
The development of China's manufacturing industry is still facing the challenge of regional imbalance. To solve the problem of development imbalance, it is necessary to realize regional development. First, we must analyze the development characteristics of different regions. To this end, we consider the requirements of the new development era and design an evaluation index system for the high-quality development level of the manufacturing industry from the dimensions of innovation, green, and efficiency. Then construct a novel hybrid model which combines the grey incidence clustering model and AP algorithm for panel data in this paper. According to the statistical data from 2014 to 2018, we find out the high-quality development of China's manufacturing industry is characterized by obvious regional differences, different development stages and different constraints.
Citation: Zhi-ying Han, Yong Liu, Xue-ge Guo, Jun-qian Xu. Regional differences of high-quality development level for manufacturing industry in China[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 4368-4395. doi: 10.3934/mbe.2022202
[1] | Mahtab Mehrabbeik, Fatemeh Parastesh, Janarthanan Ramadoss, Karthikeyan Rajagopal, Hamidreza Namazi, Sajad Jafari . Synchronization and chimera states in the network of electrochemically coupled memristive Rulkov neuron maps. Mathematical Biosciences and Engineering, 2021, 18(6): 9394-9409. doi: 10.3934/mbe.2021462 |
[2] | Stefano Cosenza, Paolo Crucitti, Luigi Fortuna, Mattia Frasca, Manuela La Rosa, Cecilia Stagni, Lisa Usai . From Net Topology to Synchronization in HR Neuron Grids. Mathematical Biosciences and Engineering, 2005, 2(1): 53-77. doi: 10.3934/mbe.2005.2.53 |
[3] | Massimo Fioranelli, O. Eze Aru, Maria Grazia Roccia, Aroonkumar Beesham, Dana Flavin . A model for analyzing evolutions of neurons by using EEG waves. Mathematical Biosciences and Engineering, 2022, 19(12): 12936-12949. doi: 10.3934/mbe.2022604 |
[4] | Karim El Laithy, Martin Bogdan . Synaptic energy drives the information processing mechanisms in spiking neural networks. Mathematical Biosciences and Engineering, 2014, 11(2): 233-256. doi: 10.3934/mbe.2014.11.233 |
[5] | Manuela Aguiar, Ana Dias, Miriam Manoel . Gradient and Hamiltonian coupled systems on undirected networks. Mathematical Biosciences and Engineering, 2019, 16(5): 4622-4644. doi: 10.3934/mbe.2019232 |
[6] | Anna Cattani . FitzHugh-Nagumo equations with generalized diffusive coupling. Mathematical Biosciences and Engineering, 2014, 11(2): 203-215. doi: 10.3934/mbe.2014.11.203 |
[7] | Guowei Wang, Yan Fu . Spatiotemporal patterns and collective dynamics of bi-layer coupled Izhikevich neural networks with multi-area channels. Mathematical Biosciences and Engineering, 2023, 20(2): 3944-3969. doi: 10.3934/mbe.2023184 |
[8] | Prasina Alexander, Fatemeh Parastesh, Ibrahim Ismael Hamarash, Anitha Karthikeyan, Sajad Jafari, Shaobo He . Effect of the electromagnetic induction on a modified memristive neural map model. Mathematical Biosciences and Engineering, 2023, 20(10): 17849-17865. doi: 10.3934/mbe.2023793 |
[9] | Stefano Fasani, Sergio Rinaldi . Local stabilization and network synchronization: The case of stationary regimes. Mathematical Biosciences and Engineering, 2010, 7(3): 623-639. doi: 10.3934/mbe.2010.7.623 |
[10] | Sridevi Sriram, Hayder Natiq, Karthikeyan Rajagopal, Ondrej Krejcar, Hamidreza Namazi . Dynamics of a two-layer neuronal network with asymmetry in coupling. Mathematical Biosciences and Engineering, 2023, 20(2): 2908-2919. doi: 10.3934/mbe.2023137 |
The development of China's manufacturing industry is still facing the challenge of regional imbalance. To solve the problem of development imbalance, it is necessary to realize regional development. First, we must analyze the development characteristics of different regions. To this end, we consider the requirements of the new development era and design an evaluation index system for the high-quality development level of the manufacturing industry from the dimensions of innovation, green, and efficiency. Then construct a novel hybrid model which combines the grey incidence clustering model and AP algorithm for panel data in this paper. According to the statistical data from 2014 to 2018, we find out the high-quality development of China's manufacturing industry is characterized by obvious regional differences, different development stages and different constraints.
The synchronization of complex networks has been one of the most critical issues in recent years [1]. Complex networks, composed of interacting dynamical systems, can be used to represent and analyze real networks [2,3]. The synchronization phenomenon is termed when two or more dynamical systems evolve in common behavior [4]. Numerous natural events can be related to synchronization [1]. Therefore, many studies have focused on the synchronization of coupled oscillators in different fields [5,6]. The synchronizability of a network is strongly associated with the network topology and the coupling between the nodes [4,7]. There are some studies that have considered the synchronization in time-varying networks [8,9]. Belykh et al. [10] showed that adding a few random connections to a network with blinking links enhances synchrony by reducing the required couplings strength for synchronization. Frasca et al. [11] investigated the synchronization of Rossler systems with switching coupling and found that switching between two coupling strengths that cannot separately synchronize the system can lead to synchronization.
