influencing factors and strategies of the flow of academic

Research ArticleInfluencing Factors and Strategies of the Flow of AcademicProfessionals inColleges andUniversities Based onConvolutionalNeural Networks

LIli Zhao

Faculty of Public Administration China University of Mining and Technology Xuzhou 221000 Jiangsu China

Correspondence should be addressed to LIli Zhao lb18090056cumteducn

Received 9 June 2021 Revised 12 July 2021 Accepted 13 August 2021 Published 23 August 2021

Academic Editor Sang-Bing Tsai

Copyright copy 2021 LIli Zhao -is is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

-e influencing factors of academic professional mobility in colleges and universities are complex and diverse and the intensity ofeach influencing factor has obvious characteristics of differentiation However scholars have few relevant studies based on theintensity of factors affecting the flow of academic professionals in colleges and universities-erefore this article aims to study andanalyze the influence of different factors on the flow of academic professionals in different types of colleges and universities whichis of great significance for promoting the rational and orderly flow of academic professionals -is paper proposes a com-prehensive application of multiple methods such as questionnaire surveys and data statistics based on ERG theory based on thefour types of universities and colleges research-oriented teaching research teaching and application -e influence intensity ofthe flow-influencing factors is comparatively studied It clarified the differences in the influence of spiritual factors economicfactors and social factors on the mobility of academic professionals in colleges and universities established a mobility factormodel and put forward policy recommendations for colleges and universities to promote the rational and orderly mobility ofacademic professionals A total of 2042 questionnaires on ldquoPolicy Improvement Factors Affecting the Flow of Academic Experts inUniversitiesrdquo were released four academic experts of different levels were assigned to universities and 1561 were effectivelysearched Among them there were 336 research universities 157 educational research universities 404 educational universitiesand 164 applied universities -e experimental results of this article show that the factors affecting the flow of academicprofessionals in universities include economic strength factor of 04945 social strength factor of 05456 and intellectual strengthfactor of 052 -erefore the factors affecting the mobility of university scholars can be used in strategic research

1 Introduction

11 Background A good talent training and developmentsystem is the foundation for the rapid progress of a countryand nation and education in this system is one of the mainways to develop and train talents While developing collegesand universities have also entered the stage of educationalreform and have put forward high-level and high-leveldevelopment requirements-ey are also actively improvingtheir own educational concepts educational capital edu-cational models and teaching facilities In recent yearscolleges and universities have continued to expand theirstudent enrollment so the demand for academic profes-sionals in colleges and universities is increasing As the main

driving force for the development of higher educationuniversity academic professionals have gradually become themain educational resource for competition among univer-sities -e lack of talents and the growing demand for talentshave further intensified the competition among academicprofessionals among universities -e reasonable flow oftalents is beneficial and necessary for the development of theuniversity but the frequent flow of talents and the con-centration effect of the flow of talents have an adverse effecton the development of the university -e frequent flow ofacademic professionals affects the stability of the develop-ment of education -is not only harms the development ofthe overall teaching level of colleges and universities but alsoaffects the self-development of high-level talents in colleges

HindawiMobile Information SystemsVolume 2021 Article ID 9663389 11 pageshttpsdoiorg10115520219663389

and universities -erefore how to reasonably regulate theflow of high-level talents in my countryrsquos universities isrelated to the prosperity and development of my countryrsquoshigher education

12Significance -eoperating quality of the academic labormarket is of extreme importance to the government uni-versities and scholars For the government the level of acountryrsquos academic labor force determines the level of highereducation development and technological innovation in theera of knowledge economy Under the combined effect of theglobalization of higher education and the new publicmanagement the international connections between uni-versities have become closer and the competition betweeneach other has become more intense Scholar resources asthe core of the competition have become the focus ofpriority competition among universities -e internationalflow of high-level human resources will determine thereordering of the status of the global technology center Foruniversities as a nonprofit organization gathered byscholars the maximization of prestige is its basic interestand how to attract and retain talents is the center of per-sonnel work -e type location and prestige of the initialacademic position even determine the limit of future aca-demic achievement If the academic labor market cannoteffectively allocate academic resources it will lead to amismatch between scholars and academic posts resulting ina large loss of academic human capital

13 RelatedWork For a long time the brain drain has beenworrying Zhou et alrsquos research found that little is knownabout the brain drain in a country China is a developingcountry that is not only experiencing the loss of overseastalents but also experiencing domestic cross-regional andcross-sectoral flows In this research he studied the flow ofhigh-level talents (HLT) based on the background ofestablishing world-class universities and disciplines(WCUD) in China and its dynamic mechanism and thendiscussed its potential impact -e results show that in thepast four decades eastern China has been a net inflow re-gion while the northeast and central and western regionshave seen net outflows -e eastern region shows moreinternal regional mobility In addition a large amount ofHLT flows from research institutions to universities (CU)Regional socioeconomic gaps imperfect systems and poormanagement are the main reasons for the movement ofhigh-level personnel [1] However due to the uncertainty ofthe experimental process there are still gaps in the exper-imental results -e dispersion of talent within the UnitedStates is not uniform Joseph et alrsquos research found that thereis sufficient statistical evidence that there is an interstatebrain drain phenomenon within the country -ey firststudied this by determining whether the country can bedivided into four categories of talents To this end Josephet al observed the relative pull or driving force of talents andobserved relative to which states tend to retain or lose theirnative talents and which states tend to attract a large or smallnumber of immigrant students seeking education results

outside their own state Once they have completed thisclassification Joseph et al will try to see if there is a set ofrandomly selected independent attributes or topics in thesegroups that are statistically important to support theseclassifications [2] However their experimental process isnot closed so there is a certain deviation in the experimentalresults Regional competitiveness depends on the power ofscience and technology the development of politics econ-omy and culture affects the demand for talents and factorsthat affect the flow of science and technology personnel-erefore there is an urgent need to solve the problem ofhow to create a good environment and attract talents toachieve effective management of them Zhao et al usedfactor analysis cluster analysis multidimensional onlineanalysis and regression analysis to explore the relationshipbetween regional environments provide guidance for theconstruction of regional environments and promote therational flow of scientific and technological talents -eyfound that different regions have different factors -e ex-pected elements of scientific and technological personnel ineastern provinces are the level of economic development andincome while the living environment and cultural envi-ronment are the main expected elements in the central andwestern regions [3] However due to the unclosed nature ofthe experiment there are still some discrepancies in theexperimental data

14 Innovation -e innovation of this research lies in thefollowing (1) it puts forward the influencing factors ofacademic professionals in colleges and universities analyzesthe intensity of the factors affecting the mobility of academicprofessionals in four different colleges and universities andestablishes a mobility factor model and (2) it puts forwardthe viewpoint that the factors affecting the mobility of ac-ademic professionals in colleges and universities are dividedinto ldquothree forcesrdquo such as spiritual factors economicfactors and social forces

