Ebook: Applied Mathematics, Modeling and Computer Simulation

This book contains the selected papers from the 2023 International Conference on Applied Mathematics, Modeling and Computer Simulation (AMMCS 2023), which was held on August 12–13, 2023, Hubei Zhongke Institute of Geology and Environment Technology, Wuhan, China. It provides scholars and researchers with an effective medium for communicating important recent developments in their areas of specialization to colleagues and to scientists in related disciplines. We had the honor of inviting Zhixin Yang, Department of Electromechanical Engineering and State Key Laboratory of Internet of Things for Smart City, Faculty of Science and Technology, University of Macau, China., to serve as our Conference Chairmen. Two-hundred-seventy individuals and enterprises from all over the world attended the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations, and online Q&A discussion. Firstly, keynote speakers were each allocated 30–35 minutes to hold their speeches. Then in the oral presentations, the excellent papers selected were presented by their authors one by one for 15–20 minutes.

On behalf of the conference AMMCS 2023 organizing committee, we would like to express our deeply appreciation to all the keynote speakers, oral speakers peer reviewers, and all the participants and supporters. More than anything, we would like to acknowledge the Advances in Transdisciplinary Engineering, for the efforts and help of all the colleagues in publishing this paper volume. We really expect the opportunity for future collaborations.

The book contains 133 peer-reviewed papers, selected from more than 250 submissions and ranging from the theoretical and conceptual to strongly pragmatic and addressing industrial best practice. It covers Mathematical Modelling and Application, Engineering Applications and Scientific Computations, Simulation of Intelligent Systems.The book shares practical experiences and enlightening ideas and will be of interest to researchers and practitioners in applied mathematics, modeling and computer simulation everywhere.

The dynamic coupling simulation of urban water supply network and non-negative pressure water supply is very important for the security of urban water supply. The three related aspects were studied in the paper. Firstly, taking an independent area of city K water supply system as an example, the coupling hydraulic modelling was built based on EPANET software. Then the nodes of residential water consumption in the network were installed with non-negative pressure equipment ergodically. Therefore, the suitable node locations for installation were obtained, and the sensitive nodes in the pipeline network were listed at the same time. Lastly, for the simultaneous multi-point utilizations of non-negative pressure equipment, the operating situation was simulated and the impact on the pressure of the municipal pipeline network was analysed. Based on the hydraulic simulation results and genetic algorithm, the layout of secondary water supply with non-negative pressure was optimized. The theory on non-negative equipment proposed in the paper is also applicative for other cities.

In this paper, we build a model to make a reasonable prediction of some data of the game, which can help the game company to make constant adjustments to the game in its operation. For the prediction of the number of participants, we use function fitting, gray prediction (Markov chain correction) and BP neural network to predict the number of participants in the game at a specific time. The model built by the gray prediction (Markov chain correction) and BP neural network fits better. Then we introduced three difficulty influence factors for word difficulty and established a set of well-fitted evaluation criteria by linking the four influence factors with two parameters of the weibull function. Finally, we used hierarchical analysis to assign values to the difficulty influence factors, and then evaluated the words according to this assignment result

Objective:

Body-esteem is an important indicator of the psychological benefits of physical exercise. To integrate inconsistent findings from previous literature, this study aimed to explore the reasons for the inconsistency.

Methods:

A meta-analysis was conducted to analyze 90 original studies published between 2008 and 2022, involving 98 independent samples and 29,251 participants.

Results:

(1) There was a significant positive correlation between physical exercise and body-esteem (r = 0.421, 95%CI [0.368, 0.472]), with a moderate effect size. (2) There were also significant positive correlations, with moderate effect sizes, between exercise duration (r = 0.386, 95%CI [0.235, 0.520]), exercise intensity (r = 0.355, 95%CI [0.227, 0.470]), exercise frequency (r = 0.405, 95%CI [0.291, 0.507]), and body-esteem. (3) Group type effects moderated the relationship between physical exercise and body-esteem (Qb = 8.088). (4) Exercise type effects partially moderated the relationship between physical exercise and body-esteem (Qb = 10.057), physical self-worth (Qb = 10.015), and physical attractiveness (Qb = 7.823). (5) Publication type effects were not significant (Qb = 2.795). (6) Exercise measurement type effects moderated the relationship between physical exercise and body-esteem (Qb = 10.304).