In the nervous system, the communications and interactions among neurons create complex networks [12,13]. Therefore, many of their activities lead to the formation and representation of collective behaviors such as synchronization [14]. Previous studies have revealed the significance of the synchronous behavior of neurons in information processing and cognitive tasks [15]. Subsequently, the study of synchronization in neuronal networks has attracted the attention of many scientists [16,17,18]. Belykh et al. [19] investigated the bursting neurons coupled nonlinearly through chemical synapses and found that synchronization depends on the receiving signals by each neuron. In contrast, synchronization of the electrically coupled neurons relies on the network's topology [20]. Previous studies have considered different network topologies such as small-world, scale-free, modular, hierarchical, etc. [21,22,23]. Furthermore, with the development of the multilayer networks that provide multiple interactions, the synchronization of the neurons was also investigated in this type of topology [8,9]. Some of the studies have considered the factors that can enhance the synchronization in neuronal networks. It has been proved that the time delay [24], the activity-dependent coupling [25], considering the neuron–astrocyte interaction [26], proper adjustment of autapse [27], etc., can help enhance the synchronous behavior among neurons.
The neuronal functions rely on the communications between them [28,29]. These communications which occur in the cellular regions are called synapses. It is known that the transmissions among neurons can be through electrical or chemical synapses. The electrical synapses occur through channels (gap junctions) when the cytoplasms of two neurons are connected [30]. While in the chemical synapses, the neurotransmitters are released from one cell and received by another [31]. These two types of transmissions exist in different brain regions and are essential for their normal function. Furthermore, Pereda [28] highlighted that the interaction between these two synapses in the healthy brain is also crucial. There is evidence that the electrical synapse transits to chemical synapses in particular brain regions [32,33,34]. In other words, the formation and elimination of electrical and chemical synapses are sequential [35,36]. Szabo et al. [37] represented this serial synaptic evolution in motoneurons from the snail Helisoma. Furthermore, there is a hypothesis that the former electrical synapses are necessary for developing the chemical synapses. Todd et al. [38] represented this fact in the leech neurons.
Motivated by the above description about the transition of synapses, this paper studies the network of coupled Hindmarsh-Rose neurons coupled with transient synapses. It is assumed that the electrical synapses are initially present, and they are replaced with chemical synapses over time. This sequence of transformations happens with a specific period. The synchronization of the neurons is studied under different conditions by varying the coupling strengths, the period of transitions, and the duration time of the presence of the synapses. The time series of the neurons is also under consideration. Therefore, in the next section, the model, the network, and the coupling are described. The results are presented in the third section, and the conclusions are given in the last section.
A network of Hindmarsh-Rose neurons with electrical and chemical synapses in a small-world structure is constructed as follows:
˙xi=yi+3x2i−x3i−zi+Iext+N∑j=1gij(ge(xj−xi)+gc(vs−xi)1+expexp(−λ(xj−ϵs))),˙yi=1−5x2i−yi,˙zi=r(s(xi+1.6)−zi),i=1,…,N | (1) |
where x,y,z represent the action potential, the fast and slow recovery variables of the neuron. The parameters of the chemical coupling are vs=2, ϵs=−0.25,λ=10. gij refers to the network structure such that gij=1 shows the presence of a link between nodes i and j. The coupling strength of the electrical and chemical synapses are ge and gc, respectively. N=100 nodes are connected in a small-world configuration with 20 nearest-neighbor couplings and the probability of 0.1. The parameters of the Hindmarsh-Rose model are set at r=0.006, s=4, Iext=2.8, such that the single neuron exhibits periodic bursting. The coupling between neutrons is through periodic time-varying coupling with electrical and chemical synapses. As discussed before, the synapses are transient and change in the time interval τ. In a part of this time interval (0<t<θτ), the electrical synapses are on, and in the next part (θτ<t<τ), the electrical synapses disappear, and the chemical synapses appear. Therefore, we have:
0<t<θτ:gc=0,ge≠0θτ<t<τ:gc≠0,ge=0 | (2) |
The equations of the network are solved numerically by using the fourth-order Runge-Kutta method with a total run time 3000 and time step 0.01. The network is investigated by considering different periods (τ) for the coupling function. The time of presence of the electrical synapses to chemical ones (θ) is also varied. To quantify the synchronization of the neurons, the average synchronization error is computed as follow:
E=⟨1N−1∑Nj=2√(x1−xj)2+(y1−yj)2+(z1−zj)2⟩t, | (3) |
At first, we consider that both synapses exist among neurons constantly. The electrical coupling strength is varied, and the synchronization error is computed. Three cases are assumed for the chemical coupling strength as: 1) gc=0.5ge, 2) gc=ge, 3) gc=2ge. The synchronization errors for three cases are illustrated in Figure 1. When the chemical coupling is smaller than the electrical, gc=0.5ge, the neurons become completely synchronous in the region 0.054<ge<0.072. The time series of the synchronous neurons is shown in Figure 2a. As the electrical coupling grows from 0.072, the complete synchronization disappears, and the neurons become burst synchronous. The time series of neurons, in this case, are shown in Figure 2b and represent that although the neurons burst synchronously, there is a small error due to the difference between neurons when they are repolarized. By strengthening the chemical coupling, the synchronization error decays to zero for smaller electrical coupling strength. But the neurons do not oscillate synchronously and reach the resting state. The time series of the neurons in ge=0.2, and for gc=ge, gc=2ge are illustrated in Figure2c and 2d, respectively.