2 Convolutional Neural Networks

A typical convolutional neural network consists of a series ofprocesses-e function of the subsampling layer is to samplethe feature map output by the convolutional layer -esampling layer is sampled by scanning the step size of thesampling area instead of continuous sampling Amongthem the coacervate and concentration layers are the firststep -e adhesive layer units are organized into featuremaps In the feature map each unit transfers a set of weightswhich are linked to the feature-level part of the upper level-e exclusion and this local weighted sum are transferred toa nonlinear function (usually called an activation function)[4 5] -e basic structure of the convolutional neural net-work is shown in Figure 1

21 Convolutional Layer -e role of the convolutional layeris essentially to extract local features and then the role of thepooling layer is to combine semantically similar features [6]Usually the pooling layer calculates the maximum value of

2 Mobile Information Systems

the local block in the feature map and the adjacent con-centrated neuron reads data from the small block by movingrows or columns -is is to reduce the dimensionality andimmutability of the data [7 8]

O 1113944 1113944 I(i j)P

times G(i j)1P

1113872 1113873 (1)

Here I represent the input feature map G represents theGaussian kernel 0 represents the output feature map andthe value of P is selected from 1 to infin For P-1 the sub-sampling layer performs mean sampling and each subregionwill be calculated -e mean value in is used as the sub-sampling result [9] when P⟶infin the subsampling layerperforms maximum sampling and the maximum value ineach subregion will be selected as the subsampling result-e sampling diagram is shown in Figure 2

Suppose there are m labeled training samples (x1 y1)(x2 y2) (xm ym) in the training set where the inputfeature x(i)isinRn+1 because it is used for binary classificationin logistic so the category label y(i)isin01 [10 11] -ehypothesis function (hypothesis function) is as follows

hθ(x) 1

1 + exp minusθTx1113872 1113873

(2)

We need to minimize the following cost function bytraining model parameters

J(θ) minus1m

1113944

m

i1y

(i)log hθ x(i)

1113872 1113873 + 1 minus y(i)

1113872 1113873log 1 minus hθ x(i)

1113872 11138731113872 1113873⎡⎣ ⎤⎦

(3)

In the given test input data x we need to use a hypothesisfunction to estimate the probability value of each category ofj (j 1 2 k) p(y j|x) that is When j 1 2 k thenthere is p (y j|x) When appearing as input the probabilityof each classification result appears [12] So our hypothesisfunction needs to output a k-dimensional vector (the sum ofeach dimensional element component is 1) and each di-mensional element component represents the probability

that the input x belongs to this category [13 14] -is hy-pothetical function has the following form

hθ xi( 1113857

p yi 1|xi θ( 1113857

p yi 2|xi θ( 1113857

p yi k|xi θ( 1113857

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

1

1113936kj1 e

θTj xi

eθT1 xi

eθT2 xi

eθT

k xi

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(4)

Here hθ(xi) represents the parameters of the model andthe fraction 11113936

kj1 eθ

Tj xi is used for normalization operation

to ensure that the sum of the elements of each dimension ofthe vector is 1 [15]

-e cost function of the Softmax classifier is as follows

J(θ) minus1m

1113944

m

i11113944

k

j11 yi j1113864 1113865y

(i)logeθT

j xi

1113936kj1 e

θTj xi

⎡⎢⎢⎢⎢⎢⎣⎤⎥⎥⎥⎥⎥⎦ (5)

Formula (5) can be used as a further extension of thelogistic regression cost function [16 17] -e logistic re-gression cost function can be changed to

J(θ) minus1m

1113944

m

i1y

(i)log hθ x(i)

1113872 1113873 + 1 minus y(i)

1113872 1113873log⎡⎣

1 minus hθ x(i)

1113872 11138731113872 11138731113961

minus1m

1113944

m

i11113944

k

j11 yi j1113864 1113865log p y

(i) j | x

(i) θ1113872 1113873⎡⎢⎢⎣ ⎤⎥⎥⎦

p y(i)

1113872 j|x(i)

θ1113873 eθT

l xi

1113936kl1 e

θTl xi

(6)

Regarding the minimization of J(θ) there is no closed-form solution [18] We usually use iterative optimizationalgorithms such as gradient descent algorithm -rough

Figure 1 Basic structure of the convolutional neural network

Mobile Information Systems 3

derivative calculation the following gradient formula isobtained

nablaθjJ(θ) minus

1m

1113944

m

i1xi 1 yi j1113864 1113865( 11138571113858 1113859 minusp yi j

1113868111386811138681113868 xi θ1113872 11138731113961 (7)

22 Training of Convolutional Neural Networks For con-volutional neural networks generally supervised methodsare directly used for training and supervised methodsusually use gradient-based methods -e batch stochasticgradient descent method is usually used [19 20] -e errorfunction formula for sample n is as follows

J(W b x y) 12

1113944

c

k1tk minus yk( 1113857

212t minus y

2 (8)

Here W and b respectively represent the weight andbias of the neural network and x and y respectively rep-resent the training sample and its corresponding label tk

represents the k-th dimension component of the predictedvalue of the sample x yk represents the k-th dimensioncomponent of the label of the training sample x and trepresents the predicted value corresponding to the trainingsample x

Assuming that δ(l+1) is the error term of the l+ 1 layercalculated according to the above formula its weight andbias parameters areW and b respectively If the l+ 1 layer iscompletely connected to the l+ 1 layer the error term of the llayer is calculated as follows

δ(l) W

(l)1113872 1113873

Tδ(l+1)

1113874 1113875 middot frsquo

z(l)

1113872 1113873 (9)

-e corresponding gradient calculation formula is asfollows

nablaW(l) J(W b x y) δ(l+1)a

(l)1113872 1113873

T

nablaW(l) J(W b x y) δ(l+1)

(10)

If the first layer is the feature extraction stage that isthe convolutional layer and the subsampling layer theerror term of the first layer is calculated by the followingformula

δ(l)k upsample W

(l)k1113872 1113873

Tδ(l)

k1113874 1113875 middot frsquo

z(l)k1113872 1113873 (11)

Here k represents the k-th convolution kernel and theupsample(middot) operation transfers the error δ(l+1)

k calculated bythe latter layer to the previous layer through the subsamplinglayer that is the convolutional layer [21] For example if weuse mean sampling upsample(middot) will simply evenly dis-tribute the error to the subregion where subsampling waspreviously performed If the maximum sampling is usedthen the subsampling operation will be performed duringthe previous forward propagation -e position selected asthe sampling value will get all the errors and the otherpositions will be 0

