ANALYSIS OF THE CURRENT SITUATION OF
UNIVERSITY-CITY INTEGRATION
DEVELOPMENT BASED ON DATA MINING
TECHNOLOGY AND EXPLORATION OF THE
OPTIMIZATION PATH
Xin Ma*
College of Foreign Languages, Zhengzhou Normal University, Zhengzhou, Henan,
450044, China.
School of Humanities and Social Sciences, University Sutera Malaysia, Kuala
Lumpur, 56000, Malaysia.
focusmaxine@163.com
Siew Eng Lin
School of Humanities and Social Sciences, University Sutera Malaysia, Kuala
Lumpur, 56000, Malaysia.
Reception: 16/03/2023 Acceptance: 22/05/2023 Publication: 08/06/2023
Suggested citation:
Ma, X. and Eng Lin, S. (2023). Analysis of the current situation of university-
city integration development based on data mining technology and
exploration of the optimization path. 3C Tecnología. Glosas de innovación
aplicada a la pyme, 12(2), 163-182.
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
163
ABSTRACT
University is an inevitable product of a city's development to a specific stage. In
different social environments and historical stages, universities always develop
symbiotically with cities, and the integration of the higher education system and
emerging technologies can accelerate the regional economic development of
universities and cities. Based on data mining technology, this study uses a neural
network algorithm to establish an algorithmic model and sigmoid function as the
incentive function to analyze the integration development of emerging technology
industry and universities in Dongguan city and provide an optimization path for the
integration development of city and universities. The research results show that in the
field of scientific and technological research results, the universities in Dongguan City
applied for 49,726 patents in 2021 and authorized 25,523, with an efficiency rate of
51.33%. In the area of urban economic development, Dongguan's GDP in 2021
showed strong momentum, achieving a regional GDP of 108.554 billion yuan, an
increase of 8.2% over the previous year.
KEYWORDS
Higher education; regional economy; data mining; integration development;
optimization path
INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. CITY AND UNIVERSITY INTEGRATION DEVELOPMENT
2.1. Characteristics of integration development
2.2. Integration development advantages
2.3. Data mining technology algorithms
3. ANALYSIS AND DISCUSSION
3.1. University-company project cooperation
3.2. R&D investment
3.3. Research results
3.4. Urban industrial structure
4. CONCLUSION
REFERENCES
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
164
ABSTRACT
University is an inevitable product of a city's development to a specific stage. In
different social environments and historical stages, universities always develop
symbiotically with cities, and the integration of the higher education system and
emerging technologies can accelerate the regional economic development of
universities and cities. Based on data mining technology, this study uses a neural
network algorithm to establish an algorithmic model and sigmoid function as the
incentive function to analyze the integration development of emerging technology
industry and universities in Dongguan city and provide an optimization path for the
integration development of city and universities. The research results show that in the
field of scientific and technological research results, the universities in Dongguan City
applied for 49,726 patents in 2021 and authorized 25,523, with an efficiency rate of
51.33%. In the area of urban economic development, Dongguan's GDP in 2021
showed strong momentum, achieving a regional GDP of 108.554 billion yuan, an
increase of 8.2% over the previous year.
KEYWORDS
Higher education; regional economy; data mining; integration development;
optimization path
INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. CITY AND UNIVERSITY INTEGRATION DEVELOPMENT
2.1. Characteristics of integration development
2.2. Integration development advantages
2.3. Data mining technology algorithms
3. ANALYSIS AND DISCUSSION
3.1. University-company project cooperation
3.2. R&D investment
3.3. Research results
3.4. Urban industrial structure
4. CONCLUSION
REFERENCES
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
1. INTRODUCTION
The 21st century is an era dominated by the knowledge economy, and knowledge
innovation and technological innovation are the main features of the era. It is the basic
requirement of the era to lead industrial transformation and upgrading through
scientific and technological innovation, and to drive the integration and development
of cities [1]. Facing the call of the new era and the profound transformation of social
life, universities have started to re-examine their functions and status, and cities are
also facing new choices in their development, and the integration of universities and
cities is the trend [2]. The research on university-led urban economic and social
development is of great theoretical value in reconceptualizing the relationship
between universities and society, especially the relationship between universities and
local communities [3]. In the knowledge economy society, universities are axial
institutions with extensive ties to society, which helps to deepen the understanding of
basic issues such as the relationship between epistemology and political theory
philosophy of higher education, the functions of universities, and universities and
society, etc. In the era when the knowledge economy is prevalent, strengthening the
ties between universities and enterprises and enhancing the interaction between
university science and technology innovation and the development of new industries
in cities is the reform and development of universities, innovation of enterprises, and
leading The inevitable requirement of society is the way to enhance the core
competitiveness of cities, and it is also an effective way to build a national innovation
system [4]. Studying the issues related to the interaction mode between university
science and technology innovation development and urban emerging industry
development is not only an important issue in the field of higher education but also a
cross-cutting issue in the fields of sociology, urban economics, and urban political
science [5].