When the neurons are completely synchronous, all of the variables of the neurons are the same, i.e., x1=x2=⋯=xN, y1=y2=⋯=yN, z1=z2=⋯=zN Therefore, the equation of the synchronous manifold can be derived as
˙xi=yi+3x2i−x3i−zi+Iext+gc(vs−xi)1+expexp(−λ(xi−ϵs))N∑j=1gij,˙yi=1−5x2i−yi,˙zi=r(s(xi+1.6)−zi). | (4) |
When the chemical coupling strength increases, the coupling term gc(vs−xi)1+expexp(−λ(xi−ϵs))∑Nj=1gij intensifies and overcomes the dynamics of the neurons and leads the neurons to be quiescent.
Next, we investigate the case of transient synapses. A specific period (τ) is considered, and in a part of the period, the electrical synapses are on (gc=0), and in the next part, the synapses are replaced with the chemical ones (ge=0). Firstly, it is assumed that the duration of both synapses is the same, i.e., θ=0.5, and the effect of the transition period is studied. As mentioned in the previous section, with the parameters selected for the Hindmarsh-Rose model, the single neuron bursts periodically, and the period of bursts is T=132. Thus, the synaptic transition period is considered to be the coefficients of the bursts periods as τ=33,66,132,264,396, and 528. The synchronization errors for different periods are presented in Figure 3. Similar to the previous case, as the chemical coupling strength increases, the error reaches its minimum in more miniature electrical couplings. This can be inferred from comparing the error curves in parts a to c. Furthermore, in all cases, the errors do not reach zero, and there is a minimum error which shows that the neurons are burst synchronized. This minimum error becomes larger by increasing the period of synaptic transitions. Moreover, it leads to a change in the waveform of the oscillations and the frequency of the bursts.
The time series of all neurons for different periods are shown in Figure 4. Although the parameters of the neurons were set in such a way to show square-wave bursting, adding the time-varying coupling term alters the neurons' firing pattern. It can be observed that for lower period values, i.e., for τ=33 (Figure 4a) and τ=66 (Figure 4b), the pattern consists of a single spike followed by a relaxation oscillation with short duration, which can be called pseudo-plateau burst. By increasing the period, the pattern changes to relaxation oscillation (for τ=132,264 in Figure 4c, 4d). Raising the period to τ=396 leads to the pattern to be constructed by multiple rapid spikes and a depolarized silence that is known as circle/fold cycle bursters or depolarization block bursting (Figure 4e) [39]. For τ=528, it seems that each burst is composed of both plateau burst type and pseudo-plateau burst type (Figure4f). As mentioned, the neurons are not fully synchronized, and there is a small error that refers to the depolarized silence duration. Figure 4g shows the enlargement of one burst of neurons relating to part e. The difference between neurons in the depolarized silence duration is obvious in this figure.
With the increase in the period of synaptic transitions, the bursts become longer, and the frequency of the bursts decreases. Comparing the period of bursts with the transition period shows that for longer periods, i.e., τ=264,396,528, the period of the bursts is equal to the transition period (τ). For shorter periods as τ=66,132, the period of the bursts is twice the transition period (2τ). For shorter periods, there is no relation between these periods. Furthermore, the spikes refer to the presence of the electrical synapses, while the silence mode refers to the time of the chemical synapses. Figure 5 represents this issue for τ=66,396. In these figures, the parts of the time series related to the presence of the electrical and chemical synapses are shown by red and blue colors, respectively.
Finally, the influence of the time of existence of the electrical and chemical synapses is studied. To this aim, the parameter θ is varied. For example, considering θ=0.1 means that in 0.1τ, the electrical synapses are on, and in the rest of 0.9τ, the chemical synapses are present. Figure 6 represents the synchronization error for τ=132 and θ=0.1,0.3,0.5,0.7, and 0.9 for three cases of chemical coupling strength. It can be observed that similar to the previous cases, the increase of the chemical coupling leads to decay of error in lower electrical strengths. However, the critical electrical coupling for synchronization doesn't depend on the value of chemical strength for θ=0.9, in which the chemical synapses exist in a short interval (0.1τ). Furthermore, as the time of electrical synapses to chemical synapses increases, the final synchronization error decreases. For θ<0.9, by increasing ge, there is a minimum error, and the neurons become burst synchronous. While for θ=0.9, the zero error is obtained, and complete synchronization can be attained. The time series of the neurons for different θ are shown in Figure 7. It can be observed that the time of the presence of synapses also impacts the waveform of the action potential.
Figure 8 presents a complete view of the synchrony error of neurons in the parameter planes by varying the transition period and the timing of synapses for gc=ge. The effects of changing the transitions period (τ) are demonstrated in part (a) for θ=0.5. It is observed that increasing τ leads to burst synchronization occurring in a slightly lower coupling strength ge. However, for higher τ, the final synchronization error by increasing ge is more significant. Part (b) of this figure illustrates the impact of varying the presence time of electrical and chemical synapses (θ) for a fixed transition period τ=132. It is attained that in very low values of θ (θ<0.1), i.e. when the present time of electrical synapses is short, the synchrony error does not decay to zero by increasing ge. For 0.1<θ<0.5, the error becomes zero for intermediate values of coupling strength, and there exists a small error when the coupling becomes stronger. As the present time of electrical synapses increases (θ>0.5), this small error does not appear for strong couplings.