Finally when calculating the gradient we need to flip theconvolution kernel as in the convolution operation -ecalculation formula is as follows

nablaW

(l)

k

J(W b x y) 1113944m

i1a

(l)i1113872 1113873lowast rot90 δ(l+1)

k 21113872 1113873

nablab

(l)

k

J(W b x y) 1113944ab

δ(l+1)k1113872 1113873

ab

(12)

Here fprime(middot) is the steering function of the activationfunction f(middot) the activation (output value) of layer a(l) and land a(l) is the input image a

(l)i lowast δ

(l+1)k represents k con-

volution kernels of i input and error items on the l layer forconvolution operation [22 23]

When the backpropagation ends the gradient descentmethod is used to update the weights and the updateformula is as follows

θ θ minus znablaθJ(θ x y) (13)

Here θ is the weight and bias parameters that need to belearned and z is the learning rate

3 The Current Situation and InfluencingFactors of the Flow of AcademicProfessionals in Colleges and Universities

Scholars have done countless researches on the factors ofuniversity teacher mobility but few have done research onthe intensity of mobility factors In order to carry out in-

convolution +nonlinearity max pooling

vec

convolution + pooling layers fully connected layer

Figure 2 Schematic diagram of sampling


depth research we divide the factors affecting the flow ofcollege teachers into ldquoeconomic factorsrdquo ldquosocial factorsrdquoand ldquomental factorsrdquo and use the difference in intensity tobuild a model of teacher mobility factors [24] At the sametime due to differences in university positioning and de-velopment strategies different types of colleges and uni-versities have different factors affecting the flow of teachers-erefore colleges and universities are divided into fourtypes research-oriented universities teaching and researchuniversities teaching universities and applied universitiesand starting research is necessary In order to study thedifference in the degree of influence of different factors onthe flow of academic professionals in colleges and uni-versities we issued the ldquoCollege Academic Professionalsrdquoto four different levels of college academic professionals inresearch-oriented universities teaching and researchuniversities teaching universities and applied universi-ties A total of 2042 questionnaires were issued in theldquoFlow Influencing Factors and Policy Improvementrdquo and1561 were effectively recovered Among them 336 areresearch-oriented universities 157 are teaching and re-search universities 404 are teaching universities and 164are applied universities -e questionnaire involved manyissues such as the basic situation of mobile academicprofessionals the structure of their academic backgroundthe influencing factors of the flow the flow intention andthe flow policy recommendations Regarding the factorsaffecting the flow of academic professionals in colleges anduniversities we conducted a survey on the influence of 30factors such as the legal environment talent policy andsocial climate -e influence of each factor is divided intoldquovery importantrdquo ldquorelatively importantrdquo and ldquofive optionsare available ldquogeneralrdquo ldquonot so importantrdquo and ldquonotimportantrdquo

31 Comparison Based on the Intensity of Influencing FactorsIn order to compare the intensity of the factors affecting themobility of academic professionals in colleges and univer-sities we added weights to the intensities of the influence ofeach factor in four different types of colleges and universitiesand performed a summation Assuming that the weight ofldquohigh intensityrdquo is 3 and the weight of ldquomedium intensityrdquo is2 and the weight of ldquolow intensityrdquo is 1 then the totalintensity of the income status research-oriented medium-intensity 2 + teaching-research high-intensity 3 + teachinghigh-intensity 3 + applied high-intensity 3 total intensity11 as shown in Tables 1ndash3

It can be seen from Tables 1ndash3 that self-development isthe most important factor among the 15 factors affectingacademic mobility of universities -e income system ismore concentrated Influencing factors are the awareness ofacademic professional team structure vocational trainingself-vision and organizational vision Housing conditionsearly childhood education safety welfare system school leveltype and academic performance will affect low-intensityfactors Finally medical safety conditions relevant laws andregulations social mobility and organizational culture arethe least influential factors

32 Economic Factors -rough the study of employee be-havior it is found that employee behavior is usually affectedby economic factors and the pursuit of economic benefits ismaximized Without exception the mobility of academicprofessionals will also consider the impact of economicfactors -e economic factors referred to in this article arebroad concepts [25] In addition to the income status ofacademic professionals they also include housing condi-tions childrenrsquos education security medical security andwelfare system

321 Income Status Income status is remuneration paid incurrency or other forms -e income of academic profes-sionals in my countryrsquos universities includes wages andallowances and subsidies Among them wages are composedof basic salary job allowances and special job allowancesstipulated by the state allowances and subsidies are com-posed of job allowances overwork subsidies reform sub-sidies holiday condolences and other items Among themFigure 3 shows the intensity of the influence of ldquowage in-comerdquo on academic professionals in different types of in-stitutions [26]

It can be seen from Figure 3 that except for academicprofessionals in research-led universities who believe thatldquowage incomerdquo is a ldquomedium-intensity influencing factorrdquofor the mobility of academic professionals academic pro-fessionals in the other three colleges all believe that ldquoincomestatusrdquo affects academic careers in universities and ldquohigh-intensity factorrdquo of the flow of people-is is because first ofall research-led university scholars have higher academic

Table 1 Intensity of factors affecting the mobility of academicprofessionals in colleges and universities

College category Total strength

Economic factor

Income status 11Housing conditions 9

Medical insurance conditions 8Childrenrsquos education guarantee 9

Welfare system 9

Table 2 Total intensities of influencing factors in the mobility ofacademic professionals in colleges and universities

Social factors

Relevant laws and regulations 7Social mobility 6School level type 9

Organizational culture 6Faculty structure 10

Table 3 Intensity of factors influencing the mobility of academicprofessionals in colleges and universities

Mental powerfactor

Talent incentive system 11Personal development space 12

Professional training opportunities 10Academic achievement 9

Self-goal and organizational visionrecognition 10


qualifications higher academic abilities and stronger mo-bility so they pay more attention to meeting advancedneeds such as psychological needs Regarding ldquosalary in-comerdquo other economic benefits are relatively good and thedegree of attention to ldquosalary incomerdquo is lower than that ofacademic professionals in other types of universities

322 Housing Conditions For colleges and universitiesraising funds to build houses has become an important wayto solve the housing problem of academic professionals Inaddition to raising funds to build houses dormitoriesturnover houses and apartments are also other ways to solvethe housing of academic professionals However collegesand universities will introduce new academic professionsand exodus academic professions every year to solve thehousing problem and ensure the quality of life which be-comes the top priority In recent years as housing pricesacross the country have doubled the number of transitionalhouses in schools has been insufficient academic profes-sionals have continued to increase and the waiting line hasincreased year by year University academic professionalshave been hindered from buying houses and academicprofessionals with low income and no accumulation are inurgent need Universities solve the housing problem Col-leges and universities usually provide one-time housingsubsidies or adopt the ldquodual systemrdquo model of ldquomonetarysubsidies plus physical placementrdquo to attract academicprofessionals to inflow Figure 4 shows the intensity of theinfluence of ldquohousing conditionsrdquo on academic professionalsin different types of colleges and universities

