Large-Scale Network Topology Reveals Heterogeneity in Individuals With at Risk Mental State for Psychosis: Findings From the Longitudinal Youth-at-Risk Study

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

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Feasibility study of eye movement desensitisation and reprocessing (EMDR) in people with an at-risk mental state (ARMS) for psychosis: study protocol

BMJ Open ◽

10.1136/bmjopen-2020-038620 ◽

2020 ◽

Vol 10 (10) ◽

pp. e038620

Author(s):

Daniela Strelchuk ◽

Nicola Wiles ◽

Katrina M Turner ◽

Catherine Derrick ◽

Stan Zammit

Keyword(s):

At Risk ◽

Eye Movement ◽

Mental Health Professionals ◽

Mental State ◽

Large Scale ◽

Traumatic Event ◽

Controlled Trial ◽

Secondary Outcome ◽

Psychotic Symptoms ◽

At Risk Mental State

IntroductionTrauma can play an important role in the development of psychosis, yet no studies have investigated whether a trauma-focused psychological therapy could prevent the onset of psychosis in people at high risk of developing this condition. This study aims to establish whether it would be feasible to conduct a multicentre randomised controlled trial (RCT) to investigate the clinical and cost-effectiveness of eye movement desensitisation and reprocessing (EMDR) therapy to prevent the onset of psychosis in people with an at-risk mental state (ARMS).Methods/analysisThis is a single-arm trial with a nested qualitative study where all participants (target n=20) will be offered EMDR. Eligible participants are those who meet criteria for ARMS; have experienced a traumatic event before the onset of ARMS symptomatology; and have at least one symptom of post-traumatic stress disorder (PTSD). Participants will be followed up at 4, 8 and 12 months after the baseline assessment. The primary outcome measure is transition to psychosis, and secondary outcome measures include severity of psychotic symptoms, PTSD, depression, anxiety, impaired functioning, health status and resource use. The analysis will aim to establish the rates of recruitment and retention for a large-scale RCT. Interviews with therapists and patients will explore their views of the study and their experiences of delivering or receiving EMDR.Ethics and disseminationThis protocol has been approved by the South West-Cornwall and Plymouth Research Ethics Committee (Reference 18/SW/0037). Findings will be disseminated through journal publications, conference presentations and meetings with service users, their families, mental health professionals and commissioners.Trial registration numberISRCTN31976295.

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Large Scale Network Topology Visualization System Based on Three.JS

Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications ◽

10.2991/icaita-16.2016.39 ◽

2016 ◽

Author(s):

Yuxiao Wang ◽

Yuanzhang Li ◽

Yu'an Tan ◽

Quanxin Zhang ◽

Jun Zheng

Keyword(s):

Network Topology ◽

Large Scale ◽

Visualization System ◽

Large Scale Network ◽

Scale Network

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VNF Chain Placement for Large Scale IoT of Intelligent Transportation

Sensors ◽

10.3390/s20143819 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3819

Author(s):

Xing Wu ◽

Jing Duan ◽

Mingyu Zhong ◽

Peng Li ◽

Jianjia Wang

Keyword(s):

Network Topology ◽

Traffic Safety ◽

Large Scale ◽

Intelligent Transportation ◽

Full Potential ◽

Service Chain ◽

Network Function ◽

Large Scale Network ◽

Scale Network ◽

Over Time

With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.

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Network Topology Discovery Algorithm Based on OSPF Link State Advertisement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.3203 ◽

2014 ◽

Vol 644-650 ◽

pp. 3203-3207

Author(s):

Zhe Jian Shen ◽

Yun Sheng Ge

Keyword(s):

Network Topology ◽

Large Scale ◽

Utilization Rate ◽

Link State ◽

Network Discovery ◽

Large Scale Network ◽

Scale Network ◽

Environment Experiment ◽

Routing Tables ◽

Discovery Algorithms

In large scale network, the problem that network in an area topology accurately be discovered should be solved. The traditional network discovery algorithms mainly use ICMP and SNMP. But these two algorithms allow routers or other devices to send request packets to other devices. It may cause the low utilization rate of network. However, OSPF routing protocol, when it exchange routing tables, it will send LSUs to other routers and receive them from the remote routers. According to analyzing the LSAs, which are encapsulated in LSUs, we can obtain the network topology. We use GNS3 to simulate OSPF environment. Experiment shows that this algorithm can obtain the network topology rapidly and accurately.

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