Discrete Dynamic Model Research Articles

Continuum Model and Numerical Method for Dislocation Structure and Energy of Grain Boundaries

We present a continuum model to determine the dislocation structure and energy of low angle grain boundaries in three dimensions. The equilibrium dislocation structure is obtained by minimizing the grain boundary energy that is associated with the constituent dislocations subject to the constraint of Frank's formula. The orientation-dependent continuous distributions of dislocation lines on grain boundaries are described conveniently using the dislocation density potential functions, whose contour lines on the grain boundaries represent the dislocations. The energy of a grain boundary is the total energy of the constituent dislocations derived from discrete dislocation dynamics model, incorporating both the dislocation line energy and reactions of dislocations. The constrained energy minimization problem is solved by the augmented Lagrangian method and projection method. Comparisons with atomistic simulation results show that our continuum model is able to give excellent predictions of the energy and dislocation densities of both planar and curved low angle grain boundaries.

Quality Improvement of College Students’ Innovation and Entrepreneurship Education Based on Big Data Analysis under the Background of Cloud Computing

China is in a critical period of national rejuvenation and national prosperity. Innovation is the soul of a nation and an inexhaustible driving force for national development. Youth are the hope and future of a country. In order to enable young people to have the entrepreneurial ability and innovative spirit of the development of the new era, it has become a very important link for college students to better enter the society and realize their own value. It has become a key link in the development of individuals and China, and has attracted the attention of the whole society. With the continuous development of Internet technology and the deepening of application exploration, in the context of cloud computing, the improvement of the quality of college students’ innovation and entrepreneurship education based on big data analysis has also become the focus of universities, society, and the country. The core of the system is the discrete dynamic modeling technology of complex systems. This paper will discuss ways to improve the quality of college students’ innovation and entrepreneurship education based on big data under the background of cloud computing by introducing the development and principle of complex system discrete dynamic modeling technology and analyzing the deficiencies and problems of college students’ innovation and entrepreneurship quality in many aspects.

Municipal solid waste supply chain management under an integrated optimization of sustainability targets

This research study presents a dynamic discrete optimization model for the treatment of municipal solid waste (MSW) with sustainability as an essential research objective. The optimization model screens capacity selections for MSW technologies over distributed sites with consideration of pretreatment biodrying technologies for waste calorific value enhancement. The choices for these MSW technologies and the network operation are described by binary and continuous variables, respectively. The MSW network economic seeks the maximization of the net present value (NPV). Key environmental impacts of MSW technology use as well as the impacts of MSW transportation are described by a minimization optimization problem. The social target of the MSW network considers the maximization of job creation. When applying the model to a case study, sustainability objective functions showed conflict in the results. Pareto optimal solutions are found from the multi-objective optimization model and a compromised solution is suggested for the considered case study.

Dynamic Discrete Inventory Control Model with Deterministic and Stochastic Demand in Pharmaceutical Distribution

This paper presents an inventory control problem in a private pharmaceutical distribution company from the Republic of Serbia. The company realizes that distribution within nine neighbouring countries and inventory control in the pharmaceutical supply chain is centralized. In order to constitute a conceptual model of the problem, we propose the modern control theory concept. The conceptual model is based on the specific practical assumptions and constraints of the supply chain. Thereafter, a dynamic discrete mathematical model of inventory control is formulated to reflect elements of the system and their relations. The model considers multiple pharmaceutical products, variable lead time, realized stochastics and deterministic demand, and different ordering policies (Lot for Lot and Fixed Order Quantity). Deterministic demand is represented as a sales forecast for each product per month, while stochastic demand is generated as a random variation of sales forecast in a range of ±20%. Two objective functions are defined as the maximization of the difference between planned average inventory level and realized average inventory level, and the minimization of stock-out situations. We develop a procedure for the determination of reorder points and the number of deliveries to achieve proposed objective functions. The model overcomes shortages of theoretically-based distribution requirements planning models and offers solutions to the limitations in inventory control practice. Real-life data, collected over two years, are used for the validation of the proposed model and the solution procedure. Numerical examples illustrate the model application and behaviour.