This paper investigated the effects of the transient electrical synapses on the synchronization of the coupled neurons. Previous studies on the brain have revealed that the electrical synapses transit to the chemical synapses in special brain regions. Motivated by this, it was assumed that the electrical synapses exist among neurons for a defined time interval and then change to chemical synapses in the next time interval, and this sequence repeats periodically. The synchronization error of the neurons was computed by considering different periods for synaptic transitions and different time durations for synapses.
At first, the network with constant electrical and chemical synapses was investigated. It was observed that when the chemical coupling was weak, the neurons were completely synchronous in a specific range of electrical coupling strength, further which the burst synchronization emerged. As the chemical coupling became more robust, the oscillations were suppressed, and the neurons stayed in a resting state. When the synapses were transient, the neurons were oscillating in all cases, and the steady resting state was not observed. The results showed that complete synchronization was only obtained when the time duration of electrical synapses was high. In other cases, the burst synchronization was observed. Increasing the period of the synaptic transitions led to the increase of the absolute synchronization error. In all cases, increasing the chemical coupling caused the decay of the synchronization error in lower electrical strengths. Moreover, both the period and the time duration of the electrical and chemical synapses influenced the shape of the time series of the action potentials.
This work is supported by the Natural Science Basic Research Program of Shaanxi (2021JM-533, 2021JQ-880, 2020JM-646), the Innovation Capability Support Program of Shaanxi (2018GHJD-21), the Science and Technology Program of Xi'an (2019218414GXRC020CG021-GXYD20.3), the Support Plan for Sanqin Scholars Innovation Team in Shaanxi Province of China, the Scientific Research Program Funded by Shaanxi Provincial Education Department (21JK0960), the Youth Innovation Team of Shaanxi Universities, the Scientific Research Foundation of Xijing University (XJ21B01), and the Scientific Research Foundation of Xijing University (XJ200202). This work is partially funded by Centre for Nonlinear Systems, Chennai Institute of Technology, India vide funding number CIT/CNS/2021/RD/064.
The authors declare that they have no conflict of interest.
[1] |
F. Yang, Y. Sun, Y. Zhang, T. Wang, Factors affecting the manufacturing industry transformation and upgrading: a case study of Guangdong-Hong Kong-Macao greater bay area, Int. J. Environ. Res. Public Health, 18 (2021), 7157. https://doi.org/10.3390/ijerph18137157 doi: 10.3390/ijerph18137157
![]() |
[2] |
Y. Fu, A. Supriyadi, T. Wang, L. Wang, G. T. Cirella, Effects of regional innovation capability on the green technology efficiency of China's manufacturing industry: evidence from listed companies, Energies, 13 (2020), 5467. https://doi.org/10.3390/en13205467 doi: 10.3390/en13205467
![]() |
[3] |
T. Li, L. Liang, D. Han, Research on the efficiency of green technology innovation in China's provincial high-end manufacturing industry based on the RAGA-PP-SFA model, Math. Probl. Eng., 2018 (2018). https://doi.org/10.1155/2018/9463707 doi: 10.1155/2018/9463707
![]() |
[4] | Y. Guo, L. G. Zhang, An empirical study on the impact of environmental regulation on R&D input of industrial enterprises, China Popul. Resour. Environ., S3 (2014), 104–107. |
[5] |
M. Perkmann, A. Neely, K. Walsh, How should firm evaluate success in university-industry alliances? A performance measurement system, R&D Manage., 41 (2011), 202–216. https://doi.org/10.1111/j.1467-9310.2011.00637.x doi: 10.1111/j.1467-9310.2011.00637.x
![]() |
[6] |
K. Frenken, G. J. Heimeriks, J. Hoekman, What drives university research performance? An analysis using the CWTS Leiden ranking data, J. Informetrics, 11 (2017), 859–872. https://doi.org/10.1016/j.joi.2017.06.006 doi: 10.1016/j.joi.2017.06.006
![]() |
[7] |
B. V. Looy, K. Debackere, P. Andries, Policies to stimulate regional innovation capabilities via university-industry collaboration analysis and an assessment, R&D Manage., 33 (2003), 209–229. https://doi.org/10.1111/1467-9310.00293 doi: 10.1111/1467-9310.00293
![]() |
[8] |
V. Dewangan, M. Godse, Towards a holistic enterprise innovation performance measurement system, Technovation, 34 (2014), 536–545. https://doi.org/10.1016/j.technovation.2014.04.002 doi: 10.1016/j.technovation.2014.04.002
![]() |
