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Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of covering a variety of nodes and edges. The members of these networks usually have metadata whose analysis can lead to the discovery of knowledge. One way to analyze such data is clustered where high-quality clustering requires effective similarity calculation. Most of the existing clustering methods do not pay attention to the use of metadata or the characteristics of network members. On the other hand, they are only able to process small and medium-sized networks due to the amount of memory and execution speed. This paper presents a hybrid approach for heterogeneous network clustering to overcome these problems. The structural similarity in this approach is calculated by the graph embedding method, which we call learning-based. Attribute similarity is calculated by a scoring method that we call similarity-based. In the experimental study, we compared the proposed method with collaborative approaches based on similarity on the real-world networks. The experimental findings demonstrate the superiority of the proposed method in terms of entropy, memory consumption, execution time, and density in certain cases. [ABSTRACT FROM AUTHOR]

This paper delves into the recent advancements of log-aesthetic curves (LAC), a family of curves for industrial design. Recently it has been redefined in the context of similarity geometry, a branch of Klein geometry. Building upon the work of Miura and Gobithaasan [1], the paper explores LAC properties, delves into the connection with similarity geometry (as detailed in Inoguchi et al. [2],[3],[4],[5]), and examines extensions of LACs, including the σ-curve [6] and τ-curve [7]. [ABSTRACT FROM AUTHOR]

Abundant datasets are critical to train models based on deep learning technologies for ship detection applications. Compared with optical images, ship detection based on synthetic aperture radar (SAR) (especially the high-Earth-orbit spaceborne SAR launched recently) lacks enough training samples. A novel cross-domain attention GAN (CDA-GAN) model is proposed for optical-to-SAR translation, which can generate high-quality SAR amplitude training samples of a target by optical image conversion. This high quality includes high geometry structure similarity of the target compared with the corresponding optical image and low background noise around the target. In the proposed model, the cross-domain attention mechanism and cross-domain multi-scale feature fusion are designed to improve the quality of samples for detection based on the generative adversarial network (GAN). Specifically, a cross-domain attention mechanism is designed to simultaneously emphasize discriminative features from optical images and SAR images at the same time. Moreover, a designed cross-domain multi-scale feature fusion module further emphasizes the geometric information and semantic information of the target in a feature graph from the perspective of global features. Finally, a reference loss is introduced in CDA-GAN to completely retain the extra features generated by the cross-domain attention mechanism and cross-domain multi-scale feature fusion module. Experimental results demonstrate that the training samples generated by the proposed CDA-GAN can obtain higher ship detection accuracy using real SAR data than the other state-of-the-art methods. The proposed method is generally available for different orbit SARs and can be extended to the high-Earth-orbit spaceborne SAR case. [ABSTRACT FROM AUTHOR]

For A ⊆ ω , the coarse similarity class of A, denoted by [ A ] , is the set of all B ⊆ ω such that the symmetric difference of A and B has asymptotic density 0. There is a natural metric δ on the space of coarse similarity classes defined by letting δ ([ A ] , [ B ]) be the upper density of the symmetric difference of A and B. We study the metric space of coarse similarity classes under this metric, and show in particular that between any two distinct points in this space there are continuum many geodesic paths. We also study subspaces of the form { [ A ] : A ∈ } where is closed under Turing equivalence, and show that there is a tight connection between topological properties of such a space and computability-theoretic properties of . We then define a distance between Turing degrees based on Hausdorff distance in the metric space (, δ). We adapt a proof of Monin to show that the Hausdorff distances between Turing degrees that occur are exactly 0, 1 / 2 , and 1, and study which of these values occur most frequently in the senses of Lebesgue measure and Baire category. We define a degree a to be attractive if the class of all degrees at distance 1 / 2 from a has measure 1, and dispersive otherwise. In particular, we study the distribution of attractive and dispersive degrees. We also study some properties of the metric space of Turing degrees under this Hausdorff distance, in particular the question of which countable metric spaces are isometrically embeddable in it, giving a graph-theoretic sufficient condition for embeddability. Motivated by a couple of issues arising in the above work, we also study the computability-theoretic and reverse-mathematical aspects of a Ramsey-theoretic theorem due to Mycielski, which in particular implies that there is a perfect set whose elements are mutually 1-random, as well as a perfect set whose elements are mutually 1-generic. Finally, we study the completeness of (, δ) from the perspectives of computability theory and reverse mathematics. [ABSTRACT FROM AUTHOR]

Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree (S A C D) , Similarity based on Average Betweenness (S A C B) , Similarity based on Average Closeness (S A C C) , and Similarity based on Average Clustering Coefficient (S A C C C) . Our approach involved determining centrality scores for each node, calculating the average centrality for the entire graph, and deriving similarity scores through common neighbors. We then applied centrality scores to these common neighbors and identified nodes with above average centrality. To evaluate our approach, we compared proposed measures with existing local similarity-based link prediction measures, including common neighbors, the Jaccard coefficient, Adamic–Adar, resource allocation, preferential attachment, as well as recent measures like common neighbor and the Centrality-based Parameterized Algorithm (C C P A) , and keyword network link prediction (K N L P) . We conducted experiments on four real-world datasets. The proposed similarity scores based on average centralities demonstrate significant improvements. We observed an average enhancement of 24% in terms of Area Under the Receiver Operating Characteristic (AUROC) compared to existing local similarity measures, and a 31% improvement over recent measures. Furthermore, we witnessed an average improvement of 49% and 51% in the Area Under Precision-Recall (AUPR) compared to existing and recent measures. Our comprehensive experiments highlight the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of biomedical literature has opened up additional opportunities to apply Information Retrieval and NLP methods for mining existing archives. Therefore, a query based retrieval application (QBR) based on hybrid similarity of string and semantic similarity can help medical professionals in their ongoing research. There are multiple benefits of utilizing various NLP applications, for example, information retrieval engine, and clinical diagnosis frameworks for decision support in medical field. These applications depend on the capacity to gauge Hybrid textual similarity (HTS) and N-Gram similarity. Hybrid similarity is the combination of weighting function and word embedding models providing similarity scores with optimum results. In this work, the main focus is on building of a new biomedical document retrieval model which can pull relevant literature for clinical decision support system based on the specific query. There is also an attempt to compare the statistical and NLP based approaches of query based biomedical document retrieval with the baseline systems. Analysis of the proposed method inclusive of semantic word embeddings shows promising results for both of the suggested similarities. [ABSTRACT FROM AUTHOR]