Discrete Dynamic Modeling Analysis of Engineering Management and Quality Optimization Innovation Mode Based on Big Data Intelligent Algorithm

With the advent of the big data era, information technology and intelligent algorithms are more and more widely used. In the engineering project management industry, big data technology and dynamic modeling have achieved good results. Enterprises attach great importance to the innovation of engineering project management. The establishment of engineering management and quality optimization innovation model can improve the management efficiency of engineering project process and enable enterprises to obtain great advantages in market competition. The use of big data technology can make project management and quality innovation more information-based and intelligent. Based on the above situation, this paper puts forward the research on discrete dynamic modeling of engineering management and optimization innovation mode based on big data intelligent algorithm. It mainly uses big data technology to collect and classify the obtained data information and uses discrete dynamic modeling technology to optimize the management system, focus on the management and innovation of green engineering, use big data dynamic modeling technology to build engineering management model, and improve the balance of project model through intelligent planning algorithm. The experimental results show that the big data intelligent algorithm combined with discrete dynamic modeling technology increases the applicability and feasibility of the system. The accurate data after big data analysis can be used for quality supervision and detection, which not only improves the project quality problems but also improves the management efficiency of the project.

Research on Village Planning and Rural Architectural Design Based on Discrete Dynamic Modeling Technology

With the advent of the era of big data, the combination of science and technology and urban and rural planning has become the focus of many countries. The improvement of village planning and the redesign of rural buildings can promote the rapid development of villages and strengthen rural cohesion. Based on the above situation, this paper proposes a dynamic programming algorithm combined with discrete dynamic modeling technology to improve rural planning. Firstly, the dynamic programming algorithm is used to reconstruct the village layout and optimize the original village model. The dynamic monitoring technology is used to update the dynamic data in real time to provide specific information for the follow-up rural architectural design. Secondly, the dynamic modeling technology is used to build the building model, which can calculate the building location and building angle of the village. In order to further improve village development, we also put forward the concept of green building design. The performance of traditional modeling technology and discrete dynamic modeling technology in the green building design model is compared. The results show that the discrete dynamic modeling technology can improve the overall performance of rural buildings and improve the operation efficiency of the system in the batch design of green buildings. The village layout improved by dynamic planning reduces the specific travel distance of villagers and provides effective help for rural construction and economic development. Compared with traditional modeling technology, dynamic modeling technology has a shorter workflow and less time. Discrete dynamic modeling technology can realize dynamic batch architecture design and has higher applicability than traditional algorithms.

Simulation and Formation Mechanisms of Urban Landscape Design Based on Discrete Dynamic Models Driven by Big Data

Urban landscape design as a contemporary art embodies postmodernist philosophical thinking, aesthetic thinking, and breaking the traditional concept of art, and it is a new way of creating and presenting art. Big data technology characterized by large scale, speed, variety, value, and uncertainty of data is used to achieve urban landscape design. In this article, during the research process, we strive to raise the revelation of the design layer rather than the brand new level of cross-fertilization and interaction between big data-driven discrete dynamic model and urban landscape design; we also reveal how the benefits of promoting urban development and harmonious life are achieved in the interactive expression of the urban landscape after the application of the big data-driven discrete dynamic model, which provides designers and related professionals with more detailed and novel design ideas at the theoretical level and makes the theory of big data-driven discrete dynamic models in landscape design interactive methods more enriched. Finally, this article puts forward its thinking and outlook on the design of the big data-driven discrete dynamic model in the interactivity of urban landscape design, hoping that artists will strengthen its functional and material design elements when creating performance. Moreover, more design means of emerging technologies of modern science and technology should be integrated so that modern urban landscape can achieve ordinary and uncommon benefits and promote the rapid development of the big data-driven discrete dynamic model in urban landscape design development.