Conclusion:

(1) There is a moderate positive correlation between physical exercise and body-esteem. (2) Research characteristics, such as group type, exercise type, and exercise measurement type, can affect the relationship between physical exercise and body-esteem, with small to moderate moderation effects.

In this paper, a singularly perturbed convection-diffusion problem with exponential boundary layers is considered. For this problems, we study a local projection stabilization method on a Shishkin triangular mesh. If piecewise polynomials of order r ≥ 1 are used, the method has uniform convergence of almost order r in the energy norm.

CA3 a-thorny pyramidal neurons have a physiological mechanism for bursting, which has been shown to be an efficient means of inducing synaptic plasticity, but how their biophysical properties influence their bursting firing activities is not yet clear. In this paper, we will focus on discussing the effects of different ion channels on the bursting firing activities of CA3 a-thorny pyramidal neurons. The obtained results show that increasing the conductance of Na-type, Ca and h-ion channels could make soma’s bursting transform to spiking firing; while increasing the conductance of K-type ion channels could change soma’s firing patterns from spiking to bursting. Thus, soma’s firing patterns of the CA3 a-thorny pyramidal neurons are dependent on ion channels, especially on the conductance of each ion channel.

The South China Sea (SCS) is a region where internal solitary waves (ISWs) are very active. The ISWs will propagate westward after they form in the northern SCS. During the propagation process of the ISWs past the DongSha Island, their shapes and amplitudes will change accordingly. This paper mainly investigates the three-dimensional evolution characteristics of the ISWs past an Island by use of the experimental method. In this experiment, an ISW propagates in a two-layer fluid with the pure water in the upper and the salt water of constant density in the lower. The waveform changes of the ISW induced by the Island are observed by the fluorescent method and are qualitatively compared with the surface signature of ISWs captured by Synthetic Aperture Radar (SAR), as well as the amplitude variations of the ISW in different locations are successfully inversed by using the fluorescent method combined with the dyeing technique after its passes the island. Further experimental results from the combination of the fluorescent and dyeing method suggest that it is possible to obtain the quantitative analysis results combining the fluorescent and dyeing method.

Iron and steel futures market plays an important role in China’s economic development, and its price prediction has always been a research hotspot. This paper selects rebar as a representative, uses model to predict its futures price, and evaluates and analyzes the prediction results. This paper selects 2865 sets of data of rebar futures price from 2009 to 2020 to forecast the price in the next 14 trading days, and compares it with the real futures price of the 14 trading days, so as to evaluate VMD—EEMD—LSTM model the prediction effect of this model. Compared with other single model and combination model, the evaluation indexes mean absolute error(MAE), root mean square error(RMSE) and mean absolute percentage error(MAPE) of the prediction model are better than other models in 10% significance level, and have better prediction effect. More accurate futures price forecast can help stakeholders make better decisions and allocate resources, and make China’s economy develop in a more healthy and efficient direction.

The purpose of the study is to reduce the export credit risk of enterprises, and the export credit risk assessment of enterprises under the belt and road strategy based on deep learning is discussed. First, the research background and deep belief networks are introduced. Second, the contrast divergence algorithm based on the deep belief model is improved on the restricted Boltzmann machine. Finally, the deep belief network of classification and partition is constructed and simulated. The results show that the test accuracy of the classification and partition of the restricted Boltzmann machine (CPRBM) constructed is higher than that of the restricted Boltzmann machine (RBM). When the accuracy of the algorithm is verified under the condition of unbalanced two classification samples in a relatively small amount of datasets, the accuracy of the CPRBM algorithm is 93.71%, and the accuracy of the RBM algorithm is 89.86%. In the optimization stage of the deep belief networks, the convergence rate of the CPRBM is slower than that of the RBM. Since the optimized system increases the penalty term in the first training stage of the deep belief networks, the penalty term is canceled in the second stage of optimization. At three time points, the algorithm accuracy of the CPRBM is higher than that of the RBM. The simulation results are consistent with the previous experimental results. Although the accuracy is not high at the third time point, the CPRBM algorithm still has some advantages. Compared with the accuracy of the support vector machine (SVM) and the deep extreme learning machine (DELM), the CPRBM algorithm based on deep belief networks has the highest accuracy at any time point. The CPRBM algorithm constructed has obvious advantages compared with common models, and the overall performance of the algorithm is better. The conclusions provide the support for the sustainable development of the economy under the Belt and Road strategy.

