A UAV-Assisted V2X Network Architecture with Separated Data Transmission and Network Control

With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.

Connectivity differences between adult male and female patients with attention deficit hyperactivity disorder according to resting-state functional MRI

Attention deficit hyperactivity disorder（ADHD） is a pervasive psychiatric disorder that affects both children and adults. Adult male and female patients with ADHD are differentially affected, but few studies have explored the differences. The purpose of this study was to quantify differences between adult male and female patients with ADHD based on neuroimaging and connectivity analysis. Resting-state functional magnetic resonance imaging scans were obtained and preprocessed in 82 patients. Group-wise differences between male and female patients were quantified using degree centrality for different brain regions. The medial-, middle-, and inferior-frontal gyrus, superior parietal lobule, precuneus, supramarginal gyrus, superior- and middle-temporal gyrus, middle occipital gyrus, and cuneus were identified as regions with significant group-wise differences. The identified regions were correlated with clinical scores reflecting depression and anxiety and significant correlations were found. Adult ADHD patients exhibit different levels of depression and anxiety depending on sex, and our study provides insight into how changes in brain circuitry might differentially impact male and female ADHD patients.

The Quantitative Law of Network Information Growth

Based on the assumptions of ＂information transfer＂ and ＂information creation＂, this paper educes the multiplied growth mechanism of network information： that the gross quantity of network information （Im） is about n times as much as the quantity of real network information （Ir）. According to this theoretical model, we give a uniform explanation to all kinds of information growth models in existence, and put forward some proposals, such as ＂forbidding information transfer＂ and ＂building up the central information base＂, to control the repeated information flooding on the network and facilitate the full use of network information.

Factors That Affect the Centrality Controllability of Scale-Free Networks

The influence of a node in a network can be characterized by its macroscopic properties such as eigenvector centrality. An issue of significant theoretical and practical interest is to modify the influence or roles of the nodes in a network, and recent advances indicate that this can be achieved by just controlling a subset of nodes： the socalled controllers. However, the relationship between the structural properties of a network and its controllability, e.g., the control of node importance, is still not well understood. Here we systematically＂ explore this relationship by constructing scale-free networks with a fixed degree sequence and tunable network characteristics. We calculate the relative size （nc＊） of the minimai controlling set required to controi the importance of each individual node in a network. It is found that while clustering has no significant impact on nc＊, changes in degree-degree correlations, heterogeneity and the average degree of networks demonstrate a discernible impact on its controllability.

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network:a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network（GRNN） model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data（including climate and topography data）, and the performance of each network model was assessed using the mean estimation error（MEE）, model efficiency factor（MEF）, and correlation coefficient（r）. The best neural network model was then selected and further applied to predict the forage production of rangelands in the future（in 2030 and 2080） under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm^2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production（0–100 kg/hm^2） will increase while the areas with moderate, moderately high and high levels of forage production（≥100 kg/hm^2） will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.

Planning for selective amygdalohippocampectomy involving less neuronal fiber damage based on brain connectivity using tractography

Temporal lobe resection is an important treatment option for epilepsy that involves removal of potentially essential brain regions. Selective amygdalohippocampectomy is a widely performed temporal lobe surgery. We suggest starting the incision for selective amygdalohippocampectomy at the inferior temporal gyrus based on diffusion magnetic resonance imaging（MRI） tractography. Diffusion MRI data from 20 normal participants were obtained from Parkinson＇s Progression Markers Initiative（PPMI） database（www.ppmi-info.org）. A tractography algorithm was applied to extract neuronal fiber information for the temporal lobe, hippocampus, and amygdala. Fiber information was analyzed in terms of the number of fibers and betweenness centrality. Distances between starting incisions and surgical target regions were also considered to explore the length of the surgical path. Middle temporal and superior temporal gyrus regions have higher connectivity values than the inferior temporal gyrus and thus are not good candidates for starting the incision. The distances between inferior temporal gyrus and surgical target regions were shorter than those between middle temporal gyrus and target regions. Thus, the inferior temporal gyrus is a good candidate for starting the incision. Starting the incision from the inferior temporal gyrus would spare the important（in terms of betweenness centrality values） middle region and shorten the distance to the target regions of the hippocampus and amygdala.