Game of Sensors: An Energy-Efficient Method to Enhance Network Lifetime in Wireless Sensor Networks Using the Game of Life Cellular Automaton

Wireless sensor networks (WSNs) have increased in popularity since they play a relevant role in many applications, such as environmental monitoring, fire detection, and health care, to mention a few. These applications periodically collect data that is relayed to a single sink employing a many-to-one communication pattern. This pattern requires energy-efficient routing protocols since sensors closer to the sink node deplete their energy faster than those sensors located further away. As a result, several techniques have been proposed to solve this problem. For instance, some pieces of research split the network into concentric coronas to provide more energy resources in areas with heavier traffic. However, these techniques use either a predefined network deployment that is not well-suited for all sensor applications or do not always guarantee homogeneous energy consumption. This paper proposes a simple energy-aware routing method based on the Game of Life cellular automaton, which provides a homogeneous energy depletion while extending the network lifespan by considering factors such as residual energy, number of active neighbors, and a sleep schedule. To this end, a discrete dynamic model that takes into account different behaviors of WSN through a set of rules combined with a variation of the A-star algorithm is proposed. Simulation results show that the proposed model accurately balances the energy consumption rate while expanding the network lifetime compared to most recent research works. Furthermore, the proposed method can be combined with path-planning algorithms to improve energy consumption in sparse WSNs.

Retracted] Construction and Application of Music Audio Database Based on Collaborative Filtering Algorithm

In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database.

Research on Discrete Dynamic System Modeling of Vocal Performance Teaching Platform Based on Big Data Environment

The traditional teaching model of national vocal music in colleges and universities has some problems, such as low quality of teaching, poor diversity of teaching, and low interest of students. Based on this, this study studies the innovation of the teaching model of national vocal music in colleges and universities based on the deep recurrent neural network algorithm and designs the teaching quality evaluation model based on the deep recurrent neural network algorithm. The collection of data and information is realized from the aspects of students’ class state, vocal music examination results, classroom interaction, etc., and the comprehensive analysis is carried out using the deep recursive neural network algorithm, to realize the multiple analysis and objective evaluation of the whole process of different teaching modes of national vocal music in music colleges and universities, according to the different characteristics and teaching objectives of the teaching process of national vocal music in music colleges and universities The standard requires accurate analysis and evaluation. The results show that the optimization model based on the deep recurrent neural network algorithm has the advantages of high feasibility, high intelligence, and wide range of applications. The experiments show that the deep recurrent neural network algorithm can analyze the effectiveness of the innovation model, and the fuzzy evaluation method can realize the comprehensive evaluation of the innovation mode, which is conducive to the improvement of students’ vocal music learning ability.

Price change prediction of Ultra high frequency financial data based on temporal convolutional network

Through in-depth analysis of Ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are proposed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market.

Research on Data Mining of Sports Wearable Intelligent Devices Based on Big Data Analysis

Traditional motion data mining models have some problems, such as poor dynamic data capture effect, low information classification effect rate, poor quantitative representation effect, and so on. Based on this, this paper studies the mining method of dynamic motion data based on neural network, constructs a data mining model based on discrete dynamic modeling technology, and realizes the collection of data information from the aspects of motion characteristics and types combined with multilayer sensors. Neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all data of dynamic motion process and accurate analysis and evaluation according to different data characteristics of different types of motion data. The results show that the data mining model based on discrete dynamic modeling technology and wearable sensor technology has the advantages of high feasibility, high intelligence, and wide application range.

Retracted] Discrete Dynamic Modeling Analysis of Rural Revitalization and Ecotourism Sustainable Prediction Based on Big Data

With the development of modern rural tourism, it has become people’s spiritual demand to deeply tap the characteristic rural culture and continuously enhance the attraction of rural tourism. However, due to the complex tourism market environment, small changes in factors will affect the final result. Therefore, traditional methods cannot accurately predict the future tourism market. In view of this situation, this paper uses the mathematical algorithm of BP neural network to simulate the structure of brain neural network to deal with this kind of nonlinear relationship problem. Through the classification of complex and changeable factors, the modeling is completed systematically, and finally the effective prediction results are obtained. For the analysis of the time evolution of tourism resources development, it can be found from the policy development theory that the innovative development presents the characteristics of S‐shaped curve in time; that is, the number adopted in the initial stage of development is less, then enters the rapid development period, and finally develops slowly. This study will help to solve the primary problem of balancing the positive and negative effects of rural tourism development and realizing the sustainable development of coastal villages.

