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The quality of the unstructured mesh has a considerable impact on the stability and accuracy of aerodynamic simulation in computational fluid dynamics (CFD). Typically, engineers spend a significant portion of their time on mesh quality evaluation to ensure a valid, high-quality mesh. The extensive manual interaction and a priori knowledge required to undertake an accurate and timely evaluation process have become a bottleneck in the idealized efficient CFD workflow. This paper aims to introduce a neural network-based quality evaluation approach for unstructured meshes to enable higher efficiency and the level of automation.

The paper investigates the capability of deep neural networks for the quality evaluation of unstructured meshes. For training the network, we build a training dataset for mesh quality learning algorithms. The dataset contains a rich variety of unstructured aircraft meshes with different mesh sizes, densities, cell distribution, growth ratios and cell numbers to ensure its diversity and availability. We also design a neural network, AircraftNet, to learn the effect of mesh quality on the convergent properties of the numerical solutions. The proposed network directly manipulates raw point data in mesh source files rather than passing it to an intermediate data representation. During training, AircraftNet extracts non-linear quality features from high-dimensional data spaces and then automatically predicts the overall quality of the input unstructured mesh.

The paper provides a series of experimental results on GPUs. It shows that AircraftNet is able to effectively analyze the quality-related features like mesh density and distribution from the extracted features and achieve high prediction accuracy on the proposed dataset with even a small number of training runs.

Because of the limited training dataset, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The paper publishes a benchmarking dataset for mesh quality learning algorithms and designs a novel neural network approach for unstructured mesh quality evaluation.

The paper aims to propose a generation approach for unstructured surface mesh to speed up mesh generation.

The paper proposes a lightweight interactive generation approach for unstructured surface mesh and presents several key technologies to support this approach.

The experimental results show that the proposed approach is feasible for unstructured meshes and it can accelerate the mesh generation process.

More geometric defects should be covered, and more convenient and efficient interactive means need to be provided.

The proposed approach and key technologies are implemented in NNW-GridStar.UG, which is the unstructured version of the mesh generation software of National Numerical Windtunnel (NNW).

This paper proposes a lightweight interactive approach for unstructured surface mesh generation, which can speed up mesh generation.

The purpose of this paper is to explore a new eco-friendly green textile dyeing. Natural plant Buddleja officinalis is traditionally used as yellow pigment addition in rice. It is worth developing its application and dyeing performance in cotton fabric.

Buddleja officinalis dried flower was extracted with ethanol aqueous. The extraction conditions including ethanol concentration, material to liquor ratio, extract time and temperature were optimized. Then cotton fabrics were dyed with Buddleja officinalis extraction under conventional and ultrasonic conditions. The effects of dyeing time, bath ratio, pH value of dyeing bath, dyeing temperature and mordants on K/S values were studied and the resulting color strength obtained by conventional and ultrasonic dyeing were compared. The ultraviolet (UV) transmittance of Buddleja officinalis dyed cotton fabric was also evaluated.

The color strength of the fabric dyed with Buddleja officinalis under ultrasonic conditions was higher than that under conventional conditions. Alum, Fe and Cu as simultaneous mordants improved the K/S value of the dyed cotton fabrics. Both washing fastness and rubbing fastness were fairly good in all Buddleja officinalis dyed cotton fabrics, washing fastness = 3–4 and rubbing fastness = 4. What’s more, the dyed cotton fabrics showed lower transmittance values as compared to undyed cotton fabrics and indicated potential UV protection capability.

Buddleja officinalis can be a new natural dye source for the ultrasonic dyeing of cotton fabric.

It is for the first time that Buddleja officinalis is used as a natural dye in cotton fabric dyeing with less water and the dyeing using ultrasound has been found to have an obvious improvement in the color strength and color-fastness.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

A digital servitization ecosystem (DSE) is a cooperation model based on the concept of value cocreation. However, capability asymmetry among enterprises can lead to unfair benefit distribution and hinder value cocreation and digital service transformation. This paper aims to investigate the impact of the varying capabilities of enterprises (manufacturers, service providers and digital technology providers) on revenue distribution when these enterprises collaborate on digital servitization transformation. This analysis is performed from an ecosystem perspective to facilitate the stable development of DSEs.

The rise of DSEs has engendered extensive literature, and the distribution of benefits within DSEs is in dire need of new mechanisms to adapt to the new competitive environment. The importance of investment contribution, digital servitization level, digitalization level, risk-taking ability, digital servitization effort level and brand awareness is determined by combining the expert scoring method and the entropy value method to determine different weights for manufacturers, service providers and digital technology providers. The Shapley value is used to design the benefit distribution mechanism for stable cooperation among DSE enterprises, thus providing a more scientific basis for the development of cooperative relationships.

Digital servitization is a collaborative process that involves multienterprise activities, and it is significantly affected by digital servitization level and digitalization level. Moreover, constructing the modified Shapley value benefit distribution mechanism according to the relevant capabilities of digital servitization can promote the stable development of DSEs and value cocreation among members.

The main contributions of this study are as follows: First, it summarizes the stability-influencing factors of DSEs on the basis of empirical and literature research on the demand for enterprise digital servitization capabilities and transformation difficulties, delves deeper into the capability composition and cooperative relationship of DSE members and combines the expert scoring method and the entropy value method to determine the weighting to design the benefit distribution mechanism. Second, it reflects system stability and development by studying the revenue distribution of DSE members, thereby expanding the ecosystem construction and business model transformation of digital servitization in the existing research.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

Security breaches have been arising issues that cast a large amount of financial losses and social problems to society and people. Little is known about how social media could be used a surveillance tool to track messages related to security breaches. This paper aims to fill the gap by proposing a framework in studying the social media surveillance on security breaches along with an empirical study to shed light on public attitudes and concerns.

