Analysis of higher education management strategy based on entropy and dissipative structure theory

Miaomiao Ren

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Detalles de la revista

Formato: Revista
eISSN: 2444-8656
Primera edición: 01 Jan 2016
Calendario de la edición: 2 veces al año
Idiomas: Inglés

With the development of modern information technology, the ideas and behavior of university managers have changed a lot. Under the new situation, university education management needs to make some changes to better meet the needs of university management. First, the information construction of university education management can better realize accurate management. The traditional education management of colleges and universities mostly adopts the way of unified management, without considering the differences of students' personality, ability and quality, growth environment and so on. Therefore, it is easy to have problems such as low management efficiency and inadequate management in the process of management. In order to promote the information construction of education management, college education managers can use big data and other technologies to investigate and summarize the use of students' On-campus learning platform, the official microblog message information of the school, etc. The visual processing of these data can help managers further grasp the behavior of students, so as to implement differentiated management for different types of students, and make the education management of colleges and universities more targeted and accurate [1]. Second, the information construction of educational management in Colleges and universities can make the educational management work develop in a scientific direction. Under the traditional education management mode, managers generally make management decisions according to the general situation of the managers. In thermodynamics, entropy is defined as the ratio between the heat Q absorbed by the object and the temperature T of the object. In microscopic theory, entropy is usually understood as the increase of chaos. If the number of states of a system is limited more, the order degree of the system will be higher and the entropy will be smaller. On the contrary, the less the number of states of the system is limited, the disorder degree of the system will be higher and the entropy will be larger. As well as previous management experience, it is easy to experience. In this process, the previous experience of managers may not be applicable to the current situation, which affects the development of management work [2]. Under the background of educational management informatization, educational managers can use the information in the database to make more reasonable and scientific management decisions and formulate educational management plans that are more in line with the development needs of students and teachers, which can effectively improve the level of school education management [3].

For example, ice below zero is low entropy, and after ice melts into water, water has fluidity, water molecules will collide with each other, but it has high entropy. Austrian physicist Boltzmann was the first to associate entropy with the description of the state probability of molecular irregular motion, and expounded the statistical properties of the second law of thermodynamics. He proposed the famous entropy law S = k × ln p, pointing out that entropy is directly proportional to the logarithm of thermodynamic probability. The formula clearly describes the increase of entropy S, that is, the increase of molecular disorder, which is determined by the appearance of macroscopic state probability P. Entropy has the following properties: (1)

Extremum: If the object has N states, the probability of occurrence of a state q is P(Q) ≥ 0, and the probability of occurrence of all States is $\sum_{Q} P (Q) = 1$ \sum\nolimits_Q {P\left( Q \right) = 1} , the entropy function takes the maximum value when $P_{Q} = \frac{1}{N}$ {P_Q} = {1 \over N} . If the system has only one state and the probability of system state is 1, the entropy of the system is equal to zero, indicating that the system is completely determined [4].

(2)

Additivity: If system a and system B are completely independent, the entropy of composite system AB is equal to that of subsystem a, plus that of subsystem B: S(AB) = S(A) + S(B).

(3)

If an impossible event Q_n(P(Q_n) = 0) is added to the system, then it will not change the process of the system[5].

(4)

Enforceability: If system a and B are statistically related, and $S (\frac{A}{B})$ S\left( {{A \over B}} \right) is the entropy (conditional entropy) of system a when system B is known, then $E (AB) = E (A) + E (\frac{A}{B})$ E\left( {AB} \right) = E\left( A \right) + E\left( {{A \over B}} \right) .

(5)

Symmetry: The entropy of the system is independent of the order in which its state probability p appears.

Buglakov A I and others proposed the entropy method to evaluate the complexity of manufacturing enterprises. This method can evaluate the complexity of simple enterprise systems according to objective data, but it is not suitable for evaluating the complexity of enterprise production process, and the evaluation cost is too high [6]. Entropy theory is also used in system engineering and process management. He Q applies entropy to the research of enterprise organization ERP. They regard entropy as the degree of confusion in the management of the organization. With the development of enterprise organization ERP to the mature stage, the degree of confusion of the enterprise becomes lower and lower [7]. Hao C and others took information entropy as an index to measure the complexity of supply chain operation, and pointed out that this method has strong flexibility and can reflect the changes of supply chain [8].