It can be seen from Figure 4 that academic professionalsin applied universities believe that ldquohousing conditionsrdquo area ldquohigh-intensity influencing factorrdquo for the mobility ofacademic professionals in colleges and universities whileacademic professionals in the other three types of collegesall believe that ldquohousing conditionsrdquo are the mobility ofacademic professionals in colleges and universities -is

may be because academic professionals in research-ledteaching-research and teaching universities have higherincome levels and housing purchase welfare systems Mostacademic professionals in such universities can buy theirown houses without living in them Regarding housingprovided by the school however for academic profes-sionals in applied universities relatively low wages andwelfare systems make housing conditions still a high-in-tensity influencing factor

323 Childrenrsquos Education Guarantee -e children of ac-ademic professionals have unique advantages in educationA large number of studies have proved that the academicperformance of children of academic professionals is betterthan the children of parents of other occupations and thesuccess rate of children of academic professionals is higherHowever academic professionals will encounter a series ofproblems such as high expectations leading to lower eyeswhen educating children stereotyped thinking leads todislocation of roles and busy work leads to negligence ofcommunication -erefore the education of children ofacademic professionals often requires more attention Fig-ure 5 shows the intensity of the impact of childrenrsquos edu-cation security on academic professionals in different typesof colleges and universities

It can be seen from Figure 5 that except for academicprofessionals in teaching and research universities who be-lieve that ldquochildrenrsquos education securityrdquo is a ldquohigh-intensityinfluencing factorrdquo that affects the mobility of academicprofessionals in colleges and universities the other threecollege academic professionals believe that ldquochildrenrsquos edu-cation securityrdquo is the ldquomedium-intensity influencing factorrdquoof the mobility of academic professionals -is may be basedon the fact that most academic professionals in teaching andresearch universitiesmust not only take into account scientificresearch work but also do a good job in teaching Takingmultiple positions allows them to realize the importance of

Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Unimportant

Influence intensity

Research-orientedTeaching research type

Teaching typeApplication type

0

5

10

15

20

25

30

35

40

45

50

Freq

uenc

y

Figure 3 Intensity of the influence of ldquowage incomerdquo on academicprofessionals in different types of colleges and universities

Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity

Research-orientedTeaching research typeTeaching type

0

10

20

30

40

50

60

Freq

uenx

y

Figure 4 Intensity of the influence of ldquohousing conditionsrdquo onacademic professionals in different types of colleges anduniversities


cultivating multiple abilities and primary and secondaryschools especially the elementary level it is an importantstage of multifield and all-round ability training so they paymore attention to the education of their children

33 Analysis of Social Factors Mayorsquos ldquosocial manrdquo hy-pothesis puts forward that social needs are the basic needs ofldquosocial manrdquo People gain a sense of identity in social in-teractions Compared with formal organizations informalorganizations have a greater impact on human behavior-esame is true for academic professionals in colleges anduniversities whose mobility behavior is usually affected bysocial factors According to Mayorsquos hypothesis academicprofessionals in colleges and universities social peoplesocial needs and gaining a sense of identity are the maininfluencing factors of social forces

331 Relevant Laws and Regulations -e lack of perfectsystems and norms for the flow of academic professionals incolleges and universities has led to frequent and disorderlyflow of academic professionals At the macrolevel it ismainly manifested as the role misalignment between na-tional regulation and the independent development of ac-ademic professionals At the microlevel it is manifested asthe low efficiency of university management Figure 6 showsthe intensity of the influence of ldquorelated laws and regula-tionsrdquo on academic professionals in different types of col-leges and universities -e changes in university teachermobility policies are mainly manifested in the changes inmobility concepts mobility mechanisms and willingness toflow-e overall presentation has evolved from a mandatorygovernment-led university faculty mobility policy to aldquogovernment guidance + market participationrdquo universityfaculty mobility policyrdquo

It can be seen from Figure 6 that except for academicprofessionals in teaching and research universities that

ldquorelevant laws and regulationsrdquo are the ldquolow-intensityinfluencing factorsrdquo for the mobility of academic profes-sionals in colleges and universities academic professionalsin the other three colleges all believe that ldquorelevant laws andregulationsrdquo are the ldquomedium-intensity influencing factorrdquoof the mobility of academic professionals -is may be basedon the fact that academic professionals in teaching andresearch universities hold multiple positions and laws andregulations are less restrictive than those of the other threetypes of academic professionals

332 School Level Type For academic professionals theuniversity level is not only a consideration of externalprestige but also a concern for university resources Oftenthe higher the level of the institution the more the resourcesthey get In the context of ldquodouble first-classrdquo ldquofirst-classuniversitiesrdquo and ldquofirst-class disciplinesrdquo are more attractiveto academic professionals on the contrary lower-levelcolleges and universities have fewer resources attractingacademic professionals to flow into the comparison difficultFigure 7 shows the intensity of the impact of school leveltypes on academic professionals in different types ofinstitutions

It can be seen from Figure 7 that except for academicprofessionals in research-led universities who believe thatldquoschool level typerdquo is a ldquohigh-intensity influence factorrdquo forthe flow of academic professionals in colleges and univer-sities academic professionals in the other three colleges allbelieve that ldquoschool level typerdquo is ldquomid-strength influencingfactorsrdquo -is may be because the resources required by theresearch of academic professionals in research-leadinguniversities are directly linked to the level of the school sothey will choose the corresponding university based on theirscientific research capabilities

4 Influencing Factors and Strategy Research onthe Flow of Academic Professionals inColleges and Universities

In order to analyze the characteristics of the factors affectingthe mobility of academic professionals in research-orientedteaching-research teaching and application-oriented uni-versities we divided 15 factors influencing the mobility ofacademic professionals in colleges and universities intoldquoeconomic factorsrdquo and ldquosocial factorsrdquo -e three types ofldquomental factorsrdquo compare the influence of the ldquothree factorsrdquoon the flow of academic professionals in four different typesof colleges and universities

41 Intensities of Factors Affecting the Mobility of AcademicProfessionals in Different Types of Colleges and UniversitiesIn order to compare the influence intensity of ldquoeconomicfactorsrdquo ldquosocial factorsrdquo and ldquomental factorsrdquo based on thetypes of institutions we have drawn the following summarytable based on Tables 1ndash3 Assuming that the sum of thethree strength distributions for each force is 100 thedistribution ratio is calculated according to the number offactors that are distributed among the ldquothree force factorsrdquo

Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y

Figure 5 Intensity of the impact of childrenrsquos education securityon academic professionals in different types of colleges anduniversities


Based on the type of colleges and universities the impact ofthe ldquothree forcesrdquo is shown in Table 4

According to Table 4 we find that ldquoeconomic powerfactorsrdquo ldquosocial power factorsrdquo and ldquomental power factorsrdquohave different influences on the mobility of academic pro-fessionals in four different types of colleges and universities-e factors of economic power and mental power are alllocated in the middle and high intensity and the distributionof social power factors is different in the high intensity Inorder to make the results easier to observe we weighted andsummed the three forces of economic power social powerandmental power respectively-e weight of low strength is1 the weight of medium strength is 2 and the weight of highstrength is 3 Figure 8 is calculated

It can be seen from Figure 8 that for the flow of academiccareers in research-led universities the influence of mentalfactors is the highest and the influence of social factors andeconomic factors is at the same level for academic careers inteaching-research teaching and application-oriented

universities in terms of mobility the influence of mentalfactors is the highest followed by economic factors and theinfluence of social factors is relatively low

42 Adjusting the Mobility Policy of Academic ProfessionalsBased on the Difference in the Strength of the ldquoltree Forcesrdquo-e inequality in the regional distribution of ordinaryuniversities especially the concept of ldquodouble first-raterdquo hasexacerbated the ldquodifferentiation between the rich and thepoorrdquo between the regions and the management resourcesof universities are relatively large -is resource gap is ev-ident in the different resources of academic professionals-is resource gap is prominently manifested in the differ-ence in resources of academic professionals As the corecompetitiveness of colleges and universities academicprofessionals have relatively high differences between theiracademic qualifications professional titles and other ldquoself-inducing factorsrdquo and graduate colleges and other

Very important More important Generallyimportant

Less important Unimportant

Influence intensity


Teaching typeApplication Type

0

10

20

30

40

50

60

Freq

uenx

y

Figure 7 Intensity of the impact of school level types on academic professionals in different types of institutions



Influence intensity

05

101520253035404550

Freq

uenx

y



Figure 6 ldquoRelevant laws and regulationsrdquo influence intensity of academic professionals in different types of colleges and universities


ldquobelonging factorsrdquo -ese differences make academic pro-fessionals different in their work focus Academic profes-sionals in leading universities have an advantage in scientificresearch and academic professionals in teaching universitieshave an advantage in teaching activities However it isimpossible for any university to focus solely on ldquoresearchrdquo orldquoteachingrdquo Both must be considered It requires both aca-demic professionals who are good at scientific research andacademic professionals who are good at teaching -ereforeit is necessary for colleges and universities to attract theinflux of academic professionals who need typology fromother types of institutions Because most indicators areinvolved in this study we will analyze the reliability of eachdimension separately and then analyze the reliability of theoverall scale -e results of the analysis are shown in Table 5

From Table 5 we can see that the alpha coefficient of 9indicators in personal attributes is 0752 the alpha coeffi-cient of 16 indicators in organizational attributes is 0854the alpha coefficient of 4 indicators in social attributes is0731 and the overall alpha coefficient of the scale is 0916and the alpha coefficients of the three dimensions are allhigher than 07 and all are in the range of 07-098-erefore the measurement results of the scale in these threedimensions have high credibility and the overall credibilityof the scale it is also in the range of high confidence

43 Grasping the Law of the Strength of the ldquoltree Forcesrdquo andStandardizing the Mobility of Academic Professionals In thefield of practice it is necessary to actively explore the flow ofacademic professionals in colleges and universities On theone hand colleges and universities do not understand theintensity of the factors affecting the flow of academic pro-fessionals and spontaneously raise the level of some factorswhich not only failed to promote the flow of academicprofessionals but also caused a waste of college resources Onthe other hand colleges and universities cannot provide theconditions for academic professionals in colleges and uni-versities and the flow of academic professionals is hinderedor causes disorderly flow of academic professionals-erefore colleges and universities have a certain degree ofblindness when using resources to promote the flow ofacademic professionals After conducting a horizontalanalysis we have made a longitudinal comparative analysisof the correlation between the influencing factors in thethree dimensions of economic power social power andspiritual power and the flow of academic professionals -eresults of the analysis are shown in Table 6

-e mean value of Pearsonrsquos correlation in Table 6 isobtained by calculating the mean value of Pearsonrsquos cor-relation of all influencing factors in each dimension In orderto regulate the mobility of academic professionals colleges

2

2

26

24

14

28

22 16

26

26 22

24

Economic power Social power Mental strength

Val

ue

Type



Figure 8 Radar chart of the influence intensity of the ldquothree forcesrdquo based on the type of institutions

Table 4 Intensity of the influence of the ldquothree forcesrdquo on the basis of the type of institution

Economic power Influence Mental strengthLow Medium () High () Low () Medium () High () Low Medium () High ()

Research-oriented 100 20 60 20 40 60Teaching research type 60 40 40 20 20 20 80Teaching type 80 20 40 60 40 60Application type 40 60 80 20 60 40


and universities should understand the intensity of thefactors affecting the mobility of academic professionals invarious types of colleges and universities formulate strat-egies for the introduction of academic professionals incolleges and universities and promote the mobility of ac-ademic professionals in a planned and purposeful mannerWhether it is a research-led university a teaching-researchuniversity a teaching university or an application-orienteduniversity if you want to introduce academic professionalsfrom other colleges and universities into the university youshould make up for the shortcomings and improve the levelof disadvantaged resources according to the strength dif-ference Attracting the influx of academic professionals fromthe same type of university requires strengthening the re-sources of high-intensity factors affecting the flow of aca-demic professionals and strengthening the level of superiorresources -is requires managers to act in accordance withthe law of flow on the basis of exploring the law of flow

