SMT: A SPATIAL MAPPING TOOL FOR STATISTICAL DATA SET

The statistical map is usually used to indicate the quantitative features of various socio economic phenomena among regions on the base map of administrative divisions or on other base maps which connected with statistical unit. Making use of geographic information system (GIS) techniques, and supported by Auto CAD software, the author of this paper has put forward a practical method for making statistical map and developed a software (SMT) for the making of small scale statistical map using C language.

Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.

Overcoming a recent impasse in the application of artificial neural networks as solid oxide fuel cells simulator with computational topology

In recent years,the solid oxide fuel cell(SOFC)scientific community has invested continuous efforts to employ artificial intelligence methods to design and develop new energy systems.It is crucial to gain a better understanding of the microscale phenomena that occur in the electrodes.In this review,we present a literature review of the field,discussing the limitations of including microstructural data in existing research and possible research directions to overcome them.This review focuses on a particular research area that uses artificial neural networks(ANNs)to predict the performance of SOFCs.Herein,we show that neural networks are used not only to conform to the newest trends but also for improving the design and providing a better understanding of microscale phenomena that occur in the electrodes.The review concludes by highlighting topological data analysis as a promising area of research that can incorporate detailed microstructure characterization in ANNs for performance prediction.

Biomolecular Topology:Modelling and Analysis

With the great advancement of experimental tools,a tremendous amount of biomolecular data has been generated and accumulated in various databases.The high dimensionality,structural complexity,the nonlinearity,and entanglements of biomolecular data,ranging from DNA knots,RNA secondary structures,protein folding configurations,chromosomes,DNA origami,molecular assembly,to others at the macromolecular level,pose a severe challenge in their analysis and characterization.In the past few decades,mathematical concepts,models,algorithms,and tools from algebraic topology,combinatorial topology,computational topology,and topological data analysis,have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge.In this work,we introduce biomolecular topology,which concerns the topological problems and models originated from the biomolecular systems.More specifically,the biomolecular topology encompasses topological structures,properties and relations that are emerged from biomolecular structures,dynamics,interactions,and functions.We discuss the various types of biomolecular topology from structures(of proteins,DNAs,and RNAs),protein folding,and protein assembly.A brief discussion of databanks(and databases),theoretical models,and computational algorithms,is presented.Further,we systematically review related topological models,including graphs,simplicial complexes,persistent homology,persistent Laplacians,de Rham-Hodge theory,Yau-Hausdorff distance,and the topology-based machine learning models.

Topological Data Analysis of Potentiometric Multisensor Measurements in Treated Wastewater

In this study,a multisensor system consisting of 23 potentiometric sensors was applied for long-term online measurements in outlet flow of the water treatment plant.Within 1 month of continuous measurements,the data set of more than 295,000 observations was acquired.The processing of this dataset with conventional chemometric tools was cumbersome and not very informative.Topological data analysis(TDA)was recently suggested in chemometric literature to deal with large spectroscopic datasets.In this research,we explore the opportunities of TDA with respect to multisensor data with only 23 variables.It is shown that TDA allows for convenient data visualization,studying the evolution of water quality during the measurements and tracking the periodical structure in the data related to the water quality depending on the time of the day and the day of the week.TDA appears to be a valuable tool for multisensor data exploration.