Based on the current research, this paper proposes a method based on entropy and dissipative structure theory. Firstly, we determine the connection length L_ab, which refers to the shortest path between two elements in the structure diagram. The length of direct connection is 1, and the length of each transfer is added by 1. Secondly, calculate the total number of microscopic states of the system, A₁, $A_{1} = \sum \sum Lab$ {A_1} = \sum {\sum {Lab} } . Third, calculate the probability value of the realization of the micro state of each connection: $P (a, b) = \frac{L_{ab}}{A_{a}}$ P\left( {a,b} \right) = {{{L_{ab}}} \over {{A_a}}} . Finally, the entropy of the organizational structure between any two elements of the vertical upper and lower levels and the horizontal same level of the system is calculated: H₁ (ab) = − P_a(ab) ln P_a(ab).

Entropy theory and management entropy

2.1

Basic concept and properties of entropy

In 1865, the German physicist Clausius called the ratio of heat absorbed by matter to temperature in the reversible process entropy, which Clausius defined as follows: (1) $Δ S = S - S_{0} = \int_{P_{0}}^{P} \frac{dQ (reversible)}{T}$ \Delta S = S - {S_0} = \int\limits_{{P_0}}^P {{{dQ\left( {{\rm{reversible}}} \right)} \over T}} Where, P₀ is the initial state, P is the end state, S₀, S are the entropy relative to P₀, P states, T is the absolute temperature, Q is the heat, and S ~ S₀ is the change of entropy. If the system goes through a rollback process. The entropy change mentioned above, ie the integral value, is a physical quantity that does not depend on the integral path, but only on the initial and final states of the system, and is a function of the state of the system. The second law of thermodynamics is now expressed in the form of entropy: During the reverse process, the entropy change of the system is equal to the ratio of the heat absorbed in the system to the temperature of the heat source. In an irreversible process, the entropy change is greater than the ratio of heat to temperature. It is concluded that the entropy change is greater than zero during the irreversible thermal process in the system, ie there is a principle of entropy increase. When the return process takes place in the system, the entropy is zero, the irreversible processes are all processes from the unbalanced state to the equilibrium state, and the equilibrium state corresponds to the maximum entropy [9].

2.2

Management entropy

We introduce the concept of entropy to management science and gain the concept of management entropy. Any kind of management organization, called management entropy, refers to a systemic policy approach, etc., and always shows an irreversible process in which the efficiency of a relatively closed organization gradually decreases and the inefficient energy increases continuously. This is a law that reduces the efficiency of organizational management.

The irreversibility of management system is similar to that of various systems in nature, We can define “management entropy” with the help of “entropy” in thermodynamics. From the perspective of management efficiency, I believe that entropy is a measure of the management efficiency presented by the organization, system, policy and method of the system in the process of relatively closed system movement. The increase of system entropy means the continuous reduction and consumption of “management efficiency” [10].

2.3

Dissipative structure theory

The theory of dissipative structure was proposed by the Belgian physicist Prigogine in 1969. The theory, called dissipative structure theory, refers to a nonlinear open system that is far from equilibrium. time, space, and function. Prigogine called it a “spreading structure” because this new orderly structure was far from equilibrium. The theory of dissipative structure is a science that studies its properties, formation, stability, and laws of evolution [11].

The theory of dissipative structure divides macroscopic systems into isolated systems, closed systems, and open systems. An open system has three states: equilibrium, near equilibrium, and far from equilibrium.

The combination of management entropy and continuing education management in Colleges and Universities -- dissipative structure theory

3.1

The continuing education system in Colleges and universities has the characteristics of the formation of dissipative structure

(1)

The continuing education system of colleges and universities is an open system. The continuing education system of colleges and universities does not exist in isolation, but in a dynamic and open state of continuous interaction with the outside world and continuous input and output. First, in the integration and coordination of continuing education resources, there is an interactive relationship between schools and society and between schools and specific implementation departments; Second, it shows the openness of the object of continuing education, that is, the socialization of the source of the object of education. Therefore, the management system of continuing education in Colleges and universities is a complex open system closely related to the outside world.