Data mining technology is the process of analyzing the correlation between data or
studying its data patterns to obtain information of application value from a large
amount of data that contains useless information [6]. Unlike traditional data analysis,
the process of data mining has no clear assumptions, and the knowledge obtained
from data analysis and mining needs to be valid and practical [7]. Among other things,
knowledge here refers not to truths, scientific theorems, or pure mathematical
formulas in the traditional sense, but rather to relative, new relationships, patterns,
and trends that hold under specific conditions and in a specific domain [8]. Data
mining technology emerged in the 1980s when its application was mainly oriented to
the problems encountered in traditional data processing applications. With the advent
of the big data era, more and more open source data have become easily accessible,
and these complex and massive data are rich in value, but the huge amount of useful
information is often mixed with useless data, which is difficult to identify, resulting in
the phenomenon of "data explosion and knowledge paucity" [9]. This requires a more
efficient way to find and explore the value in the huge amount of data, and data
mining is one of the key technologies. Data mining technology has been widely used
in the fields of urban air environment monitoring, urban traffic management, and urban
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emerging technology industry combination, among which Zhang L et al [10] selected
the weather monitoring data of a city and analyzed the haze weather using a general
joint matrix decomposition framework for data integration and its systematic algorithm,
and gave a case proof of the proposed data mining algorithm, and the results showed
that the algorithm was high accuracy. Mcevoy D et al [11] proposed a data mining
algorithm to support climate-adaptive urban development and empirically analyzed the
impact of meteorological elements on urban haze. Masey et al [12] used data mining
techniques based on bilinear transformation and ICEEMDAN framework to analyze
the main reasons for the degradation of urban air quality from an economic point of
view, which are the rapid development of urban economy, high-emission industrial and
energy structure, and backward environmental protection technology, Korres M P et al
[13] used the clustering algorithm in data mining technology to drill down and analyze
the causes of haze formation and its impact on all sectors, and indirectly and directly
give adjustment suggestions and optimization paths to achieve urban haze
management at the source. In urban traffic management, Zhang Q [14] and Cali S
[15] et al. mined big data through the integration of intuitionistic fuzzy multi-criteria
evaluation for marketing, supply, and purchasing decisions. B Z Z [16], Simovici D [17]
et al. provided an idea to discriminate traffic status based on data mining techniques
and validated the idea by observing the traffic flow information that The features such
as average occupancy, green light phase saturation, and traffic flow were selected for
traffic state discrimination, and how to build a traffic state data mining clustering matrix
was discussed. Using data preparation techniques, traffic engineering techniques
collect traffic data through the loop coil detector of the road, through which four
clustering matrices of smooth flow, stable flow, congested flow, and blocked flow can
be calculated. Broto [18] based on data mining techniques through spatiotemporal
analysis and deep residual networks to analyze the problems in urban governance
and construction, and can to some extent realistically reflect the bottlenecks of
development and provide a reference for the steady development of cities, Zhang Y
[19] based on data mining using big data and knowledge mining methods oriented to
intelligent production found that emerging technology industries are most closely
related to economic growth, and considered that vigorous development of high-tech
industries is a key strategy for stable economic growth, Yan Q [20] used the clustering
algorithm in data mining for the integration, data mining, and decision support in
informatics of integration, data mining, and decision support, and found that high-tech
industries have obvious development advantages and prospects. The continuous
development and progress of cities, it is bound to affect the construction process of
universities. Kim D Y [21], by establishing an analytical model of simplification and
integration methods of data between strategic urban industries and university talent
training, concluded that universities should accelerate the cultivation of science and
technology innovation talents that adapt to the development of emerging industries in
cities and accelerate the pace of university-enterprise interaction, besides, there are
scholars who, from the perspective of professional construction, give In addition,
some scholars give suggestions on the cultivation of talents from the perspective of
professional construction. There are three main characteristics of universities
supporting urban development: first, highlighting technical disciplines to support
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
166
emerging technology industry combination, among which Zhang L et al [10] selected
the weather monitoring data of a city and analyzed the haze weather using a general
joint matrix decomposition framework for data integration and its systematic algorithm,
and gave a case proof of the proposed data mining algorithm, and the results showed
that the algorithm was high accuracy. Mcevoy D et al [11] proposed a data mining
algorithm to support climate-adaptive urban development and empirically analyzed the
impact of meteorological elements on urban haze. Masey et al [12] used data mining
techniques based on bilinear transformation and ICEEMDAN framework to analyze
the main reasons for the degradation of urban air quality from an economic point of
view, which are the rapid development of urban economy, high-emission industrial and
energy structure, and backward environmental protection technology, Korres M P et al
[13] used the clustering algorithm in data mining technology to drill down and analyze
the causes of haze formation and its impact on all sectors, and indirectly and directly
give adjustment suggestions and optimization paths to achieve urban haze
management at the source. In urban traffic management, Zhang Q [14] and Cali S
[15] et al. mined big data through the integration of intuitionistic fuzzy multi-criteria
evaluation for marketing, supply, and purchasing decisions. B Z Z [16], Simovici D [17]
et al. provided an idea to discriminate traffic status based on data mining techniques
and validated the idea by observing the traffic flow information that The features such
as average occupancy, green light phase saturation, and traffic flow were selected for
traffic state discrimination, and how to build a traffic state data mining clustering matrix
was discussed. Using data preparation techniques, traffic engineering techniques
collect traffic data through the loop coil detector of the road, through which four
clustering matrices of smooth flow, stable flow, congested flow, and blocked flow can
be calculated. Broto [18] based on data mining techniques through spatiotemporal
analysis and deep residual networks to analyze the problems in urban governance
and construction, and can to some extent realistically reflect the bottlenecks of
development and provide a reference for the steady development of cities, Zhang Y
[19] based on data mining using big data and knowledge mining methods oriented to
intelligent production found that emerging technology industries are most closely
related to economic growth, and considered that vigorous development of high-tech
industries is a key strategy for stable economic growth, Yan Q [20] used the clustering
algorithm in data mining for the integration, data mining, and decision support in
informatics of integration, data mining, and decision support, and found that high-tech
industries have obvious development advantages and prospects. The continuous
development and progress of cities, it is bound to affect the construction process of
universities. Kim D Y [21], by establishing an analytical model of simplification and
integration methods of data between strategic urban industries and university talent
training, concluded that universities should accelerate the cultivation of science and
technology innovation talents that adapt to the development of emerging industries in
cities and accelerate the pace of university-enterprise interaction, besides, there are
scholars who, from the perspective of professional construction, give In addition,
some scholars give suggestions on the cultivation of talents from the perspective of
professional construction. There are three main characteristics of universities
supporting urban development: first, highlighting technical disciplines to support
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
technological innovation, second, deep cross-fertilization of disciplines to contribute to
breakthroughs in urban industrial groups, and third, the combination of disciplinary
layout points to synergize urban industrial development [22]. To achieve the integrated
sustainable development of strategic cities, universities should promote the research
and development of urban technology in professional construction on the one hand,
and provide a continuous supply of scientific and technological innovation talents on
the other hand [23].Tang M et al [24] based on the application of multi-attribute large-
scale group decision-making in circular economy development by data mining and
group leadership, which is considered to be inextricably linked to the development of
urban emerging industries Therefore, it is necessary to optimize the professional
structure setting of universities to better connect with the upgrading of urban emerging
industrial structure and market demand. From the perspective of science and
technology innovation results, the effective docking mechanism between university
science and technology innovation results and urban industrial demand development
is discussed in depth, and local universities should strive to enhance the ability of
science and technology innovation results transformation to provide scientific and
technological support for urban industrial development [25-27]. Regarding the
research on the integration mode of university science and technology innovation and
urban emerging industry development, Sain K et al [28] found that after exploring the
academic behaviors of university researchers by developing an integrated early
warning system based on artificial intelligence, the interaction modes between
universities and urban industries mainly include four categories of joint participation,
mutual influence, joint action, and close relationship, due to the different starting
points and interest-driven degrees of research behaviors Ali M [29] and Mariani D [30]
suggested that through the integration of demand analysis and local cultural wisdom,
it is possible to scientifically differentiate talent teams, establish a differentiated talent
evaluation system, innovate a "comprehensive + dynamic" training model for scientific
and innovative talents, and establish a talent pool for urban development. Zabit M N
[31] took the development and validation of the integrated learning method of problem
learning as an example of the science and technology evaluation system of "basic
research for the world and applied research for the market", revised the relevant
policies and texts, innovated the interaction mode with evaluation as the main body,
and promoted universities to actively adapt to the development of urban emerging
industries. Son K S et al [32] proposed a triple interaction model of "R&D platform-
research team-technology innovation" based on the integration of university and city,
taking into account the development characteristics of urban emerging industries and
the impact of common cause failure and periodic testing.
