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The purpose of this study is to perform a detailed review on the numerical modeling of multiphase and multicomponent flows in microfluidic system using phase-field method. The phase-field method is of emerging importance in numerical computation of transport phenomena involving multiple phases and/or components. This method is not only used to model interfacial phenomena typical to multiphase flows encountered in engineering and nature but also turns out to be a promising tool in modeling the dynamics of complex fluid-fluid interfaces encountered in physiological systems such as dynamics of vesicles and red blood cells). Intrinsically, a priori unknown topological evolution of interfaces offers to be the most concerning challenge toward accurate modeling of moving boundary problems. However, the numerical difficulties can be tackled simultaneously with numerical convenience and thermodynamic rigor in the paradigm of the phase field method.

The phase-field method replaces the macroscopically sharp interfaces separating the fluids by a diffuse transition layer where the interfacial forces are smoothly distributed. As against the moving mesh methods (Lagrangian) for the explicit tracking of interfaces, the phase-field method implicitly captures the same through the evolution of a phase-field function (Eulerian). In contrast to the deployment of an artificially smoothing function for the interface as used in the volume of a fluid or level set method, however, the phase-field method uses mixing free energy for describing the interface. This needs the consideration of an additional equation for an order parameter. The dynamic evolution of the system (equation for order parameter) can be described by Allen–Cahn or Cahn–Hilliard formulation, which couples with the Navier–Stokes equation with the aid of a forcing function that depends on the chemical potential and the gradient of the order parameter.

In this review, first, the authors discuss the broad motivation and the fundamental theoretical foundation associated with phase-field modeling from the perspective of computational microfluidics. They subsequently pinpoint the outstanding numerical challenges, including estimations of the model-free parameters. They outline some numerical examples, including electrohydrodynamic flows, to demonstrate the efficacy of the method. Finally, they pinpoint various emerging issues and futuristic perspectives connecting the phase-field method and computational microfluidics.

This paper gives unique perspectives to future directions of research on this topic.

The idea of implementing supply chain management (SCM) principles for the construction industry was embraced by construction stakeholders to enhance the sector's performance. The analysis from the literature revealed that the implementation of SCM in the construction industry enhances the industry's value in terms of cost-saving, time savings, material management, risk management and others. The construction supply chain (CSC) can be managed using the pull or push system. This chapter also discusses the origin and proliferation of SCM into the construction industry. The chapter revealed that the concept of SCM has passed through five different eras: the creation era, the use of ERP, globalisation stage, specialisation stage and electronic stage. The findings from the literature revealed that we are presently in the fourth industrial revolution (4IR) era. At this stage, the SCM witnesses the adoption of technologies and principles driven by the 4IR. This chapter also revealed that the practice of SCM in the construction industry is centred around integration, collaboration, communication and the structure of the supply chain (SC). The forms and challenges hindering the adoption of these practices were also discussed extensively in this chapter.

Tacit knowledge in frontline operations is primarily reflected in the holders’ intuition about dynamic systems. Despite the implicit nature of tacit knowledge, the understanding of complex systems it encapsulates can be displayed through formalization methods. This study seeks to develop a methodology for formalizing tacit knowledge in a dynamic delivery system.

This study employs a structured survey to gather experiential knowledge from dispatchers engaged in last-mile delivery operations. This knowledge is then formalized using a value function approximation approach, which transforms tacit insights into structured inputs for dynamic decision-making. We apply this methodology to optimize delivery operations in an online-to-offline pharmacy context.

The raw system feature data are not strongly correlated with the system’s development trends, making them ineffective for guiding dynamic decision-making. However, the system features obtained through preprocessing the raw data increase the predictiveness of dynamic decisions and improve the overall effectiveness of decision-making in delivery operations.

This research provides a foundational framework for studying sequential dynamic decision problems, highlighting the potential for improved decision quality and system optimization through the formalization and integration of tacit knowledge.

This approach proposed in this study offers a method to preserve and formalize critical operational expertise. By embedding tacit knowledge into decision-making systems, organizations can enhance real-time responsiveness and reduce operational costs.