(2)

The continuing education system in Colleges and universities is far from equilibrium. Since the reform and opening up, with the establishment of socialist market economy and the booming demand for all kinds of continuing education, continuing education is gradually developing towards a socialized, diversified and open pattern, and various non-governmental training institutions have actively involved in this field. By virtue of their new system and flexible mechanism, they give full play to their own advantages, bring vitality and vitality to the field of continuing education, and attract the attention of the society. Needless to say, this will drive the continuing education system of colleges and universities away from balance, and this imbalance has a growing trend [12].

(3)

The interaction between the elements in the continuing education system of colleges and universities is nonlinear. There are a large number of nonlinear interaction mechanisms among the elements of the continuing education system of colleges and universities. People's subjective initiative can not be measured by simple numbers. The social system is nonlinear in essence. In all the work of continuing education management in Colleges and universities, there are strong psychological, emotional, intellectual and other spiritual factors. Whether in terms of quality or quantity, the constituent elements can not be a simple linear relationship.

3.2

Calculation method of organization entropy

We have used this method for reference and made minor modifications to the calculation process of tissue entropy. The specific calculation method is as follows:

First, determine the connection length O_ab, which refers to the shortest path between two elements in the structure diagram. The length of direct connection is 1, and the length of each transfer is added by 1.

Secondly, the total number of micro states of the system A₁ is calculated, $A_{1} = \sum \sum O_{ab}$ {A_1} = \sum {\sum {{O_{ab}}} } Third, calculate the probability value of the realization of the micro state of each connection: $P (a, b) = \frac{O_{ab}}{A_{a}}$ P\left( {a,b} \right) = {{{O_{ab}}} \over {{A_a}}}

Finally, calculate the organizational structure entropy between any two elements at the same level vertically and horizontally: H₁ (ab) = − P_a(ab) ln P_a(ab).

The entropy method is used to measure and determine the expected content of information, which has subsequently become a very important method in social science and physics. In information theory, entropy is represented by uncorrelated P_a. The uncertain measurement methods are: (2) $S (P_{1}, \dots, P_{h}) = - \sum_{l = 1}^{h} pl \times ln pl$ S\left( {{P_1}, \ldots ,\,{P_h}} \right) = - \sum\nolimits_{l = 1}^h {pl \times \ln \,pl}

Among them, S represents the size of the information entropy, h represents the possible h states of the system, and the probability of each state appearing is pl. In the comprehensive evaluation, the information entropy evaluation can be applied to obtain the order degree of the system information and the utility value of the information. The larger S is, the higher the disorder degree of information is, and the smaller the utility value of information is; on the contrary, the smaller S is, the lower the disorder degree of information is, and the greater the utility value of information is [13]. When $g = \frac{1}{ln (h)}$ g = {1 \over {\ln \left( h \right)}} , ensure that s is between 0–1. 0 entropy represents the maximum amount of information and 1 represents the minimum amount of information. If all PL values are equal, $Pl = \frac{1}{h}$ Pl = {1 \over h} , S reaches the maximum.

The evaluation grade of weight can also be obtained by entropy method. The N attributes of M indicators form matrix R. (3) $\begin{array}{l} B_{1} \dots B_{n} \\ R = \begin{matrix} A_{1} \\ \dots \\ A_{m} \end{matrix} [\begin{matrix} r 1_{1} & \dots & r 1_{n} \\ \dots & \dots & \dots \\ {rm}_{1} & \dots & {rm}_{n} \end{matrix}] \end{array}$ \matrix{ {{B_1} \ldots {B_n}} \hfill \cr {R = \matrix{ {{A_1}} \hfill \cr \ldots \hfill \cr {{A_m}} \hfill \cr } \left[ {\matrix{ {r{1_1}} & \ldots & {r{1_n}} \cr \ldots & \ldots & \ldots \cr {r{m_1}} & \ldots & {r{m_n}} \cr } } \right]} \cr }