In summary, at this stage, researchers have conducted a lot of research on data
mining technology and analyzed it's supervising and promoting effects on urban
development, but often ignore the correlation between cities and universities, and use
data mining technology in the research of integration development of cities and
universities is almost not involved. In this paper, we use data mining technology to
systematically study the interaction mode between university science and technology
innovation and urban emerging industry development, and propose specific innovation
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paths, and the study amends and supplements the existing theories in light of China's
reality and the special characteristics of higher education development, which is
important for enriching and developing theories of higher education, education
economics, and urban sociology in China. According to the current situation of
regional development, the optimal path for the integration and development of cities
and universities is explored.
2. CITY AND UNIVERSITY INTEGRATION
DEVELOPMENT
Urban culture provides rich nourishment for the formation of local university culture.
The role of city culture in local university culture is mainly expressed in rich soil,
sufficient nutrients, and innovative sources. What kind of characteristic city culture
there is, there will be what kind of characteristic local university culture. The city
culture puts a distinctive regional imprint on the local university culture, and the two
forms a unique cultural form through extensive interaction, communication, and
penetration. In the process of building the university culture, the university will
continuously draw on the cultural nutrients of the city, such as the city's history and
humanities, excellent traditional culture, red culture, etc., and then become a
participant and creator of the city culture, further revealing the connotation and
essence of the city culture. As an important part of the city culture, the local university
culture is inevitably influenced by the subtle influence of the city culture, and the city
culture supports the construction and development of the local university culture.
2.1. CHARACTERISTICS OF INTEGRATION DEVELOPMENT
University is the product of the development of urban civilization to a specific stage,
and likewise, the city is the fertile ground for the emergence and development of the
university. From the perspective of human civilization, a history of higher education
development is also a history of interactive development between universities and
cities and continuous integration with society, which runs through all stages of higher
education from elitism to popularization and has profound inner inevitability. The
relationship between universities and cities can be traced back to medieval Europe.
The majority of medieval universities, formed by the market or founded by the church,
emerged in the central cities of Europe. In the United States, for example, universities
were generally established in the economically prosperous "cities" of the future. The
American Civil War accelerated the process of urbanization, and by the end of the
19th century, a network of American cities had taken shape nationwide. The gradual
urbanization of universities had a great impact on higher education in terms of
enrollment scale, types of institutions, sources of students, etc. In the 1960s,
universities, and cities not only strengthened their spatial ties with cities, but also
developed comprehensive interactions in the fields of economy, science and
technology, and culture. After the 1990s, as the U.S. entered the metropolitan era,
universities and cities became more and more interdependent in various fields,
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forming a symbiotic relationship of "you in me, I in you", showing an obvious
endogenous demand. In the process of constant balancing and coordination,
universities and cities find common needs and balance points for their development
and increasingly become a community of relationships, interests, and destiny.
2.2. INTEGRATION DEVELOPMENT ADVANTAGES
In the large system of social development, universities, and cities are two
interdependent, mutually cooperative, and mutually promoting subsystems. The
university subsystem provides talent training, science and technology innovation, and
other services for the strategic layout of the city development through the interaction
and opening with the city and other external environment, while the city subsystem
provides various kinds of rich resources for the survival and development of the
university, providing a constant source of nutrients for the leapfrog development of the
university. It can be seen that the integration and development of universities and
cities is a process of mutually beneficial cooperation, two-way empowerment, and
two-way service between universities and cities, which is characterized by interactive
two-way nature. From the macroscopic point of view, a stable two-way interactive flow
is formed between the two systems of university and city, which is mainly reflected in
the continuous flow and exchange of capital, technology, information, human
resources, and culture between the two systems, and both sides strive to find the right
integration and power point in spatial layout, talent cultivation, collaborative
innovation, and cultural leadership, to realize the win-win development of university
and city with two-way empowerment and two-way service. Microscopically, the
development of various high-tech industries, financial services, transportation, medical
security, housing reform, and community construction within the city system provides
important social security for the university's schooling needs and also put forward
higher level and multi-level service demands for the university's social services. By
focusing on the advantages of disciplines within the university system, and taking into
account applied and development research while emphasizing basic research, the
university system continues to enhance its role in serving the promotion of scientific
and technological innovation and transformation of scientific and technological
achievements in the city, forming a two-way synergistic innovation service system with
the city innovation system, and focusing on the major needs of the city development,
actively integrating with the subsystems of the city system through the integration of
industry and education, science and education, etc. It also promotes the formation of
different types and levels of diversified service modes by combining the university's
characteristics and forms two-way empowerment development with the city in political,
economic, and cultural fields.