Retracted] Construction and Research of Guqin Sound Synthesis and Plucking Big Data Simulation Model Based on Computer Synthesis Technology

The application and economic efficiency evaluation mode of traditional composition in the field of modern music cannot meet the needs of various types of music in China, especially Guqin music. Based on this, this paper studies the big data simulation model of Guqin sound synthesis and plucking based on computer composition technology. The computer composition technology uses the discrete dynamic modeling technology of complex system to complete the computer simulation of Guqin sound through the analysis of the correlation between Guqin music data and realizes the storage and analysis of the generated Guqin sound data. In addition, the technology can analyze the data information of composition mode in the piano sound plucking simulation model with the classical Guqin music data stored in the cloud system over the years and then feed it back to relevant professionals for verification. The experimental results show that the Guqin sound simulation model can efficiently compare and analyze the melody and other data of classical Guqin sound with the simulated Guqin sound and can realize secondary data mining. This paper studies the application of computer composition method based on discrete dynamic modeling technology in Guqin sound simulation, which has certain reference significance for improving the cloud data in China’s modern music field and the intelligent construction of Guqin sound data cloud storage system.

Forest Ecological Diversity Change Prediction Discrete Dynamic Model

Forest ecosystem is the most important terrestrial ecosystem type of species diversity in the world. The protection and sustainable use of forest ecological diversity is of great significance to forest protection and sustainable management. In order to maintain biodiversity, the traditional concept of forest resources management must be changed. In recent years, the mining and application of big data have become the frontier content of international ecological diversity and macroecology research. By establishing the discrete dynamic model of forest ecological diversity change, this study finds a method that can keep the number of trees stable in a short time and ensure the sustainable development of forest resources. This article studies and constructs the forest information system and constructs an open‐source WebGIS (network geographic information system) scheme for the field of spatial information and supporting OGC (occupationally generated content). At the same time, the grey theory GM (1,1) model is used to predict the development trend of forest resources in China. The results show that under the promotion of policies and research projects, China’s forestry development has realized the double growth of forest area and volume, and the forest coverage rate has reached a new level.

One discrete dynamical model on the Wolbachia infection frequency in mosquito populations

One discrete dynamical model on the Wolbachia infection frequency in mosquito populations

Discrete Dynamic Modeling and Change Trend Analysis of Regional Economy Based on Big Data

The driving force of high-quality development of regional economy is inseparable from the support of technology. With the support of big data, we need to solve this problem in order to solve the difficulty of large-scale experimental testing and accurately reflect the feasibility growth of data sample changes. This paper proposes a discrete dynamic modeling technology based on big data background to analyze the development and change of regional economy. The reliability AMSAA model is usually used for dynamic discrete modeling. It can be combined with the change data provided by big data to form a dynamic modeling method for reliability growth evaluation. Then, the Bayesian regression method is used to predict the change parameters of the model, and the spatial econometric method is used to analyze the regional economic change. The results show that compared with the traditional methods, the discrete dynamic modeling method is more accurate and can effectively solve the problem of reliable growth under the condition of big data. After introducing the spatial effect measurement model, it can also reflect the main factors of the growth and change of regional economic real output value. In addition to the development of high and new technology, terrain factors, investment, and government support have also had different effects. Therefore, according to the above results, it is proved that the discrete dynamic modeling technology can accurately obtain the experimental data and provide reliable technical support for dynamic data processing.