The result of matrix r_m established by index A_m and attribute B_n can be calculated by the following formula: (4) $P = [\begin{matrix} \frac{r_{11}}{v_{1}} & \dots & \frac{r_{1 n}}{v_{1}} \\ \dots & \dots & \dots \\ \frac{r_{m 1}}{vm} & \dots & \frac{r_{mn}}{vm} \end{matrix}] = [\begin{matrix} p_{1} & \dots & p_{1 n} \\ \dots & \dots & \dots \\ p_{m 1} & \dots & p_{mn} \end{matrix}]$ P = \left[ {\matrix{ {{{{r_{11}}} \over {{v_1}}}} & \ldots & {{{{r_{1n}}} \over {{v_1}}}} \cr \ldots & \ldots & \ldots \cr {{{{r_{m1}}} \over {vm}}} & \ldots & {{{{r_{mn}}} \over {vm}}} \cr } } \right] = \left[ {\matrix{ {{p_1}} & \ldots & {{p_{1n}}} \cr \ldots & \ldots & \ldots \cr {{p_{m1}}} & \ldots & {{p_{mn}}} \cr } } \right] Here: (5) $Pab = \frac{rab}{va}, va = \sum_{b}^{n} rab$ Pab = {{rab} \over {va}},\,va = \sum\nolimits_b^n {rab}

The entropy weight e_b of the index is defined as: (6) $e_{a} = - \sum_{b = 1}^{n} (pab) log (pab), a = 1, \dots, m$ {e_a} = - \sum\nolimits_{b = 1}^n {\left( {pab} \right)\log \left( {pab} \right),\,a = 1, \ldots ,m}

As a method to evaluate the relative importance of attributes, entropy weight method can sensitively reflect the content of information. Therefore, in this study, the calculation method of entropy weight method is used for reference:

Firstly, if the operation of the system has m evaluation indexes and n evaluation objects, the following evaluation index eigenvalue matrix X can be established: (7) $X = [\begin{matrix} X_{11} & \dots & X_{1 n} \\ \dots & \dots & \dots \\ X_{m 1} & \dots & X_{mn} \end{matrix}]$ X = \left[ {\matrix{ {{X_{11}}} & \ldots & {{X_{1n}}} \cr \ldots & \ldots & \ldots \cr {{X_{m1}}} & \ldots & {{X_{mn}}} \cr } } \right] Where X_ab represents the index value of b evaluation object under i evaluation index.

Secondly, X is standardized to obtain matrix X′: (8) $X^{'} = [\begin{matrix} X_{11}^{'} & \dots & X_{1 n}^{'} \\ \dots & \dots & \dots \\ X_{m 1}^{'} & \dots & X_{mn}^{'} \end{matrix}]$ X^\prime = \left[ {\matrix{ {X_{11}^\prime} & \ldots & {X_{1n}^\prime} \cr \ldots & \ldots & \ldots \cr {X_{m1}^\prime} & \ldots & {X_{mn}^\prime} \cr } } \right] Where, for profitability indicators: (9) $X_{ab}^{'} = \frac{X_{ab} - min X_{ab}}{max X_{ab} - min X_{ab}}$ X_{ab}^\prime = {{{X_{ab}} - \min {X_{ab}}} \over {\max \,{X_{ab}} - \min \,{X_{ab}}}}

For profit and loss indicators: (10) $X_{ab}^{'} = \frac{max X_{ab} - X_{ab}}{max X_{ab} - min X_{ab}}$ X_{ab}^\prime = {{\max {X_{ab}} - {X_{ab}}} \over {\max \,{X_{ab}} - \min \,{X_{ab}}}}

Standardized processing can classify each attribute value into a dimensionless quantity, which is convenient for addition, subtraction, multiplication and division between attributes of different units for comparison.

