Toward a Neurophysiological Measure of Image Quality Perception Based on Algebraic Topology Analysis

The bandwidth of internet connections is still a bottleneck when transmitting large amounts of images,making the image quality assessment essential.Neurophysiological assessment of image quality has highlight advantages for it does not interfere with natural viewing behavior.However,in JPEG compression,the previous study is hard to tell the difference between the electroencephalogram(EEG)evoked by different quality images.In this paper,we propose an EEG analysis approach based on algebraic topology analysis,and the result shows that the difference between Euler characteristics of EEG evoked by different distortion images is striking both in the alpha and beta band.Moreover,we further discuss the relationship between the images and the EEG signals,and the results implied that the algebraic topological properties of images are consistent with that of brain perception,which is possible to give birth to braininspired image compression based on algebraic topological features.In general,an algebraic topologybased approach was proposed in this paper to analyze the perceptual characteristics of image quality,which will be beneficial to provide a reliable score for data compression in the network and improve the network transmission capacity.

A Combined Shape and Topology Optimization Based on Isogeometric Boundary Element Method for 3D Acoustics

A combined shape and topology optimization algorithm based on isogeometric boundary element method for 3D acoustics is developed in this study.The key treatment involves using adjoint variable method in shape sensitivity analysis with respect to non-uniform rational basis splines control points,and in topology sensitivity analysis with respect to the artificial densities of sound absorption material.OpenMP tool in Fortran code is adopted to improve the efficiency of analysis.To consider the features and efficiencies of the two types of optimization methods,this study adopts a combined iteration scheme for the optimization process to investigate the simultaneous change of geometry shape and distribution of material to achieve better noise control.Numerical examples,such as sound barrier,simple tank,and BeTSSi submarine,are performed to validate the advantage of combined optimization in noise reduction,and to demonstrate the potential of the proposed method for engineering problems.

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.

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.

Plant multiscale networks:charting plant connectivity by multi-level analysis and imaging techniques

In multicellular and even single-celled organisms,individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation.Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes.Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project.In plant science,network analysis has similarly been applied to study the connectivity of plant components at the molecular,subcellular,cellular,organic,and organism levels.Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype.In this review,we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities.We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants.Finally,we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.

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.

Operational Risk Assessment for Integrated Transmission and Distribution Networks

To satisfy the requirements of accurate operationalrisk assessment of integrated transmission and distribution networks (I-T&D), an integrated operational risk assessment (IORA) algorithm is proposed. Specific cases demonstrate thatan I-ORA is necessary because it provides accurate handlingof the coupling between transmission and distribution networks,accurate analysis of power supply mode (PSM) changes ofimportant users and helps to improve security and stability ofpower grid operations. Two key technical requirements in theI-ORA algorithm are realized, i.e., integrated topology analysisand integrated power flow calculation. Under a certain contingency, integrated topology analysis is used to assess the risksof substation power cuts, network split and PSM changes ofimportant users, while the integrated power flow calculation,based on the self-adaptive Levenburg-Marquard method andNewton method, can be implemented to assess risks of heavyload/overload and voltage deviation. In addition, the graphicsprocessing unit is used to parallelly process some computationintensive steps. Numerical experiments show that the proposedI-ORA algorithm can realize accurate assessment for the entireI-T&D. In addition, the efficiency and convergence are satisfying,indicating the proposed I-ORA algorithm can significantly benefitreal practice in the coordination operation of I-T&D in the future.

Synchrophasor-based Online State Estimated in Large-scale Power Grid

With integration of a larger amount of clean power sources and power electronic equipment,operation and dynamic characteristics of the power grid are becoming more and more complicated and stochastic.Therefore,it is necessary and urgent to obtain accurate real-time states,which is difficult from traditional state estimation.This paper systematically develops a phasor measurement unit(PMU)based real-time state estimator for a realistic large-scale power grid for the first time.The estimator mainly relies on three refined algorithms,i.e.,an improved linear state estimation algorithm,a practical bad data identification method and a distributed topology check technique.Furthermore,a novel system architecture is designed and implemented for the China Southern Power Grid.Numerical simulations and extensive field operation results of the state estimator recorded under both normal and abnormal situations are presented.All the tests and field results demonstrate the advantages of the proposed algorithms in terms of online system monitoring and feasibility of refreshing the states of the whole system at intervals of tens of milliseconds.

NORMA:The Network Makeup Artist—A Web Tool for Network Annotation Visualization

The Network Makeup Artist(NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations(e.g., Gene Ontology, Pathway enrichment, community detection,or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists,algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefy, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/Pavlopoulos Lab/NORMA.

Self-assembly of highly ordered DNA origami lattices at solid-liquid interfaces by controlling cation binding and exchange

The surface-assisted hierarchical self-assembly of DNA origami lattices represents a versatile and straightforward method for the organization of functional nanoscale objects such as proteins and nanoparticles.Here,we demonstrate that controlling the binding and exchange of different monovalent and divalent cation species at the DNA-mica interface enables the self-assembly of highly ordered DNA origami lattices on mica surfaces.The development of lattice quality and order is quantified by a detailed topological analysis of high-speed atomic force microscopy(HS-AFM)images.We find that lattice formation and quality strongly depend on the monovalent cation species.Na^(+)is more effective than Li^(+)and K^(+)in facilitating the assembly of high-quality DNA origami lattices,because it is replacing the divalent cations at their binding sites in the DNA backbone more efficiently.With regard to divalent cations,Ca^(2+)can be displaced more easily from the backbone phosphates than Mg^(2+)and is thus superior in guiding lattice assembly.By independently adjusting incubation time,DNA origami concentration,and cation species,we thus obtain a highly ordered DNA origami lattice with an unprecedented normalized correlation length of 8.2.Beyond the correlation length,we use computer vision algorithms to compute the time course of different topological observables that,overall,demonstrate that replacing MgCl_(2) by CaCl_(2) enables the synthesis of DNA origami lattices with drastically increased lattice order.