[9] |
J. Youtie, P. Shapira, Building an innovation hub: a case study of the transformation of university roles in regional technological and economic development, Res. Policy, 37 (2008), 1188–1204. https://doi.org/10.1016/j.respol.2008.04.012 doi: 10.1016/j.respol.2008.04.012
![]() |
[10] | H. N. Tian, Q. Q. Sun, Research on the evaluation of green technology innovation capability of automobile manufacturing enterprises based on cloud model, Manage. Rev., 32 (2020), 102–114. |
[11] |
Y. Ma, G. Hou, Q. Yin, B. Xin, Y. Pan, The sources of green management innovation: Does internal efficiency demand pull or external knowledge supply push?, J. Cleaner Prod., 202 (2018), 582–590. https://doi.org/10.1016/j.jclepro.2018.08.173 doi: 10.1016/j.jclepro.2018.08.173
![]() |
[12] |
P. Li, Y. Ouyang, Technical change and green productivity, Environ. Resour. Econ., 76 (2020), 271–298. https://doi.org/10.1007/s10640-020-00424-1 doi: 10.1007/s10640-020-00424-1
![]() |
[13] | J. X. Li, Research on the evaluation of the degree of intelligentization of China's manufacturing industry and its influencing factors, China Soft Sci., 1 (2020), 154–163. |
[14] |
Y. Deng, D. You, J. Wang, Optimal strategy for enterprises' green technology innovation from the perspective of political competition, J. Cleaner Prod., 235 (2019), 930–942. https://doi.org/10.1016/j.jclepro.2019.06.248 doi: 10.1016/j.jclepro.2019.06.248
![]() |
[15] |
B. Lu, Expedited patent examination for green inventions: developing countries' policy choices, Energy Policy, 61 (2013), 1529–1538. https://doi.org/10.1016/j.enpol.2013.06.028 doi: 10.1016/j.enpol.2013.06.028
![]() |
[16] |
X. Pan, B. Ai, C. Li, X. Pan, Y. Yan, Dynamic relationship among environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China, Technol. Forecast. Soc. Change, 144 (2019), 428–435. https://doi.org/10.1016/j.techfore.2017.12.012 doi: 10.1016/j.techfore.2017.12.012
![]() |
[17] | X. Fan, D. P. Zhao, Y. He, Enterprise innovation efficiencies of university-industry cooperation and their influential factors, Sci. Res. Manage., 2 (2012), 33–39. |
[18] | Y. Y. Ma, X. L. Zhang, Y. T. Sun, Environmental regulations motivate enterprises to make R&D efforts? Empirical evidence from thermal power enterprises, Sci. Res. Manage., 39 (2018), 66–74. |
[19] | J. Y. Liu, J. Xie, Environmental regulation and manufacturing export quality upgrade—based on the perspective of factor input structure heterogeneity, China Popul. Resour. Environ., 28 (2018), 158–167. |
[20] |
S. Roper, P. Vahter, J. H. Love, Externalities of openness in innovation, Res. Policy, 42 (2013), 1544–1554. https://doi.org/10.1016/j.respol.2013.05.006 doi: 10.1016/j.respol.2013.05.006
![]() |
[21] | L. Qian, R. Q. Xiao, Z. W. Chen, Research on green technology innovation efficiency and regional differences of industrial enterprises in my country—based on common frontier theory and DEA model, Econ. Theory Econo. Manage., 1 (2015), 26–43. |
[22] |
D. Li, Y. Zhao, L. Zhang, X. Chen, C. Cao, Impact of quality management on green innovation, J. Cleaner Prod., 170 (2018), 462–470. https://doi.org/10.1016/j.jclepro.2017.09.158 doi: 10.1016/j.jclepro.2017.09.158
![]() |
[23] |
G. F. Zhu, D. Wang, Analysis and prospects of the current development of China's manufacturing industry: based on the evaluation index system of manufacturing powers, J. Manage. Eng., 31 (2017), 1–7. https://doi.org/10.13587/j.cnki.jieem.2017.04.001 doi: 10.13587/j.cnki.jieem.2017.04.001
![]() |
[24] | S. R. Liang, L. W. Luo, The dynamic effect of technology spillovers of international R&D capital on the efficiency of green technology innovation, Sci. Res. Manage., 40 (2019), 21–29. |
[25] | Y. M. Wu, A spatial panel econometrics analysis on industrial R&D, university-industry cooperation and innovation performance, Sci. Res. Manage., 36 (2015), 118–127. |
[26] |
J. Zhang, Y. Chang, L. Zhang, D. Li, Do technological innovations promote urban green development?—A spatial econometric analysis of 105 cities in China, J. Cleaner Prod., 182 (2018), 395–403. https://doi.org/10.1016/j.jclepro.2018.02.067 doi: 10.1016/j.jclepro.2018.02.067
![]() |
[27] |
J. Schleich, R. Walz, M. Ragwitz, Effects of policies on patenting in wind-power technologies, Energy Policy, 108 (2017), 684–695. https://doi.org/10.1016/j.enpol.2017.06.043 doi: 10.1016/j.enpol.2017.06.043
![]() |
[28] | W. H. Li, The spatiotemporal evolution and influencing factors of China's provincial industrial green technological innovation output: an empirical study based on 30 provincial data, Chin. J. Manage. Eng., 31 (2017), 9–19. |
[29] |
