Discrete Dynamic Model Research Articles

Analysis of Digital Operation of Rural Sewage Treatment System in Big Data Environment by Discrete Dynamic Modeling Technology

Sewage treatment is a process with high energy consumption and pollution. In the current situation of energy shortage and fierce competition in the paper industry market, how to reduce the energy cost is related to the survival and development of every sewage treatment plant. With the rise of big data environment in the internet, discrete dynamic modeling technology has developed very rapidly. Discrete dynamic modeling technology is a dynamic model system that can connect the information of different things, so as to form a dynamic model system that can control all devices or data. The object attributes processed by the model system also determine the nature of the dynamic model system. As we all know, the big data environment is a large database with complex data types, so the dynamic model system in the big data environment is nonlinear. This paper mainly studies the historical development trend of big data environment and discrete dynamic modeling technology, as well as the stability method of five level H-bridge when discrete dynamic modeling technology controls rural sewage treatment system. Finally, the control of chaotic bifurcation in discrete dynamic model system under digital background is studied. It includes the rectifier modeling of the current data required by large devices in the sewage treatment system and the result analysis of the generated values.

Research on Discrete Dynamic Modeling of Learner Behavior Analysis in English Teaching.

The current English teaching mode focuses on the traditional offline teaching and online teaching. In order to solve the problems that some students are inefficient and cannot teach students according to their aptitude in the teaching process, this paper uses the big data analysis strategy based on a neural network algorithm. This paper studies the discrete dynamic modeling method of learner behavior analysis in English teaching. Firstly, it summarizes the current situation of English teaching and the research status of the hybrid application of discrete dynamic modeling technology. Secondly, combined with English teaching content and teaching objectives, through the analysis of various data of students' learning behavior, this paper evaluates students' English teaching quality from five aspects that affect the students' English teaching quality and puts forward a personalized English teaching quality evaluation model based on discrete dynamic modeling technology and learners' behavior analysis. Finally, through the practical teaching application in a university, the feasibility of the discrete dynamic English teaching model is verified. The results show that compared with the current innovative English teaching methods based on a dynamic iterative decision algorithm, the personalized discrete dynamic English teaching model based on learner behavior analysis significantly improves the quality of English teaching and students' academic performance.

Feasibility Analysis and Discrete Dynamic Modeling of Physical Education Teaching Strategy Based on Intelligent Computing.

With the advent of the information age, computer technology is also widely used in various fields. In the field of education, physical education teaching strategy has always been the focus of many educators. In order to optimize physical education teaching strategies and improve teaching quality, this paper proposes a new intelligent computing technology. This technology has excellent innovation in the engineering design field of physical education teaching strategy reform and innovation and combines Intelligent Computing with physical education teaching strategy to explore the feasibility and effectiveness of physical education teaching strategy reform and innovation. On the basis of intelligent computing algorithm, this paper analyzes the visualization strategy scheme brought by intelligent computing classroom to physical education teaching. This paper analyzes the feasibility of students' feedback after the reform of the physical education teaching strategy. Finally, the big data discrete dynamic modeling technology is used to dynamically model and analyze the students' learning behavior and effect after the reform and innovation of physical education teaching strategy. Combined with the analysis data and students' behavior feedback, this paper analyzes the feasibility of the physical education teaching strategy after the reform and innovation. The results show that the visualization scheme of physical education teaching strategy based on intelligent computing can help students understand theoretical knowledge and realize the transformation from static classroom to dynamic classroom. It enhances students' practical activities and perceptual knowledge and is of great help to students' physical education learning effect. In the discrete dynamic modeling analysis, the feasibility of physical education teaching strategy reform is very important.

An opinion dynamics model for unrelated discrete opinions

Unrelated discrete opinions can be generally found in social media-based data about a certain topic, which cannot be well measured and analyzed using existing opinion dynamics models. To fill this gap, this study proposes a new discrete opinion dynamics model based on the sentiment–opinion transformation mechanism. In the model, a matrix formulating the transformation relationship between sentiments and opinions is considered. In addition, the model is initialized based on an initial-sentiment matrix of the information instead of a certain mathematical distribution. Subsequently, we design the simulation experiments using different values of parameters and different network topologies to study four effects on sentiment and opinion dynamics, namely, the effects of the threshold, the effects of the initial-sentiment matrix, the effects of the sentiment–opinion matrix, and the effects of the second piece of information. The results highlight the importance of transformation relation between sentiment and opinion.

