Structural similarity of lithospheric velocity models of Chinese mainland

Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features,with dissimilarities due to different data and methods used in model generation.The quantification of model structural similarity can help in interpreting the geophysical properties of Earth's interior and establishing unified models crucial in natural hazard assessment and resource exploration.Here we employ the complex wavelet structural similarity index measure(CW-SSIM)active in computer image processing to analyze the structural similarity of four lithospheric velocity models of Chinese mainland published in the past decade.We take advantage of this method in its multiscale definition and insensitivity to slight geometrical distortion like translation and scaling,which is particularly crucial in the structural similarity analysis of velocity models accounting for uncertainty and resolution.Our results show that the CW-SSIM values vary in different model pairs,horizontal locations,and depths.While variations in the inter-model CW-SSIM are partly owing to different databases in the model generation,the difference of tomography methods may significantly impact the similar structural features of models,such as the low similarities between the full-wave based FWEA18 and other three models in northeastern China.We finally suggest potential solutions for the next generation of tomographic modeling in different areas according to corresponding structural similarities of existing models.

Joint inversion of gravity and vertical gradient data based on modified structural similarity index for the structural and petrophysical consistency constraint

Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion.The current joint inversion methods can be divided into the structural consistency constraint and petrophysical consistency constraint methods,which are mutually independent.Currently,there is a need for joint inversion methods that can comprehensively consider the structural consistency constraints and petrophysical consistency constraints.This paper develops the structural similarity index(SSIM)as a new structural and petrophysical consistency constraint for the joint inversion of gravity and vertical gradient data.The SSIM constraint is in the form of a fraction,which may have analytical singularities.Therefore,converting the fractional form to the subtractive form can solve the problem of analytic singularity and finally form a modified structural consistency index of the joint inversion,which enhances the stability of the SSIM constraint applied to the joint inversion.Compared to the reconstructed results from the cross-gradient inversion,the proposed method presents good performance and stability.The SSIM algorithm is a new joint inversion method for petrophysical and structural constraints.It can promote the consistency of the recovered models from the distribution and the structure of the physical property values.Then,applications to synthetic data illustrate that the algorithm proposed in this paper can well process the synthetic data and acquire good reconstructed results.

Color Image Quality Assessment Based on Structural Similarity

It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.

An Improved Image Denoising Algorithm Based on Structural Similarity and Curvelet

An image denoising method based on curvelet within the framework of non-local means(NLM) is proposed in this paper. We use Structural Similarity(SSIM) to compute the value of SSIM between the reference patch and its similar versions, and remove the dissimilar pixels. Besides, the curvelet is adopted to adjust the coefficients of these patches with low SSIM. Experiments show that the proposed method has the capacity to denoise effectively, improves the peak signal-to-noise ratio of the image, and keeps better visual result in edges information reservation as well.

Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques

A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.

An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.

A Structure Similarity Analysis Method for On－Line Recognition of Handwritten Chinese Characters

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A Structure Similarity Analysis of Secreted Proteins in Magnaporthe oryzae and Its Host Oryza sativa

The structure similarity of secreted proteins in rice blast fungus Magnaporthe oryzae and its host Oryza sativa was analyzed. One thousand two hundred and forty one proteins were predicted as secreted proteins using four algorithms based on 11 074 proteins in genome of M. oryzae. One hundred and forty six secreted proteins（ 11. 8% of M. oryzae secretome） were aligned with 116 rice proteins（ 0. 21% of 56 278 rice proteins） using BLAST search on rice genome. One hundred sixteen rice similar proteins participated in rice cell wall modification（ cell wall associated enzymes） and signal transduction（ proteases）. These results imply that both cell wall involved proteins and signal transduction are probably hijacks pathway between host pants and pathogenic fungi. Because these proteins are highly conserved among fungi and plants,the express patterns of these protein coding genes during the interaction process are valuable to study in detail.

Modeling of 3D-structure for regular fragments of low similarity unknown structure proteins

Because it is hard to search similar structure for low similarity unknown structure proteins directly from the Protein Data Bank （PDB） database, 3D-structure is modeled in this paper for secondary structure regular fragments （α-Helices, β-Strands） of such proteins by the protein secondary structure prediction software, the Basic Local Alignment Search Tool （BLAST） and the side chain construction software SCWRL3. First, the protein secondary structure prediction software is adopted to extract secondary structure fragments from the unknown structure proteins. Then, regular fragments are regulated by BLAST based on comparative modeling, providing main chain configurations. Finally, SCWRL3 is applied to assemble side chains for regular fragments, so that 3D-structure of regular fragments of low similarity unknown structure protein is obtained. Regular fragments of several neurotoxins are used for test. Simulation results show that the prediction errors are less than 0.06nm for regular fragments less than 10 amino acids, implying the simpleness and effectiveness of the proposed method.