2.3. DATA MINING TECHNOLOGY ALGORITHMS
In the same data mining process, the process mainly includes three steps: data
preprocessing, model building, and model evaluation, and the commonly used models
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are the clustering algorithm and neural network algorithm. Clustering analysis is the
analysis process of grouping objects into multiple clusters composed of similar
objects, which is an important branch of data mining technology. The clustering
process is based on certain attributes of the data, and the data is divided into different
categories or clusters by setting rules, and the data within the same cluster are more
similar, and the data within different clusters are more different. Cluster analysis is a
kind of unsupervised learning, and no classification or grouping information in each
cluster indicates the class of data. In data mining techniques, neural network
algorithms can efficiently mine the correlation between one or more sets of data and
the target data based on a large amount of known data. Therefore, it is often widely
used in scenarios such as data prediction or analyzing the influence weight of multiple
groups of data on a single group of data, while the neural network algorithm is
selected for data mining techniques in this study, and its main model is as follows.
(1)
Where is the sigmoid excitation function, represents the input data of the
neuron, and the input in the sigmoid neuron can be any value between 0 and 1, from
which the excitation function of the Sigmoid neuron can be derived, as in equation (2).
(2)
where denotes the weight value corresponding to each input data and is the
threshold value of that neuron.
When using a neural network, it is necessary to train first and adjust the weight
threshold to make the network adapt to the correlation between data and achieve the
purpose of prediction. Training includes the following steps: (1) Neural network
initialization. The number of nodes in each layer of the network is determined
according to the system input and output , the weight threshold is given an initial
value, and the excitation function is given. Calculate the output of the hidden
layer. Based on the input data , the weights between the input layer and the
hidden layer, and the hidden layer threshold , the hidden layer output is
calculated.
(3)
Calculate the output value of the output layer. Calculate the neural network output
value based on the hidden layer output , the weight between the hidden layer and
the output layer as and the threshold of each neuron in the output layer as .
σ
(Z) =
1
1+e−
Z
σ(Z)
Z
f(x) =
1
1+e−(
wx
+
b
)
(x,y)
f(x)
x
wij
aj
H
H
j=f
(n
∑
i=1
wij xi−aj
)
H
wjk
bk
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are the clustering algorithm and neural network algorithm. Clustering analysis is the
analysis process of grouping objects into multiple clusters composed of similar
objects, which is an important branch of data mining technology. The clustering
process is based on certain attributes of the data, and the data is divided into different
categories or clusters by setting rules, and the data within the same cluster are more
similar, and the data within different clusters are more different. Cluster analysis is a
kind of unsupervised learning, and no classification or grouping information in each
cluster indicates the class of data. In data mining techniques, neural network
algorithms can efficiently mine the correlation between one or more sets of data and
the target data based on a large amount of known data. Therefore, it is often widely
used in scenarios such as data prediction or analyzing the influence weight of multiple
groups of data on a single group of data, while the neural network algorithm is
selected for data mining techniques in this study, and its main model is as follows.
(1)
Where is the sigmoid excitation function, represents the input data of the
neuron, and the input in the sigmoid neuron can be any value between 0 and 1, from
which the excitation function of the Sigmoid neuron can be derived, as in equation (2).
(2)
where denotes the weight value corresponding to each input data and is the
threshold value of that neuron.
When using a neural network, it is necessary to train first and adjust the weight
threshold to make the network adapt to the correlation between data and achieve the
purpose of prediction. Training includes the following steps: (1) Neural network
initialization. The number of nodes in each layer of the network is determined
according to the system input and output , the weight threshold is given an initial
value, and the excitation function is given. Calculate the output of the hidden
layer. Based on the input data , the weights between the input layer and the
hidden layer, and the hidden layer threshold , the hidden layer output is
calculated.
(3)
Calculate the output value of the output layer. Calculate the neural network output
value based on the hidden layer output , the weight between the hidden layer and
the output layer as and the threshold of each neuron in the output layer as .
σ(Z) = 1
1+e−Z
σ(Z)
Z
f(x) = 1
1+e−(wx+b)
(x,y)
f(x)
x
wij
aj
H
Hj=f(n
∑
i=1
wij xi−aj)
H
wjk
bk
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
(4)
According to the neural network output value and the actual value , the error of
each neuron in the output layer is calculated .
(5)
3. ANALYSIS AND DISCUSSION
The interaction mode of university science and technology innovation and urban
new industry development not only reflects how the two sides interact but also reflects
the different interests of each subject in the interaction. On the one hand, we should
analyze the practice subjects and practice elements of the interaction mode, and
grasp and sort out the interaction mode of China on a deeper level from the whole, on
the other hand, we should actively study the interaction cases of foreign universities'
science and technology innovation and urban emerging industry development, and
explore the interaction law and compare the experience through the method of
comparative research, so this study takes Dongguan city as an example and uses
data mining technology to fully explore the Therefore, this study takes Dongguan City
as an example and uses data mining technology to fully explore the path of integration
and development of city and university.
3.1. UNIVERSITY-COMPANY PROJECT COOPERATION
The ultimate goal of the cooperation between universities and urban emerging
industries is to transform science and technology into real productivity and obtain
economic benefits. In the operation process of the interaction mode between the two
sides, the selection of projects determines to a certain extent the participation of the
interaction subjects and the way of interaction. Generally speaking, the government
tends to play the role of macro regulation and actively guides universities and
emerging enterprises to participate in pre-collaborative innovation through organizing
science and technology project exchange meetings and other means. However,
based on the difference in starting points and interest demands of different subjects,
the cooperation rate of the project cannot be raised, or the cooperation is interrupted
halfway, which makes the project cooperation effect not reach the expected goal, and
the possibility of achieving cluster innovation development is smaller and the cluster
effect is insufficient. The said R&D activities are mainly divided into three categories:
basic research, applied research, and experimental development, which is an act of
innovation and novelty in one by using scientific methods to generate new knowledge
or create new applications, and it has a wide range of cooperation objects. The project
cooperation between universities and enterprises in Dongguan is shown in Table 1
and Figure 1.