The rolling bearing fault signals are accompanied with the specific periodic impulse components in case of fault. Due to the background noise, the periodic impulse component is extremely weak and difficult to be extracted. The periodic segment matrix (PSM) has excellent singular value aggregation property and can accurately separate the periodic impulse components from the signal. Based on this, a symplectic period mode decomposition (SPMD) method is proposed. Firstly, the singular value squared difference ratio (SVSDR) spectrum is defined, and the embedded dimension of PSM is determined. Then, the symplectic geometry component matrix is obtained by symplectic geometry similarity transformation, and the first symplectic geometry period component (SPC) is obtained by periodic impact intensity (PII). Finally, the spectral L2/L1 norm (SNL2/L1) index is used as the termination condition, and the iterative decomposition is terminated when the SNL2/L1 of the residual signal decreases. The emulation and experimental signals results indicate that the SPMD method can extract periodic impulse components accurately and is an effective rolling bearing fault diagnosis method. [ABSTRACT FROM AUTHOR]

Sentence clustering plays a central role in various text-processing activities and has received extensive attention for measuring semantic similarity between compared sentences. However, relatively little focus has been placed on evaluating clustering performance using available similarity measures that adopt low-dimensional continuous representations. Such representations are crucial in domains like sentence clustering, where traditional word co-occurrence representations often achieve poor results when clustering semantically similar sentences that share no common words. This article presents a new implementation that incorporates a sentence similarity measure based on the notion of embedding representation for evaluating the performance of three types of text clustering methods: partitional clustering, hierarchical clustering, and fuzzy clustering, on standard textual datasets. This measure derives its semantic information from pre-training models designed to simulate human knowledge about words in natural language. The article also compares the performance of the used similarity measure by training it on two state-of-the-art pre-training models to investigate which yields better results. We argue that the superior performance of the selected clustering methods stems from their more effective use of the semantic information offered by this embedding-based similarity measure. Furthermore, we use hierarchical clustering, the best-performing method, for a text summarization task and report the results. The implementation in this article demonstrates that incorporating the sentence embedding measure leads to significantly improved performance in both text clustering and text summarization tasks. [ABSTRACT FROM AUTHOR]

Cyclodextrins (CDs), due to its unique 'outer hydrophilic and inner hydrophobic' structure, can be used as excellent carriers to protect active aroma compounds. The hydrodynamic conditions in the reactor substantially influence the characteristics of the cyclodextrin inclusions. Based on this, this study investigated the effects of the impeller type and the rotational speed on the characteristics of the inclusion complexes, meanwhile the corresponding scale-up process was also investigated. Results showed that the pitched blade turbine was the optimum impeller due to better axial flow performance. The average particle size, polydispersity index (PDI), loading capacity and the major aroma concentration of the inclusion complexes prepared at 5 and 120 L reactors did not have significant difference compared to the inclusion complexes prepared at 0.5 L reactor under conditions of geometry similarity and constant power per unit volume, which verified the feasibility of the scale-up rule of the encapsulation process. [ABSTRACT FROM AUTHOR]

Ligand-based virtual screening aims to reduce the cost and duration of small molecule drug discovery campaigns. Shape similarity can be used to screen large databases, with the goal of predicting potential new hits by comparing with molecules with known favourable properties. This paper presents the theory underpinning RGMolSA, a new alignment-free and mesh-free surface-based molecular shape descriptor derived from the mathematical theory of Riemannian geometry. The treatment of a molecule as a series of intersecting spheres allows the description of its surface geometry using the Riemannian metric, obtained by considering the spectrum of the Laplacian. This gives a simple vector descriptor constructed of the weighted surface area and eight non-zero eigenvalues, which capture the surface shape. We demonstrate the potential of our method by considering a series of PDE5 inhibitors that are known to have similar shape as an initial test case. RGMolSA displays promise when compared with existing shape descriptors and in its capability to handle different molecular conformers. [ABSTRACT FROM AUTHOR]

A compact and tractable two-dimensional model to generate the topological network structure of domain walls in BiFeO3 thin films is presented in this study. Our method combines the stochastic geometry parametric model of the centroidal Voronoi tessellation optimized using the von Neumann entropy, a novel information-theoretic tool for networks. The former permits the generation of image-based stochastic artificial samples of domain wall networks, from which the network structure is subsequently extracted and converted to the graph-based representation. The von Neumann entropy, which reflects information diffusion across multiple spatiotemporal scales in heterogeneous networks, plays a central role in defining a fitness function. It allows the use of the network as a whole rather than using a subset of network descriptors to search for optimal model parameters. The optimization of the parameters is carried out by a genetic algorithm through the maximization of the fitness function and results in the desired graph-based network connectivity structure. Ground truth empirical networks are defined, and a dataset of network connectivity structures of domain walls in BiFeO3 thin films is undertaken through manual annotation. Both a versatile tool for manual network annotation of noisy images and a new automatic network extraction method for highquality images are developed. [ABSTRACT FROM AUTHOR]

