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To present the detailed implementation processes of the IDEAL algorithm for two-dimensional compressible flows based on Delaunay triangular mesh, and compare the performance of the SIMPLE and IDEAL algorithms for solving compressible problems. What’s more, the implementation processes of Delaunay mesh generation and derivation of the pressure correction equation are also introduced.

Programming completely in C++.

Five compressible examples are used to test the SIMPLE and IDEAL algorithms, and the comparison with measurement data shows good agreement. The IDEAL algorithm has much better performance in both convergence rate and stability over the SIMPLE algorithm.

The detail solution procedure of implementing the IDEAL algorithm for compressible flows based on Delaunay triangular mesh is presented in this work, seemingly first in the literature.

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

The role of inventors' creativity is crucial for technological innovation within enterprises. The mobility of inventors among different enterprises is a primary source for companies to acquire external knowledge. The mechanism of “learning-by-hiring” is widely recognized by companies. Therefore, it is important to determine how to allocate network resources to enhance the creativity of inventors when companies hire mobile inventors.

The study suggests an analytical framework that analyzes alterations in tie strength and structural holes resulting from the network embeddedness of mobile inventors as well as the effect of the interaction between these two variables on changes in inventor’s creativity after the mobility. In addition, this paper examines the moderating impact of cognitive richness of mobile inventors and cognitive distance between mobile inventors and new employers on the correlation between network embeddedness and creativity.

This study found that: (1) The increase of tie strength has a significant boost in creativity. (2) Increasing structural holes can significantly improve the creativity of mobile inventors. (3) When both the tie strength and the structural holes increase, the creativity of the mobile inventors significantly increases. (4) It is important to note that when there is a greater cognitive distance, stronger tie strength promotes the creativity of mobile inventors. Additionally, cognitive richness plays a significant role in moderating the relationship between changes in structural holes and the creativity of mobile inventors.

These findings provide theoretical guidance for firms to effectively manage mobile inventors and optimize collaborative networks within organizations.

The purpose of this paper is to investigate three issues: how does an innovative search (local search and boundary-spanning search) impact firm innovation performance of latecomers; how does capability reconfiguration (capability evolution and capability substitution) mediates the relationship between innovative search and firm innovation performance; and how does the technological leapfrogging process (initial stage, following stage, synchronization stage and leading stage) moderate the relationship between capability reconfiguration and firm innovation performance.

A “resource-capability-performance” theoretical framework was developed to explore the relationships between local/boundary-spanning search, capability reconfiguration and firm innovation performance. The data were collected by sending out surveys to managers and employees in various industries in mainland China. These hypotheses were tested using structural equation models and hierarchical regressions.

The results showed that: innovative search has a direct causal relationship to capability reconfiguration; local search and boundary-spanning search are conducive to improve the innovation performance of latecomers; the impact of local search and boundary-spanning search on innovation performance is realized through the completion of mediating role of capability reconfiguration; there are differences in the path of local search and boundary-spanning search affecting the capability reconfiguration of enterprise innovation performance; and the relationship between innovative search, capability reconfiguration and enterprise innovation performance evolves with the enterprise in different stages of technological leapfrogging.

This study explores the relationship and the path of innovative search to firm innovation performance and analyzes the path difference between local search and boundary-spinning search, which enriches the research of organizational search and enterprise innovation. This paper reveals the whole path of innovative search affecting innovation performance, discusses the important role of capability reconfiguration and makes incremental contributions to dynamic capability theory. It studies the evolution of innovative search on innovation performance under the background of technological leapfrogging, which provides a new perspective for the study of organizational search and capability-based theory.

This paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems.

Firstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis.

In this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis.

The proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

This chapter aims to analyze how digital entrepreneurship is developed in the food industry of the European Union, comparing digital skills and big data indicators in all enterprises and the food industry. Using Eurostat Digital Economy and Society database, the authors obtained data between 2016 and 2020 – including the indicators: information and communications technology (ICT) specialists and ICT training to digital skills, and smart devices, geolocation, and social media to big data assessment. Furthermore, we compared all enterprises with those that manufacture beverages, food, and tobacco products. The authors identified that the food sector is still behind the other sectors regarding digitalization. Consequently, this research contributes to understanding entrepreneurs’ digital skills and how them relate to the use of big data in the food industry. Moreover, it also allows identifying the digital indicators of the food industry as less innovative than other industry digital indicators.

Taoist thought is not easily penetrable for Westerners, though it has growing importance with the globalisation of Chinese commerce. The purpose of this paper is to explore some aspects of Chinese and Taoist thought, and how they can be expressed in cybernetic terms, using a knowledge cybernetics (KC) schema.

KC operates through metaphor the role of which is considered with respect to its application to the specific area of urban landscaping.

A new methodological approach is indicated that is capable of linking Western landscape theory with Taoist feng shui. The traditional approach to critical planning has been in principle enhanced through the idea of landscape canonical harmony that comes from Taoist feng shui. It is also shown that the activity phases in urban landscape design are ontologically different, and use distinct types of energy measures.

The research uses KC as a vehicle for the development of a landscaping methodology that draws on both traditional Western and Chinese Taoist approaches. It requires application to real situations to draw out its practical capacities.

This is apparently the first time that a synergy between Western and Chinese approaches to landscape design and development has been attempted that results in the proposal of a complete methodology.

The successful use of this methodology could demonstrate that Western and Chinese approaches to landscape design are relatable.

Three-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers.

The authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C).

It has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions.

The designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

Although a laser profile sensor (LPS) can be used to measure dimensions, the “shadow region” generally degrades the accuracy and precision of width measurements. The accuracy and precision of such measurements should be improved.

In this paper, the authors propose herein a technique that combines high dynamic range (HDR) imaging with logistic fitting. First, a HDR image is composed of several images acquired with different exposure times, which augments the grayscale of the object profile and significantly reduces overexposure. Next, the profile is fit to a logistic function, which provides accurate and precise edge coordinates. Finally, given the edge coordinates, the object width is calculated.

To verify the stability of this logistic algorithm, the authors simulate different noise conditions and different degrees of incomplete edge data. In addition, the progressiveness of the algorithm is demonstrated by comparing the results with those of other algorithms and with the height measurement. Furthermore, the suitability of the system is verified experimentally.

Because of the limitation of the condition of laboratory, in the experimental section, this paper cannot represent perfectly the industrial situation. It makes this section limited in demonstration.

In this paper, the results show that the measurement accuracy and precision of the width is improved and exceeds that of the height measurement. The proposed HDR imaging method with logistic fitting may be applied to LPS width measurements, which should significantly aid the development of LPSs.

Purpose — The Department for Transport's 2011 GPS National Travel Survey (NTS) pilot study investigated whether personal GPS devices and automated data processing could be used in place of the 7-day paper diary. Using GPS technology could reduce the relatively high burden that the diary places upon respondents, reduce costs and improve data quality.

Design/methodology/approach — Data was collected from c.900 respondents. Practical changes were made to the existing methodology where necessary, including the collection of information to support data processing. Processing was undertaken using the University of Eindhoven's Trace Annotator. Results from the GPS pilot were then compared to those from the main NTS diaries for the same period.

Findings — There were no insurmountable problems using GPS devices to collect data; however, the processed GPS data did not resemble the diary outputs, making GPS unsuitable for the NTS. The GPS data produced fewer and longer trips than the diary data. The purpose of a quarter of the GPS trips was unclear, and a disproportionate share started and ended at home.

Research limitations — Further work to manually inspect trips identified via validation as unfeasible and subsequently refine the processing algorithms would have been desirable had time permitted. GPS data processing may have been hindered by missing GPS data, particularly in the case of rail travel.

Originality/value — This research used an accelerometer-equipped GPS device to better predict the method of travel. It also combined addresses that respondents reported having visited during the travel week with GIS data to code the purpose of trips without using a post-processing prompted-recall survey.