O
k=
t
∑
j=1
Hjwjk −b
k
O
Y
e
ek=Yk−Ok
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Table 1. Number and distribution of project topics
Figure 1. The situation of each index of the subject project
Projects
Number of
subjects
Inputs (hundred
people)
Investment in R&D (ten
million yuan)
National Science
and Technology
Projects
285 883 120
Local Science
and Technology
Projects
438 548 11
Other Science
and Technology
Projects
145 315 17
Self-selected
Science and
Technology
Projects
168 164 4.5
Projects
entrusted by
enterprises
53 107 4.2
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Table 1. Number and distribution of project topics
Figure 1. The situation of each index of the subject project
Projects
Number of
subjects
Inputs (hundred
people)
Investment in R&D (ten
million yuan)
National Science
and Technology
Projects
285
883
120
Local Science
and Technology
Projects
438
548
11
Other Science
and Technology
Projects
145
315
17
Self-selected
Science and
Technology
Projects
168
164
4.5
Projects
entrusted by
enterprises
53
107
4.2
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The analysis of the data shows that, firstly, the participation of enterprises in R&D
activities entrusted by science and technology projects is low, accounting for only
4.9% of the overall projects, indicating that enterprises, as the main body of applied
and experimental research, have less demand for cooperation in producing new
products and conducting new R&D. Secondly, the participation of domestic
universities is also low, indicating that, as one of the main bodies of basic research,
the original intention of universities is to advance scientific and technological
knowledge and Not much consideration is given to long-term economic benefits or
social benefits. In summary, universities are not committed to applying their scientific
and technological achievements to solve practical problems or to transfer them to
sectors dedicated to their application, such as emerging enterprises, and similarly,
enterprises are not willing to seek partners, such as universities, thus resulting in a
situation where the concept of cooperation between the two sides lags and project
cooperation is not strong, and the chances of cluster innovation development resulting
from the clustering of projects are reduced.
3.2. R&D INVESTMENT
The characteristics of new urban industries vary at different stages of development,
but they all grow and develop gradually in the midst of various unknown risks. After
the birth of scientific and technological achievements, new products begin to
transform from theories and concepts to physical experiments. Faced with the
unknown market prospect at this stage, traditional venture capital institutions such as
banks and insurance companies tend to adopt a wait-and-see policy first, preferring
well-known enterprises or enterprises that have reached a mature level of
development in the selection of investment targets. At the same time, due to the
incomplete relevant safeguard policies, the mismatch of the legal system of venture
capital, the lack of professional investment talents, and the fragile exit mechanism of
venture capital, the current venture capital system is slightly single, which is unable to
guarantee the normal funding operation of the interaction between university science
and technology innovation achievements and the market of emerging industries in the
city, the research expenditure in Dongguan in recent years is shown in Table 2 and
Figure 2.
Table 2. Expenditures of funds by year (billion yuan)
Year Basic Research Applied Research Experimental Development
2016 0.62 3.1 11.06
2017 0.73 3.57 11.45
2018 0.82 3.68 12
2019 1.12 3.93 12.23
2020 1.32 4.02 12.65
2021 1.43 4.63 13.27
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Figure 2. Expenditures
As can be seen from the graph, the funding expenditures become progressively
larger over time, while the funding for experimental development accounts for a
relatively large share, accounting for 68.65% of all funding expenditures by 2021. For
the integration of universities and cities, regardless of the mode of interaction, its
smooth operation or not is inseparable from financial support. The scientific and
technological innovations born in Chinese universities face the problem of shortage of
funds, whether in the early stage of research and development, in the middle stage of
technology diffusion, or the late stage of industrialization. Although universities have
been supported by special state funds in establishing university science and
technology parks, science and technology innovation bases, business incubators, and
even in the process of matching with enterprises, they have played a role in
supporting the transfer and diffusion of technology. However, in recent years,
investment in R&D in China is still at a low level and the proportion of GDP has been
low, as shown in Table 3 and Figure 3.
Table 3. Comparison of R&D spending to GDP volume by country (%)
Year
China
American
Japan
France
Germany
2017
1.51
2.75
3.35
2.20
2.76
2018
1.76
2.55
3.30
2.24
2.85
2019
1.87
2.67
3.51
2.23
2.78
2020
1.98
2.78
3.67
2.22
2.87
2021
2.01
2.84
3.48
2.21
3.89
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Figure 2. Expenditures
As can be seen from the graph, the funding expenditures become progressively
larger over time, while the funding for experimental development accounts for a
relatively large share, accounting for 68.65% of all funding expenditures by 2021. For
the integration of universities and cities, regardless of the mode of interaction, its
smooth operation or not is inseparable from financial support. The scientific and
technological innovations born in Chinese universities face the problem of shortage of
funds, whether in the early stage of research and development, in the middle stage of
technology diffusion, or the late stage of industrialization. Although universities have
been supported by special state funds in establishing university science and
technology parks, science and technology innovation bases, business incubators, and
even in the process of matching with enterprises, they have played a role in
supporting the transfer and diffusion of technology. However, in recent years,
investment in R&D in China is still at a low level and the proportion of GDP has been
low, as shown in Table 3 and Figure 3.
Table 3. Comparison of R&D spending to GDP volume by country (%)
Year
China
American
Japan
France
Germany
2017
1.51
2.75
3.35
2.20
2.76
2018
1.76
2.55
3.30
2.24
2.85
2019
1.87
2.67
3.51
2.23
2.78
2020
1.98
2.78
3.67
2.22
2.87
2021
2.01
2.84
3.48
2.21
3.89
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Figure 3. Comparison of R&D expenditure as a share of GDP
As shown in Chart 3 and Figure 3, the Chinese government's investment effort in
R&D funding has developed from 1.51% in 2017 to 2.01% in 2021, which is an
increasing trend but clearly lags behind by a large margin compared to developed
countries such as the United States, Japan, Germany, and France. Looking closely at
the data for 2017-2021, it is easy to see that the level of investment that China will
reach in 2021 has been put in place or even exceeded by many in countries such as
the United States a few years ago. The limited investment in R&D in China, which
cannot meet the actual needs of the market, objectively hinders the transformation of
university science and technology innovation results into urban emerging industries.