Geometries of eroding landscapes contain important information about geologic, climatic, biotic and geomorphic processes. They are also characterized by variability, which makes disentangling their origins challenging. Observations and physical models of fluvial processes, which set the pace of erosion on most continents, emphasize complexity and variability. By contrast, the spectral content of longitudinal river profiles and similarity of geometries at scales greater than approximately 100km highlight relatively simple emergent properties. A general challenge then, addressed in this manuscript, is development of a theory of landscape evolution that embraces such scale-dependent insights. We do so by incorporating randomness and probability into a theory of fluvial erosion. First, we explore the use of stochastic differential equations of the Langevin type, and the Fokker--Planck equation, for predicting migration of erosional fronts. Second, analytical approaches incorporating distributions of driving forces, critical thresholds and associated proxies are developed. Finally, a linear programming approach is introduced, that, at its core, treats evolution of longitudinal profiles as a Markovian stochastic problem. The theory is developed essentially from first principles and incorporates physics governing fluvial erosion. We explore predictions of this theory, including the natural growth of discontinuities and scale-dependent evolution, including local complexity and emergent simplicity. [ABSTRACT FROM AUTHOR]

Multimodal change detection (CD) is an increasingly interesting yet highly challenging subject in remote sensing. To facilitate the comparison of multimodal images, some image regression methods transform one image to the domain of the other image, allowing for images comparison in the same domain as in unimodal CD. In this paper, we begin by analyzing the limitations of previous image structure based regression models that only rely on similarity relationships. Then, we highlight the significance of incorporating dissimilarity relationships as a complementary approach to more comprehensively characterize and utilize the image structure. In light of this, we propose a novel method for multimodal CD called Similarity and Dissimilarity induced Image Regression (SDIR). Specifically, SDIR construct a similarity based k-nearest neighbors (KNN) graph and a dissimilarity based k-farthest neighbors (KFN) graph, where the former mainly characterizes the low-frequency information and the latter captures the high-frequency information in spectral domain. Therefore, the proposed SDIR that incorporates similarity (low-frequency) and dissimilarity (high-frequency) relationships enables to achieve better regression performance. After completing the image regression, we utilize a Markovian based fusion segmentation model to combine the change fusion and change extraction processes for improving the final CD accuracy. The proposed method's effectiveness is demonstrated through experiments on six real datasets and compared with eleven advanced and widely used methods, achieving 5.6% improvements in the average Kappa coefficient. The source code is accessible at https://github.com/yulisun/SDIR. [ABSTRACT FROM AUTHOR]

This article presents a clustering effectiveness measurement model based on merging similar clusters to address the problems experienced by the affinity propagation (AP) algorithm in the clustering process, such as excessive local clustering, low accuracy, and invalid clustering evaluation results that occur due to the lack of variety in some internal evaluation indices when the proportion of clusters is very high. First, depending upon the "rough clustering" process of the AP clustering algorithm, similar clusters are merged according to the relationship between the similarity between any two clusters and the average inter-cluster similarity in the entire sample set to decrease the maximum number of clusters Kmax. Then, a new scheme is proposed to calculate intra-cluster compactness, inter-cluster relative density, and inter-cluster overlap coefficient. On the basis of this new method, several internal evaluation indices based on intra-cluster cohesion and inter-cluster dispersion are designed. Results of experiments show that the proposed model can perform clustering and classification correctly and provide accurate ranges for clustering using public UCI and NSL-KDD datasets, and it is significantly superior to the three improved clustering algorithms compared with it in terms of intrusion detection indices such as detection rate and false positive rate (FPR). [ABSTRACT FROM AUTHOR]

This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal-to-noise environments. Thus, in this work, a comprehensive single-channel mixed signal separation algorithm was proposed based on the combination of Symplectic Geometry Mode Decomposition (SGMD) and the FastICA algorithm. First, SGMD-FastICA uses SGMD to decompose single-channel mixed signals, and then it uses the Pearson correlation coefficient to select the Symplectic Geometry Components that exhibit higher correlation coefficients with the mixed signals. Then, these components are expanded with the single-channel mixed signals into virtual multi-channel signals and input into the FastICA algorithm. The simulation results show that the SGMD algorithm could eliminate noise interference while keeping the raw time series unchanged, which is achievable through symplectic geometry similarity transformation during the decomposition of mixed signals. Comparative experiment results also show that compared with the EMD-FastICA and VMD-FastICA, the SGMD-FastICA algorithm has the best separation effect for single-channel mixed signals. The SGMD-FastICA algorithm represents an improved solution that addresses the limitations of the FastICA algorithm, enabling the direct separation of single-channel mixed signals, while also addressing the challenge of proper signal separation in noisy environments. [ABSTRACT FROM AUTHOR]

The mutual identification of spatial objects is a fundamental issue when updating geographic data with other data sets. Representations of spatial objects in different sources may not have the same identifiers, which would unambiguously assign them to each other. Intersections of spatial objects can be used for this purpose, but this does not allow for the detection of possible changes and their quantification. The aim of this paper is to propose a simple, applicable procedure for calculating the shape similarity measure, which should be able to efficiently identify different representations of spatial objects in two data sources, even though they may be changed or generalised. The main result is the aggregated index of shape similarity and instructions for its calculation and implementation. The shape similarity index is based on the calculation of the set similarity, the distance of the boundaries, and the differences in the area, perimeter, and number of the vertices of areal spatial objects. In the case study, the footprints of the building complexes in Dúbravka (part of the city of Bratislava, the capital of Slovakia) are compared using data from OpenStreetMap and INSPIRE (Infrastructure for Spatial Information in Europe) Buildings. A contribution to the quality check of the OpenStreetMap data is then a secondary result. The proposed method can be effectively used in the semi-automatic integration of heterogeneous data sources, updating the data source with other spatial data, or in their quality control. [ABSTRACT FROM AUTHOR]