5 Conclusions

By analyzing the differences in the intensities of factorsaffecting the mobility of academic professionals in collegesand universities we gain insight into the dynamics of themobility of academic professionals and the theory of aca-demic professionalsrsquo mobility is further enriched In pre-vious studies scholars focused on the analysis of the stateand types of influencing factors of the mobility of academicprofessionals in colleges and universities but few peoplehave conducted empirical analysis and comparison of theinfluencing factors of the mobility of academic professionalsin different types of colleges and universities For collegesand universities the establishment of a team of academicprofessionals with optimized structure and high quality isthe key to improving the level of colleges and universitiesenhancing the overall strength of colleges and universitiesand entering the ranks of ldquodouble first-classrdquo In the processof development colleges and universities have unscrupu-lously introduced high-level academic professionals in orderto recruit high-quality student resources -rough the studyof the influencing factors of the mobility of academicprofessionals in the academic labor market the types ofcolleges are divided the model of academic professionalsmobility factors is established and the policy

recommendations to promote the mobility of academicprofessionals are provided to universities which is condu-cive to helping colleges and universities to understandcorrectly Factors affecting the mobility of academic pro-fessionals promote the mobility of academic professionals incolleges and universities It can also positively guide themobility of academic professionals in colleges and univer-sities regulate mobility behaviors make the academic labormarket more perfect operate more effectively and avoidvicious competition -e shortcomings of this study are dueto the high difficulty of stratified sampling this article adoptsa combination of online survey and paper questionnairesurvey making the questionnaire stratified sampling fail toreach the initial design of the survey and some colleges havefewer samples -e survey data between universities is notbalanced but fortunately the collected samples meet thestatistical requirements Although not good enough it doesnot affect the final statistical analysis Secondly the statisticalanalysis method of data is relatively simple lacking in-depthresearch on the correlation of related factors or causality

Data Availability

No data were used to support this study

Conflicts of Interest

-e author declares no conflicts of interest

Acknowledgments

-is study was supported by the Education and TeachingReform Project of Heilongjiang Province (SJGY20180422and SJGY20200602)

References

[1] Y Zhou Y Guo and Y Liu ldquoHigh-level talent flow and itsinfluence on regional unbalanced development in ChinardquoApplied Geography vol 91 pp 89ndash98 2018

[2] A Joseph P Rutz S Stachowiak and S Jaume ldquoHighereducation analyticsrdquo International Journal of InformationSystems and Social Change vol 8 no 1 pp 58ndash70 2017

[3] S L Zhao D Y Zhu X B Peng andW Song ldquoAn empiricalanalysis of the regional competitiveness based on SampT talentsflowrdquo Human Systems Management vol 35 no 1 pp 1ndash102016

[4] P Finch ldquoAwards and competitions showthattalent will outrdquolte Architectsrsquo Journal vol 244 no 13 16 pages 2017

[5] B Q Cai and X H Huang ldquoEvaluating the coordinateddevelopment of regional innovation ecosystem in ChinardquoEkoloji vol 27 no 106 pp 1123ndash1132 2018

[6] B Leavy ldquoCustomer-centered innovation improving theodds for successrdquo Strategy amp Leadership vol 45 no 2pp 3ndash11 2017

[7] L Maxwell ldquoSuperbosses how exceptional leaders master theflow of talentrdquo Library Journal vol 141 no 2 84 pages 2016

[8] G William and O Superbosses ldquoHow exceptional leadersmaster the flow of talent by sydney finkelsteinrdquo OrganizationManagement Journal vol 13 no 4 pp 230ndash232 2016

Table 5 Reliability analysis

Cronbachrsquos alpha N of itemsEconomic factors 0752 9Social factors 0854 16Spiritual factors 0731 4Overall scale 0916 29

Table 6 Summary of correlation analysis

Relevance to teachermobility

Economicfactors

Socialfactors

Spiritualfactors

Pearson correlationmean 04945 05456 052


[9] M J E Superbosses ldquoHow exceptional leaders master theflow of talentrdquo Journal of Applied Management and Entre-preneurship vol 21 no 3 pp 124ndash127 2016

[10] D Hopkins J Higham S Tapp and T Duncan ldquoAcademicmobility in the Anthropocene era a comparative study ofuniversity policy at three New Zealand institutionsrdquo Journalof Sustainable Tourism vol 24 no 3 pp 376ndash397 2016

[11] M Paula ldquoVulnerability gender and resistance in transna-tional academic mobilityrdquo Tertiary Education and Manage-ment vol 24 no 3 pp 254ndash265 2018

[12] X Hao G Zhang and S Ma ldquoDeep learningrdquo InternationalJournal of Semantic Computing vol 10 no 3 pp 417ndash4392016

[13] G Litjens T Kooi B E Bejnordi et al ldquoA survey on deeplearning in medical image analysisrdquo Medical Image Analysisvol 42 no 9 pp 60ndash88 2017

[14] D S Kermany M Goldbaum W Cai et al ldquoIdentifyingmedical diagnoses and treatable diseases by image-based deeplearningrdquo Cell vol 172 no 5 pp 1122ndash1131 2018

[15] D Shen G Wu and H-I Suk ldquoDeep learning in medicalimage analysisrdquo Annual Review of Biomedical Engineeringvol 19 no 1 pp 221ndash248 2017

[16] Y Chen Z Lin Z Xing et al ldquoDeep learning-based classi-fication of hyperspectral datardquo IEEE Journal of Selected Topicsin Applied Earth Observations amp Remote Sensing vol 7 no 6pp 2094ndash2107 2017

[17] X Wang L Gao S Mao et al ldquoCSI-based fingerprinting forindoor localization a deep learning approachrdquo IEEE Trans-actions on Vehicular Technology vol 66 no 1 pp 763ndash7762016

[18] S Albarqouni C Baur F Achilles V Belagiannis S Demirciand N Navab ldquoAggNet deep learning from crowds formitosis detection in breast cancer histology imagesrdquo IEEETransactions onMedical Imaging vol 35 no 5 pp 1313ndash13212016

[19] D Ravi C Wong F Deligianni et al ldquoDeep learning forhealth informaticsrdquo IEEE Journal of Biomedical and HealthInformatics vol 21 no 1 pp 4ndash21 2017

[20] D Marmanis M Datcu T Esch and U Stilla ldquoDeep learningearth observation classification using ImageNet pretrainednetworksrdquo IEEE Geoscience and Remote Sensing Lettersvol 13 no 1 pp 105ndash109 2016

[21] G Li Y Zhang T Deng et al ldquoResponses of tree stem sapflow and its main influencing factors to bud pruningrdquo NongyeGongcheng XuebaoTransactions of the Chinese Society ofAgricultural Engineering vol 37 no 5 pp 131ndash139 2021

[22] Y Z Hou P Li J Zhang et al ldquoIdentification of criticalinfluencing factors in dropping process of XuesaitongDropping Pillsrdquo Zhongguo Zhong yao za zhiZhongguozhongyao zazhiChina journal of Chinese materia medicavol 46 no 1 pp 103ndash109 2021

[23] F Xiong L Gan and W Sun ldquoCharacterization of reservoirpermeability and analysis of influencing factors in fracture-pore mediumrdquo Chinese Journal of Geophysics-Chinese Editionvol 61 no 1 pp 279ndash288 2021

[24] P Singh and R Agrawal ldquoA customer centric best connectedchannel model for heterogeneous and IOTnetworksrdquo Journalof Organizational and End User Computing vol 30 no 4pp 32ndash50 2018