3.3. RESEARCH RESULTS
Through data mining techniques, it is found that the conversion of university
science and technology innovation results is an important way to build a cooperation
platform between universities and urban emerging industries, and is also the key to
the smooth operation of each interaction model, and the study takes the high or low
conversion rate as an important indicator of the value of science and technology
activities. A lower conversion rate of science and technology innovation results causes
waste of university science and technology innovation resources on the one hand,
and affects the timeliness of enterprises' access to the latest science and technology
innovation results on the other, leading to problems such as slow product upgrading
and weak market competitiveness of urban emerging industries. In addition, an
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important manifestation of scientific and technological innovation results is the patent
application, the development of new products must first pass patent approval, and
then can be put into production and then transferred to the market operation, so the
efficiency of the patent is also one of the important indicators to determine the value of
scientific and technological innovation activities. In the past five years, although the
Dongguan government has encouraged universities and enterprises to actively apply
for patents under the premise of scientific and technological innovation, the number of
applications and authorizations has been increasing year by year, as shown in Table 4
and Figure 4.
Table 4. Patent applications and grants
Figure 4. Patent Applications and Grants
Year Patent Application Patent Licensing Authorization rate (%)
2017 25680 15901 61.9
2018 27803 16553 59.54
2019 33620 21740 64.66
2020 44826 22967 51.24
2021 49726 25523 51.33
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important manifestation of scientific and technological innovation results is the patent
application, the development of new products must first pass patent approval, and
then can be put into production and then transferred to the market operation, so the
efficiency of the patent is also one of the important indicators to determine the value of
scientific and technological innovation activities. In the past five years, although the
Dongguan government has encouraged universities and enterprises to actively apply
for patents under the premise of scientific and technological innovation, the number of
applications and authorizations has been increasing year by year, as shown in Table 4
and Figure 4.
Table 4. Patent applications and grants
Figure 4. Patent Applications and Grants
Year
Patent Application
Patent Licensing
Authorization rate (%)
2017
25680
15901
61.9
2018
27803
16553
59.54
2019
33620
21740
64.66
2020
44826
22967
51.24
2021
49726
25523
51.33
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The above analysis data show that the results of scientific and technological
innovation often do not match the market demand. The reasons for this are mainly the
following: firstly, the universities' awareness of the transformation of scientific and
technological innovation results is weak, and the purpose of scientific research is
slightly single; secondly, in the process of the patent application, the situation of
emphasizing quantity rather than quality is very common; again, compared with the
United States and other countries with the mature transformation of scientific and
technological achievements, the incentive mechanism of scientific and technological
innovation results in China has great problems, focusing on spiritual incentives such
as title evaluation, which is difficult to mobilize teachers and student's motivation. As
can be seen from Table 4 and Figure 4, the growth of patent efficiency rate, however,
is not obvious, and even appears to regress. 61.9% of patent efficiency rate in
Dongguan City in 2017, with signs of decline in 2018 and 2020, and a slight
turnaround in 2019, but still lower than in 2017.
The patents and emerging technologies developed by universities, as important
research results, have to be applied in actual production life to play an important role.
The patents and technology transfer of universities in Dongguan in recent years are
shown in Table 5 and Figure 5.
Table 5. Patents and Technology Transfer in Higher Education Institutions
Figure 5. Patents and Technology Transfer
Technology transfer (yuan) Patent assignment (yuan)
Year Amount sold Actual income Amount sold
Actual
income
2013 3894 2913 9229 8460
2014 111345 71911 15102 7855
2015 131805 75437 20109 12663
2016 102149 64994 18491 10150
2017 97301 66282 27471 14855
2018 62149 72423 44766 17665
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Where Figure a shows the transfer of university technology and Figure b shows the
transfer of patents, it can be seen through Figure 5 that for the transfer of university
technology, its sales gradually decreased from 2015, but the overall change in actual
income is small, which shows from the side that with the continuous research and the
gradual development of high precision technology, resulting in a significant increase in
the amount of transfer of individual technologies, thus ensuring sufficient revenue. For
patent transfer, from 2013 to 2018 in an upward trend, the actual income increased
from 8460 to 17665.
3.4. URBAN INDUSTRIAL STRUCTURE
Under the progress of the integration and development of the university and the
city, the gross product of Dongguan City is shown in Table 6 and Figure 6, in which
Dongguan achieved a gross regional product of 108.554 billion yuan in 2021, an
increase of 8.2% over the previous year. Among them, the value added of primary
industry is 3.466 billion yuan, an increase of 11.8%, contributing 0.4% to the growth of
regional GDP; the value added of the secondary industry is 631.941 billion yuan, an
increase of 10.5 %, contributing 73.0% to the growth of regional GDP; the value
added of tertiary industry is 450.128 billion yuan, an increase of 5.1%, contributing to
the growth of regional GDP 26.6%, overall strong economic development, thanks to
the city's support and assistance to universities, the comprehensive quality of various
undergraduate and higher vocational institutions in Dongguan has also continued to
progress and always maintained a strong momentum of development.
Table 6. The growth rate of gross production
Year Gross production value (billion yuan) Growth rate (%)
2016 7260.92 8
2017 8079.2 8.3
2018 8818.11 7.5
2019 9474.43 7.4
2020 9756.77 1.1
2021 10855.4 8.2
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Where Figure a shows the transfer of university technology and Figure b shows the
transfer of patents, it can be seen through Figure 5 that for the transfer of university
technology, its sales gradually decreased from 2015, but the overall change in actual
income is small, which shows from the side that with the continuous research and the
gradual development of high precision technology, resulting in a significant increase in
the amount of transfer of individual technologies, thus ensuring sufficient revenue. For
patent transfer, from 2013 to 2018 in an upward trend, the actual income increased
from 8460 to 17665.