Sedimentary structures provide critical information for the reconstruction of ancient environments of Earth and other planets. Fluvial dune cross sets, structures that record dune migration via the filling of leading troughs along ancient riverbeds, are particularly useful. Most quantitative methods for interpreting cross sets require thickness measurements, but the growth of planview imaging of sedimentary rocks on Earth and Mars has introduced a need for methods to interpret planview exposures of cross sets. Here, we measured 359 cross-set widths exposed along ancient channel belts of the Cretaceous Cedar Mountain Formation exposed in Utah, USA, and compared these widths to published thicknesses (n = 350). We found that the mean cross-set width was 1.29 m, 9.3 times the mean thickness. The normalized distributions of widths and thicknesses were statistically similar and thus contained similar paleoenvironmental information. Numerical experiments representing a series of trough cut-and-fill structures show that cross-set reworking is equally important in setting width as thickness and that the observed cross-set widths could be explained by a gamma-distributed range of trough widths with a mean of 2.96 ± 0.25 m, ~2.3 times cross-set width. Given the similarity of dune-field geometries across depositional settings and planetary boundary conditions, we suggest this cross-set width-to-thickness relationship could be useful for interpreting other cross sets exposed in planview. [ABSTRACT FROM AUTHOR]

In marine applications, the scaled wind turbine model (SWTM) based aerodynamic testing apparatus is regarded as one of the most effective approaches to predict the aerodynamic performance of offshore floating wind turbines (OFWTs). To fully disclose the aerodynamics of OFWTs, such kind of apparatus should meet two design requirements: (1) accurate representation of the aerodynamic characteristics of reference wind turbines and (2) reasonable reproduction of the environmental factors from real-world wind-wave-coupled field. Baring with these considerations, the authors propose a cost-effective SWTM-based aerodynamic testing apparatus, which consists of an innovative SWTM, a wind-wave simulator (WWS) and a data acquisition and analyses system. The proposed SWTM is featured by a double-deck tower, which satisfies the requirements of similarity in geometry, stiffness and mass, and a set of redesigned blades. The proposed WWS is capable of simulating both the time-varying inflow wind and the wave-induced platform motions through a wind field generator and a motion simulator. Based on the developed apparatus, a series of aerodynamic tests are conducted to validate the effectiveness of the apparatus design. In addition, the coupling effects of inflow wind and platform motions on the aerodynamics of OFWT are further revealed through experimental tests. [ABSTRACT FROM AUTHOR]

Vertebrates and cephalopods are the two major animal groups that view the world through sophisticated camera-type eyes. There are of course exceptions: nautiloid cephalopods have more simply built pinhole eyes. Excellent camera type eyes are also found in other animals, such as some spider groups, a few snails, and certain marine worms, but the vast majority of large camera-type eyes belong to cephalopods and vertebrates. Vertebrates and cephalopods also devote major parts of their brains to the processing of visual information. Obviously, there are differences in eye performance among cephalopods and vertebrates, but there are no major subgroups where vision seems to have low priority. The similarity in eye geometry is striking, especially between fish and coleoid cephalopods, with a hemispherical retina centred around a spherical lens. Do these similarities mean that vertebrate and cephalopod eyes are equally good? Comparing the eyes of vertebrates and cephalopods reveals many fundamental differences with surprisingly small consequences for vision, but also one difference that means that cephalopods and vertebrates do not share the same visual world. In this Primer, Dan-E. Nilsson, Sönke Johnsen and Eric Warrant compare the development, geometry, and performance of the camera-type eyes of vertebrates and cephalopods. [ABSTRACT FROM AUTHOR]

This paper considers various numerical functions that determine the degree of similarity between two finite sequences. These similarity measures are based on the concept of embedding for sequences, which we define here. A special case of this embedding is a subsequence. Other cases additionally require equal distances between adjacent symbols of a subsequence in both sequences. This is a generalization of the concept of the substring with unit distances. Moreover, equality of distances from the beginning of the sequences to the first embedded symbol or from the last embedded symbol to the end of the sequences may be required. In addition to the last two cases, an embedding can occur in the sequence more than once. In the literature, functions such as the number of common embeddings or the number of pairs of occurrences of embeddings in a sequence are used. We introduce three additional functions: the sum of lengths of common embeddings, the sum of the minimum numbers of occurrences of a common embedding in both sequences, and the similarity function based on the longest common embedding. In total, we consider 20 numerical functions; for 17 of these functions, algorithms (including new ones) of polynomial complexity are proposed; for two functions, algorithms of exponential complexity with a reduced exponent are proposed. In Conclusions, we briefly compare these embeddings and functions. [ABSTRACT FROM AUTHOR]

s t r i n g U t i l s. c o n v e r t A b s t r a c t M a t h H t m l (formulaUtilTools.convertMathHtml( s t r i n g U t i l s. c o n v e r t M m l (!article.abstractinfoEn))) Ontology serves as a central technique in the semantic web to elucidate domain knowledge. The challenge of dealing with the heterogeneity introduced by diverse domain ontologies necessitates ontology matching, a process designed to identify semantically interconnected entities within these ontologies. This task is inherently complex due to the broad, diverse entities and the rich semantics inherent in vocabularies. To tackle this challenge, we bring forth a new interactive ontology matching method with local and global similarity deviations (IOM-LGSD) for ontology matching, which consists of three novel components. First, a local and global similarity deviation (LGSD) metrics are presented to measure the consistency of similarity measures (SMs) and single out the less consistent SMs for user validation. Second, we present a genetic algorithm (GA) based SM selector to evolve the SM subsets. Lastly, a problem-specific induced ordered weighting aggregating (IOWA) operator based SM aggregator is proposed to assess the quality of selected SMs. The experiment evaluates IOM-LGSD with the ontology alignment evaluation initiative (OAEI) Benchmark and three real-world sensor ontologies. The evaluation underscores the effectiveness of IOM-LGSD in efficiently identifying high-quality ontology alignments, which consistently outperforms comparative methods in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Helping learners to develop a solid grasp of geometric concepts poses a challenge for teachers. Therefore, it is important that teachers have a sound understanding of the geometry they teach. The aim of this qualitative study was to explore pre-service teachers' (PST's) understanding of the concept of similarity in Euclidean geometry and to use van Hiele's theory to explain misconceptions evidenced by the PSTs. Data in this study were collected from 34 first-year PSTs studying for a Bachelor of Education degree in high school mathematics. The authors analysed the written responses to a 13-item worksheet and also conducted interviews with seven of the participants. The analysis of the data was guided by van Hiele's theory which was used to identify misconceptions amongst PST's who had not yet developed the appropriate reasoning skills linked to particular van Hiele levels of geometric thought. It was found that these students used reasoning that is characteristic of the elementary levels to make judgments. Many PST's faced challenges with similarity notation and the process of proving the similarity between two figures. This study recommends that PST's should be given more opportunities to connect visual and analytic representations of similarity. [ABSTRACT FROM AUTHOR]