In this study, the authors propose a framework for real-time monitoring of public perception to security breach events using social media metadata. Then, an empirical study was conducted on a sample of 1,13,340 related tweets collected in August 2015 on Twitter. By text mining a large number of unstructured, real-time information, the authors extracted topics, opinions and knowledge about security breaches from the general public. The time series analysis suggests significant trends for multiple topics and the results from sentiment analysis show a significant difference among topics.

The study confirms that social media monitoring provides a supplementary tool for the traditional surveys which are costly and time-consuming to track security breaches. Sentiment score and impact factors are good predictors of real-time public opinions and attitudes to security breaches. Unusual patterns/events of security breaches can be detected in the early stage, which could prevent further destruction by raising public awareness.

The sample data were collected from a short period of time on Twitter. Future study could extend the research to a longer period of time or expand key words search to observe the sentiment trend, especially before and after large security breaches, and to track various topics across time.

The findings could be useful to inform public policy and guide companies responding to consumer security breaches in shaping public perception.

This study is the first of its kind to undertake the analysis of social media (Twitter) content and sentiment on public perception to security breaches.

How do entrepreneurs use simple rules to build their business models? Based on an inductive study of three Chinese Internet and technology firms, the authors find that business models emerge from simple rules that entrepreneurs learn from their experience. Simple rules also guide entrepreneurs to actualize and exploit opportunities in the marketplace, and they can help business models evolve through market feedback, especially in internationalization. This paper aims to delve into the black box of entrepreneurial decision-making and offer a better depiction of the business model development process in uncertain and fast-changing environments and thus provide guidance for future entrepreneurs.

Following the case method (Eisenhardt, 1989; Yin, 2003), the authors first present a thick description of characteristics of three companies and the dynamics of their business models. They then code these descriptions into first-order measures. Finally, they aggregate these measures into abstract constructs. They constantly compare the theoretical constructs and the emerging theory with the existing literature on business models.

The authors generate three key insights from the findings: business models emerge from simple rules learned from entrepreneurs’ experience, simple rules help entrepreneurs exploit and actualize opportunities in the marketplace and simple rules help businesses expand and evolve business models through market feedback, especially in internationalization.

This paper falls into the intersection of strategy and entrepreneurship – an emerging new field of strategic entrepreneurship – and is concerned with how businesses create and sustain a competitive advantage while simultaneously identifying and exploiting new opportunities. The authors bring people – the individual decision-makers for businesses – back in strategy research and depict a more realistic picture of the behavior of successful entrepreneurs and their business model development process.

The purpose of this paper is to investigate the time-varying finite-time formation tracking control problem for multiple unmanned aerial vehicle systems under switching topologies, where the states of the unmanned aerial vehicles need to form desired time-varying formations while tracking the trajectory of the virtual leader in finite time under jointly connected topologies.

A consensus-based formation control protocol is constructed to achieve the desired formation. In this paper, the time-varying formation is specified by a piecewise continuously differentiable vector, while the finite-time convergence is guaranteed by utilizing a non-linear function. Based on the graph theory, the finite-time stability of the close-loop system with the proposed control protocol under jointly connected topologies is proven by applying LaSalle’s invariance principle and the theory of homogeneity with dilation.

The effectiveness of the proposed protocol is verified by numerical simulations. Consequently, the proposed protocol can successfully achieve the predefined time-varying formation in finite time under jointly connected topologies while tracking the trajectory generated by the leader.

This paper proposes a solution to simultaneously solve the control problems of time-varying formation tracking, finite-time convergence, and switching topologies.

Megaproject knowledge innovation (MKI) is perceived as a critical strategy for engineering value co-creation and industrial chain upgrading. Ascertaining the impact mechanism of MKI is a crucial initial step towards improving management practices. Within the framework of complex systems in megaprojects, factors exhibit intricate interdependencies. However, the current domain of knowledge has either overlooked or oversimplified this relationship and therefore cannot propose pragmatic and efficacious strategies for enhancing MKI. To close this gap, this study develops a Bayesian network (BN) model aiming to investigate the interdependencies among MKI-related factors and their impact on MKI.

First, this study implements literature review, expert interview and field investigation to identify the influencing factor nodes for the network model development. Second, a Bayesian network was constructed by integrating the expert knowledge with Dempster-Shafer theory. Next, a MKI measurement model was established using 253 training samples. Finally, the factor significance and optimal MKI improvement strategies are identified from the sensitivity analysis and probabilistic reasoning within the BNs.

The results indicate that (1) the BN model exhibits significant reliability and holds promotion and application value in formulating MKI management strategies; (2) knowledge sharing, shared vision and leadership are the key influencing factors of MKI; and (3) simultaneously improving institutional pressure, leadership and knowledge sharing is the most optimal strategy to enhance MKI.

This study innovatively introduced the BN method into the domain of MKI management, providing an appropriate approach for modelling complex relationships among factors and investigate nonlinear influences. The developed model raises megaproject stakeholders’ awareness about factors influencing MKI and presents quantified strategies that increase the likelihood of maximising MKI levels. Its ease of generalisability positions it as a promising decision support tool, facilitating the implementation of sustainable MKI practices.