3.4. URBAN INDUSTRIAL STRUCTURE
Under the progress of the integration and development of the university and the
city, the gross product of Dongguan City is shown in Table 6 and Figure 6, in which
Dongguan achieved a gross regional product of 108.554 billion yuan in 2021, an
increase of 8.2% over the previous year. Among them, the value added of primary
industry is 3.466 billion yuan, an increase of 11.8%, contributing 0.4% to the growth of
regional GDP; the value added of the secondary industry is 631.941 billion yuan, an
increase of 10.5 %, contributing 73.0% to the growth of regional GDP; the value
added of tertiary industry is 450.128 billion yuan, an increase of 5.1%, contributing to
the growth of regional GDP 26.6%, overall strong economic development, thanks to
the city's support and assistance to universities, the comprehensive quality of various
undergraduate and higher vocational institutions in Dongguan has also continued to
progress and always maintained a strong momentum of development.
Table 6. The growth rate of gross production
Year
Gross production value (billion yuan)
Growth rate (%)
2016
7260.92
8
2017
8079.2
8.3
2018
8818.11
7.5
2019
9474.43
7.4
2020
9756.77
1.1
2021
10855.4
8.2
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Figure 6. City GDP and Growth Rate
4. CONCLUSION
In the context of a knowledge-based economy, universities, and enterprises, as
important subjects in the interactive development of university science and technology
innovation and new industries in cities, realize the interaction mode of
interdependence, close connection, and seeking a win-win situation, which is of great
significance to the in-depth implementation of the concept of integration of industry
and education and the construction of national innovation system. This paper
analyzes the cooperation between urban development and various universities in
Dongguan City with data mining technology as the research method and proposes
suggestions for the integration development path, the specific research findings are as
follows:
1. The interconnection and development between urban enterprises and
universities are not close enough. Currently, urban enterprises in China
commission fewer science and technology projects to participate in R&D
activities, accounting for only 4.9% of the overall projects, indicating that
enterprises, as the main body of applied and experimental research, have less
demand for collaborative production of new products.
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2. Through data mining technology to analyze between universities, enterprises,
research institutions, and the government of Dongguan City, based on
integration and innovation, to build a model of the inner action mechanism of
regional universities and the development of science and technology
innovation in the city, and analyze the path selection relationship between
technology trading and integrated operation mode, the results show that with
continuous investment and research and development each university's patent
application and technology transfer increase in 2021 No., Dongguan City
applied for a total of 49,726 patent applications, 25,523 authorized, with an
authorization rate of 51.33%.
3.
The scientific and technological research and development achievements of
the university will act on the development of the city, promote the
transformation and upgrading of urban industries and improve the
competitiveness of the city. The research results show that the GDP of
Dongguan City in 2021 showed strong momentum, achieving a regional GDP
of 108.554 billion yuan, an increase of 8.2% over the previous year, and the
rapid development of the city will also drive the university to progress together,
forming a good closed-loop development.
REFERENCES
(1)
Cetin, M., Aksoy, T., Cabuk, S. N., et al. (2021). Employing remote sensing
technique to monitor the influence of newly established universities in creating
an urban development process on the respective cities. Land Use Policy, 109.
(2) Winston, N. (2022). Sustainable community development: Integrating social and
environmental sustainability for sustainable housing and communities.
Sustainable Development, 30.
(3) Koch, J. R. , Wagner, B. G. , & Roberts, A. E. . (2021). Christian universities as
moral communities: drinking, sex, and drug use among university students in the
united states. The Social Science Journal(1), 1-13.
(4)
Wang, J. (2018). Integrating Indigenous with Scientific Knowledge for the
Development of Sustainable Agriculture: Studies in Shaanxi Province. Asian
Journal of Agriculture and Development, 15.
(5)
Mohamed, N., & Salama, M. (2022). Data Mining-Based Cyber-Physical Attack
Detection Tool for Attack-Resilient Adaptive Protective Relays. Energies, 15.
(6) Schmidt, C. (2017). Viability of alternative online news media in developing and
transition countries. In Universities, Entrepreneurship and Enterprise
Development in Africa, 5.
(7) Wang, J., Jiang, K., & Wu, Y. (2022). On congestion games with player-specific
costs and resource failures. Automatica, 142, 110367.
(8)
Kashan, A. J., Lay, J., Wiewiora, A., et al. (2022). The innovation process in
mining: Integrating insights from innovation and change management.
Resources Policy, 76, 102575-.
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
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Ed.44 | Iss.12 | N.2 April - June 2023
180
2. Through data mining technology to analyze between universities, enterprises,
research institutions, and the government of Dongguan City, based on
integration and innovation, to build a model of the inner action mechanism of
regional universities and the development of science and technology
innovation in the city, and analyze the path selection relationship between
technology trading and integrated operation mode, the results show that with
continuous investment and research and development each university's patent
application and technology transfer increase in 2021 No., Dongguan City
applied for a total of 49,726 patent applications, 25,523 authorized, with an
authorization rate of 51.33%.
3. The scientific and technological research and development achievements of
the university will act on the development of the city, promote the
transformation and upgrading of urban industries and improve the
competitiveness of the city. The research results show that the GDP of
Dongguan City in 2021 showed strong momentum, achieving a regional GDP
of 108.554 billion yuan, an increase of 8.2% over the previous year, and the
rapid development of the city will also drive the university to progress together,
forming a good closed-loop development.
REFERENCES
(1) Cetin, M., Aksoy, T., Cabuk, S. N., et al. (2021). Employing remote sensing
technique to monitor the influence of newly established universities in creating
an urban development process on the respective cities. Land Use Policy, 109.
(2) Winston, N. (2022). Sustainable community development: Integrating social and
environmental sustainability for sustainable housing and communities.
Sustainable Development, 30.
(3) Koch, J. R. , Wagner, B. G. , & Roberts, A. E. . (2021). Christian universities as
moral communities: drinking, sex, and drug use among university students in the
united states. The Social Science Journal(1), 1-13.