Prune dwarf virus (PDV) infection affects the production of stone fruits (Prunus spp.) around the world, including in Turkey. In this study, we analyzed the available nucleotide (nt) and amino acid (aa) sequences of P1, P2, movement protein (MP), and coat protein (CP) genes to assess the molecular variability and structure of non-Turkish and Turkish PDV populations. Global isolates were clustered into three major groups in nt-based phylogenetic trees of MP and CP. Comparison of geographically different isolates showed that the MP aa sequences were more conserved than the CP aa sequences. The major aa changes in MP and CP showed the most effect on group 3 isolates. MP and CP-based analyses revealed the lowest number of variable sites (S), total number of mutations (η), average number of nt differences between sequences (k), and nt diversity (per site) (π) values in group 3 isolates, indicating high genomic similarities. The Turkish isolates manifested the highest S and η, and the second highest k and π values were observed in the comparison with CP, confirmed the high intra-group divergence of the Turkish isolates belonging to the three phylogroups. A stronger purifying selection pressure was observed in the MP region than in the CP region. Neutrality tests deduced negative values for all the MP and CP phylogroups and positive values for the P1, P2, Bulgarian, and Polish populations. MP and CP comparisons showed the divergence of the three phylogroups, as revealed by the results of the KS*, KST*, Z*, and Snn permutation statistical tests. The fixation index (FST) values suggested that non-Turkish PDV isolates had a higher genetic distance compared with Turkish isolates from group 3 and the Turkish isolates from the other two groups. [ABSTRACT FROM AUTHOR]

Summary: Collaborative filtering recommendation systems, which analyze sets of user ratings, have been applied to various domains and have resulted in considerable improvements in the traditional recommendation system. However, they still have problems with data sparsity and cold‐start of the user ratings. To solve these problems, we present a hybrid recommendation approach by combining collaborative filtering methods and word embedding‐based content analysis. This study focuses on the movie domain, and therefore, the contents of the items are represented as a set of features such as titles, genres, directors, actors, and plots. The main aim of this paper is to understand the content of the movie plot using a word embedding to improve the measurement of similarity of each plot content to other plot content (called plot embedding). To enhance the accuracy in measuring the similarity between movies, we also consider other features such as titles, genres, directors, and actors extracted from movies. In the experiments, the movie dataset was collected by our crowdsourcing platform, which is the OMS platform. The experimental findings indicate that the proposed approach can enhance the efficiency of applied collaborative filtering recommendation systems. [ABSTRACT FROM AUTHOR]

Recent years have witnessed an increasing focus on graph-based semi-supervised learning with Graph Neural Networks (GNNs). Despite existing GNNs having achieved remarkable accuracy, research on the quality of graph supervision information has inadvertently been ignored. In fact, there are significant differences in the quality of supervision information provided by different labeled nodes, and treating supervision information with different qualities equally may lead to sub-optimal performance of GNNs. We refer to this as the graph supervision loyalty problem, which is a new perspective for improving the performance of GNNs. In this paper, we devise FT-Score to quantify node loyalty by considering both the local feature similarity and the local topology similarity, and nodes with higher loyalty are more likely to provide higher-quality supervision. Based on this, we propose LoyalDE (Loyal Node D iscovery and E mphasis), a model-agnostic hot-plugging training strategy, which can discover potential nodes with high loyalty to expand the training set, and then emphasize nodes with high loyalty during model training to improve performance. Experiments demonstrate that the graph supervision loyalty problem will fail most existing GNNs. In contrast, LoyalDE brings about at most 9.1% performance improvement to vanilla GNNs and consistently outperforms several state-of-the-art training strategies for semi-supervised node classification. [ABSTRACT FROM AUTHOR]

There are several educational software (ES) used in the classroom environment for the teaching and learning of geometric contents that are part of the Portuguese basic education mathematics program. There are studies that show that the use of this type of artifact has a fundamental role in the behavior of students, raising, among other aspects, a greater motivation for learning mathematics. The aim of this work is to explore and describe implications for the behavior and learning of students in the 7th grade of Portuguese basic education, in face of a pedagogical practice that involves carrying out tasks using ES Plickers, in the theme similarities of the domain geometry and measurement, throughout intervention carried out. The adopted methodology presents characteristics of a quasi-experimental study. The participants were 61 students from three classes of a school in the north of Portugal, followed during eight consecutive classes. A set of tasks using Plickers, tests and a questionnaire survey were used as instruments for data collection. The results point to positive increments, at a behavioral level, as well as in the evolution of learning, in view of the use of this methodology in the classroom. [ABSTRACT FROM AUTHOR]