Finally, b index entropy: (11) $H_{b} = - \sum Pab ln Pab$ {H_b} = - \sum {Pab\,\ln \,Pab}

Where: (12) $P_{ab} = \frac{X_{ab}^{'}}{\sum K_{ab}^{'}}$ {P_{ab}} = {{X_{ab}^\prime} \over {\sum {K_{ab}^\prime} }}

Experimental results and analysis

The calculation result of internal entropy is the sum of organizational entropy and team construction and management entropy. Where H₁ is the organization entropy and H₂ is the team building and management entropy [14]. The calculation results of tissue entropy are as follows:

The five randomly selected management teams are teaching building A and B, two teams, teacher management team and student management team. The specific structure analysis is shown in Figure 1.

Organizational structure of management team

Using the calculation method of organization entropy, first determine the connection length L_ij, and the specific calculation results of the connection length are as follows:

Table 1

Calculation results of contact degree

The contact length is 1	The contact length is 2	The contact length is 3	The contact length is 4
(1,2) (1,3) (2,5)	(2,3) (2,4) (2,5) (3,6)	(1,5) (1,6) (1,7)	(6,7) (6,8) (7,9) (7,8)
(2,5) (3,6) (3,7)	(2,6) (2,7) (2,8) (3,5)	(2,9) (4,8) (4,9)
(4,7) (4,8)	(3,5) (4,5) (6,7) (7,9)	(4,5) (4,6) (4,7)
		(4,9) (5,6) (5,7)

Then the structural entropy of the system: (13) $H_{1} = - \sum P_{a} (ab) \times ln P_{a} (ab) = - \frac{\sum Lab}{A_{1}} \times ln (\frac{Lab}{A_{1}}) = 3.517$ {H_1} = - \sum {{P_a}\left( {ab} \right) \times \,\ln \,{P_a}\left( {ab} \right) = - {{\sum {Lab} } \over {{A_1}}}} \times \ln \left( {{{Lab} \over {{A_1}}}} \right) = 3.517

Team building and management entropy include three indicators: the score rate of Q12 questionnaire, the proportion of undergraduates and the proportion of people taking the postgraduate entrance examination. The index value is shown in Figure 2.

Entropy index of team building and management

First, substitute the index value into the matrix X mentioned in the previous chapter. (14) $X = [\begin{matrix} X_{11} & \dots & X_{1 n} \\ \dots & \dots & \dots \\ X_{m 1} & \dots & X_{mn} \end{matrix}]$ X = \left[ {\matrix{ {{X_{11}}} & \ldots & {{X_{1n}}} \cr \ldots & \ldots & \ldots \cr {{X_{m1}}} & \ldots & {{X_{mn}}} \cr } } \right]

The result is: (15) $X = \begin{matrix} 0.82 & 0.93 & 0.70 & 1 & 0.78 \\ 0.23 & 0.29 & 0.15 & 0.33 & 0.26 \\ 0.33 & 0.34 & 0.5 & 0.09 & 0.16 \end{matrix}$ X = \matrix{ {0.82} & {0.93} & {0.70} & 1 & {0.78} \cr {0.23} & {0.29} & {0.15} & {0.33} & {0.26} \cr {0.33} & {0.34} & {0.5} & {0.09} & {0.16} \cr }

Normalize the matrix to obtain the standard matrix X′. (16) $X^{'} = \begin{matrix} 0.5 & 0.5 & 0 & 1 & 0.63 \\ 0.54 & 0.86 & 0 & 1 & 0.74 \\ 0.54 & 0.6 & 1 & 0 & 0.34 \end{matrix}$ X^\prime = \matrix{ {0.5} & {0.5} & 0 & 1 & {0.63} \cr {0.54} & {0.86} & 0 & 1 & {0.74} \cr {0.54} & {0.6} & 1 & 0 & {0.34} \cr } Then (17) $H_{2} = \sum - P_{ab} \times ln Pab = 5.15$ {H_2} = \sum { - {P_{ab}} \times \ln \,Pab = 5.15}

To sum up, internal entropy (18) $H_{Within the} = H_{1} + H_{2} = 6.547$ {H_{{\rm{Within}}\,{\rm{the}}}} = {H_1} + {H_2} = 6.547

The calculation indicators of external entropy flow include the proportion of team members participating in the meeting of the whole hospital, the number of communication between the team and teachers in a week, the number of performance appraisal of the team in a year, the proportion of team members commended by the whole school, the timeliness and accuracy of team members' feedback to teachers on students' achievements, and students' satisfaction [15].