R. Leoncini, A. Marzucchi, S. Montresor, F. Rentocchini, U. Rizzo, 'Better late than never': the interplay between green technology and age for firm growth, Small Bus. Econo., 52 (2019), 891–904. https://doi.org/10.1007/s11187-017-9939-6 doi: 10.1007/s11187-017-9939-6
![]() |
[30] |
S. Lin, J. Sun, D. Marinova, D. Zhao, Evaluation of the green technology innovation efficiency of China's manufacturing industries: DEA window analysis with ideal window width, Technol. Anal. Strategic Manage., 30 (2018), 1166–1181. https://doi.org/10.1080/09537325.2018.1457784 doi: 10.1080/09537325.2018.1457784
![]() |
[31] | H. X. Tang, X. Z. Zhang, Y. Wu, Z. C. He, Research on the development quality of China's manufacturing industry and the improvement of international competitiveness, China Soft Sci., 2 (2019), 128–142. |
[32] |
H. Z. Zheng, Research on enterprise green technology innovation and diffusion dynamics under environmental regulation, Sci. Manage. Res., 34 (2016), 77–88. https://doi.org/10.19445/j.cnki.15-1103/g3.2016.05.020 doi: 10.19445/j.cnki.15-1103/g3.2016.05.020
![]() |
[33] |
J. Chen, J. Cheng, S. Dai, Regional eco-innovation in China: an analysis of eco-innovation levels and influencing factors, J. Cleaner Prod., 153 (2017), 1–14. https://doi.org/10.1016/j.jclepro.2017.03.141 doi: 10.1016/j.jclepro.2017.03.141
![]() |
[34] | M. Wei, S. H. Li, Research on the measurement of China's economic high-quality development level in the new era, J. Quant. Econo. Tech. Econo., 35 (2018), 3–20. |
[35] | L. Sun, C. Miao, L. Yang, Ecological-economic efficiency evaluation of green technology innovation in strategic emerging industries based on entropy weighted TOPSIS method, Ecol. Indic., 73 (2017), 554–558. |
[36] |
Q. Luo, C. Miao, L. Sun, X. Meng, M. Duan, Efficiency evaluation of green technology innovation of China's strategic emerging industries: an empirical analysis based on Malmquist-data envelopment analysis index, J. Cleaner Prod., 238 (2019), 117782. https://doi.org/10.1016/j.jclepro.2019.117782 doi: 10.1016/j.jclepro.2019.117782
![]() |
[37] |
Y. Yu, G. Xin, Y. Chen, Deconstruction of the "black box" in industry-university-research institute's technology transfer and evaluation of its efficiency, Sci. Res. Manage., 4 (2017), 28–37. https://doi.org/10.19571/j.cnki.1000-2995.2017.04.004 doi: 10.19571/j.cnki.1000-2995.2017.04.004
![]() |
[38] |
S. Jain, K. P. Triantis, S. Liu, Manufacturing performance measurement and target setting: A data envelopment analysis approach, Eur. J. Oper. Res., 214 (2011), 616–626. https://doi.org/10.1016/j.ejor.2011.05.028 doi: 10.1016/j.ejor.2011.05.028
![]() |
[39] | Y. L. Zhao, J. J. Gu, Measurement and comparative research on the development quality of manufacturing industries in China and America, Quant. Econo. Tech. Econo. Res., 35 (2018), 116–133. |
[40] | W. Zt, Research on the cultivation of practical innovation ability of local college students based on CDIO concept, Res. Explor. Lab., 36 (2017), 220–224. |
[41] | L. X. Wang, X. G. Chen, X. L. Yao, X. Y. Li, C. T. Zhang, Research on the responsiveness of Chinese industrial enterprises to environmental regulation policies, China Soft Sci., 10 (2017), 143–152. |
[42] | J. L. Liu, M. S. Ran, Research on the impact of environmental regulations on industrial production technology progress, Sci. Res. Manage., 36 (2015), 107–114. |
[43] |
M. Buesa, J. Heijs, M. M. Pellitero, T. Baumert, Regional systems of innovation and the knowledge production function: the spanish case, Technovation, 26 (2006), 463–472. https://doi.org/10.1016/j.technovation.2004.11.007 doi: 10.1016/j.technovation.2004.11.007
![]() |
[44] |
L. Luo, S. Liang, Study on the efficiency and regional disparity of green technology innovation in China's industrial companies, Chin. J. Popul. Resour. Environ., 14 (2016), 262–270. https://doi.org/10.1080/10042857.2016.1258799 doi: 10.1080/10042857.2016.1258799
![]() |
[45] |
J. Quan, B. Zeng, D. Liu, Green supplier selection for process industries using weighted grey incidence decision model, Complexity, 2018 (2018). https://doi.org/10.1155/2018/4631670 doi: 10.1155/2018/4631670
![]() |
[46] | X. Tang, N. Xie, Research on the evaluation of tourism development potential of tea intangible cultural heritage based on grey clustering, Grey Syst. Theory Appl., 9 (2019), 295–304. |
[47] |
B. Zeng, X. Ma, M. Zhou, A new-structure grey Verhulst model for China's tight gas production forecasting, Appl. Soft Comput., 96 (2020), 106600. https://doi.org/10.1016/j.asoc.2020.106600 doi: 10.1016/j.asoc.2020.106600
![]() |
[48] |
