scholarly journals Star varietal cube: A New Large Scale Parallel Interconnection Network

2011 ◽  
pp. 37-44
Author(s):  
Binod Nag ◽  
Debendra Pradhan ◽  
Nirmal Keshari Swain ◽  
Nibedita Adhikari
Keyword(s):  
Network Topology ◽  
Efficient Method ◽  
Large Scale ◽  
Interconnection Network ◽  
Average Distance ◽  
Building Blocks ◽  
Star Graph ◽  
Recursive Structure ◽  
Strong Connectivity ◽  
The Hierarchical Structure

This paper proposes a new interconnection network topology, called the Star varietalcube SVC(n,m), for large scale multicomputer systems. We take advantage of the hierarchical structure of the Star graph network and the Varietal hypercube to obtain an efficient method for constructing the new topology. The Star graph of dimension n and a Varietal hypercube of dimension m are used as building blocks. The resulting network has most of the desirable properties of the Star and Varietal hypercube including recursive structure, partionability, strong connectivity. The diameter of the Star varietal hypercube is about two third of the diameter of the Star-cube. The average distance of the proposed topology is also smaller than that of the Star-cube.

Multitriangle: A Constant Node Degree Interconnection Network

1997 ◽  
Vol 08 (02) ◽  
pp. 187-209 ◽  
Author(s):  
Jie Wu ◽  
Haifeng Qian
Keyword(s):  
Network Topology ◽  
Interconnection Network ◽  
Average Distance ◽  
Node Degree ◽  
Formal Definition ◽  
Corner Cutting ◽  
Network Topologies ◽  
Salient Features ◽  
Definition Of ◽  
Node Routing

We propose a constant node degree network topology, multitriangle, which is hierarchical, recursive, and expansive. First we introduce a corner cutting approach that generates a set of new network topologies (including multitriangles), followed by a formal definition of the multitriangle network and discussion of its properties. The salient features of this network are that it is a constant node degree network and it can be viewed as a hierarchical ring, a popular topology which has been adopted in several commercial systems. Algorithms for node-to-node routing, hierarchical ring routing, optimal ring routing, and broadcasting are presented. The multitriangle network is analyzed in terms of diameter, degree, average distance, and message density, and results are compared with other relevant networks.

On Component Connectivity of Hierarchical Star Networks

2020 ◽  
Vol 31 (03) ◽  
pp. 313-326
Author(s):  
Mei-Mei Gu ◽  
Jou-Ming Chang ◽  
Rong-Xia Hao
Keyword(s):  
Fault Tolerance ◽  
Network Topology ◽  
Interconnection Network ◽  
Building Blocks ◽  
Star Graphs ◽  
Combinatorial Properties ◽  
Star Networks ◽  
Minimum Number ◽  
Text Component ◽  
Disconnected Graph

For an integer [Formula: see text], the [Formula: see text]-component connectivity of a graph [Formula: see text], denoted by [Formula: see text], is the minimum number of vertices whose removal from [Formula: see text] results in a disconnected graph with at least [Formula: see text] components or a graph with fewer than [Formula: see text] vertices. This naturally generalizes the classical connectivity of graphs defined in term of the minimum vertex-cut. This kind of connectivity can help us to measure the robustness of the graph corresponding to a network. The hierarchical star networks [Formula: see text], proposed by Shi and Srimani, is a new level interconnection network topology, and uses the star graphs as building blocks. In this paper, by exploring the combinatorial properties and fault-tolerance of [Formula: see text], we study the [Formula: see text]-component connectivity of hierarchical star networks [Formula: see text]. We obtain the results: [Formula: see text], [Formula: see text] and [Formula: see text] for [Formula: see text].

scholarly journals Measuring the effect of distance on the network topology of the Global Container Shipping Network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dimitrios Tsiotas ◽  
César Ducruet
Keyword(s):  
Network Topology ◽  
Large Scale ◽  
Spatial Dimension ◽  
Spatial Distance ◽  
Container Shipping ◽  
Decomposition Approach ◽  
Spatial Coverage ◽  
Geographical Scale ◽  
The Hierarchical Structure ◽  
Exporting Trade

AbstractThis paper examines how spatial distance affects network topology on empirical data concerning the Global Container Shipping Network (GCSN). The GCSN decomposes into 32 multiplex layers, defined at several spatial levels, by successively removing connections of smaller distances. This multilayer decomposition approach allows studying the topological properties of each layer as a function of distance. The analysis provides insights into the hierarchical structure and (importing and exporting) trade functionality of the GCSN, hub connectivity, several topological aspects, and the distinct role of China in the network’s structure. It also shows that bidirectional links decrease with distance, highlighting the importance of asymmetric functionality in carriers’ operations. It further configures six novel clusters of ports concerning their spatial coverage. Finally, it reveals three levels of geographical scale in the structure of GCSN (where the network topology significantly changes): the neighborhood (local connectivity); the scale of international connectivity (mesoscale or middle connectivity); and the intercontinental market (large scale connectivity). The overall approach provides a methodological framework for analyzing network topology as a function of distance, highlights the spatial dimension in complex and multilayer networks, and provides insights into the spatial structure of the GCSN, which is the most important market of the global maritime economy.