Innovation and Discrete Dynamic Modeling of College Music Teaching Model Based on Multiple Intelligences Theory

The development of music teaching mode in colleges and universities needs to take music as the main body and carrier to spread and inherit the music theory system. With the continuous innovation and development of science and technology, the teaching mode and teaching system have also ushered in new changes. How to let students understand the process of music teaching and better appreciate the charm of music is the main problem faced by educators. Faced with the above situation, starting from the theory of multiple intelligences, this paper studies the innovation and discrete dynamic modeling of music teaching mode in colleges and universities. Firstly, this paper discusses the application effect of this method in music teaching based on the theory of multiple intelligences. This paper investigates the actual development of multiple intelligences theory in college music teaching. Combined with the characteristics of multiple intelligences theory, modeling and analysis of students’ interest changes in the intelligent music education model represented by the space vector model are carried out. Finally, this paper studies the discrete dynamic modeling of students’ learning effect after the optimization and innovation of music teaching mode in colleges and universities under the theory of multiple intelligences. The results show that in the innovation of music teaching mode, personalized learning services should be provided based on students’ interests. The theory of multiple intelligences can help teachers to effectively analyze the diversity characteristics and changes of students in teaching activities, and it is of great help to improve students’ musical performance.

Application of agent-based approach for heat conduction processes simulation

Heat transfer processes are the basis of the most technological processes of the energy sector. Thus, the development of modern approaches for computer simulation and visualization of the phenomena of thermal energy transfer in various objects are of great importance and is relevant. Classical models in the form of differential equations of various types describe processes in continuous space and time. So, it is difficult to apply classical models to study nonlinear phenomena, and processes in inhomogeneous media in the presence of discontinuous solutions at the boundaries. In these cases, simplifying assumptions are used, thus, the adequacy of the models is reduced. It is of great interest to apply fundamentally different approaches to describe transfer processes, which include discrete dynamic models. The purpose of this project is to study the possibilities to apply discrete approaches to simulate nonlinear heat transfer processes under conditions of material inhomogeneity and the presence of volume sources of variable power. The paper studies the possibilities to apply the agent-based approach to simulate models of complex systems. This approach allows us to consider a continuum as a set of interacting elements (agents). The behavior of the elements is completely described by local dependencies. At the same time, the laws of functioning of individual elements are accepted as deterministic and they correspond to the fundamental principles of the theory of heat transfer. The possibility to apply a discrete approach for simulating the process of heat transfer by the molecular mechanism has been studied. The general methodology to develop an agent-based deterministic model is described. Its applicability to describe quasi-linear and nonlinear heat conduction processes is considered. The examples of simulation of combustion processes complicated by exothermic and endothermic effects are considered. The advantages and disadvantages of the proposed method are indicated. The results of the study have shown that discrete agent models are a good alternative to classical continuum approaches to study heat transfer processes in inhomogeneous media. The results obtained do not contradict modern approaches to the description of thermal processes. It has also been found that the simulation algorithms used in the agent-based approach are quite universal and easily adapt to changes under the conditions of problem setting. The analysis of the results makes it possible to recommend a discrete agent-based approach to develop simulation models of complex technological processes and systems.