B. Zeng, H. Li, Prediction of coalbed methane production in China based on an optimized grey system model, Energy Fuels, 35 (2021), 4333-4344. https://doi.org/10.1021/acs.energyfuels.0c04195 doi: 10.1021/acs.energyfuels.0c04195
![]() |
[49] | X. F. Pan, Y. Yang, Evaluation of industrial enterprise's innovation efficiency in China excluding the influence of environment variables, Oper. Res. Manage. Sci., 23 (2014), 244-251. |
[50] |
Y. Deng, Y. Wu, H. Xu, Political connections and firm pollution behaviour: an empirical study, Environ. Resour. Econ., 75 (2020), 867-898. https://doi.org/10.1007/s10640-020-00410-7 doi: 10.1007/s10640-020-00410-7
![]() |
[51] |
W. Gu, T. L. Saaty, L. Wei, Evaluating and optimizing technological innovation efficiency of industrial enterprises based on both data and judgments, Int. J. Inf. Technol. Decis. Making, 17 (2018), 9-43. https://doi.org/10.1142/S0219622017500390 doi: 10.1142/S0219622017500390
![]() |
[52] |
L. Fraccascia, V. Albino, C. A. Garavelli, Technical efficiency measures of industrial symbiosis networks using enterprise input-output analysis, Int. J. Prod. Econ., 183 (2017), 273-286. https://doi.org/10.1016/j.ijpe.2016.11.003 doi: 10.1016/j.ijpe.2016.11.003
![]() |
[53] | L. W. Luo, S. R. Liang, Green technology innovation efficiency and factor decomposition of China's industrial enterprises, China Popul. Resour. Environ., 26 (2016), 149-157. |
[54] |
C. Rusinko, Green manufacturing: An evaluation of environmentally sustainable manufacturing practices and their impact on competitive outcomes, IEEE Trans. Eng. Manage., 54 (2007), 445-454. https://doi.org/10.1109/TEM.2007.900806 doi: 10.1109/TEM.2007.900806
![]() |
[55] |
H. Forsman, Innovation capacity and innovation development in small enterprises. A comparison between the manufacturing and service sectors, Res. Policy, 40 (2011), 739-750. https://doi.org/10.1016/j.respol.2011.02.003 doi: 10.1016/j.respol.2011.02.003
![]() |
[56] | Y. H. Zhou, X. D. He, Y. Shen, An evaluation of the efficiency of Chinese industry enterprises' innovation performance, Econ. Res. J., 5 (2012), 107–119. |
[57] | Z. J. Feng, W. Chen, Research on the efficiency of R&D and innovation in China's high-tech industry: a new perspective based on the resource-constrained two-stage DEA model, Syst. Eng. Theory Prac., 34 (2014), 1202–1212. |
[58] | C. Wu, S. W. Yang, P. C. Tang, T. Wu, S. K. Fu, Construction of a green innovation efficiency improvement model for China's heavy pollution industries, China Popul. Resour. Environ., 28 (2018), 40–48. |
[59] | C. Zhang, B. N. Guo, T. S. Yu, Pollution heterogeneity, optimal environmental regulation intensity and production technology progress, Sci. Res. Manage., 36 (2015), 138–144. |
[60] |
N. Yalcin, A. Bayrakdaroglu, C. Kahraman, Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries, Expert Syst. Appl., 39 (2011), 350–364. https://doi.org/10.1016/j.eswa.2011.07.024 doi: 10.1016/j.eswa.2011.07.024
![]() |
[61] | Z. Liu, Y. G. Dang, W. Y. Qian, W. J. Zhou, Grey grid correlation model based on panel data, Syst. Eng. Theory Prac., 34 (2014), 991–996. |
[62] |
B. J. Frey, D. Dueck, Clustering by passing messages between data points, Science, 315 (2007), 972–976. https://doi.org/10.1126/science.1136800 doi: 10.1126/science.1136800
![]() |
1. | Hongliang Miao, Amit Gupta, Coordinated Development of China’s Regional Economy and Ethnic Diversity under the Background of Big Data and the Internet of Things, 2022, 2022, 1875-905X, 1, 10.1155/2022/6424505 | |
2. | Yule Wang, Arwa Abdulkreem AL‐Huqail, Shadi Salimimoghadam, Khidhair Jasim Mohammed, Amin Jan, H. Elhosiny Ali, Mohamed Amine Khadimallah, Hamid Assilzadeh, The metaheuristic optimization of the mechanical properties of sustainable energies using artificial neural networks and genetic algorithm: A case study by eggshell fine waste, 2022, 46, 0363-907X, 21338, 10.1002/er.8255 | |
3. | Khaled Benkouider, Sundarapandian Vaidyanathan, Aceng Sambas, Esteban Tlelo-Cuautle, Ahmed A. Abd El-Latif, Bassem Abd-El-Atty, Ciro Fabian Bermudez-Marquez, Ibrahim Mohammed Sulaiman, Aliyu Muhammed Awwal, Poom Kumam, A New 5-D Multistable Hyperchaotic System With Three Positive Lyapunov Exponents: Bifurcation Analysis, Circuit Design, FPGA Realization and Image Encryption, 2022, 10, 2169-3536, 90111, 10.1109/ACCESS.2022.3197790 | |
4. | Linsen Li, Wen-Tsao Pan, Node Location Method of Ecological Environment Monitoring Network Based on Zigbee, 2022, 2022, 1607-887X, 1, 10.1155/2022/5854569 | |
5. | Hong Huo, Huanning Xu, Arpit Bhardwaj, Construction of Emergency Procurement System and System Improvement Based on Convolutional Neural Network, 2022, 2022, 1687-5273, 1, 10.1155/2022/6139706 | |