[25] S Ivanaj G-B Nganmini and A Antoine ldquoMeasuringE-learnersrsquo perceptions of service qualityrdquo Journal of Orga-nizational and End User Computing vol 31 no 2 pp 83ndash1042019

[26] C-L Wei and C-T Ho ldquoExploring signaling roles of serviceprovidersrsquo reputation and competence in influencing per-ceptions of service quality and outsourcing intentionsrdquoJournal of Organizational and End User Computing vol 31no 1 pp 86ndash109 2019


and universities -erefore how to reasonably regulate theflow of high-level talents in my countryrsquos universities isrelated to the prosperity and development of my countryrsquoshigher education

12Significance -eoperating quality of the academic labormarket is of extreme importance to the government uni-versities and scholars For the government the level of acountryrsquos academic labor force determines the level of highereducation development and technological innovation in theera of knowledge economy Under the combined effect of theglobalization of higher education and the new publicmanagement the international connections between uni-versities have become closer and the competition betweeneach other has become more intense Scholar resources asthe core of the competition have become the focus ofpriority competition among universities -e internationalflow of high-level human resources will determine thereordering of the status of the global technology center Foruniversities as a nonprofit organization gathered byscholars the maximization of prestige is its basic interestand how to attract and retain talents is the center of per-sonnel work -e type location and prestige of the initialacademic position even determine the limit of future aca-demic achievement If the academic labor market cannoteffectively allocate academic resources it will lead to amismatch between scholars and academic posts resulting ina large loss of academic human capital

13 RelatedWork For a long time the brain drain has beenworrying Zhou et alrsquos research found that little is knownabout the brain drain in a country China is a developingcountry that is not only experiencing the loss of overseastalents but also experiencing domestic cross-regional andcross-sectoral flows In this research he studied the flow ofhigh-level talents (HLT) based on the background ofestablishing world-class universities and disciplines(WCUD) in China and its dynamic mechanism and thendiscussed its potential impact -e results show that in thepast four decades eastern China has been a net inflow re-gion while the northeast and central and western regionshave seen net outflows -e eastern region shows moreinternal regional mobility In addition a large amount ofHLT flows from research institutions to universities (CU)Regional socioeconomic gaps imperfect systems and poormanagement are the main reasons for the movement ofhigh-level personnel [1] However due to the uncertainty ofthe experimental process there are still gaps in the exper-imental results -e dispersion of talent within the UnitedStates is not uniform Joseph et alrsquos research found that thereis sufficient statistical evidence that there is an interstatebrain drain phenomenon within the country -ey firststudied this by determining whether the country can bedivided into four categories of talents To this end Josephet al observed the relative pull or driving force of talents andobserved relative to which states tend to retain or lose theirnative talents and which states tend to attract a large or smallnumber of immigrant students seeking education results

outside their own state Once they have completed thisclassification Joseph et al will try to see if there is a set ofrandomly selected independent attributes or topics in thesegroups that are statistically important to support theseclassifications [2] However their experimental process isnot closed so there is a certain deviation in the experimentalresults Regional competitiveness depends on the power ofscience and technology the development of politics econ-omy and culture affects the demand for talents and factorsthat affect the flow of science and technology personnel-erefore there is an urgent need to solve the problem ofhow to create a good environment and attract talents toachieve effective management of them Zhao et al usedfactor analysis cluster analysis multidimensional onlineanalysis and regression analysis to explore the relationshipbetween regional environments provide guidance for theconstruction of regional environments and promote therational flow of scientific and technological talents -eyfound that different regions have different factors -e ex-pected elements of scientific and technological personnel ineastern provinces are the level of economic development andincome while the living environment and cultural envi-ronment are the main expected elements in the central andwestern regions [3] However due to the unclosed nature ofthe experiment there are still some discrepancies in theexperimental data

14 Innovation -e innovation of this research lies in thefollowing (1) it puts forward the influencing factors ofacademic professionals in colleges and universities analyzesthe intensity of the factors affecting the mobility of academicprofessionals in four different colleges and universities andestablishes a mobility factor model and (2) it puts forwardthe viewpoint that the factors affecting the mobility of ac-ademic professionals in colleges and universities are dividedinto ldquothree forcesrdquo such as spiritual factors economicfactors and social forces

2 Convolutional Neural Networks

A typical convolutional neural network consists of a series ofprocesses-e function of the subsampling layer is to samplethe feature map output by the convolutional layer -esampling layer is sampled by scanning the step size of thesampling area instead of continuous sampling Amongthem the coacervate and concentration layers are the firststep -e adhesive layer units are organized into featuremaps In the feature map each unit transfers a set of weightswhich are linked to the feature-level part of the upper level-e exclusion and this local weighted sum are transferred toa nonlinear function (usually called an activation function)[4 5] -e basic structure of the convolutional neural net-work is shown in Figure 1

21 Convolutional Layer -e role of the convolutional layeris essentially to extract local features and then the role of thepooling layer is to combine semantically similar features [6]Usually the pooling layer calculates the maximum value of



O 1113944 1113944 I(i j)P

times G(i j)1P

1113872 1113873 (1)



hθ(x) 1

1 + exp minusθTx1113872 1113873

(2)


J(θ) minus1m

1113944

m

i1y

(i)log hθ x(i)

1113872 1113873 + 1 minus y(i)

1113872 1113873log 1 minus hθ x(i)

1113872 11138731113872 1113873⎡⎣ ⎤⎦

(3)



hθ xi( 1113857

p yi 1|xi θ( 1113857

p yi 2|xi θ( 1113857

p yi k|xi θ( 1113857

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

1

1113936kj1 e

θTj xi

eθT1 xi

eθT2 xi

eθT

k xi

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(4)


kj1 eθ




J(θ) minus1m

1113944

m

i11113944

k

j11 yi j1113864 1113865y

(i)logeθT

j xi

1113936kj1 e

θTj xi

⎡⎢⎢⎢⎢⎢⎣⎤⎥⎥⎥⎥⎥⎦ (5)


J(θ) minus1m

1113944

m

i1y

(i)log hθ x(i)

1113872 1113873 + 1 minus y(i)

1113872 1113873log⎡⎣

1 minus hθ x(i)

1113872 11138731113872 11138731113961

minus1m

1113944

m

i11113944

k

j11 yi j1113864 1113865log p y

(i) j | x

(i) θ1113872 1113873⎡⎢⎢⎣ ⎤⎥⎥⎦

p y(i)

1113872 j|x(i)

θ1113873 eθT

l xi

1113936kl1 e

θTl xi

(6)





nablaθjJ(θ) minus

1m

1113944

m


1113868111386811138681113868 xi θ1113872 11138731113961 (7)


J(W b x y) 12

1113944

c


212t minus y

2 (8)