The calculation method of external entropy is the same as that of team internal construction and management. The calculation results are as follows: (19) $H_{outside} = \sum - P_{ab} \times ln P_{ab} = 7.412$ {H_{{\rm{outside}}}} = \sum { - {P_{ab}} \times \ln \,{P_{ab}} = 7.412}

According to the calculation results of the entropy model, the internal entropy of the teacher's management team is 6.547 and the external entropy is 7.412. The external entropy is less than the internal entropy, but the gap is small. According to the dissipative structure model of team development mentioned above, the development of the group is in an orderly stage, which indicates that the team is achieving satisfactory performance. The small difference between internal entropy and external entropy also indicates that the team is in a mature stage and in a stable management mode. Student satisfaction is considered to be an important dimension of management team performance evaluation. Through the survey of student satisfaction, it is found that the student satisfaction rate of two teams is 100%, the student satisfaction rate of two teams is 99%, and the satisfaction rate of one team is 98.5%, which shows that the management team has good performance. This is just consistent with our research results, which shows that the management team in the case is in an orderly development stage with excellent performance. The entropy model of team performance evaluation has been verified in the case study. It has certain practical value and can be used as a work to evaluate team performance evaluation. In the case study, we found that the school management team can maintain the high performance of the team under the working conditions of great pressure and challenges, and the students have a high evaluation of its performance. What is the reason? As the previous theoretical summary of the management team, when the team is used in management, a stable management mode is formed, including personnel composition, three shift system, training and reward system. Although the management mode will be slightly transferred in different schools and different periods, some basic management methods have not changed. It may be this mature and stable management model that has an impact on the performance of the team and maximizes the performance of the team.

The management mode of school management team brings us profound inspiration. The stable management mode formed by the organization can not be easily changed. The change of the organization has its own laws to follow. The kind of reform that mechanically applies other organizational development models and runs counter to organizational development can only lead to poor efficiency, adaptability and other important characteristics of the organization, and finally eliminated. The complexity science represented by entropy and dissipative structure theory emphasizes the self-organization phenomenon in the development of organizations. Even if some organizations are organized at the beginning of the operation process, they also follow the law of self-organization in the subsequent operation process.

Conclusion

On the basis of entropy and dissipative structure, the existence of team entropy and dissipative structure is verified. Under the effect of entropy increase, the internal efficiency of the team decreases and eventually moves towards a state of disorder. If you want the team to be always full of vitality and maintain an orderly state, the team must expand its openness and actively introduce negative entropy flow. On the basis of team entropy, this paper analyzes the factors affecting team performance, judges the internal entropy flow and external entropy flow that cause the existence and evolution of team, and establishes the entropy model for evaluating team performance and the calculation method of entropy model. Through empirical research, the feasibility of team performance evaluation entropy model is proved, which provides a dynamic method for team performance evaluation. The study of the case team found that the development of the team has its own development law. A stable management model will have an impact on the performance of the team, while the blind reform of the management model will only make the team decline.

Figuras y tablas

Calculation results of contact degree

The contact length is 1	The contact length is 2	The contact length is 3	The contact length is 4
(1,2) (1,3) (2,5)	(2,3) (2,4) (2,5) (3,6)	(1,5) (1,6) (1,7)	(6,7) (6,8) (7,9) (7,8)
(2,5) (3,6) (3,7)	(2,6) (2,7) (2,8) (3,5)	(2,9) (4,8) (4,9)
(4,7) (4,8)	(3,5) (4,5) (6,7) (7,9)	(4,5) (4,6) (4,7)
		(4,9) (5,6) (5,7)

Referencias

[1] Wang C, Hashimoto T, Wang Y, et al. Formation of Dissipative Structures in the Straight Segment of Electrospinning Jets[J]. Macromolecules, 2020, 53(18):7876–7886. WangC HashimotoT WangY Formation of Dissipative Structures in the Straight Segment of Electrospinning Jets [J] Macromolecules 2020 53 18 7876 7886 10.1021/acs.macromol.0c01343 Search in Google Scholar

