Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

Spectrum-Efficient User Association in Caching Enabled Dense Small Cell Network

In this paper,we reveal the fundamental limitation of network densification on the performance of caching enabled small cell network(CSCN)under two typical user association rules,namely,contentand distance-based rules.It indicates that immoderately caching content would significantly change the interference distribution in CSCN,which may degrade the network area spectral efficiency(ASE).Meanwhile,it is shown that content-based rule outperforms the distance-based rule in terms of network ASE only when small cell base stations(BSs)are sparsely deployed with low decoding thresholds.Moreover,it is proved that network ASE under distance-based user association serves as the upper bound of that under content-based rule in dense BS regime.To enable more spectrum-efficient user association in dense CSCN,we further optimize network ASE by designing a probabilistic content retrieving strategy based on distance-based rule.With the optimized retrieving probability,network ASE could be substantially enhanced and even increase with the growing BS density in dense BS regime.

Network Learning-Enabled Sensor Association for Massive Internet of Things

The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single GW.This is mitigated by allotting various channels to adjoining GWs.Furthermore,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network execution.Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs.Recently,in remote IoT networks,the utilization of machine learning(ML)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these ML techniques.Therefore,this paper proposes the use of an RL algorithm for GW determination and association in IoT networks.For this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(MAB)calculation,to investigate and determine the optimal GW with which to associate.In this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network topologies.The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches.

Uplink Performance Analysis in Multi-Tier Heterogeneous Cellular Networks with Power Control and Biased User Association

A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint and transmits data when it has sufficient transmit power to perform channel inversion. With biased user association, each user is associated with a base station(BS) that provides the maximum received power weighted by a bias factor, but not their nearest BS. Stochastic geometry is used to evaluate the performances of the proposed system model in terms of the outage probability and ergodic rate for each tier as functions of the biased and power control parameters. Simulations validate our analytical derivations. Numerical results show that there exists a trade-off introduced by the power cut-off threshold and the maximum user transmit power constraint. When the maximum user transmit power becomes a binding constraint, the overall performance is independent of BS densities. In addition, we have shown that it is beneficial for the outage and rate performances by optimizing different network parameters such as the power cut-off threshold as well as the biased factors.

A Clique-Based Approach to the Identification of Common Gene Association Sub-Networks

We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.

Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

To support dramatically increased traffic loads,communication networks become ultra-dense.Traditional cell association(CA)schemes are timeconsuming,forcing researchers to seek fast schemes.This paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is associated.In the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until convergence.In the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user distribution.Simulations demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional schemes.Meanwhile,performance metrics,such as capacity and fairness,can be guaranteed.

Track-to-Track Association Technique in Radar Network in the Presence of Systematic Errors

The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-to-track association algorithms. Consequently, the influence of radar systematic errors on tracks from different radars, which is described as some rotation and translation, has been analyzed theoretically in this paper. In addition, a novel approach named alignment-correlation method is developed to estimate and reduce this effect, align and correlate tracks accurately without prior registration using phase correlation technique and statistic binary track correlation algorithm. Monte-Carlo simulation results illustrate that the proposed algorithm has good performance in solving the track-to-track association problem with systematic errors in radar network and could provide effective and reliable associated tracks for the next step of registration.

Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.

Delay-Based User Association in Heterogeneous Networks with Backhaul

The Internet of things(IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when Io T applications meet heterogeneous networks(HetNets) where macro cells are overlaid with small cells, some traditional problems need rethinking. In this paper, we investigate the delay-addressed association problem in two-tier Het Nets considering different backhaul technologies. Specifically, millimeter wave and fiber links are used to provide high-capacity backhaul for small cells. We first formulate the user association problem to minimize the total delay which depends on the probability of successful transmission, the number of user terminals(UTs), and the number of base stations(BSs). And then two algorithms for active mode and mixed mode are proposed to minimize the network delay. Simulation results show that algorithms based on mutual selection between UTs and BSs have better performance than those based on distance. And algorithms for mixed modes have less delay than those for active mode when the number of BSs is large enough, compared to the number of UTs.

Joint User Association and Resource Allocation for Self-Backhaul Ultra-Dense Networks

In order to meet the exponentially increasing demand on mobile data traffic, self-backhaul ultra-dense networks(UDNs) combined with millimeter wave(mm Wave) communications are expected to provide high spatial multiplexing gain and wide bandwidths for multi-gigabit peak data rates. In selfbackhaul UDNs, how to make the radio access rates of small cells match their backhaul rates by user association and how to dynamically allocate bandwidth for the access links and backhaul links to balance two-hop link resources are two key problems on improving the overall throughputs. Based on this, a joint scheme of user association and resource allocation is proposed in self-backhaul ultra-dense networks. Because of the combinatorial and nonconvex features of the original optimization problem, it has been divided into two subproblems. Firstly, to make the radio access rates of small base stations match their backhaul rates and maximize sum access rates per Hz of all small cells, a proportional constraint is introduced, and immune optimization algorithm(IOA) is adopted to optimize the association indicator variables and the boresight angles of between users and base stations. Then, the optimal backhaul and access bandwidths are calculated by differentiating the general expression of overall throughput. Simulation results indicatethat the proposed scheme increases the overall throughputs significantly compared to the traditional minimum-distance based association scheme.