SIMILARITY TRANSFORMATION, THE STRUCTURE OF THE TRAVELING WAVES SOLUTION AND THE EXISTENCE OF A GLOBAL SMOOTH SOLUTION TO GENERALIZED KURAMOTO SIVASHINSKY TYPE EQUATIONS

The existence of a global smooth solution for the initial value problem of generalized Kuramoto-Sivashinsky type equations have been obtained. Similarty siolutions and the structure of the traveling waves solution for the generalized KS equations are discussed and analysed by using the qualitative theory of ODE and Lie's infinitesimal transformation respectively.

The Golden Ratio Theorem: A Framework for Interchangeability and Self-Similarity in Complex Systems

The Golden Ratio Theorem, deeply rooted in fractal mathematics, presents a pioneering perspective on deciphering complex systems. It draws a profound connection between the principles of interchangeability, self-similarity, and the mathematical elegance of the Golden Ratio. This research unravels a unique methodological paradigm, emphasizing the omnipresence of the Golden Ratio in shaping system dynamics. The novelty of this study stems from its detailed exposition of self-similarity and interchangeability, transforming them from mere abstract notions into actionable, concrete insights. By highlighting the fractal nature of the Golden Ratio, the implications of these revelations become far-reaching, heralding new avenues for both theoretical advancements and pragmatic applications across a spectrum of scientific disciplines.

Community Detection by Neighborhood Similarity

Detection of the community structure in a network is important for understanding the structure and dynamics of the network.By exploring the neighborhood of vertices,a local similarity metric is proposed,which can be quickly computed.The resulting similarity matrix retains the same support as the adjacency matrix.Based on local similarity,an agglomerative hierarchical clustering algorithm is proposed for community detection.The algorithm is implemented by an efficient max-heap data structure and runs in nearly linear time,thus is capable of dealing with large sparse networks with tens of thousands of nodes.Experiments on synthesized and real-world networks demonstrate that our method is efficient to detect community structures,and the proposed metric is the most suitable one among all the tested similarity indices.

Systematic Method of Applying Structural Characteristics of Natural Organisms to Mechanical Structures

Structural bionic design lacks mature and scientific theories, and the excellent structural characteristics of natural organisms sometimes cannot be transferred into engineering structures effectively. Aiming at overcoming the existing problems, this paper summarizes three related theories: similarity theory, fuzzy evaluation theory and optimization theory. Based on the related theories, a method of structural bionic design is introduced, which includes four steps: selecting the most useful structural characteristic of natural organism; analyzing the structural characteristic finally chosen for engineering problem; completing the structural bionic design for engineering structure; and verifying the structural bionic design. Similarity theory and fuzzy evaluation theory are employed to achieve Step 1. In Step 2 and Step 3, optimization theory is employed to analyze the parameters of structures. Together with the thoughts of simplification and grouping, optimization theory can reveal the relationship between organism structure and engineering structure, providing a way to structural bionic design. A general evaluation criterion is proposed in Step 4, which is feasible to evaluate the performance of different structures. Finally, based on the method, a structural bionic design of thin-walled cylindrical shell is introduced.

The Similar Structures and Control Problems of Complex Systems

In this paper, the naturally evolving complex systems, such as biotic and social ones, are considered. Focusing on their structures, a feature is noteworthy, i.e., the similarity in structures. The relations between the functions and behaviors of these systems and their similar structures will be studied. Owing to the management of social systems and the course of evolution of biotic systems may be regarded as control processes, the researches will be within the scope of control problems. Moreover, since it is difficult to model for biotic and social systems, it will start with the control problems of complex systems, possessing similar structures, in engineering. The obtained results show that for either linear or nonlinear systems and for a lot of control problems similar structures lead to a series of simplifications. In general, the original system may be decomposed into reduced amount of subsystems with lower dimensions and simpler structures. By virtue of such subsystems, the control problems of original system can be solved more simply. At last, it turns round to observe the biotic and social systems and some analyses are given.

