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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.

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.

This study aimed to explore the relationship between supply chain resilience (SCR) and organizational performance (OP), with innovation (INN) serving as a mediator and information sharing (IS) acting as a moderator.

The study comprehensively examined the connections between SCR, OP, INN and IS. An exploratory approach and quantitative methods were employed. The data were collected from small and medium-sized manufacturing enterprises of three cities Xian, Hainan and Guangzhou of China via online questionnaire surveys conducted through Emails and WeChat. SmartPLS-4 was used for data analysis.

The findings indicated that SCR has a positive effect on sustainability efforts. Additionally, INN and effective IS both mediated and moderated this relationship, playing crucial roles in improving sustainability within the supply chain.

The study offered practical insights for businesses to enhance their sustainability efforts. Managers can use these findings to develop strategies that improve SCR, foster INN and encourage effective IS, ultimately resulting in a more sustainable supply chain.

This study enriched the existing knowledge base by investigating the intricate relationships among SCR, OP, INN and IS, all within the context of achieving sustainability. By exploring these elements holistically, the research introduced originality and highlighted effective strategies for sustainable supply chain management.

This paper aims to develop a cost-effective, camera-less advanced driver assistance system (ADAS) for electric vehicles. It will use sensor fusion of ultrasonic and radar sensors to implement adaptive cruise control (ACC), blind spot detection (BSD) and reverse parking (RP).

The system was tested on an electric vehicle test bench, using strategically placed ultrasonic and radar sensors. Sensor fusion enabled accurate object detection and distance measurement. The system’s performance was evaluated through simulated obstacle scenarios, with responses monitored via a graphical user interface. Sensor and GPS data were transmitted to the cloud for potential vehicle-to-vehicle communication.

The sensor fusion approach effectively supported ACC, BSD and RP functions, demonstrating accuracy in obstacle detection, speed adjustment and emergency braking. The real-time system visualization confirmed reliability across various scenarios and cloud integration showed promise for future communication enhancements.

Ultrasonic and radar sensors have limited range and accuracy compared to cameras. Ultrasonic sensors are less effective at longer distances and in adverse weather conditions, whereas radar can face challenges in detecting small or stationary objects. Sensor performance can be affected by environmental factors such as rain, fog or snow, which may reduce the effectiveness of both ultrasonic and radar sensors. Sensor performance can be affected by environmental factors such as rain, fog or snow, which may reduce the effectiveness of both ultrasonic and radar sensors.

Improved obstacle detection and collision avoidance contribute to overall vehicle safety. Drivers benefit from advanced features like ACC, BSD and RP without the high cost of traditional camera-based systems. The use of ultrasonic and radar sensors makes advanced driver assistance features more affordable, allowing broader adoption across various vehicle segments, including budget-friendly and mid-range models. The system’s responsiveness and obstacle detection capabilities can lead to more efficient driving, reducing the likelihood of accidents and improving traffic flow.

Enhanced safety features such as ACC, BSD and RP contribute to reducing traffic accidents and injuries. By making advanced driver assistance features more affordable, the system improves vehicle safety for a broader range of drivers, including those in lower-income brackets. The introduction of such systems can raise public awareness about the benefits of ADAS technologies and their role in enhancing road safety.

This study introduces a novel ADAS system that eliminates the need for cameras by leveraging the strengths of radar and ultrasonic sensors. The approach offers a practical and innovative solution for enhancing vehicle safety at a reduced cost.