[2] Sci G. On Well-Posedness of 2D Dissipative Quasi-Geostrophic Equation in Critical Mixed Norm Lebesgue Spaces[J]. Analysis in Theory and Applications, 2020, 36(2):111–127. SciG On Well-Posedness of 2D Dissipative Quasi-Geostrophic Equation in Critical Mixed Norm Lebesgue Spaces [J] Analysis in Theory and Applications 2020 36 2 111 127 10.4208/ata.OA-0018 Search in Google Scholar

[3] Buglakov A I, Ivanov V A, Komarov P V, et al. A Study of Films Based on Acrylic Copolymers: Mesoscopic Simulation[J]. Polymer Science, Series A, 2020, 62(3):307–319. BuglakovA I IvanovV A KomarovP V A Study of Films Based on Acrylic Copolymers: Mesoscopic Simulation [J] Polymer Science, Series A 2020 62 3 307 319 10.1134/S0965545X20030049 Search in Google Scholar

[4] He Q, Dong S, Cheng Y. Ordering Method and Empirical Study on Multiple Factor Sensitivity of Group Social Attitudes Based on Entropy Theory[J]. International Journal of Computer Systems Science & Engineering, 2019, 34(4):225–230. HeQ DongS ChengY Ordering Method and Empirical Study on Multiple Factor Sensitivity of Group Social Attitudes Based on Entropy Theory [J] International Journal of Computer Systems Science & Engineering 2019 34 4 225 230 10.32604/csse.2019.34.225 Search in Google Scholar

[5] Hao C, Weidong S, Wei L I, et al. Energy Loss Analysis of Novel Self-Priming Pump Based on the Entropy Production Theory[J]. Journal of Thermal Science, 2019, 28(02):150–162. HaoC WeidongS WeiL I Energy Loss Analysis of Novel Self-Priming Pump Based on the Entropy Production Theory [J] Journal of Thermal Science 2019 28 02 150 162 Search in Google Scholar

[6] Zhang H, Xie J, Ge J, et al. Hybrid particle swarm optimization algorithm based on entropy theory for solving DAR scheduling problem[J]. Tsinghua Science and Technology, 2019, 24(03):282–290. ZhangH XieJ GeJ Hybrid particle swarm optimization algorithm based on entropy theory for solving DAR scheduling problem [J] Tsinghua Science and Technology 2019 24 03 282 290 10.26599/TST.2018.9010052 Search in Google Scholar

[7] Ueda Y. Characterization of the dissipative structure for the symmetric hyperbolic system with non-symmetric relaxation[J]. Journal of Hyperbolic Differential Equations, 2021, 18(01):195–219. UedaY Characterization of the dissipative structure for the symmetric hyperbolic system with non-symmetric relaxation [J] Journal of Hyperbolic Differential Equations 2021 18 01 195 219 10.1142/S0219891621500053 Search in Google Scholar

[8] Ustinov A I, Demchenko V S A, Melnychenko T V, et al. Effect of structure of high entropy CrFeCoNiCu alloys produced by EB PVD on their strength and dissipative properties[J]. Journal of Alloys and Compounds, 2021, 887(3):161408. UstinovA I DemchenkoV S A MelnychenkoT V Effect of structure of high entropy CrFeCoNiCu alloys produced by EB PVD on their strength and dissipative properties [J] Journal of Alloys and Compounds 2021 887 3 161408 10.1016/j.jallcom.2021.161408 Search in Google Scholar

[9] Pieczywek P M, W Paziński, Zdunek A. Dissipative particle dynamics model of homogalacturonan based on molecular dynamics simulations[J]. Scientific Reports, 2020, 10(1):14691. PieczywekP M PazińskiW ZdunekA Dissipative particle dynamics model of homogalacturonan based on molecular dynamics simulations [J] Scientific Reports 2020 10 1 14691 10.1038/s41598-020-71820-2747756032895471 Search in Google Scholar