δ(l) W

(l)1113872 1113873

Tδ(l+1)


z(l)

1113872 1113873 (9)



(l)1113872 1113873

T


(10)


δ(l)k upsample W

(l)k1113872 1113873

Tδ(l)


z(l)k1113872 1113873 (11)




nablaW

(l)

k

J(W b x y) 1113944m

i1a

(l)i1113872 1113873lowast rot90 δ(l+1)

k 21113872 1113873

nablab

(l)

k

J(W b x y) 1113944ab

δ(l+1)k1113872 1113873

ab

(12)


(l)i lowast δ









vec












Economic factor



Welfare system 9


Social factors




Mental powerfactor











Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Unimportant

Influence intensity



0

5

10

15

20

25

30

35

40

45

50

Freq

uenc

y


Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y













Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y










Influence intensity



0

10

20

30

40

50

60

Freq

uenx

y




Influence intensity

05

101520253035404550

Freq

uenx

y









2

2

26

24

14

28

22 16

26

26 22

24


Val

ue

Type









5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors























O 1113944 1113944 I(i j)P

times G(i j)1P

1113872 1113873 (1)



hθ(x) 1

1 + exp minusθTx1113872 1113873

(2)


J(θ) minus1m

1113944

m

i1y

(i)log hθ x(i)

1113872 1113873 + 1 minus y(i)

1113872 1113873log 1 minus hθ x(i)

1113872 11138731113872 1113873⎡⎣ ⎤⎦

(3)



hθ xi( 1113857

p yi 1|xi θ( 1113857

p yi 2|xi θ( 1113857

p yi k|xi θ( 1113857

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

1

1113936kj1 e

θTj xi

eθT1 xi

eθT2 xi

eθT

k xi

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(4)


kj1 eθ




J(θ) minus1m

1113944

m

i11113944

k

j11 yi j1113864 1113865y

(i)logeθT

j xi

1113936kj1 e

θTj xi

⎡⎢⎢⎢⎢⎢⎣⎤⎥⎥⎥⎥⎥⎦ (5)


J(θ) minus1m

1113944

m

i1y

(i)log hθ x(i)

1113872 1113873 + 1 minus y(i)

1113872 1113873log⎡⎣

1 minus hθ x(i)

1113872 11138731113872 11138731113961

minus1m

1113944

m

i11113944

k

j11 yi j1113864 1113865log p y

(i) j | x

(i) θ1113872 1113873⎡⎢⎢⎣ ⎤⎥⎥⎦

p y(i)

1113872 j|x(i)

θ1113873 eθT

l xi

1113936kl1 e

θTl xi

(6)





nablaθjJ(θ) minus

1m

1113944

m


1113868111386811138681113868 xi θ1113872 11138731113961 (7)


J(W b x y) 12

1113944

c


212t minus y

2 (8)




δ(l) W

(l)1113872 1113873

Tδ(l+1)


z(l)

1113872 1113873 (9)



(l)1113872 1113873

T


(10)


δ(l)k upsample W

(l)k1113872 1113873

Tδ(l)


z(l)k1113872 1113873 (11)




nablaW

(l)

k

J(W b x y) 1113944m

i1a

(l)i1113872 1113873lowast rot90 δ(l+1)

k 21113872 1113873

nablab

(l)

k

J(W b x y) 1113944ab

δ(l+1)k1113872 1113873

ab

(12)


(l)i lowast δ









vec












Economic factor



Welfare system 9


Social factors




Mental powerfactor











Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Unimportant

Influence intensity



0

5

10

15

20

25

30

35

40

45

50

Freq

uenc

y


Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y













Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y










Influence intensity



0

10

20

30

40

50

60

Freq

uenx

y




Influence intensity

05

101520253035404550

Freq

uenx

y









2

2

26

24

14

28

22 16

26

26 22

24


Val

ue

Type









5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors























nablaθjJ(θ) minus

1m

1113944

m


1113868111386811138681113868 xi θ1113872 11138731113961 (7)


J(W b x y) 12

1113944

c


212t minus y

2 (8)




δ(l) W

(l)1113872 1113873

Tδ(l+1)


z(l)

1113872 1113873 (9)



(l)1113872 1113873

T


(10)


δ(l)k upsample W

(l)k1113872 1113873

Tδ(l)


z(l)k1113872 1113873 (11)




nablaW

(l)

k

J(W b x y) 1113944m

i1a

(l)i1113872 1113873lowast rot90 δ(l+1)

k 21113872 1113873

nablab

(l)

k

J(W b x y) 1113944ab

δ(l+1)k1113872 1113873

ab

(12)


(l)i lowast δ









vec












Economic factor



Welfare system 9


Social factors




Mental powerfactor











Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Unimportant

Influence intensity



0

5

10

15

20

25

30

35

40

45

50

Freq

uenc

y


Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y













Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y










Influence intensity



0

10

20

30

40

50

60

Freq

uenx

y




Influence intensity

05

101520253035404550

Freq

uenx

y









2

2

26

24

14

28

22 16

26

26 22

24


Val

ue

Type









5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors






























Economic factor



Welfare system 9


Social factors




Mental powerfactor











Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Unimportant

Influence intensity



0

5

10

15

20

25

30

35

40

45

50

Freq

uenc

y


Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y













Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y










Influence intensity



0

10

20

30

40

50

60

Freq

uenx

y




Influence intensity

05

101520253035404550

Freq

uenx

y









2

2

26

24

14

28

22 16

26

26 22

24


Val

ue

Type









5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors




























Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Unimportant

Influence intensity



0

5

10

15

20

25

30

35

40

45

50

Freq

uenc

y


Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y













Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y










Influence intensity



0

10

20

30

40

50

60

Freq

uenx

y




Influence intensity

05

101520253035404550

Freq

uenx

y









2

2

26

24

14

28

22 16

26

26 22

24


Val

ue

Type









5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors
































Veryimportant

Moreimportant

Generallyimportant

Lessimportant

Influence intensity


0

10

20

30

40

50

60

Freq

uenx

y










Influence intensity



0

10

20

30

40

50

60

Freq

uenx

y




Influence intensity

05

101520253035404550

Freq

uenx

y









2

2

26

24

14

28

22 16

26

26 22

24


Val

ue

Type









5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors





























Influence intensity



0

10

20

30

40

50

60

Freq

uenx

y




Influence intensity

05

101520253035404550

Freq

uenx

y









2

2

26

24

14

28

22 16

26

26 22

24


Val

ue

Type









5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors


























2

2

26

24

14

28

22 16

26

26 22

24


Val

ue

Type









5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors























5 Conclusions



Data Availability




Acknowledgments


References













Economicfactors

Socialfactors

Spiritualfactors






















influencing factors and strategies of the flow of academic

Documents