Nowadays Cloud Computing (CC) has been to a period of flourishing development in the worldwide. Hyper Converged Infrastructure (HCI) has been leading software defined datacenter of CC to a new stage. Service Oriented Architecture (SOA) in HCI CC platform provides the agile and abstract distributed IT architecture mode, and it will dramatically change the service style and efficient of software defined datacenter. The revised simplex method is used to solve the optimization question of resources schedule in SOA based HCI CC platform, and the result shows at least 25% profit promotion, while in medium or large-scale CC platform the promotion will be greater.

The purpose of this paper is to provide reference for safety warning and accident recovery of tank truck rollover. The calculation method of critical lateral acceleration of tank truck considering liquid sloshing is studied by combining analytical method and multi-body dynamics simulation method. In this paper, three methods are presented to solve the critical lateral acceleration of tank truck rollover considering liquid sloshing, which are analytical method, step steer simulation method and tilt table simulation method respectively. The liquid sloshing is simplified to two-dimensional liquid sloshing by assuming that the lateral acceleration of the tank truck is constant. The change of liquid center of gravity is used to be equivalent to the effect of liquid sloshing on tank truck rollover. The influence data of liquid sloshing, tire, suspension system and tank truck center of gravity on the rollover of tank truck were obtained. The research results can be applied to the safety warning and accident recovery of tank truck.

Lithium-ion batteries with potential safety hazards require accurate forecasts of their remaining useful life to be maintained and replaced. An online forecasting method is proposed, in which the training dataset before a pre-set operating time for the start of forecasting is automatically extended in order to train support vector regression model and then the key hyperparameters of the model is optimized by tree-structured parzen estimator algorithm to update the model used to forecast battery capacity for the next cycle. Verified by NASA lithium-ion batteries datasets, this method provides higher forecasting accuracy and generalization ability, which is suitable for the scenario of online accurate short-term forecasting based on a limited amount of historical data.

As one of the most important fracturing tools, the ball seat is widely used in the hydraulic fracturing process in the horizontal well. Due to the erosion of sand-carrying fluid on the inner wall of the ball seat, severe wear will occur on the surface of the material. This will lead to leak pressure and cause the fracturing failure. This study aims to investigate the erosion behavior of different ball seat structures and optimize the structure to increase erosion-resistant performance during the fracturing process. In this study, a two-section model of the ball seat combined with curved surface thought is proposed, and four models are designed. Based on the computational fluid dynamics (CFD) with Fluent software, four models are simulated and analyzed in actual working conditions using the dynamic mesh method with erosive wear deformation analysis for the first time. The simulation results show that the convex and concave curve surface structure has the lowest maximum erosion rate and most minor erosive wear deformation. Besides, from the point of view of pressure, research reveals the mechanism of different erosion behavior of ball seat structures and finds the optimal structure. Based on this structure, the curvature parameters are optimized by analyzing a set of models with different curvature. The results show that the optimal structure, convex and concave curve surface structure, with the 49 mm curvature radius of the convex curve and 57 mm curvature radius of the concave curve has the best performance to resist erosion wear. This research can offer a significant reference for structural optimization of the ball seat, simulation methods and prediction of the lifespan study of the ball seat.