Natural characteristic and vibration analysis of nonlinear articulated multi-beam ring structure for modeling ring truss antenna under base excitation

The investigation of natural characteristic and dynamic response of the ring truss antenna is essential for its design and vibration control since its high flexibility and nonlinearity. This paper investigates the dynamic properties of the articulated multi-beam ring structure for modeling the ring truss antenna under base excitation. The mathematical model of the multi-beam ring structure is established based on the global mode method. The matching conditions of force and displacement at connected points and boundary conditions of the ring structure are obtained. The global mode method and separation of variables method are applied to derive the analytical characteristic equation. Then natural frequency and global mode of the system are obtained. The orthogonality of global modes is proved. Based on the Galerkin procedure, the global modes which are explicitly functions are truncated to be substituted into the nonlinear partial differential equations of the system to yield the low dimensional discrete dynamic model with high accuracy described by ODEs which is useful for designing active vibration controllers. Afterwards the validity of the dynamic model is verified by comparing natural frequencies and mode shapes with those obtained by FEM. Finally, the effects of system parameters including external excitation, linear stiffness, third-order nonlinear stiffness, damping and friction torque on natural characteristic and dynamic response for the first resonance are analyzed. The results show that parameters of the structure have a significant effect on the whole system. The dynamic analysis in the paper is meaningful for design and vibration control of the antenna for the future study.

The Trend Prediction of the New Public Management Model Based on the Discrete Dynamic Evolution Model

At present, Western theoretical circles have gradually reached a goal-oriented consensus on “civic participation in public management.” This study provides an in-depth discussion of this synchronic view. At present, China’s political and economic environment, government, and citizens’ quality have undergone positive changes. The Chinese government has accelerated the practice of public management reform. Of course, there are also regional imbalances in the process of improving the public management mechanism in China. At the level of citizens, government, and society, there is a mismatch between supply and demand. With the increasing demands of citizens, the contradictions and crises in social life and public management are still intensifying. After clarifying these “risks” and “lag,” the study makes recommendations for the practice of “citizen-participatory public management” in China. This paper studies from three dimensions of citizen’s subjectivity, interpersonality, and sociality and specifically explores organizational flexibility, flattening, and diversity. The study expects to build a cooperative relationship between citizens and the environment and then build a “citizen participation” government. “Public management” has not been carried out in China for a long time, and its practicality is still limited. In this new management mode, realizing the benign interaction between citizens and the government is the key to the research. An important breakthrough point is to continue to nurture and develop community organizations (nonprofit organizations). This is because, by nurturing and developing community organizations, citizens can be provided with areas to realize their potential and achieve their aspirations. At the same time, this will also contribute to the multidimensional and diversified development of the organization. Most importantly, community organizations make public organizations more adaptable to the adjustment and integration of the external environment.

Modeling Immigration in Spain: Causes, Size and Consequences

This paper deals with the construction of a discrete dynamic population model addressed to estimate the expected size of the immigration population in a finite short period of time in Spain. By paying attention to a special subpopulation of interest, such as an irregular immigrant, unaccompanied minor immigrant and regular immigrant, a vector discrete population model is built after the discussion and introduction of proper hypotheses linked to economy, host and country of origin regulation policies, political interest and others. The model allows us to study the change of the results under variation of the parameters.

Algebraic Systems with Positive Coefficients and Positive Solutions

The paper is devoted to the existence, uniqueness and nonuniqueness of positive solutions to nonlinear algebraic systems of equations with positive coefficients. Such systems appear in large numbers of applications, such as steady-state equations in continuous and discrete dynamical models, Dirichlet problems, difference equations, boundary value problems, periodic solutions and numerical solutions for differential equations. We apply Brouwer’s fixed point theorem, Krasnoselskii’s fixed point theorem and monotone iterative methods in order to extend some known results and to obtain new results. We relax some hypotheses used in the literature concerning the strict monotonicity of the involved functions. We show that, in some cases, the unique positive solution can be obtained by a monotone increasing iterative method or by a monotone decreasing iterative method. As a consequence of one of our results, we recover the existence of a non-negative solution of the Leontief system and describe a monotone iterative method to find it.

Study on Discrete Dynamic Modeling of College Students' Innovative Employment Security Mechanism under the Environment of Internet of Things.

The innovative mechanism of college students' employment security is a series of security measures implemented by the state and society to solve the mismatch between the scale growth of college graduates and the jobs provided by the society. In order to promote the development of emerging Internet of things technologies, users can find interesting and valuable information from a large number of data sets and use this information to meet the needs of users. This article mainly studies the historical development trend and discrete dynamic modeling and analysis technology of college students' innovative employment security mechanism under the environment of Internet of things. Using two different modeling methods of Bayesian network and BP neural network, this article makes discrete dynamic modeling on the influencing factors and employment security mechanism of college students' employment, so as to improve the college biological network innovation employment security mechanism and better help college students' employment.