We describe a method for the post-hoc interpretation of a neural network (NN) trained on the global and local minima of neutral water clusters. We use the structures recently reported in a newly published database containing over 5 × 106 unique water cluster networks (H2O)N of size N = 3–30. The structural properties were first characterized using chemical descriptors derived from graph theory, identifying important trends in topology, connectivity, and polygon structure of the networks associated with the various minima. The code to generate the molecular graphs and compute the descriptors is available at https://github.com/exalearn/molecular-graph-descriptors, and the graphs are available alongside the original database at https://sites.uw.edu/wdbase/. A Continuous-Filter Convolutional Neural Network (CF-CNN) was trained on a subset of 500 000 networks to predict the potential energy, yielding a mean absolute error of 0.002 ± 0.002 kcal/mol per water molecule. Clusters of sizes not included in the training set exhibited errors of the same magnitude, indicating that the CF-CNN protocol accurately predicts energies of networks for both smaller and larger sizes than those used during training. The graph-theoretical descriptors were further employed to interpret the predictive power of the CF-CNN. Topological measures, such as the Wiener index, the average shortest path length, and the similarity index, suggested that all networks from the test set were within the range of values as the ones from the training set. The graph analysis suggests that larger errors appear when the mean degree and the number of polygons in the cluster lie further from the mean of the training set. This indicates that the structural space, and not just the chemical space, is an important factor to consider when designing training sets, as predictive errors can result when the structural composition is sufficiently different from the bulk of those in the training set. To this end, the developed descriptors are quite effective in explaining the results of the CF-CNN (a.k.a. the "black box") model. [ABSTRACT FROM AUTHOR]

Objectives: In the field of line matching and checking, the geometric attributes of the individual line are weakly stable, and the non one-to-one matching results are difficult to be checked. To address the problems above, a line matching algorithm based on pair-wise geometric features and individual line descriptor constraints was proposed. Methods: For matching process, first, two line segments which satisfied certain geometric constraints in the neighborhood were grouped into a line pair and matched as a whole. Second, the epipolar constraint of the intersection in the line pair was used to determine the matching range. And characteristic angles within line pairs, distance ratio between line segments and the radiation information of the neighborhood of line pairs were used to narrow the range of the matching candidates. Finally, the final matching pairs was obtained by calculating the gray similarity of triangle region. For checking process, initially according to the angle of line with epipolar and the slope of line, the corresponding relation between individual lines was established. Moreover, each corresponding line pair was split into two groups of corresponding individual lines. Then, descriptors were established for two single lines in each group and the similarity between the two line descriptors was calculated. Eventually, collinear geometry and descriptor similarity were combined to check the matching results, eliminate the false matches. Collinear lines in the results were merged and one-to-one matching results were obtained. Results: Aerial images with typical texture features and close-range images of different transformation types were selected for experiments. The results demonstrate that the proposed algorithm has a high matching accuracy. The matching accuracy rate is higher than 95% in complex scenes with similar texture, perspective change, rotation change, scale change and illumination change. Conclusions: The proposed algorithm has good robustness to different types of images, and also has advantages for complex line matching check. [ABSTRACT FROM AUTHOR]

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Context. The development of effective distance metrics and similarity measures for categorical features is an important task in data analysis, machine learning, and decision theory since a significant portion of object properties is described by non-numerical values. Typically, the dependence between categorical features may be more complex than simply comparing them for equality or inequality. Such attributes can be relatively similar, and to construct an effective model, it is necessary to consider this similarity when calculating distance or similarity measures. Objective. The aim of the study is to improve the efficiency of solving practical data analysis problems by developing mathematical tools for determining the similarity of objects based on categorical ordered features. Method. A distance based on weighted Manhattan distance and a similarity measure for determining the similarity of objects based on categorical ordinal features (i.e. a linear order with scales of preference considering the problem domain can be specified on the attribute value set) are proposed. It is proven that the distance formula satisfies the axioms of non-negativity, symmetry, triangle inequality, and upper bound, and therefore is a distance metric in the space of ranked categorical features. It is also proven that the similarity measure presented in the study satisfies the axioms of boundedness, symmetry, maximum and minimum similarity, and is described by a decreasing function. Results. The developed approach has been implemented in an applied problem of determining the degree of similarity between objects described by ordered categorical features. Conclusions. In this study, mathematical tools were developed to determine similarity between structured data described by categorical attributes that can be ordered based on a specific priority in the form of a ranking system with preferences. Their properties were analyzed. Experimental studies have shown the convenience and “intuitive understanding” of the logic of data processing in solving practical problems. The proposed approach can provide the opportunity to conduct new meaningful research in data analysis. Prospects for further research lie in the experimental use of the proposed tools in practical tasks and in studying their effectiveness. [ABSTRACT FROM AUTHOR]

Various detection methods have been proposed for defense against group shilling attacks in recommender systems; however, these methods cannot effectively detect attack groups generated based on adversarial attacks (e.g., GOAT) or mixed attack groups. In this study, we propose a two-stage method, called KC-GCN, which is based on k -cliques and graph convolutional networks. First, we construct a user relationship graph, generate suspicious candidate groups, and extract influential users by calculating the user nearest-neighbor similarity. We construct the user relationship graph by calculating the edge weight between any two users through analyzing their similarity over suspicious time intervals on each item. Second, we combine the extracted user initial embeddings and the structural features hidden in the user relationship graph to detect attackers. On the Netflix and sampled Amazon datasets, the detection results of KC-GCN surpass those of the state-of-the-art methods under different types of group shilling attacks. The F1-measure of KC-GCN can reach above 93% and 87% on these two datasets, respectively. [ABSTRACT FROM AUTHOR]

