Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

Influence of Al_(2)O_(3)/SiO_(2) Ratio on the Structure and Properties of Na^(+)/K^(+)Ion Exchange Na_(2)O-MgO-Al_(2)O_(3)-SiO_(2) Glasses

In this work,the structure,viscosity and ion-exchange process of Na_(2)O-MgO-Al_(2)O_(3)-SiO_(2) glasses with different Al_(2)O_(3)/SiO_(2) molar ratios were investigated.The results showed that,with increasing Al_(2)O_(3)/SiO_(2) ratio,the simple structural units Q_(1) and Q_(2) transformed into highly aggregated structural units Q_(3) and Q_(4),indicating the increase of polymerization degree of glass network.Meanwhile,the coefficient of thermal expansion decreased from 9.23×10^(-6)℃^(-1) to 8.88×10^(-6)℃^(-1).The characteristic temperatures such as melting,forming,softening and glass transition temperatures increased with the increase of Al_(2)O_(3)/SiO_(2) ratio,while the glasses working temperature range became narrow.The increasing Al_(2)O_(3)/SiO_(2) ratio and prolonging ion-exchange time enhanced the surface compressive stress(CS)and depth of stress layer(DOL).However,the increase of ion exchange temperature increased the DOL and decreased the CS affected by stress relaxation.There was a good linear relationship between stress relaxation and surface compressive stress.Chemical strengthening significantly improved the hardness of glasses,which reached the maximum value of(622.1±10)MPa for sample with Al_(2)O_(3)/SiO_(2) ratio of 0.27 after heat treated at 410℃for 2 h.

Effect of Fe_(2)O_(3)on the Structure,Physical Properties and Crystallization of CaO-Al_(2)O_(3)-SiO_(2)Glass

The calcium aluminosilicate-based glasses(CaO-Al_(2)O_(3)-SiO_(2),CAS)with different Fe_(2)O_(3)content(0.10wt%,0.50wt%,0.90wt%,and 1.30wt%)were prepared by traditional melt-quenching method.The glass network structure,thermal and mechanical properties,and crystallization behavior changes were investigated by nuclear magnetic resonance spectrometer,Fourier-transform infrared spectro-photometer,X-ray diffractometer,differential scanning calorimetry and field emission scanning electron microscope measurements.The change of Q^(n)in glass structures reveals the glass network connectivity decreases due to the increasing content of Fe_(2)O_(3)addition,resulting in the increasing of non-bridging number in glass structure.The glass densities slightly rise from 2.644 to 2.681 g/cm^(3),while Vickers’s hardness increases at first,from 6.469 to 6.901 GPa,then slightly drops to 6.745 GPa,with Fe_(2)O_(3)content increase.There is almost no thermal expansion coefficient change from different Fe_(2)O_(3)content.The glass transmittance in visible range gradually decreases with higher Fe_(2)O_(3)content,resulting from the strong absorption of Fe^(2+)and Fe^(3+)ions.The calculated activation energy from thermal analysis results first decreases from 282.70 to 231.18 kJ/mol,and then increases to 244.02 kJ/mol,with the Fe_(2)O_(3)content increasing from 0.10wt%to 1.30wt%.Meanwhile,the maximum Avrami constant of 2.33 means the CAS glasses exhibit two-dimensional crystallization.All of the CAS glass-ceramics samples contain main crystal phase of anorthite,the microstructure appears lamellar and columnar crystals.

Analysis of Urban Agglomeration Network Structure Based on Baidu Migration Data: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Urban Agglomeration

The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure of urban agglomeration in the Greater Bay Area through the use of social network analysis method.This is the inaugural application of big data based on location services in the study of urban agglomeration network structure,which represents a novel research perspective on this topic.The study reveals that the density of network linkages in the Greater Bay Area urban agglomeration has reached 100%,indicating a mature network-like spatial structure.This structure has given rise to three distinct communities:Shenzhen-Dongguan-Huizhou,Guangzhou-Foshan-Zhaoqing,and Zhuhai-Zhongshan-Jiangmen.Additionally,cities within the Greater Bay Area urban agglomeration play different roles,suggesting that varying development strategies may be necessary to achieve staggered development.The study demonstrates that large datasets represented by LBS can offer novel insights and methodologies for the examination of urban agglomeration network structures,contingent on the appropriate mining and processing of the data.

A Tree-Based Data Collecting Network Structure for Wireless Sensor Networks

In a sensor network with a large number of densely populated sensor nodes, a single target of interest may be detected by multiple sensor nodes simultaneously. Data collected from the sensor nodes are usually highly correlated, and hence energy saving using in-network data fusion becomes possible. A traditional data fusion scheme starts with dividing the network into clusters, followed by electing a sensor node as cluster head in each cluster. A cluster head is responsible for collecting data from all its cluster members, performing data fusion on these data and transmitting the fused data to the base station. Assuming that a sensor node is only capable of handling a single node-to-node transmission at a time and each transmission takes T time-slots, a cluster head with n cluster members will take at least nT time-slots to collect data from all its cluster members. In this paper, a tree-based network structure and its formation algorithms are proposed. Simulation results show that the proposed network structure can greatly reduce the delay in data collection.