CAGCN:Centrality-Aware Graph Convolution Network for Anomaly Detection in Industrial Control Systems

In industrial control systems,the utilization of deep learning based methods achieves improvements for anomaly detection.However,most current methods ignore the association of inner components in industrial control systems.In industrial control systems,an anomaly component may affect the neighboring components;therefore,the connective relationship can help us to detect anomalies effectively.In this paper,we propose a centrality-aware graph convolution network(CAGCN)for anomaly detection in industrial control systems.Unlike the traditional graph convolution network(GCN)model,we utilize the concept of centrality to enhance the ability of graph convolution networks to deal with the inner relationship in industrial control systems.Our experiments show that compared with GCN,our CAGCN has a better ability to utilize this relationship between components in industrial control systems.The performances of the model are evaluated on the Secure Water Treatment(SWaT)dataset and the Water Distribution(WADI)dataset,the two most common industrial control systems datasets in the field of industrial anomaly detection.The experimental results show that our CAGCN achieves better results on precision,recall,and F1 score than the state-of-the-art methods.

Special issue on future network:software-defined networking

Computer networks have to support an everincreasing array of applications,ranging from cloud computing in datacenters to Internet access for users.In order to meet the various demands,a large number of network devices running different protocols are designed and deployed in networks.

Analysis on Economic Resilience of the Guangdong-Hong Kong-Macao Greater Bay Area:A Perspective from the Economic Connection Network

In the face of an increasingly complex and unstable external development environment,enhancing economic resilience has become a key task in the construction of the Guangdong-Hong Kong-Macao Greater Bay Area.This paper analyzes the changes in the economic resilience of the Guangdong-Hong Kong-Macao Greater Bay Area and its constituent cities after the 2008 global financial crisis by virtue of the regional economic resilience assessment method proposed by Martin et al.It constructs an economic connection network for the Guangdong-Hong Kong-Macao Greater Bay Area using the data from corporate headquarters and branches of A-share listed companies to analyze its impact on the economic resilience of the Area.The study reveals the three following conclusions.Firstly,the economic resilience of the Guangdong-Hong Kong-Macao Greater Bay Area generally outperforms the national average level,seeing a rapid boost over the recent years and exceeding the level witnessed during the 2008 financial crisis.However,there are marked disparities in the economic resilience ofvarious cities within the Greater Bay Area,with Shenzhen,Guangzhou,and Dongguan emerging as the most robust in this regard.Secondly,the economic connection network has a positive impact on the economic resilience of the cities in the Guangdong-Hong Kong-Macao Greater Bay Area.Specifically,there is a positive correlation between a city's economic resilience and its centrality in the economic connectionnetwork.Suchcentrality exerts a positive spillover effect on the economic resilience of surrounding cities.Thirdly,from the perspective of industryspecific networks,circulation and service industry networks are more conducive to improving the economic resilience of a city.Given the significant role of the economic connection network in shaping regional and urban economic resilience,it is imperative for the Guangdong-Hong Kong-Macao Greater Bay Area to prioritize ensuring economic development security and enhancing economic resilience,promote the development of the economic connection network,and enhance the network centrality of its constituent cities.This can improve the economic resilience of itselfand its constituentcities inaneffective manner.

Protein interaction networks:centrality,modularity,dynamics,and applications

In the post-genomic era,proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies.Especially the rapid accumulation of protein-protein interactions(PPIs)provides a foundation for constructing protein interaction networks(PINs),which can furnish a new perspective for understanding cellular organizations,processes,and functions at network level.In this paper,we present a comprehensive survey on three main characteristics of PINs:centrality,modularity,and dynamics.1)Different centrality measures,which are used to calculate the importance of proteins,are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information;2)Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced;3)The dynamics of proteins,PPIs and sub-networks are discussed,respectively.Finally,the main applications of PINs in the complex diseases are reviewed,and the challenges and future research directions are also discussed.

