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.

Structural characteristics and influencing factors of spatial correlation network for regional high-quality development in China

On the basis of measuring the regional high-quality development in China from 2011 to 2020,this study uses gravity model to build spatial correlation network,and uses social network analysis method to analyze the structural characteristics and influencing factors of correlation network.The results are shown as follows.First,from 2011 to 2020,the level of regional high-quality development in China is rising gradually,and the discrete characteristics between regions are gradually obvious,showing a step-like distribution structure decreasing from east to west.Second,the network density of regional high-quality development is generally low and tends to decline,but it has strong stability and correlation strength.Third,the spatial correlation network has an obvious core-edge structure.Shanghai is always at the center of the network and plays a significant intermediary role,while Qinghai and Xinjiang are always at the edge of the network.Fourth,the regional high-quality development association network can be divided into four major sectors:main benefit,net benefit,net spillover,and broker,showing the spatial correlation characteristics of inter-plate contact and intra-plate agglomeration.Fifth,the level of economic development,the level of urbanization and geographical proximity have a significant impact on the formation of regional high-quality development correlation network.

Existence and exponential stability of almost-periodic solutions for MAM neural network with distributed delays on time scales

This paper is concerned with multidirectional associative memory neural network with distributed delays on almost-periodic time scales.Some sufficient conditions on the existence,uniqueness and the global exponential stability of almost-periodic solutions are established.An example is presented to illustrate the feasibility and effectiveness of the obtained results.

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.

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.

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.

LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks

Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These conditions were formulated as easily verifiable linear matrix inequalities (LMIs), whose conservativeness is relatively low. The approach proposed extends the known stability results, and can also be applied to other forms of recurrent neural networks (RNNs).

Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach

Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.

Delay-Dependent Exponential Stability Criterion for BAM Neural Networks with Time-Varying Delays

By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neural networks with time-varying delays.The proposed condition can be checked easily by LMI control toolbox in Matlab.A numerical example is given to demonstrate the effectiveness of our results.

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

Gut microbial interactions based on network construction and bacterial pairwise cultivation

Association networks are widely applied for the prediction of bacterial interactions in studies of human gut microbiomes.However,the experimental validation of the predicted interactions is challenging due to the complexity of gut microbiomes and the limited number of cultivated bacteria.In this study,we addressed this challenge by integrating in vitro time series network(TSN)associations and cocultivation of TSN taxon pairs.Fecal samples were collected and used for cultivation and enrichment of gut microbiome on YCFA agar plates for 13 days.Enriched cells were harvested for DNA extraction and metagenomic sequencing.A total of 198 metagenome-assembled genomes(MAGs)were recovered.Temporal dynamics of bacteria growing on the YCFA agar were used to infer microbial association networks.To experimentally validate the interactions of taxon pairs in networks,we selected 24 and 19 bacterial strains from this study and from the previously established human gut microbial biobank,respectively,for pairwise co-cultures.The co-culture experiments revealed that most of the interactions between taxa in networks were identified as neutralism(51.67%),followed by commensalism(21.67%),amensalism(18.33%),competition(5%)and exploitation(3.33%).Genome-centric analysis further revealed that the commensal gut bacteria(helpers and beneficiaries)might interact with each other via the exchanges of amino acids with high biosynthetic costs,short-chain fatty acids,and/or vitamins.We also validated 12 beneficiaries by adding 16 additives into the basic YCFA medium and found that the growth of 66.7%of these strains was significantly promoted.This approach provides new insights into the gut microbiome complexity and microbial interactions in association networks.Our work highlights that the positive relationships in gut microbial communities tend to be overestimated,and that amino acids,short-chain fatty acids,and vitamins are contributed to the positive relationships.

Almost periodic solutions of memristive multidirectional associative memory neural networks with mixed time delays

Traditional biological neural networks cannot simulate the real situation of the abrupt synaptic connections between neurons while modeling associative memory of human brains.In this paper,the memristive multidirectional associative memory neural networks(MAMNNs)with mixed time-varying delays are investigated in the sense of Filippov solution.First,three steps are given to prove the existence of the almost periodic solution.Two new lemmas are proposed to prove the boundness of the solution and the asymptotical almost periodicity of the solution by constructing Lyapunov function.Second,the uniqueness and global exponential stability of the almost periodic solution of memristive MAMNNs are investigated by a new Lyapunov function.The sufficient conditions guaranteeing the properties of almost periodic solution are derived based on the relevant definitions,Halanay inequality and Lyapunov function.The investigation is an extension of the research on the periodic solution and almost periodic solution of bidirectional associative memory neural networks.Finally,numerical examples with simulations are presented to show the validity of the main results.

Tank-dependence of the functionality and network differentiation of activated sludge community in a full-scale anaerobic/anoxic/aerobic municipal sewage treatment plant

Understanding the structures and dynamics of bacterial communities in activated sludge(AS)in full-scale wastewater treatment plants(WWTPs)is of both engineering and ecological significance.Previous investigations have mainly focused on the AS communities of WWTP aeration tanks,and the differences and interactions between the communities in anaerobic and anoxic tanks of the AS system remain poorly understood.Here,we investigated the structures of bacterial communities and their inter-connections in three tanks(anaerobic,anoxic,and aerobic)and influent from a full-scale WWTP with conventional anaerobic/anoxic/aerobic(A/A/O)process over a year to explore their functionality and network differentiation.High-throughput sequencing showed that community compositions did not differ appreciably between the different tanks,likely due to the continuous sludge community interchange between tanks.However,network analysis showed significant differences in inter-species relationships,OTU topological roles,and keystone populations in the different AS communities.Moreover,the anoxic network is expected to be more unstable and easily affected by environmental disturbance.Tank-associated environmental factors,including dissolved oxygen,pH,and nutrients,were found to affect the relative abundance of functional genera(i.e.,AOB,NOB,PAOs,and denitrifiers),suggesting that these groups were more susceptible to environmental variables than other bacteria.Therefore,this work could assist in improving our understanding of tank-associated microbial ecology,particularly the response of functional bacteria to seasonal variations in WWTPs employing A/A/O process.