[10] Oldofredi A, Ttinger H C. The dissipative approach to quantum field theory: conceptual foundations and ontological implications[J]. European Journal for Philosophy of Science, 2021, 11(1):1–36. OldofrediA TtingerH C The dissipative approach to quantum field theory: conceptual foundations and ontological implications [J] European Journal for Philosophy of Science 2021 11 1 1 36 10.1007/s13194-020-00330-9774656033365106 Search in Google Scholar

[11] Montuori R, Nastri E, Todisco P. Influence of the seismic shear proportioning factor on steel MRFs seismic performances[J]. Soil Dynamics and Earthquake Engineering, 2020, 141(2):106498. MontuoriR NastriE TodiscoP Influence of the seismic shear proportioning factor on steel MRFs seismic performances [J] Soil Dynamics and Earthquake Engineering 2020 141 2 106498 10.1016/j.soildyn.2020.106498 Search in Google Scholar

[12] B, E, Schmidt, et al. Improvements in the accuracy of wavelet-based optical flow velocimetry (wOFV) using an efficient and physically based implementation of velocity regularization[J]. Experiments in Fluids, 2020, 61(2):1–17. SchmidtB. E. Improvements in the accuracy of wavelet-based optical flow velocimetry (wOFV) using an efficient and physically based implementation of velocity regularization [J] Experiments in Fluids 2020 61 2 1 17 10.1007/s00348-019-2869-0 Search in Google Scholar

[13] Yong, Ma, Qi-qi, et al. Analysis of transient mold friction under different scales based on wavelet entropy theory[J]. Journal of Iron and Steel Research, International, 2019, 26(10):1061–1068. MaYong Qi-qi Analysis of transient mold friction under different scales based on wavelet entropy theory [J] Journal of Iron and Steel Research, International 2019 26 10 1061 1068 10.1007/s42243-019-00314-x Search in Google Scholar

[14] Susana Gomez and Benjamin Ivorra and Angel M. Ramos. Designing optimal trajectories for a skimmer ship to clean, recover and prevent the oil spilled on the sea from reaching the coast[J]. Applied Mathematics and Nonlinear Sciences, 2018, 3(2) : 553–570. GomezSusana IvorraBenjamin RamosAngel M. Designing optimal trajectories for a skimmer ship to clean, recover and prevent the oil spilled on the sea from reaching the coast [J]. Applied Mathematics and Nonlinear Sciences 2018 3 2 553 570 10.2478/AMNS.2018.2.00043 Search in Google Scholar

[15] Sk. Sarif Hassan and Moole Parameswar Reddy and Ranjeet Kumar Rout. Dynamics of the Modified n-Degree Lorenz System[J]. Applied Mathematics and Nonlinear Sciences, 2019, 4(2) : 315–330. Sarif HassanSk. ReddyMoole Parameswar RoutRanjeet Kumar Dynamics of the Modified n-Degree Lorenz System [J]. Applied Mathematics and Nonlinear Sciences 2019 4 2 315 330 10.2478/AMNS.2019.2.00028 Search in Google Scholar

Applied Mathematics and Nonlinear Sciences

Analysis of higher education management strategy based on entropy and dissipative structure theory

Miaomiao Ren
Miaomiao Ren

Publicado en línea: 15 Jul 2022

Volumen & Edición: AHEAD OF PRINT

Páginas: -

Recibido: 15 Apr 2022

Aceptado: 21 May 2022

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Vista previa del PDF

Artículo

Figure 1

Figure 2

Figuras y tablas

Figure 1

Figure 2

Calculation results of contact degree

Referencias

Artículos Recientes

Planifique su conferencia remota con Sciendo

Applied Mathematics and Nonlinear Sciences

Analysis of higher education management strategy based on entropy and dissipative structure theory

Miaomiao RenMiaomiao Ren

Publicado en línea: 15 Jul 2022

Volumen & Edición: AHEAD OF PRINT

Páginas: -

Recibido: 15 Apr 2022

Aceptado: 21 May 2022

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Vista previa del PDF

Artículo

Figure 1

Figure 2

Figuras y tablas

Figure 1

Figure 2

Calculation results of contact degree

Referencias

Artículos Recientes

Planifique su conferencia remota con Sciendo

Miaomiao Ren
Miaomiao Ren