STRUCTURAL MODELING OF WAN TRAFFIC

High resolution traffic measurements from modern communications networks provide unique opportunities for developing and validating mathematical models for aggregate traffic, or WAN (wide area network) traffic. To exploit these opportunities, this paper emphasized the need for structural model that takes into account specific physical feature of the underlying communication network structure. This approach is in sharp contrast to the traditional black box modeling methodology from this time series analysis that ignores, in general, specific physical structure. The paper demonstrated, in particular, how the proposed structural modeling approach provides a direct link between the observed self similarity characteristic of measured aggregate network traffic and the strong empirical evidence in favor of heavy tailed, infinite variance phenomena at the level of individual network connections.

A weight's agglomerative method for detecting communities in weighted networks based on weight's similarity

This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge＇s weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes＇ functions effectively. In order to detect the community structure efficiently, a threshold coefficient t~ to evaluate the equivalence of edges＇ weights and a new weighted modularity based on the weight＇s similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism, it presents a weight＇s agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time O（n2 log~）. Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of t~ the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.

THE STRUCTURE AND APPROXIMATION OF A.S. SELF-SIMILAR SET

The structure of any a.s. self-similar set K(w) generated by a class of random elements {gn,wσ} taking values in the space of contractive operators is given and the approximation of K(w) by the fixed points {Pn,wσ} of {gn,ow} is obtained. It is useful to generate the fractal in computer.

Extractive Summarization Using Structural Syntax, Term Expansion and Refinement

This paper investigates a procedure developed and reports on experiments performed to studying the utility of applying a combined structural property of a text’s sentences and term expansion using WordNet [1] and a local thesaurus [2] in the selection of the most appropriate extractive text summarization for a particular document. Sentences were tagged and normalized then subjected to the Longest Common Subsequence (LCS) algorithm [3] [4] for the selection of the most similar subset of sentences. Calculated similarity was based on LCS of pairs of sentences that make up the document. A normalized score was calculated and used to rank sentences. A selected top subset of the most similar sentences was then tokenized to produce a set of important keywords or terms. The produced terms were further expanded into two subsets using 1) WorldNet;and 2) a local electronic dictionary/thesaurus. The three sets obtained (the original and the expanded two) were then re-cycled to further refine and expand the list of selected sentences from the original document. The process was repeated a number of times in order to find the best representative set of sentences. A final set of the top (best) sentences was selected as candidate sentences for summarization. In order to verify the utility of the procedure, a number of experiments were conducted using an email corpus. The results were compared to those produced by human annotators as well as to results produced using some basic sentences similarity calculation method. Produced results were very encouraging and compared well to those of human annotators and Jacquard sentences similarity.

A Novel Prediction Method of Protein Structural Classes Based on Protein Super-Secondary Structure

At present, the feature extraction of protein sequences is the most basic issue to predict protein structural classes and is also the key problem to decide the quality of prediction. In order to predict protein structural classes accurately, we construct a 14-dimensional feature vector based on protein secondary and super-secondary structure information to reflect the content and spatial ordering of the given protein sequences. Among the vector, seven features about -helix bundle, hairpin motifs, Rossman folds, -plaits and other super-secondary structure information are first proposed in our paper. Experiments show that our method improves overall accuracy of lower similarity datasets 1189 and 640 by 0.9% - 3.8% and 0.5% - 4.2% respectively compared with other methods and has a competitive advantage for predicting proteins in and classes.

Comparison of Ontology-Based Semantic-Similarity Measures in the Biomedical Text

In recent years, there are many types of semantic similarity measures, which are used to measure the similarity between two concepts. It is necessary to define the differences between the measures, performance, and evaluations. The major contribution of this paper is to choose the best measure among different similarity measures that give us good result with less error rate. The experiment was done on a taxonomy built to measure the semantic distance between two concepts in the health domain, which are represented as nodes in the taxonomy. Similarity measures methods were evaluated relative to human experts’ ratings. Our experiment was applied on the ICD10 taxonomy to determine the similarity value between two concepts. The similarity between 30 pairs of the health domains has been evaluated using different types of semantic similarity measures equations. The experimental results discussed in this paper have shown that the Hoa A. Nguyen and Hisham Al-Mubaid measure has achieved high matching score by the expert’s judgment.