Discrete Dynamic Modeling of Learner Behavior Analysis in Physical Education Teaching.

With the advent of the big data era, the combination of information technology and education has become an important way for the development of the industry. The large-scale realization of teaching tasks under the background of information data requires the prediction and analysis of learners' characteristics, behavior, and development trend. Based on the above situation, this paper uses discrete dynamic modeling technology in big data environment to study the learners' behavior in physical education teaching. By quantifying the learning process data, the feature points of each learner are extracted to realize the personalized construction of dynamic learning data. Due to the rapid development of network technology, we mainly analyze the online education platform and explore the influencing factors of learners' behavior characteristics from many aspects. Finally, it carries out dynamic modeling and prediction for physical education learners from the aspect of achievement change, uses the grey model to build the achievement change system, and combines the dynamic modeling technology to reflect the development trend of achievement. The results show that the main factor affecting learners' behavior change in physical education is video learning. Most students are passive and lack initiative in the learning process. Discrete dynamic modeling technology can improve the accuracy of predicting student achievement changes and provide effective data for the research content.

Construction and Application Analysis of University Management System Based on Association Rule Mining Algorithm Apriori

With the rapid development of data mining and dynamic data modeling, this technology has been applied in most groups. In recent years, with the continuous development of the data processing technology, society has entered the digital era. Big data technology is widely used in economics, medicine, computer, and other fields. Based on the development status of the big data technology, an association rule mining algorithm Apriori is proposed to study the characteristics of massive data in a university management system. Combined with the big data discrete dynamic modeling technology, the cloud storage problem in the management system is optimized and improved. Dynamic modeling technology is used to optimize the energy-saving and energy storage functions of the system. Secondly, the Apriori algorithm is used to mine the student achievement data, and the factors affecting the change of student achievement are analyzed. Finally, the running efficiency of the whole university management system is dynamically modeled and analyzed. The results show that the system performance of Apriori algorithm mining has high client compatibility. Association rule algorithm is mainly used in student personality analysis and personal information management, The algorithm used in this paper can optimize the overall performance of the university management system and has certain effectiveness and applicability. It can quickly query the required information under the background of big data and has strong applicability. Dynamic modeling and optimization of the storage system can also improve the utilization of the storage system.

Research on Audit Simulation of Accounting Computerization Based on Internet Complex Discrete Dynamic Modeling Technology

With the rapid development of science and technology, modeling technology involves more and more fields and the data objects studied are more and more abstract. Based on the Internet plus complex discrete dynamic modeling technology, this paper studies the auditing and control of computerized accounting computerization. In order to maintain the steady state of the discrete dynamic system, the lifting method boosting, least square method, gradient boost, and LS-Ensem algorithm are added in the modeling process. Then, in order to improve the data recognition ability of the system, the recognition algorithm is added. The results show that the complex discrete dynamic system composed of the above algorithms is different from the ordinary system and can be better suitable for small-scale data. It also improves the stability of the whole system and then improves the accuracy of the model. Compared with ordinary discrete systems, the complex discrete dynamic systems studied in this paper can better overcome external factors and avoid errors. The system can quickly analyze and process the data that need statistics and has good application value.

Adaptive Model of Discrete Real-Time Linear Dynamic Logic for the Optimization of Big Data Game Education Mode

