reflects the potential impact of digital initiatives on the sensing, seizing and transforming capabilities of dynamic capabilities, this chapter reveals the tremendous potential of digital initiatives to help firms become more adaptive to their environment and create sustainable competitive advantages that elicit positive market responses. This conceptual framework represents an original contribution to the literature. It enhances the understanding of the resource-based view and efficient market hypothesis, providing a fresh perspective on the influence of digital initiatives on firm performance and the dynamic capabilities mechanism that has hitherto been overlooked. As a result, this chapter enables researchers to develop testable hypotheses that examine the causal relationships between digital initiatives, dynamic capabilities and market performance using robust quantitative research methods. Furthermore, this chapter offers valuable insights for managers seeking to develop a more focused approach to digital transformation and enhance their competitive advantage. By exploring the impact of digital initiatives on sensing, seizing and transforming capabilities, managers can gain a deeper understanding of how they can leverage digital initiatives to improve their organisational performance and respond more effectively to the demands of an ever-changing landscape.

Despite the wide use of quantitative assessment to identify the relationship between green logistics (GL) practices and the sustainability performance (SP) of firms, results of these studies are inconsistent. A lack of theoretical foundation has been cited as a potential reason for these contradictory findings. This study aims to explore the relationship between GL practices and SP qualitatively and to provide a theoretical foundation for this link.

Following a multi-methodology approach, the authors used the grounded theory method (GTM) to investigate perceived relationships through qualitative analysis and adopted the system thinking (ST) approach to identify causal relationships using causal loop diagrams (CLDs).

The authors identified different sustainability practices under three major categories: logistics capabilities, resource-related practices and people-related practices. This analysis showed the relationships among these practices are non-linear. Based on the results, the authors developed three propositions and introduced a theoretical foundation for the relationship between GL practices and SP.

Managerial personnel can use the theoretical foundation provided by this study when making decisions on GL practices adoption. This theoretical foundation suggests applying a holistic approach that can help optimize SP by selecting suitable practices. On the other hand, researchers can use a multi-methodology approach suggested by this study to explore complex social issues.

This study contributes to the knowledge from a methodology perspective as no previous studies have been conducted to identifying the relationship between GL practices and SP by combining GTM and ST approaches. This combination can be extended to build system dynamics models for sustainable logistics impacts bringing novelty to the research field of sustainable logistics.

The purpose of this study is to examine the mechanism of benevolent leadership on employee cheating behavior through two paths – employee uncertainty and perceived acceptability of norm violation – and also reveal the possible dark side of benevolent leadership. Meanwhile, the moderating effects of leader behavioral integrity in the cognition dual path process are also discussed.

This study invites 383 employees and their superiors in seven Chinese enterprises as the research objects and conducts a paired survey at three time points, and then Mplus 7.4 software is used to analyze the empirical data.

The results are shown as follows. Benevolent leadership plays a positive role on uncertainty and perceived acceptability of norm violation. Uncertainty and perceived acceptability of norm violation mediate the relationship between benevolent leadership and cheating behavior, respectively. Leader behavioral integrity moderates the positive role of benevolent leadership on uncertainty and perceived acceptability of norm violation. Leader behavioral integrity moderates the indirect effect of benevolent leadership on employees' cheating behavior through uncertainty and perceived acceptability of norm violation.

This study reveals the mechanism behind the negative role of benevolent leadership through the cognition reaction of employees to benevolent leadership and broadens the research scope of benevolent leadership. Meanwhile, it provides some practical inspiration for leaders to effectively use the benevolent leadership style and restrain employees' cheating behavior.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

This study aims to empirically examine the relationship of ethical leadership and psychological capital with knowledge creation. It also investigates the effect of psychological capital as a mediator in the relationship between ethical leadership and knowledge creation.

This study is based on quantitative research methodology. The data was gathered using a survey questionnaire from 286 members of public-sector research organizations (PSROs) in India. Structural equation modelling (SEM) was used for hypotheses testing.

The findings of this study show that ethical leadership and psychological capital have a positive influence on knowledge creation. Further, psychological capital mediated the relationship between ethical leadership and knowledge creation.

This study is a quantitative cross-sectional study. However, future researchers may use qualitative research methodology and longitudinal data collection to supplement this study.

This study provides new understanding into the creation of knowledge by emphasizing on the critical role played by ethical leadership and psychological capital and, thus, makes significant theoretical contribution. It emphasizes that managers should not only be ethical but also use interventions to strengthen psychological capital of employees to strengthen knowledge creation.

To the best of authors’ knowledge, this is the first study to examine the underlying mechanism of psychological capital in explaining the links of ethical leadership with knowledge creation.

This paper aims to comprehensively review the preparation methods of superhydrophobic surfaces (SHPS) for corrosion protection of Mg alloy in recent years.

The preparation methods, wettability and corrosion resistance of SHPS on Mg alloy in the past three years are systematically described in this paper.

Two types of SHPS, including single-layer and multilayer coatings for corrosion protection of Mg alloy are summarized. Preparing multilayered coatings with multifunction is the current trend in developing SHPS on Mg alloy.

This paper reviewed the preparation methods and corrosion resistance of SHPS on Mg alloys. It provides a valuable reference for researchers to develop highly durable SHPS with excellent corrosion resistance for Mg alloys.

As competition in the industry intensifies, companies must use market-oriented approaches to gain competitive superiority; one of the approaches that can lead to the success of companies in the competitive market is to undertake social co-creation with the help of customers. Although the use of social media for the development of social interactions has expanded, very little attention has been paid to how the concept of social co-creation is formed on social media by users. Therefore, this study aims to investigate the effect of personality traits and website quality on social co-creation, with the mediating role of trust in tourism websites.

This research, in terms of purpose, is practical, and in terms of information collection, it is a descriptive survey. The research statistical population is all users of active tourism sites in Iran. The sampling method is non-probability and available sampling. The questionnaire was designed based on the Likert scale and was distributed electronically among the statistical sample. After collecting and reviewing the questionnaires, 203 were used for analysis. The data analysis method in this study is hierarchical multiple regression.

The results indicated that personality traits and website quality are correlated with trust and social co-creation. The dimensions of website quality, including quality of information, quality of system and quality of service on tourism websites, have considerable and positive effects on trust. Also, all dimensions of the personality traits, except extraversion and neuroticism, have a considerable and positive effect on trust. Moreover, the correlation between trust and social co-creation is positive.

According to the review of the digital marketing literature, some researchers examined the influential factors in co-creation, but there is little research about how the interaction of these three concepts (personality traits, website quality and trust) enhances co-creation. This study contributes to the existing literature with empirical evidence of how personality traits and website quality influence co-creation by mediating the role of trust.