In the era of digital economy, the development of digital supply chain has brought many transaction disputes and transaction security problems. Establishing a traceability system has become an effective means to solve these problems. However, traditional traceability technology has problems such as opaque information and easy data tampering, which requires a more secure and reliable way to trace digital supply chain. Therefore, this paper proposes a digital supply chain traceability method based on blockchain technology and using Pailler algorithm, Merkle Tree and smart contract, aiming to improve the security and reliability of transactions. This method was developed on the FISCO BCOS platform and was evaluated for performance and visual effects, proving its effectiveness and feasibility.

Based on the index data of the direct beneficiary area of Saihanba Machinery Forest Farm, this paper establishes the ecological environment assessment model for the period from 1990 to 2020; in addition, RSEI is established and applied to determine the site that can take in the Saihanba model in the country. Obvious impact of Saihanba Machinery Forest Farm on the ecological environment has been found since 2005 from the ecological environment score of the study object. According to the eco-index, the site area of the ecological zone to be built is 983,500 square kilometers, which is roughly 1:105 to the area of Saihanba Machinery Forest Farm.

Digital empowerment of China’s power energy sector is a key factor in increasing its economic and social benefits, and named entity recognition technology is the most fundamental and core task of information extraction technology in the digital empowerment process. Therefore, we propose a multimodal named entity recognition model PE-MNER for power equipment based on deep neural networks. Compared to text multimodality, text and image multimodality can use image information to supplement missing information in the text, thus enabling more accurate entity extraction. The model first obtains a BERT neural network through incremental training, and then extracts Chinese character features through the network. Then, a hierarchical visual prefix fusion network is used to fuse image information. From the comparative experimental results, it can be seen that the proposed model has the best performance compared to the benchmark model, with an improvement of 4.08%∼7.20% in the F1 score compared to the benchmark model.

China’s chemical industry has developed rapidly, but chemical production safety is still in the primary stage, hazardous chemical fire occurs frequently. The ontology model Hazardous Chemicals Fire Emergency Reaction Ontology (HC-FERO) is constructed on the basis of the knowledge domain and the breadth and depth of ontology. First, ire emergency response upper ontology is constructed with the help of domain experts. Then, a quintuple is established from the aspects of Fire response sequence, response rules, fire extinguishing principle, etc, then we choose protege to generate the hazardous chemical fire emergency ontology. At last, we select the qualitative evaluation to evaluate HC-FERO.

It is important to detect failure companies in order to protect the financial market and investors.This study introduces a back propagation (BP) failure detection model to identify failure firms. In terms of solution accuracy, the empirical results document the superior measurement of CWOA with BP compared with standard BP.

This paper overcomes the traditional simulation method which does not consider the coupling effect between tank truck movement and liquid sloshing in the rollover process, simulates the rollover process of tank truck more truly, and calculates the critical value of tank truck rollover more accurately. The data interaction between ADAMS CAR and FLUENT is realized indirectly by using SIMULINK as the intermediate transfer software. Through the secondary development of UDF and S function, data exchange between FLUENT and SIMULINK is realized by means of socket communication. ADAMS CAR transmits the translation velocity and angular velocity of the tank truck to FLUENT, and FLUENT then transmits the resultant force and moment of liquid sloshing to ADSMS CAR, so as to realize the co-simulation of the rollover process of the tank truck by using ADAMS CAR, SIMULINK and FLUENT. The critical acceleration of tank truck rollover was calculated by step steer simulation, and the influence data of liquid sloashing were obtained. The research results can be applied to the safety warning and accident recovery of tank truck. In this paper, the accident recovery of the “June 13” tank truck explosion accident in Wenling section of Shenyang-Haikou Expressway in 2020 is carried out. The calculated results are consistent with the facts, which proves that the results of the co-simulation method are reliable.

We study financing and information asymmetry issue in a two-echelon supply chain consisting of a commercial bank, a supplier and a retailer with capital constraint, in which the retailer can apply for loans from the commercial bank or apply for trade credit financing and guarantor credit financing from the supplier. The equilibrium shows that when bank credit finance is viable, the retailer decides whether to share information is neutral. When trade credit financing or guarantor credit financing is viable, the retailer shares information with the supplier. Blockchain technology provides capacity for the retailer to convey the downstream information to the supplier. The result also shows that when the guarantee ratio is relatively large, commercial bank tends to lower interest rate while the supplier is unwilling to provide GCF to the retailer. Finally, we also study the financing mode decision and demonstrate that a lower production cost promotes the supplier to provide TCF under information sharing. When there is no information sharing, the retailer’s optimal financing choice depends on the supplier’s estimation of market demand.