Study on the Networks of "Nature-Family-Component" of Chinese Medicinal Herbs Based on Association Rules Mining

Objective： To explore appropriate methods for the research of the theory of Chinese medicine nature property and find the relationship between Nature-Family-Component of Chinese herbs. Methods： From perspective of systems biology, we used Associate Network to identify useful relationships among ＂Nature- Family-Component＂ of Herbs. In this work, Associate Network combines association rules mining method and network construction method to evaluate the complicate relationship among ＂Nature-Family-Component＂ of herbs screened. Results： The results of association rules mining showed that the families had a close relationship with nature properties of herbs. For example, the families of Magnoliaceae, Araceae had a close relationship with hot nature with confidence of 100%, the families of Cucurbitaceae has a close relationship to cold nature with confidence of 90,91%. Moreover, the results of constructed Associate Network implied that herbs belonging to the same families generally had the same natures. In addition, some herbs belonging to different families may also have same natures when they contain the same main components. Conclusion： These results implied that the main components of herbs might affect their natures; the relationships between families and natures were based on the main compounds of herbs.

DAN:a deep association neural network approach for personalization

The collaborative filtering technology used in traditional recommendation systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional recommendation algorithms, thus leading to the emergence of recommendation systems based on deep learning. At present, deep learning recommendations mostly use deep neural networks to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep neural network recommendation algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the recommendation. Aimed at this problem, in this paper we propose a feedforward deep neural network recommendation method, called the deep association neural network(DAN), which is based on the joint action of multiple categories of information, for implicit feedback recommendation. Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the recommendation is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint recommendations can provide better recommendation performance.

Global stability of bidirectional associative memory neural networks with continuously distributed delays

Global asymptotic stability of the equilibrium point of bidirectional associative memory (BAM) neural networks with continuously distributed delays is studied. Under two mild assumptions on the activation functions, two sufficient conditions ensuring global stability of such networks are derived by utilizing Lyapunov functional and some inequality analysis technique. The results here extend some previous results. A numerical example is given showing the validity of our method.

The Complexity of Recognition in the Single-Layered PLN Network with Feedback Connections

Regarding a single-layered PLN network with feedback connections as an associative memory network,the complexity of recognition is discussed.We have the main result:if the size of the network N is m,then the complexity of recognition is an exponential function of m.The necessary condition under which the complexity of recognition is polynomial is given.

EXPONENTIAL STABILITY AND PERIODIC SOLUTION OF HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

In this paper, we study the existence, uniqueness, and the global exponential stability of the periodic solution and equilibrium of hybrid bidirectional associative memory neural networks with discrete delays. By ingeniously importing real parameters di > 0 (i = 1,2, …, n) which can be adjusted, making use of the Lyapunov functional method and some analysis techniques, some new sufficient conditions are established. Our results generalize and improve the related results in [9]. These conditions can be used both to design globally exponentially stable and periodical oscillatory hybrid bidirectional associative neural networks with discrete delays, and to enlarge the area of designing neural networks. Our work has important significance in related theory and its application.

Interspecific association of sika deer in terrestrial animal communities of Liancheng National Nature Reserve, China

The prevention and control of invasive of alien species is an important work for nature reserves.This study analyzes the development trend of the alien species sika deer in Liancheng National Nature Reserve.From October 2019 to June 2020,3523 valid photos and videos of terrestrial animals were acquired from 130 camera traps,and sika deer were recorded in 21 photos from 13 traps.The survival of the sika deer population was investigated by means of morphological identification,population structure analysis,species relative abundance indices,and species spatial association analysis.A total of 13 sika deer individuals were identified by camera trapping,including two kids and three subadults representing the reproductive capacity of the population.Spatially,sika deer is not associated with any local species and was outside the spatial association network of terrestrial animals in Liancheng National Nature Reserve,indicating that the sika deer population has not been integrated into the local community and has failed to perform its ecological function.It is worth noting that the reserve provides habitat suitable for sika deer and that the population has adequate reproductive capacity.Due to the lack of large apex predators in the reserve,the population size of ungulates such as sika deer,red deer,and Siberian roe deer may expand and lead to population outbreaks and the associated problems for the ecosystem.To restore large-and medium-sized carnivores and avoid the population outbreak of the species,the present challenges require immediate attention in Liancheng National Nature Reserve.

A comprehensive review of integrative pharmacology-based investigation:A paradigm shift in traditional Chinese medicine

Over the past decade,traditional Chinese medicine(TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization.Thus,integrative pharmacology-based traditional Chinese medicine(TCMIP) was proposed as a paradigm shift in TCM.This review focuses on the presentation of this novel concept and the main research contents,methodologies and applications of TCMIP.First,TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics(PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes in vivo.Then,the main research contents of TCMIP are introduced as follows:chemical and ADME/PK profiles of TCM formulas;confirming the three forms of active substances and the three action modes;establishing the qualitative PK-PD correlation;and building the quantitative PK-PD correlations,etc.After that,we summarize the existing data resources,computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods.Finally,we further discuss the applications of TCMIP for the improvement of TCM quality control,clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs,especially TCM-related combination drug disco very.