Resource Allocation and User Association for HTTP Adaptive Streaming in Heterogeneous Cellular Networks with Small Cells

Video streaming,especially hypertext transfer protocol based(HTTP) adaptive streaming(HAS) of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

A Distributed User Association and Resource Allocation Method in Cache-Enabled Small Cell Networks

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5 G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming(MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

INTS-MFS:A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Identifying associations between microRNAs(miRNAs)and diseases is very important to understand the occurrence and development of human diseases.However,these existing methods suffer from the following limitation:first,some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources,i.e.,disease similarity,protein interaction network,gene expression.Second,little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs.In this paper,we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity(INTS-MFS).The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics.INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies.As a results,30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0.29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC,PhenomiR2.0 and experimental reports.Moreover,INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer,which have not been reported.It provides biologists new clues for diagnosing breast and lung cancer.

Personalized Recommendation Algorithm Based on Rating System and User Interest Association Network

In most available recommendation algorithms, especially for rating systems, almost all the high rating information is utilized on the recommender system without using any low-rating information, which may include more user information and lead to the accuracy of recommender system being reduced. The paper proposes a algorithm of personalized recommendation (UNP algorithm) for rating system to fully explore the similarity of interests among users in utilizing all the information of rating data. In UNP algorithm, the similarity information of users is used to construct a user interest association network, and a recommendation list is established for the target user with combining the user interest association network information and the idea of collaborative filtering. Finally, the UNP algorithm is compared with several typical recommendation algorithms (CF algorithm, NBI algorithm and GRM algorithm), and the experimental results on Movielens and Netflix datasets show that the UNP algorithm has higher recommendation accuracy.

STABILITY OF BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DELAYS

In this paper the globally asymptotic stability of more general two-layer nonlinear feedback associative memory neural networks with time delays is examined. The sufficient conditions of existence, uniqueness and globally asymptotic stability of the equilibrum position are given. Finally, two interesting examples to illustrate the theory are given.

QUALITATIVE ANALYSIS OF BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS

In this paper, the global exponential stability of an equilibrium position for general bidirectional associative memory neural networks are studied. The sufficient conditions of existence and uniqueness of the equilibrium position are given. The method of energy function is examined. Two examples are given to illustrate the theory.

Immediate effects of scalp acupuncture with twirling reinforcing manipulation on hemiplegia following acute ischemic stroke: a hidden association study

Data mining has the potential to provide information for improving clinical acupuncture strategies by uncovering hidden rules between acupuncture manipulation and therapeutic effects in a data set. In this study, we performed acupuncture on 30 patients with hemiplegia due to acute ischemic stroke. All participants were pre-screened to ensure that they exhibited immediate responses to acupuncture. We used a twirling reinforcing acupuncture manipulation at the specific lines between the bilateral Baihui（GV20） and Taiyang（EX-HN5）. We collected neurologic deficit score, simplified Fugl-Meyer assessment score, muscle strength of the proximal and distal hemiplegic limbs, ratio of the maximal H-reflex to the maximal M-wave（Hmax/Mmax）, muscle tension at baseline and immediately after treatment, and the syndromes of traditional Chinese medicine at baseline. We then conducted data mining using an association algorithm and an artificial neural network backpropagation algorithm. We found that the twirling reinforcing manipulation had no obvious therapeutic difference in traditional Chinese medicine syndromes of ＂Deficiency and Excess＂. The change in the muscle strength of the upper distal and lower proximal limbs was one of the main factors affecting the immediate change in Fugl-Meyer scores. Additionally, we found a positive correlation between the muscle tension change of the upper limb and Hmax/Mmax immediate change, and both positive and negative correlations existed between the muscle tension change of the lower limb and immediate Hmax/Mmax change. Additionally, when the difference value of muscle tension for the upper and lower limbs was 〉 0 or 〈 0, the difference value of Hmax/Mmax was correspondingly positive or negative, indicating the scalp acupuncture has a bidirectional effect on muscle tension in hemiplegic limbs. Therefore, acupuncture with twirling reinforcing manipulation has distinct effects on acute ischemic stroke patients with different symptoms or stages of disease. Improved muscle tension in the upper and lower limbs, reflected by the variation in the Hmax/Mmax ratio, is crucial for recovery of motor function from hemiplegia.

CHAOTIC NEURAL NETWORK FOR ASSOCIATIVE MEMORY

Based on current research on applications of chaotic neuron network for information processing, the stability and convergence of chaotic neuron network are proved from the viewpoint of energy function. Moreover, a new auto-associative matrix is devised for artificial neural network composed of chaotic neurons, thus, an improved chaotic neuron network for associative memory is built up. Finally, the associative recalling process of the network is analyzed in detail and explanations of improvement are given.

Multi-Valued Associative Memory Neural Network

A novel learning method for multi-valued associative memory network is introduced, which is based on Hebb rule, but utilizes more information. According to the current probe vector, the connection weights matrix could be chosen dynamically. Double-valued and multi-valued associative memory are all realized in our simulation experiment. The experimental results show that the method could enhance the associative success rate.

Synthesization of high-capacity auto-associative memories using complex-valued neural networks

In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results.