Development of mean-field continuum dislocation kinematics with junction reactions using de Rham currents and graph theory

An accurate description of the evolution of dislocation networks is an essential part of discrete and continuum dislocation dynamics models. These networks evolve by motion of the dislocation lines and by forming junctions between these lines via cross slip, annihilation and junction reactions. In this work, we introduce these dislocation reactions into continuum dislocation models using the theory of de Rham currents. We introduce dislocations on each slip system as potentially open lines whose boundaries are associated with junction points and, therefore, still create a network of collectively closed lines that satisfy the classical relations α=curlβp and divα=0 for the dislocation density tensor α and the plastic distortion βp. To ensure this, we leverage Frank’s second rule at the junction nodes and the concept of virtual dislocation segments. We introduce the junction point density as a new state variable that represents the distribution of junction points within the crystal containing the dislocation network. Adding this information requires knowledge of the global structure of the dislocation network, which we obtain from its representation as a graph. We derive transport relations for the dislocation line density on each slip system in the crystal, which now includes a term that corresponds to the motion of junction points. We also derive the transport relations for junction points, which include source terms that reflect the topology changes of the dislocation network due to junction formation.

Context-specific regulation of lysosomal lipolysis through network-level diverting of transcription factor interactions

Plasticity in multicellular organisms involves signaling pathways converting contexts-either natural environmental challenges or laboratory perturbations-into context-specific changes in gene expression. Congruently, the interactions between the signaling molecules and transcription factors (TF) regulating these responses are also context specific. However, when a target gene responds across contexts, the upstream TF identified in one context is often inferred to regulate it across contexts. Reconciling these stable TF-target gene pair inferences with the context-specific nature of homeostatic responses is therefore needed. The induction of the Caenorhabditis elegans genes lipl-3 and lipl-4 is observed in many genetic contexts and is essential to survival during fasting. We find DAF-16/FOXO mediating lipl-4 induction in all contexts tested; hence, lipl-4 regulation seems context independent and compatible with across-context inferences. In contrast, DAF-16-mediated regulation of lipl-3 is context specific. DAF-16 reduces the induction of lipl-3 during fasting, yet it promotes it during oxidative stress. Through discrete dynamic modeling and genetic epistasis, we define that DAF-16 represses HLH-30/TFEB-the main TF activating lipl-3 during fasting. Contrastingly, DAF-16 activates the stress-responsive TF HSF-1 during oxidative stress, which promotes C. elegans survival through induction of lipl-3 Furthermore, the TF MXL-3 contributes to the dominance of HSF-1 at the expense of HLH-30 during oxidative stress but not during fasting. This study shows how context-specific diverting of functional interactions within a molecular network allows cells to specifically respond to a large number of contexts with a limited number of molecular players, a mode of transcriptional regulation we name "contextualized transcription."

Comparison of discrete dynamic pipeline models for operational optimization of District Heating Networks

Optimal operation of District Heating Networks (DHNs) is a very challenging task. One of the main challenges for DHNs optimization tool designers is the choice of an adequate dynamic thermal pipeline model which gives a good tradeoff between accurately modeling the physics of the thermodynamic processes and simultaneously yielding a numerically efficient model. To address this, the paper states the main Partial Differential Equation (PDE) which is used to describe the convection of hot water throughout the literature, together with reasonable assumptions that lead to minor deviations from measurements. Then, different approaches are described which can be used to solve the respective PDE. More specifically, the very common Node Method (NM), approximations of the NM, the lagrangian approach and different Finite Difference (FD) approaches are presented. The main aim of this work is to provide a qualitative and quantitative comparison of these modeling approaches in the context of optimal DHN operation. Our quantitative results show, that by comparing the different approaches to measurement data, the NM yields the smallest modeling errors for most of the temporal discretization sizes. The qualitative comparison identifies that the lagrangian method lacks the differentiability necessary for the implementation in optimization tools. The advantages of the FD approaches include guaranteeing a fixed number of variables, a constant information depth of the temperature distribution along the pipeline and the simplicity of implementation into optimization tools. The approximations of the NM bring benefits when varying mass flow directions need to be considered, which is a crucial aspect in 4th generation DHNs.