The event that predicts the next item that the people will buy in the next basket is defined as the next basket recommendation task. Though this task has been widely studied, previous works still have the following two challenges: 1) Previous methods usually make predictions via only considering the correlations between items within the basket, while ignoring the similarity relationships between baskets, which may also have the potential capability of improving the basket modeling. 2) Previous studies usually use the Recurrent Neural Network (RNN), especially attention-based RNN and fixed attention mechanisms, to model the relationships between items, which fail to capture the local network structures and their high-order sequential relationships. To overcome the above challenges, in this work, we propose a Hyperedge-based Graph Attention Network for Next Basket Recommendation, namely HGAT-BR, as our solution. To be more specific, to incorporate the similarity relationship between two baskets, we treat baskets as sets of items, and further model them as hyperedges in a hypergraph. Then, the basket representation learning can be converted to the hyperedge embedding task, where a hyperedge-based graph attention network is proposed. To further consider the correlation information among items, we treat items within a basket as nodes in a vanilla graph and learn node representations via another graph neural network. Then, we concatenate these two types of representations to make predictions. Note that, we train the basket and note representation learning simultaneously in an end-to-end manner. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed method compared with several state-of-the-art next basket recommendation methods. [ABSTRACT FROM AUTHOR]

Existing image segmentation algorithms used for the computed tomography (CT) images of asphalt concrete mostly ignore the similarity of aggregate phase geometry between adjacent CT slices, thus increasing the variability in the aggregate phase pixel values between adjacent slices and leading to a large number of model defects, e.g., interconnected aggregates, flaky aggregates, and incomplete aggregates. The developed mesostructural models with these defects pose a challenge to following simulation operations. To address this issue, an improved procedure for the 3D reconstruction of asphalt concrete mesostructures considering the similarity of aggregate phase geometry between adjacent slices was developed, which includes two adjacent-slice pixel-value-correction algorithms, a multi-directional multiple-correction method, and an image pixel interpolation process. First, the bilinear interpolation algorithm was employed to improve the pixel density of 2D CT images and the average filtering algorithm was used to reduce the noise of the CT images. Subsequently, the OTSU method was employed to separate the asphalt mortar matrix phase from the aggregate phase, and the marker-based watershed segmentation method was used to separate the interconnected aggregates. Finally, the adjacent-slice pixel-value-correction algorithm was used to recover the similarity of aggregate phase geometry between adjacent CT slices, and the multi-directional multiple-correction method was used to further enhance the geometric similarity. The results show that the developed 3D reconstruction procedure removes most of the model defects in the 3D mesostructural model of asphalt concrete, thus realistically maintaining the 3D spatial distribution features and contour characteristics. [ABSTRACT FROM AUTHOR]

When dealing with approximate wave functions, molecular properties can be computed either as expectation values or as derivatives of the energy with respect to a corresponding perturbation. In this work, the intermediate state representation (ISR) formalism for the computation of expectation values is compared to the Lagrange formalism following a derivative ansatz, which are two alternative approaches of which neither one can be considered superior in general. Within the ISR formalism, terms are included up to a given order of perturbation theory only, while in the Lagrange formalism, all terms are accounted for arising through the differentiation. Similarities and differences of the Lagrange and ISR formalism are illustrated using explicit working equations for selected methods and analyzing numerical results for a range of coupled-cluster as well as algebraic-diagrammatic construction (ADC) methods for excited states. The analysis explains why the ADC(3/2) method is able to yield a large amount of the orbital-relaxation effects for p-h states in contrast to ADC(2) although the same second-order ISR is used to represent the corresponding operator. [ABSTRACT FROM AUTHOR]

Cluster perturbation (CP) theory was developed in Paper I [F. Pawłowski et al., J. Chem. Phys. 150, 134108 (2019)] for a coupled cluster (CC) target state and is extended in this paper to comprehend a cluster linear (CL) target state, for which the embedding of a CC parent state in the target excitation space is described using a linear parametrization. The theory is developed for determining the energy and molecular properties for a CL state. When CP theory is applied to a CL target state, a series of corrections is determined in orders of the CC parent-state similarity-transformed fluctuation potential, where the zeroth-order term is the energy or molecular property of the CC parent state and where the series formally converges to the energy or molecular property of the CL target state. The determination of energies and molecular properties is simpler for a CL state than for a CC state because the CL state is linearly parametrized. The amplitude equations are quadratic for a CL target state, while quartic for a CC target state, and molecular property expressions for a CL target state have the same simple structure as for a configuration interaction state. The linear parametrization introduces non-size-extensive contributions in the energy and molecular property expressions. However, since the linear parametrization describes the embedding of the CC parent state in the target excitation space, the energy and molecular properties for a CL state are weakly size-extensive. For the energy, weak size-extensivity means that non-size-extensive contributions enter in sixth and higher orders in the CP energy series, whereas for molecular properties, weak size-extensivity means that non-size-extensive contributions enter in second and higher orders. Weak size-extensivity therefore has a little or vanishing effect on calculated energies or molecular properties. The determination of the CP energy and molecular property corrections does not require that amplitude or response equations are solved explicitly for the target state and it becomes computationally tractable to use low-order corrections from these series to obtain energies and molecular properties of CL target state quality. For three simple molecules, HF, N2, and CH2, the accuracy of the CL approach for ground-state energies is tested using a parent state including single and double excitations (i.e., the CC singles-and-doubles state, CCSD) and a target state that includes triple excitations. It is found that the size-extensive fifth-order CL energies deviate by less than 0.0001 hartree from the energies of a target CC that includes triple excitations (i.e., the CC singles-doubles-and-triples state, CCSDT). CP theory with a CL target state therefore becomes a very attractive replacement of standard CC theory for high-accuracy energy and molecular property calculations, in which triple and higher excitation levels are considered. [ABSTRACT FROM AUTHOR]

