Optimizing Performance of Composite Services in Multiple Networks Enterprise Environment

2011 ◽  
Vol 10 (4) ◽  
pp. 807-815
Author(s):  
Shuai Zhang ◽  
Jianling Sun ◽  
Yuanhong Shen ◽  
Yixi Chen ◽  
Aleksander J. Kavs
Keyword(s):  
Multiple Networks ◽  
Composite Services

Performance Optimization for Composite Services in Multiple Networks Environment

2010 ◽  
Author(s):  
Shuai Zhang ◽  
Jianling Sun ◽  
Dongming Lu ◽  
Yuanhong Shen ◽  
Aleksander J. Kavs
Keyword(s):  
Performance Optimization ◽  
Multiple Networks ◽  
Composite Services

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification

2021 ◽  
Vol 13 (8) ◽  
pp. 1602
Author(s):  
Qiaoqiao Sun ◽  
Xuefeng Liu ◽  
Salah Bourennane
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Great Promise ◽  
Learning Models ◽  
Single Level ◽  
Multiple Networks ◽  
Multi Level ◽  
Hyperspectral Classification

Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features.

A network of networks

2017 ◽  
Vol 31 (2) ◽  
pp. 223-236 ◽  
Author(s):  
Rick Iedema ◽  
Raj Verma ◽  
Sonia Wutzke ◽  
Nigel Lyons ◽  
Brian McCaughan
Keyword(s):  
Health Service ◽  
Health System ◽  
Network Governance ◽  
System Reform ◽  
System Transformation ◽  
Content Type ◽  
Multiple Networks ◽  
Network Of Networks

Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author’s participation in network activities and discussions with the agency’s staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency’s implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of “centralised decentralisation”, co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid – the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a “network of networks” through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture “strategic hybrids”, and enhance the impact of network activities on health system reform. Social implications Network governance requires more from network members than participation in a single network, as it involves health service professionals and consumers in a multi-network dynamic. This dynamic requires deliberations and collaborations to be flexible, and it increasingly positions members as “strategic hybrids” – people who have moved on from singular taken-as-given stances and identities, towards hybrid positionings and flexible perspectives. Originality/value This paper is novel in that it identifies a critical feature of health service reform and large system transformation: network governance is empowered through the dynamic co-location of and collaboration among healthcare networks, particularly when complemented with “enabler” teams of people specialising in programme implementation and evaluation.

scholarly journals Bayesian inference of hub nodes across multiple networks

Biometrics ◽  
2018 ◽  
Vol 75 (1) ◽  
pp. 172-182
Author(s):  
Junghi Kim ◽  
Kim‐Anh Do ◽  
Min Jin Ha ◽  
Christine B. Peterson
Keyword(s):  
Bayesian Inference ◽  
Multiple Networks

scholarly journals Homology-Based Interactions between Small RNAs and Their Targets Control Dominance Hierarchy of Male Determinant Alleles of Self-Incompatibility in Arabidopsis lyrata

2021 ◽  
Vol 22 (13) ◽  
pp. 6990
Author(s):  
Shinsuke Yasuda ◽  
Risa Kobayashi ◽  
Toshiro Ito ◽  
Yuko Wada ◽  
Seiji Takayama
Keyword(s):  
Dominance Hierarchy ◽  
Male Dominance ◽  
Self Incompatibility ◽  
Arabidopsis Halleri ◽  
Plant Family ◽  
Arabidopsis Lyrata ◽  
Multiple Networks ◽  
Target Sites ◽  
Gains And Losses ◽  
Linear Dominance Hierarchy

Self-incompatibility (SI) is conserved among members of the Brassicaceae plant family. This trait is controlled epigenetically by the dominance hierarchy of the male determinant alleles. We previously demonstrated that a single small RNA (sRNA) gene is sufficient to control the linear dominance hierarchy in Brassica rapa and proposed a model in which a homology-based interaction between sRNAs and target sites controls the complicated dominance hierarchy of male SI determinants. In Arabidopsis halleri, male dominance hierarchy is reported to have arisen from multiple networks of sRNA target gains and losses. Despite these findings, it remains unknown whether the molecular mechanism underlying the dominance hierarchy is conserved among Brassicaceae. Here, we identified sRNAs and their target sites that can explain the linear dominance hierarchy of Arabidopsis lyrata, a species closely related to A. halleri. We tested the model that we established in Brassica to explain the linear dominance hierarchy in A. lyrata. Our results suggest that the dominance hierarchy of A. lyrata is also controlled by a homology-based interaction between sRNAs and their targets.

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