(4) Wang, J. (2018). Integrating Indigenous with Scientific Knowledge for the
Development of Sustainable Agriculture: Studies in Shaanxi Province. Asian
Journal of Agriculture and Development, 15.
(5) Mohamed, N., & Salama, M. (2022). Data Mining-Based Cyber-Physical Attack
Detection Tool for Attack-Resilient Adaptive Protective Relays. Energies, 15.
(6) Schmidt, C. (2017). Viability of alternative online news media in developing and
transition countries. In Universities, Entrepreneurship and Enterprise
Development in Africa, 5.
(7) Wang, J., Jiang, K., & Wu, Y. (2022). On congestion games with player-specific
costs and resource failures. Automatica, 142, 110367.
(8) Kashan, A. J., Lay, J., Wiewiora, A., et al. (2022). The innovation process in
mining: Integrating insights from innovation and change management.
Resources Policy, 76, 102575-.
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
(9)
Tang, Y., & Lan, Y. (2021). Design of University Financial Decision-Making
Platform Based on Data Mining. Journal of Physics: Conference Series, 1881(4),
042063-.
(10)
Zhang, L., & Zhang, S. (2019). A General Joint Matrix Factorization Framework
for Data Integration and Its Systematic Algorithmic Exploration. IEEE
Transactions on Fuzzy Systems, PP(99), 1-1.
(11)
Mcevoy, D., Iyerraniga, U., Ho, S., et al. (2019). Integrating Teaching and
Learning with Inter-Disciplinary Action Research in Support of Climate Resilient
Urban Development. Sustainability, 11.
(12)
Masey, Nicola, Hamilton, Scott, Beverland, & Iain, et al. (2017). Estimation of
spatial patterns of urban air pollution over a 4-week period from repeated 5-min
measurements. Atmo spheric Environment.
(13)
Korres, M. P. (2022). Integrating Evaluation as a Componential Element in the
Development of the Course: The Case of Two Courses in the Faculty of
Education (AUTH) during the Pandemic. Journal of Education and Training
Studies, 10.
(14)
Zhang, Q., Lian, B., Cao, P., et al. (2020). Multi-Source Medical Data Integration
and Mining for Healthcare Services. IEEE Access, 8, 165010-165017.
(15)
Cali, S., & Balaman, S. Y. (2019). Improved decisions for marketing, supply and
purchasing: Mining big data through an integration of sentiment analysis and
intuitionistic fuzzy multi criteria assessment. Computers & Industrial Engineering,
129(MAR.), 315-332.
(16)
B Z Z A, B X Z A, A Q S, et al. (2017). Integrated sustainable development
evaluation based on human well-being indices and pressure indices: A case
study of the South China Sea Neighboring Countries. The Social Science
Journal, 54(3), 346-357.
(17)
Simovici, D. (2018). Mathematical Analysis for Machine Learning and Data
Mining || Integration. 10.1142/10702: 485-593.
(18)
Castán Broto, & Vanesa. (2017). Urban governance and the politics of climate
change. World Development, 93, 1-15.
(19)
Zhang, Y., Chen, K., Tao, F., et al. (2018). Data and knowledge mining with big
data towards smart production. Journal of Industrial Information Integration, 9,
1-13.
(20)
Yan, Q. (2017). Data Integration, Data Mining, and Decision Support in
Biomedical Informatics. In Translational Bioinformatics and Systems Biology
Methods for Personalized Medicine (pp. 41-52).
(21)
Kim, D. Y., Lim, B., Kim, J. M., et al. (2022). Integrated transcriptome analysis for
the hepatic and jejunal mucosa tissues of broiler chickens raised under heat
stress conditions. Journal of Animal Science and Biotechnology, 13(1), 1-17.
(22)
Zhou, W., & Yang, T. (2021). Application Analysis of Data Mining Technology in
Ideological and Political Education Management. Journal of Physics: Conference
Series, 1915(4), 042040 (7pp).
(23)
Thisse, & Jacques-Fran?ois. (2018). Human capital and agglomeration
economies in urban development. Developing Economies.
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
181
(24)
Tang, M., & Liao, H. (2021). Multi-attribute large-scale group decision making
with data mining and subgroup leaders: An application to the development of the
circular economy. Technological Forecasting and Social Change, 167(6),
120719.
(25)
Turugare, M., & Rudhumbu, N. (2020). Integrating technology in teaching and
learning in universities in Lesotho: opportunities and challenges. Education and
Information Technologies, 25(1).
(26)
Wu, Y., Zhang, J., & Shen, T. (2022). A logical network approximation to optimal
control on continuous domain and its application to HEV control. Science China
Information Sciences.
(27)
Zheng, C., & Zhou, W. (2021). Research on Information Construction and
Management of Education Management Based on Data Mining. Journal of
Physics: Conference Series, 1881(4), 042073 (6pp).
(28)
Sain, K. (2022). Need for Development of AI-based Integrated Warning System
(IWS) for Mitigation of Glaciers/Glacial-lakes Related Hazards with Special
Reference to Uttarakhand Himalaya. Journal of the Geological Society of India,
98(7), 1012-1014.
(29)
Ali, M., Wahjoedi, Wahyono, H., et al. (2017). Analysis of the development
requirement of democratic economy-based integrated entrepreneurship
education program on higher education program in East Lombok Regency.
International Journal of Applied Business & Economic Research, 15(20),
473-483.
(30)
Mariani, D., & Usmeldi. (2019). Needs analysis in the development of natural
science student books connected type integrated of local cultural wisdom.
Journal of Physics Conference Series, 1185(1), 012071.
(31)
Zabit, M. N. M., Zachariah, T. Z., Abdullah, N., et al. (2020). The Development
and Validation of an Integrated Learning Method Based on Problem-Based
Learning in a Pedagogy, Technology and Assessment Course at Malaysia Public
Universities. International Journal of Asian Social Science, 10.
(32)
Meng, S., & Zhang, X. (2021). Translog function in government development of
low-carbon economy. Applied Mathematics and Nonlinear Sciences. https://
doi.org/10.2478/AMNS.2021.2.00138
https://doi.org/10.17993/3ctecno.2023.v12n2e44.163-182
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
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