With the advent of the era of big data and mobile Internet, the traditional teaching model of colleges and universities has been hit unprecedentedly. In the current big data and artificial intelligence network environment, new teaching concepts, characteristics, and model applications should be analyzed, and an education model based on artificial intelligence adaptive control and negative feedback adjustment mechanisms under the big data environment should be explored. The study of this model can provide practical experience and reference value for the application, popularization, and innovation of teaching information technology in the future. Combining the characteristics of big data games, this paper proposes a college student education model based on discrete dynamic model big data game. The scores of college students represent comprehensive information such as their qualifications to provide big data university education, the credit of historical records, and the degree of reliable data transmission. Based on the general model of the adaptive learning system, we have perfected the functions of each module in the overall structure of the domain-based special subject system and designed the overall workflow of the system. Computerized evaluation is introduced into the learner module, and an adaptive evaluation system structure based on cognitive diagnosis theory is designed to achieve a personalized topic selection model for different learners and enrich learners’ experience through more detailed and specialized evaluation results. The model lays the foundation for adaptive course path recommendation. This paper proposes a discrete real-time linear dynamic logic that can express discrete real-time characteristics and all regular characteristics. It can easily describe the behavior of some complex real-time systems. Experiments show that the discrete dynamic model big data game efficiency is improved by 35.71%, which can better optimize the education model of college students. In addition, it can be concluded that the research on the effectiveness of college student education in the era of big data not only determines the safety of the ideological field of colleges and universities but also affects the thinking orientation of college students.

A discrete tick population dynamics model with continuous and seasonal birth breeding

In this paper, we formulate a discrete two-stage model with two types of birth mechanisms, continuous and seasonal. We divide tick population into two subgroups, i.e. immature and mature. We also consider diapause as an important adaptive process for ticks in respond to climate change in both stages. We derive a formula for the inherent net reproductive numbers and explore their stability analysis. When breeding is continuous, there exists a unique globally asymptotically stable positive fixed point provided that the inherent net reproductive number is larger than one; the extinction fixed point is globally asymptotically stable if the inherent net reproductive number is less than one. When breeding is seasonal with 2-periodic birth function, there exists a unique globally asymptotically stable periodic solution provided that the inherent net reproductive number is larger than one, while the population goes to extinction if this value is less than one. For the cases with multi-periodic birth function, we use numerical simulation to compare their complex behaviors.

An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions

The importance for the accurate forecast of carbon emissions affected by many factors is gradually emerging. Carbon emissions usually lag behind the related factors, which cannot be dynamically reflected in the existing grey forecasting models. Therefore, investigating the dynamic lag relationships remains the key challenge to carbon emissions forecast. For this purpose, an enhanced dynamic time-delay discrete grey forecasting model, denoted as DTDGM(1,N,τ), is proposed to predict the systems having dynamic time-lag effects. More specifically, a time-lag driving term consisting of both the interval and intensity of the time lags is developed to reflect the lag process of different factors to carbon emissions. The impulse response analysis of the vector autoregressive (VAR) model is carried out for determining the dynamic lags between carbon emissions and the related factors. In addition, a linear correction term is designed in the proposed model to extend the grey forecasting theory. Extensive experimental results about carbon emissions prediction from 1995 to 2017 show that the DTDGM(1,N,τ) model considering the delayed relationships can significantly improve the fitting and prediction performance of the model in comparison with the six benchmark models, including the three existing grey forecasting models, two machine learning models and one statistical prediction approach.

Recurrent Neural Dynamics Models for Perturbed Nonstationary Quadratic Programs: A Control-Theoretical Perspective

Recent decades have witnessed a trend that control-theoretical techniques are widely leveraged in various areas, e.g., design and analysis of computational models. Computational methods can be modeled as a controller and searching the equilibrium point of a dynamical system is identical to solving an algebraic equation. Thus, absorbing mature technologies in control theory and integrating it with neural dynamics models can lead to new achievements. This work makes progress along this direction by applying control-theoretical techniques to construct new recurrent neural dynamics for manipulating a perturbed nonstationary quadratic program (QP) with time-varying parameters considered. Specifically, to break the limitations of existing continuous-time models in handling nonstationary problems, a discrete recurrent neural dynamics model is proposed to robustly deal with noise. This work shows how iterative computational methods for solving nonstationary QP can be revisited, designed, and analyzed in a control framework. A modified Newton iteration model and an improved gradient-based neural dynamics are established by referring to the superior structural technology of the presented recurrent neural dynamics, where the chief breakthrough is their excellent convergence and robustness over the traditional models. Numerical experiments are conducted to show the eminence of the proposed models in solving perturbed nonstationary QP.

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.