Effects of ZnO,FeO and Fe_(2)O_(3)on the spinel formation,microstructure and physicochemical properties of augite-based glass ceramics

Augite-based glass ceramics were synthesised using ZnO,FeO,and Fe_(2)O_(3)as additives,and the spinel formation,matrix structure,crystallisation thermodynamics,and physicochemical properties were investigated.The results showed that oxides resulted in numerous preliminary spinels in the glass matrix.FeO,ZnO,and Fe_(2)O_(3)influenced the formation of spinel,while FeO simplified the glass network.FeO and ZnO promoted bulk crystallisation of the parent glass.After adding oxides,the grains of augite phase were refined,and the relative quantities of augite crystal planes were also influenced.All samples displayed good mechanical properties and chemical stability.The 2wt%ZnO-doping sample displayed the maximum flexural strength(170.3 MPa).Chromium leaching amount values of all the samples were less than the national standard(1.5 mg/L),confirming the safety of the materials.In conclusion,an appropriate amount of zinc-containing raw material is beneficial for the preparation of augite-based glass ceramics.

Analysis and Modeling of k-regular and k-connected Protection Structure in Ultra-High Capacity Optical Networks

This paper proposes k-regular and k-connected(k&k) structure against multifaults in ultra-high capacity optical networks.Theoretical results show that pre-configured k&k structure can reach the lower bound on logical redundancy.The switching time of k&k protection structure is as quickly as ringbased protection in SDH network.It is the optimal protection structure in ultra-high capacity optical networks against multi-faults.We develop the linear programming model for k&k structure and propose a construction method for k&k structure design.Simulations are conducted for spare spectrum resources effi ciency of the pre-confi gured k&k structure under multi-faults on representative COST239 and NSFnet topologies.Numerical results show that the spare spectrum resources efficiency of k&k structure can reach the lower bound on logical redundancy in static networks.And it can largely improve spare spectrum resources effi ciency compared with p-cycles based protection structure without reducing protection effi ciency under dynamic traffi cs.

Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.

DNEF:A New Ensemble Framework Based on Deep Network Structure

Deep neural networks have achieved tremendous success in various fields,and the structure of these networks is a key factor in their success.In this paper,we focus on the research of ensemble learning based on deep network structure and propose a new deep network ensemble framework(DNEF).Unlike other ensemble learning models,DNEF is an ensemble learning architecture of network structures,with serial iteration between the hidden layers,while base classifiers are trained in parallel within these hidden layers.Specifically,DNEF uses randomly sampled data as input and implements serial iteration based on the weighting strategy between hidden layers.In the hidden layers,each node represents a base classifier,and multiple nodes generate training data for the next hidden layer according to the transfer strategy.The DNEF operates based on two strategies:(1)The weighting strategy calculates the training instance weights of the nodes according to their weaknesses in the previous layer.(2)The transfer strategy adaptively selects each node’s instances with weights as transfer instances and transfer weights,which are combined with the training data of nodes as input for the next hidden layer.These two strategies improve the accuracy and generalization of DNEF.This research integrates the ensemble of all nodes as the final output of DNEF.The experimental results reveal that the DNEF framework surpasses the traditional ensemble models and functions with high accuracy and innovative deep ensemble methods.

Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

Next-generation networks,including the Internet of Things(IoT),fifth-generation cellular systems(5G),and sixth-generation cellular systems(6G),suf-fer from the dramatic increase of the number of deployed devices.This puts high constraints and challenges on the design of such networks.Structural changing of the network is one of such challenges that affect the network performance,includ-ing the required quality of service(QoS).The fractal dimension(FD)is consid-ered one of the main indicators used to represent the structure of the communication network.To this end,this work analyzes the FD of the network and its use for telecommunication networks investigation and planning.The clus-ter growing method for assessing the FD is introduced and analyzed.The article proposes a novel method for estimating the FD of a communication network,based on assessing the network’s connectivity,by searching for the shortest routes.Unlike the cluster growing method,the proposed method does not require multiple iterations,which reduces the number of calculations,and increases the stability of the results obtained.Thus,the proposed method requires less compu-tational cost than the cluster growing method and achieves higher stability.The method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication networks.The developed method is evaluated for two different network structures and compared with the cluster growing method.Results validate the developed method.

Transition of plasticity and fracture mode of Zr-Al-Ni-Cu bulk metallic glasses with network structures

Effect of network structure on plasticity and fracture mode of Zr？Al？Ni？Cu bulk metallic glasses （BMGs） was investigated. The microstructures of transversal and longitudinal sections were exposed by chemical etching and observed by scanning electron microscopy （SEM）. The mechanical properties were examined by room-temperature uniaxial compression test. The results show that both plasticity and fracture mode are significantly affected by the network structure and the alteration occurs when the size of the network structure reaches up to a critical value. When the cell size （dc） of the network structure is ~3μm, Zr-based BMGs characterize in plasticity that decreases with increasingdc. The fracture mode gradually transforms from single 45° shear fracture to double 45° shear fracture and then cleavage fracture with increasingdc. In addition, the mechanisms of the transition of the plasticity and the fracture mode for these Zr-based BMGs are also discussed.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