Electrochemical Oxidative Esterification of Thiophenols: Efficient Access to Sulfinic Esters

2020 ◽  
Vol 17 (7) ◽  
pp. 540-547
Author(s):  
Chun-Hui Yang ◽  
Cheng Wu ◽  
Jun-Ming Zhang ◽  
Xiang-Zhang Tao ◽  
Jun Xu ◽  
...  
Keyword(s):  
Organic Synthesis ◽  
Efficient Method ◽  
High Efficiency ◽  
Building Blocks ◽  
Constant Current ◽  
Good Efficiency ◽  
Oxidative Esterification ◽  
Efficient Access ◽  
New Applications ◽  
Sulfinic Esters

Background: The sulfinic esters are important and useful building blocks in organic synthesis. Objective: The aim of this study was to develop a simple and efficient method for the synthesis of sulfinic esters. Materials and Methods: Constant current electrolysis from thiols and alcohols was selected as the method for the synthesis of sulfinic esters. Results and Discussion: A novel electrochemical method for the synthesis of sulfinic esters from thiophenols and alcohols has been developed. Up to 27 examples of sulfinic esters have been synthesized using the current methods. This protocol shows good functional group tolerance as well as high efficiency. In addition, this protocol can be easily scaled up with good efficiency. Notably, heterocycle-containing substrates, including pyridine, thiophene, and benzothiazole, gave the desired products in good yields. A plausible reaction mechanism is proposed. Conclusion: This research not only provides a green and efficient method for the synthesis of sulfinic esters but also shows new applications of electrochemistry in organic synthesis. It is considered that this green and efficient synthetic protocol used to prepare sulfinic esters will have good applications in the future.

A Drug Target Interaction Prediction Based on LINE-RF Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 750-757
Author(s):  
Jihong Wang ◽  
Yue Shi ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Network Topology ◽  
Drug Target ◽  
Large Scale ◽  
Representation Learning ◽  
New Drugs ◽  
Combination Method ◽  
Learning Methods ◽  
Network Representation ◽  
On Line ◽  
Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

scholarly journals A Comprehensive Review: Sphingolipid Metabolism and Implications of Disruption in Sphingolipid Homeostasis

2021 ◽  
Vol 22 (11) ◽  
pp. 5793
Author(s):  
Brianna M. Quinville ◽  
Natalie M. Deschenes ◽  
Alex E. Ryckman ◽  
Jagdeep S. Walia
Keyword(s):  
Nervous System ◽  
Large Scale ◽  
System Development ◽  
Building Blocks ◽  
Cell Types ◽  
Nervous System Development ◽  
Sphingolipid Metabolism ◽  
Lysosomal Storage ◽  
Bioactive Lipids ◽  
Comprehensive Review

Sphingolipids are a specialized group of lipids essential to the composition of the plasma membrane of many cell types; however, they are primarily localized within the nervous system. The amphipathic properties of sphingolipids enable their participation in a variety of intricate metabolic pathways. Sphingoid bases are the building blocks for all sphingolipid derivatives, comprising a complex class of lipids. The biosynthesis and catabolism of these lipids play an integral role in small- and large-scale body functions, including participation in membrane domains and signalling; cell proliferation, death, migration, and invasiveness; inflammation; and central nervous system development. Recently, sphingolipids have become the focus of several fields of research in the medical and biological sciences, as these bioactive lipids have been identified as potent signalling and messenger molecules. Sphingolipids are now being exploited as therapeutic targets for several pathologies. Here we present a comprehensive review of the structure and metabolism of sphingolipids and their many functional roles within the cell. In addition, we highlight the role of sphingolipids in several pathologies, including inflammatory disease, cystic fibrosis, cancer, Alzheimer’s and Parkinson’s disease, and lysosomal storage disorders.

scholarly journals Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes

Biology ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Apurva Badkas ◽  
Thanh-Phuong Nguyen ◽  
Laura Caberlotto ◽  
Jochen G. Schneider ◽  
Sébastien De Landtsheer ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Topology ◽  
Large Scale ◽  
Metabolic Diseases ◽  
Critical Factor ◽  
Disease Genes ◽  
Alcoholic Fatty Liver ◽  
Large Scale Network ◽  
Depth Study ◽  
Public Datasets

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.

Fault tolerance of the star graph interconnection network

1994 ◽  
Vol 49 (3) ◽  
pp. 145-150 ◽  
Author(s):  
Zoran Jovanović ◽  
Jelena Mišić
Keyword(s):  
Fault Tolerance ◽  
Interconnection Network ◽  
Star Graph
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