Network analysis of eight industrial symbiosis systems

Industrial symbiosis is the quintessential characteristic of an eco-industrial park. To divide parks into different types, previous studies mostly focused on qualitative judgments, and failed to use metrics to conduct quantitative research on the intemal structural or functional characteristics of a park. To analyze a park＇s structural attributes, a range of metrics from network analysis have been applied, but few researchers have compared two or more symbioses using multiple metrics. In this study, we used two metrics （density and network degree centraliza- tion） to compare the degrees of completeness and dependence of eight diverse but representative industrial symbiosis networks. Through the combination of the two metrics, we divided the networks into three types： weak completeness, and two forms of strong completeness, namely ＂anchor tenant＂ mutualism and ＂equality-oriented＂ mutualism. The results showed that the networks with a weak degree of completeness were sparse and had few connections among nodes; for ＂anchor tenant＂ mutualism, the degree of completeness was relatively high, but the affiliated members were too dependent on core members; and the members in ＂equality-oriented＂ mutualism had equal roles, with diverse and flexible symbiotic paths. These results revealed some of the systems＇ internal structure and how different structures influenced the exchanges of materials, energy, and knowledge among members of a system, thereby providing insights into threats that may destabilize the network. Based on this analysis, we provide examples of the advantages and effectiveness of recent improvement projects in a typical Chinese eco-industrial park （Shandong Lubei）.

New insight into genes in association with asthma： literature-based mining and network centrality analysis

Background Asthma is a heterogeneous disease for which a strong genetic basis has been firmly established. Until now no studies have been undertaken to systemically explore the network of asthma-related genes using an internally developed literature-based discovery approach. This study was to explore asthma-related genes by using literature- based mining and network centrality analysis. Methods Literature involving asthma-related genes were searched in PubMed from 2001 to 2011. Integration of natural language processing with network centrality analysis was used to identify asthma susceptibility genes and their interaction network. Asthma susceptibility genes were classified into three functional groups by gene ontology （GO） analysis and the key genes were confirmed by establishing asthma-related networks and pathways. Results Three hundred and twenty-six genes related with asthma such as IGHE （IgE）, interleukin （IL）-4, 5, 6, 10, 13, 17A, and tumor necrosis factor （TNF）-alpha were identified. GO analysis indicated some biological processes （developmental processes, signal transduction, death, etc.）, cellular components （non-structural extracellular, plasma membrane and extracellular matrix）, and molecular functions （signal transduction activity） that were involved in asthma. Furthermore, 22 asthma-related pathways such as the Toll-like receptor signaling pathway, hematopoietic cell lineage, JAK-STAT signaling pathway, chemokine signaling pathway, and cytokine-cytokine receptor interaction, and 17 hub genes, such as JAK3, CCR1-3, CCR5-7, CCR8, were found. Conclusions Our study provides a remarkably detailed and comprehensive picture of asthma susceptibility genes and their interacting network. Further identification of these genes and molecular pathways may play a prominent role in establishing rational therapeutic approaches for asthma.

The roles of anurans in antagonistic networks are explained by life-habit and body-size

Interactions among living beings are the structuring basis of ecosystems,and studies of networks allow us to identify the patterns and consistency of such interactions.Antagonistic networks reflect the energy flow of communities,and identifying network structure and the biological aspects that influence its stability is crucial to understanding ecosystem functioning.We used antagonistic anuran interactions-predator-prey and host-parasite-to assess structural patterns and to identify the key anuran species structuring these networks.We tested whether anuran body-size and life-habit are related to their roles in these networks.We collected individuals of 9 species of anurans from an area of the Atlantic Forest in Brazil and identified their prey and helminth parasites.We used network(modularity,specialization,and nestedness)and centrality metrics(degree,closeness,and betweenness)to identify the role of anuran species in both networks.We then evaluated whether anuran body-size or life-habit were related to anuran centrality using generalized linear mixed models.The networks formed specialized interactions in compartments composed by key species from different habits.In our networks,anurans with rheophilic and cryptozoic habit are central in predator-prey networks,and those with larger body size and arboreal and cryptozoic habit in the host-parasite network.This study represents a step towards a better understanding of the influential factors that affect the structure of anuran antagonist networks,as well as to recognize the functioning roles of anuran species.