The Structure Entropy of Social Networks

Micro triadic structure is an important motif and serves the building block of complex networks.In this paper,the authors define structure entropy for a social network and explain this concept by using the coded triads proposed by Davis and Leinhardt in 1972.The proposed structure entropy serves as a new macro-evolution index to measure the network’s stability at a given timestamp.Empirical analysis of real-world network structure entropy discloses rich information on the mechanism that yields given triadic motifs frequency distribution.This paper illustrates the intrinsic link between the micro dyadic/triadic motifs and network structure entropy.Importantly,the authors find that the high proportion of reciprocity and transitivity results in the emergence of hierarchy,order,and cooperation of online social networks.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Effect of polymer solution structure on displacement efficiency

In this paper, a series of experiments, including atomic force microscope （AFM）, environmental scanning electron microscope （ESEM）, and core displacement tests were conducted to investigate the effect of polymer solution structure on solution properties and oil displacement efficiency. The results show that in the HPAM solution polymer coils were formed and then aggregated into a loose structure, while the HAP2010 solution formed a strong network structure, which would significantly improve the solution viscosity and flow resistance so as to upgrade the capacity of piston-like oil displacement in highly permeable porous media. Meanwhile, the retention of the HAP2010 solution at pore throats were also enhanced, which could reduce water production during subsequent water flooding and enlarge the swept volume during polymer flooding. Therefore, enhancing the interaction among polymer molecules is an effective way to improve the displacement efficiency of polymer solutions in heavy oil reservoirs with high permeability.

Structure of Chinese City Network as Driven by Technological Knowledge Flows

Based on patent cooperation data,this study used a range of city network analysis approaches in order to explore the structure of the Chinese city network which is driven by technological knowledge flows.The results revealed the spatial structure,composition structure,hierarchical structure,group structure,and control structure of Chinese city network,as well as its dynamic factors.The major findings are:1) the spatial pattern presents a diamond structure,in which Wuhan is the central city;2) although the invention patent knowledge network is the main part of the broader inter-city innovative cooperation network,it is weaker than the utility model patent;3) as the senior level cities,Beijing,Shanghai and the cities in the Zhujiang(Pearl) River Delta Region show a strong capability of both spreading and controlling technological knowledge;4) whilst a national technology alliance has preliminarily formed,regional alliances have not been adequately established;5) even though the cooperation level amongst weak connection cities is not high,such cities still play an important role in the network as a result of their location within ′structural holes′ in the network;and 6) the major driving forces facilitating inter-city technological cooperation are geographical proximity,hierarchical proximity and technological proximity.

The structure and ability of the China Seismological Gravity Monitoring System

This paper assesses the structure and ability of Local Seismological Gravity Monitoring Network (LSGMN) in China main tectonic zone and China Seismological Gravity Monitoring System (CSGMS) which formed after the project of 'China Crustal Movement Observation Network (CCMON)' has been performed. The main conclusions drawn are as follows: ①LSGMN has good monitoring and prediction ability for the earthquake of M_s about 5. But it lacks ability to monitor and predict the strong earthquake of M_s>6 because of the little range of the observation network;②CSGMS has good ability to monitor and predict the earthquake of M_s>7, but the resolving power is not enough for the earthquake magnitude from M_s=6 to M_s=7 because the observation stations are too sparse.

Fabrication and abrasive wear properties of metal matrix composites reinforced with three-dimensional network structure

Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.

Optimal network structure to induce the maximal small-world effect

In this paper, the general efficiency, which is the average of the global efficiency and the local efficiency, is defined to measure the communication efficiency of a network. The increasing ratio of the general efficiency of a small-world network relative to that of the corresponding regular network is used to measure the small-world effect quantitatively. The more considerable the small-world effect, the higher the general efficiency of a network with a certain cost is. It is shown that the small-world effect increases monotonically with the increase of the vertex number. The optimal rewiring probability to induce the best small-world effect is approximately 0.02 and the optimal average connection probability decreases monotonically with the increase of the vertex number. Therefore, the optimal network structure to induce the maximal small-world effect is the structure with the large vertex number （〉 500）, the small rewiring probability （≈0.02） and the small average connection probability （〈 0.1）. Many previous research results support our results.

Synthesis and Crystal Structure of [{Cd(hmbdc)(H_2O)_3}·2H_2O]_n

A novel coordination polymer [{Cd（hmbdc）（H20）3}-2H20],, （hmbdc = 5-hydroxyisophthalic acid） has been synthesized and characterized by elemental analysis, IR spectra and single-crystal X-ray diffraction. The crystal belongs to monoclinic, space group P2Jc, with a = 9.599（3）, b = 18.699（5）, c = 7.557（2） A, r= 108.198（4）°, V= 1288.6（6） A3, Z= 4, M,.= 382.60, Dc = 1.972 g/cm^, F（000） = 760, p = 1.740, the final R =0.0555 and wR = 0.0995 for 1732 observed reflections with 1 〉 2σ（I）. The structural analysis shows that the intermolecular hydrogen bonds and π-π interactions result in a three-dimensional supramolecular framework.