Pedestrian Bridges are common in urban areas of Chongqing and are one of the main street crossings in the city. The pedestrian bridge is beautiful in shape and convenient in construction. It can not only solve the problem of separating people and vehicles, but also increase the beauty of the city. In recent years, due to the increasingly prominent problem of aging population, it is particularly important to add escalators to existing pedestrian Bridges, which is not only the need of urban development, but also a livelihood project. This paper focuses on the design points of adding escalators to existing pedestrian Bridges, and the corresponding technical analysis.

With the popularity of Internet technology, the changing needs of consumers and the transformation and upgrading of the retail industry, new retail has emerged. The industrial structure shows a trend towards green, and people begin to pursue the quality and safety of products, and also green consumption, putting forward higher requirements for the supply chain. A green supply chain performance evaluation index system for fresh e-commerce enterprises containing six indexes is constructed, which includes: procurement costs, storage costs, packaging costs, transportation costs, distribution costs and information costs. Then, based on the realistic requirements of the green supply chain performance evaluation model for fresh e-commerce enterprises, a fuzzy comprehensive evaluation system model is established which uses the deviation maximization weighting method to determine the evaluation index weights and uses the intuitionistic hesitancy fuzzy method and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for comprehensive evaluation. Finally, an empirical analysis is conducted with Freshippo as an example to verify the applicability and objectivity of the performance evaluation index system and the evaluation model. The results show that Freshippo is at the upstream position of the industry and the new retail industry has more room for improvement.

The overset grid is an effective method to handle the moving boundary problem with complex geometries or massed movement in Computational Fluid Dynamics (CFD) simulations. As a critical part of the method, the Overset Grid Assembly (OGA) determines the point types. Hole cutting is one of the key processes of OGA to find out the hole points, which will not participate in the subsequent calculation of the simulation. However, repeated hole cutting is required when the boundary moves, restricting the assembly efficiency. In this paper, we propose a Block Corresponding Cutting (BCC) method to accelerate the process by reducing the redundancy of hole cutting with Rigid-body motion. Based on the open-source platform OpenFOAM, the experiments demonstrate that, compared with the classic SAM hole cutting method, the efficiency of BCC-based hole cutting is improved by 28.1% on average.

Traffic sign recognition plays a vital role in intelligent transportation systems, enabling driver assistance systems to effectively reduce traffic accidents. In this paper, a traffic sign recognition system based on an improved version of YOLOv5s was developed. Firstly, the addition of a channel attention module (CBAM) enhanced the network’s ability to extract informative features from the input data in both spatial and channel dimensions. Then, the original target box in YOLOv5s network which was not suitable for this detection task is improved, and the clustering method of target box obtained from the original network was optimized to K-Means++ clustering method. The researchers trained this method using the CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB) Dataset and achieved promising results in recognizing traffic signs. The experimental results demonstrated that the improved YOLOv5s outperformed YOLOv5s, achieving a Precision of 97.8% and an mAP of 98.8%.

There are downhole safety valves and permanent packers in the completion string of the three high deep wells. Conventional pump processes such as rod pumps and electric pumps cannot meet the requirements of drainage depth, and pump processes are not suitable for acidic corrosion environments, gas lift and bubble drainage are not mature in well depths, and are limited by acidic environments. However, it is necessary to establish channels that can circulate. This article focuses on permanent packer wells, and conducts research on gas lift parameters, gas lift tools, and how to establish drainage and gas production channels in the oil casing space. It points out the corresponding displacement of different gas lift parameters, and proposes different methods and supporting processes and tools for establishing gas lift circulation channels, providing a solution for the safe and efficient development of three high deep wells.