6. | Qian Wang, Wen-Tsao Pan, Population Economic Transfer Function Model Based on Genetic Tabu Search Algorithm, 2022, 2022, 1607-887X, 1, 10.1155/2022/1356909 | |
7. | Liping Chen, Amit Gupta, Coordinated Development of Smart City and Regional Industrial Economy under the Background of Internet of Things, 2022, 2022, 1875-905X, 1, 10.1155/2022/6986090 | |
8. | K. Immanuvel Arokia James, R. Prabakaran, A. Karthikeyan, R. R. Prianka, Co-operative beam forming selection with energy balanced operation for wireless sensor network, 2022, 28, 1022-0038, 3653, 10.1007/s11276-022-03067-w | |
9. | Nimet Korkmaz, İbrahim Ethem Saçu, An alternative perspective on determining the optimum fractional orders of the synaptic coupling functions for the simultaneous neural patterns, 2022, 110, 0924-090X, 3791, 10.1007/s11071-022-07782-z | |
10. | Fang Li, Tao Li, Wen-Tsao Pan, Tourism Consumer Demand Forecasting under the Background of Big Data, 2022, 2022, 1563-5147, 1, 10.1155/2022/4335718 | |
11. | Kimia Latifi, Ahoo Ebrahimi, Mehdi Ranjbaran, Arman Mirzaei, Zahra Fakhri, Efficient customer relationship management systems for online retailing: The investigation of the influential factors, 2022, 1833-3672, 1, 10.1017/jmo.2022.65 | |
12. | Tingting Cai, Dongmin Yu, Huanan Liu, Fengkai Gao, Computational Analysis of Variational Inequalities Using Mean Extra-Gradient Approach, 2022, 10, 2227-7390, 2318, 10.3390/math10132318 | |
13. | Lan Zheng, Ning Cao, Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model, 2022, 2022, 1687-5273, 1, 10.1155/2022/1835798 | |
14. | Caixia Wu, Zhenrong Zhao, Yuanyuan Liu, Bayan Omar Mohammed, Quantum-dot cellular automata-based design for three-level nanoscale full-subtractor, 2022, 05779073, 10.1016/j.cjph.2022.10.014 | |
15. | Shanshan Yu, Hao Wang, Yajun Wang, Pradeep Tomar, Optimization Design of Street Public Space Layout on Account of Internet of Things and Deep Learning, 2022, 2022, 1687-5273, 1, 10.1155/2022/7274525 | |
16. | Eman A. Atta, Ahmed F. Ali, Ahmed A. Elshamy, Kathiravan Srinivasan, A modified weighted chimp optimization algorithm for training feed-forward neural network, 2023, 18, 1932-6203, e0282514, 10.1371/journal.pone.0282514 | |
17. | Seyed Sajad Ahmadpour, Nima Jafari Navimipour, Mohammad Mosleh, Senay Yalcin, Nano-design of ultra-efficient reversible block based on quantum-dot cellular automata, 2023, 24, 2095-9184, 447, 10.1631/FITEE.2200095 | |
18. | Prasina Alexander, Fatemeh Parastesh, Ibrahim Ismael Hamarash, Anitha Karthikeyan, Sajad Jafari, Shaobo He, Effect of the electromagnetic induction on a modified memristive neural map model, 2023, 20, 1551-0018, 17849, 10.3934/mbe.2023793 | |
19. | Arash Heidari, Nima Jafari Navimipour, Hasan Dag, Mehmet Unal, Deepfake detection using deep learning methods: A systematic and comprehensive review, 2024, 14, 1942-4787, 10.1002/widm.1520 | |
20. | Gabrielle S. Prince, Molly Reynolds, Verdion Martina, HaoSheng Sun, Gene-environmental regulation of the postnatal post-mitotic neuronal maturation, 2024, 40, 01689525, 480, 10.1016/j.tig.2024.03.006 | |
21. | Xiaodi Li, Ying Xu, Energy level transition and mode transition in a neuron, 2024, 112, 0924-090X, 2253, 10.1007/s11071-023-09147-6 | |
22. | Xinqing Duan, Lei Li, Zehui Peng, Mingqiang Wang, Yanxin Liu, Dar‐Jen Hsieh, Kuan‐Chang Chang, Ultralow Power, Cleft Size‐Adjustable and pH‐Sensitive Hyaluronic Acid (HA) Biodevices for Acid‐Sensing Ion Channels Emulation, 2024, 1613-6810, 10.1002/smll.202405207 | |
23. | Jindong Liu, Zhen Wang, Huaigu Tian, Fei Xie, Zeljko Stevic, Complex Dynamic Analysis, Circuit Design and Simplified Predefined Time Synchronization for a Jerk Absolute Memristor Chaotic System, 2023, 2023, 1099-0526, 1, 10.1155/2023/5912191 | |
24. | Xinlin Song, Feifei Yang, A light-temperature neuron and its adaptive regulation, 2024, 99, 0031-8949, 125247, 10.1088/1402-4896/ad8fe4 | |
25. | Mahtab Mehrabbeik, Fatemeh Parastesh, Karthikeyan Rajagopal, Sajad Jafari, Matjaz Perc, Riccardo Meucci, Minimal universal laser network model: Synchronization, extreme events, and multistability, 2024, 13, 2192-8029, 10.1515/nleng-2024-0044 | |
26. | Zhenhua Yu, Kailong Zhu, Ya Wang, Feifei Yang, Dynamics of a neuron with a hybrid memristive ion channel, 2025, 194, 09600779, 116233, 10.1016/j.chaos.2025.116233 | |
27. | Xiaowei Han, Hagiwara Akifumi, Pin Lv, Jiahuan Liu, Xiaowei Huang, Renyuan Liu, Xiaojing Long, Yang Liu, Jiangong Zhang, Guolin Ma, Bing Zhang, Low‐Intensity Focused Ultrasound Ameliorates Cisplatin‐Induced Cognitive Impairment by Attenuating Hippocampal Neuroinflammation and Enhancing Synaptic Plasticity in Rats, 2025, 2834-2860, 10.1002/ird3.70003 |