This study presents a novel approach to integrating tacit knowledge into dynamic decision-making frameworks, demonstrated in a real-world last-mile delivery context. Unlike previous research that focuses primarily on explicit data-driven methods, our approach leverages the implicit, experience-based insights of operational staff, leading to more informed and effective decision-making strategies.

As the search engine platform, Baidu has already developed keyword advertising as one of its main business scopes, while in-feed advertising is emerging as another intelligent choice for the company. Our purpose is to validate the effectiveness of keyword and retargeted in-feed advertising on offline sales and whether the effectiveness of these two advertising strategies relies on keyword attributes work.

We utilize data from the ad campaigns of a prominent manufacturer within the machinery and equipment (hereinafter referred to as “the company”) on Baidu. To scrutinize the research hypotheses, we have employed linear regression models. Subsequently, we address potential endogeneity issues and use various techniques to ascertain the reliability of the results.

Empirical evidence indicates that both keyword and in-feed advertising enhance offline sales. Upon examining the moderating role of keyword attributes (specificity and length), we observe that specific keywords (price and word-of-mouth (WOM)) accelerate the boosting effect of advertising on sales; similarly, the longer the keywords, the more obvious the enhanced impact of advertising on sales. Moreover, the positive influence of specific keywords (price and WOM) on advertising effectiveness is more outstanding when the keywords are longer.

To our knowledge, no empirical investigation has yet to analyze keyword and retargeted in-feed advertising concurrently within the search engine context. Our research is the inaugural work to reveal that they serve as mutual substitutes regarding their impact on sales. Furthermore, this paper pioneers examining the moderating effects exerted by keyword attributes (specificity and length) on the effectiveness of these two ad types. The findings presented herein offer valuable insights into the harmonious coexistence and collaboration among companies, advertisers, users and search engine platforms.

This paper aims to, from a Taoism perspective, one of Chinese inveterate cultures and mindsets, add knowledge into how Chinese indigenous cultures and mindsets will affect the way of Chinese people perceive and process guanxi. Specifically, this paper outlines the mechanism of guanxi from a culture perspective. Cultures significantly affect local people behavior.

A mixed-methods survey (Study 1) – analyzed with one-way ANOVA – and interview (Study 2) – analyzed with grounded theory – were used to answer the research questions. In total, 248 surveys were collected for Study 1, and 34 interviews for Study 2 that were transcribed into a word file, which consists of 609 pages with 327,463 Chinese characters, were processed.

The findings show that guanxi is determined by positive and negative forces between instrumental and affective components. Further, two essential conditions – fitness of personality and clear contract, which would determine when a positive or negative force would emerge in a guanxi – were identified.

The main contribution is that this paper clarifies the guanxi mechanism based upon one of the most significant Chinese cultures and mindsets. Or guanxi is viewed from a new perspective – how Taoism affects Chinese people’s perception and evaluation of guanxi. This paper also finds evidence for the main arguments based upon the two studies.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc.

Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed.

The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments.

In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

This paper aims to present a novel structural reliability analysis scheme with considering the structural strength degradation for the wing spar of a generic hypersonic aircraft to guarantee flight safety and structural reliability.

A logarithmic model with strength degradation for the wing spar is constructed, and a reliability model of the wing spar is established based on stress-strength interference theory and total probability theorem.

It is demonstrated that the proposed reliability analysis scheme can obtain more accurate structural reliability and failure results for the wing spar, and the strength degradation cannot be neglected. Furthermore, the obtained results will provide an important reference for the structural safety of hypersonic aircraft.

The proposed reliability analysis scheme has not implemented in actual flight, as all the simulations are conducted according to the actual experiment data.

The proposed reliability analysis scheme can solve the structural life problem of the wing spar for hypersonic aircraft and meet engineering practice requirements, and it also provides an important reference to guarantee the flight safety and structural reliability for hypersonic aircraft.

To describe the damage evolution more accurately, with consideration of strength degradation, flight dynamics and material characteristics of the hypersonic aircraft, the stress-strength interference method is first applied to analyze the structural reliability of the wing spar for the hypersonic aircraft. The proposed analysis scheme is implemented on the dynamic model of the hypersonic aircraft, and the simulation demonstrates that a more reasonable reliability result can be achieved.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.