Mining of closely spaced multilayer orebodies brings the problems of significant disturbance between adjacent mining layers and drastic structural changes in surrounding rock, which brings the need for a more effective stope support method. Previous research has made sound analysis on filling or bolt support, but neither of them can solely provide ideal support effects. Thus, a novel bolt-filling support method is proposed by utilizing the synergistic effect of rock bolts (cable bolts) and filling. Numerical simulation and similarity experiments were conducted in this research to analyze the support effect of this method for multilayer ore mining. For numerical simulation, the distinct-element modelling framework PFC2D (Particle Flow Code in 2 Dimensions) was applied for four support scenarios based on the calibration of the microscopic parameters of particles in vanadium shale ores. The numerical simulation results show that the number of fractures decreases from 1311 without support through 652 with 95% filling support to 410 with bolt-filling support, which is resulted from the redistribution of the force chains due to support change. On the other hand, a 300 cm ×180 cm × 40 cm similarity model with a geometry similarity constant of 100 was established based on the 4# rock layer profile of Mount Shangheng. Two parts of similarity experiments were conducted to investigate the strains around the stopes in multi-layer ore mining for three support scenarios. The experiment results prove that the highest strain is in the center of the roof on the upper goaf, and the roof-bolt filling support induces smaller strains than zero support and conventional filling support. Finally, an effective bolt-filling support system has been developed and validated, which can improve the safety and the stability of the roofs and interlayers during the mining process of closely spaced multilayer orebody by reducing the overall load and fractures in surrounding rock. [ABSTRACT FROM AUTHOR]

In the shaking table test of a soil–pile–bridge structure system, it is difficult to keep the similarity relations of the model structure and that of the model soil consistent. Due to the difference of geometry and material similarity ratios for the model structure and model soil, the determination of the duration compression ratio of input motions is a key problem. The spectrum characteristics of input motions will be varied by the duration compression ratio so that the seismic responses of structure and soil system will be affected. There are three commonly used approaches to determine the duration compression ratio of input motions in shaking table tests: the time similarity ratio of model structure; the time similarity ratio of model soil; and uncompressed. To study the influence of the duration compression ratio on the seismic response of a soil–structure system in shaking table tests, the El Centro record and the Wolong record were chosen as the input motions, and the durations were compressed by the three commonly used approaches in this paper. The influence of the duration compression ratios of the input motions on the acceleration response of a soil–pile–bridge structure system was compared and analyzed through a series of shaking table tests. The results showed that the duration compression ratio affected the acceleration response of the model soil and the model structure, and the effect was more obvious when the peak ground acceleration (PGA) was small. If the research is focused on the seismic response of the soil, it is recommended to use the time similarity ratio of the model soil to compress the input motions. If the research is focused on the seismic response of the structure, it is recommended to use the time similarity ratio of the model structure to compress the input motions. This study could provide a reference for the design of the shaking table test of a soil–pile–bridge structure system. [ABSTRACT FROM AUTHOR]

Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity. [ABSTRACT FROM AUTHOR]

Hypergraph matching has been attractive in the application of computer vision in recent years. The interference of external factors, such as squeezing, pulling, occlusion, and noise, results in the same target displaying different image characteristics under different influencing factors. After extracting the image feature point description, the traditional method directly measures the feature description using distance measurement methods such as Euclidean distance, cosine distance, and Manhattan distance, which lack a sufficient generalization ability and negatively impact the accuracy and effectiveness of matching. This paper proposes a metric-learning-based hypergraph matching (MLGM) approach that employs metric learning to express the similarity relationship between high-order image descriptors and learns a new metric function based on scene requirements and target characteristics. The experimental results show that our proposed method performs better than state-of-the-art algorithms on both synthetic and natural images. [ABSTRACT FROM AUTHOR]

Entity alignment is used to determine whether entities from different sources refer to the same object in the real world. It is one of the key technologies for constructing large-scale knowledge graphs and is widely used in the fields of knowledge graphs and knowledge complementation. Because of the lack of semantic connection between the visual modality face attribute of the person entity and the text modality attribute and relationship information, it is difficult to model the visual and text modality into the same semantic space, and, as a result, that the traditional multimodal entity alignment method cannot be applied. In view of the scarcity of multimodal person relation graphs datasets and the difficulty of the multimodal semantic modeling of person entities, this paper analyzes and crawls open-source semi-structured data from different sources to build a multimodal person entity alignment dataset and focuses on using the facial and semantic information of multimodal person entities to improve the similarity of entity structural features which are modeled using the graph convolution layer and the dynamic graph attention layer to calculate the similarity. Through verification on the self-made multimodal person entity alignment dataset, the method proposed in this paper is compared with other entity alignment models which have a similar structure. Compared with AliNet, the probability that the first item in the candidate pre-aligned entity set is correct is increased by 12.4% and average ranking of correctly aligned entities in the candidate pre-aligned entity set decreased by 32.8, which proves the positive effect of integrating multimodal facial information, applying dynamic graph attention and a layer-wise gated network to improve the alignment effect of person entities. [ABSTRACT FROM AUTHOR]

Heterogeneous graph refers to a type of graph data characterized by its diverse node types and relation types, containing rich structures, features and heterogeneous information. How to fully utilize and capture these key information to generate effective node representations poses a great challenge in heterogeneous graph analysis and mining. To better tackle this problem, a heterogeneous graph representation learning model based on hybrid-attention mechanism is proposed, namely Heterogeneous Graph Relation Attention Network (HGRAN). The main contributions of HGRAN are listed as follows. First, a novel framework was proposed for better representing heterogeneous information originating from various relations and comprehensive usage of both structural and feature information instead of employing meta-path based framework. Second, a novel hybrid-attention mechanism which combines relation attention and node attention was proposed within this framework. Third, a novel feature similarity based relation attention is proposed to capture heterogeneous information originating from different relations. Fourth, in order to better implement node attention in heterogeneous graphs, a new transforming method that transforms adjacency matrices of diverse relations into a unified manner is proposed. Finally, extensive experiments on multiple real-world heterogeneous graph datasets are conducted to verify HGRAN, and the results support its superiority in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Link prediction is a crucial aspect of graph machine learning, with applications as diverse as disease prediction, social network recommendations, and drug discovery. It involves the prediction of potential new links between nodes within a network. Despite its importance, current models for link prediction exhibit notable limitations. Graph Convolutional Networks have shown high efficiency in link prediction across various datasets. However, they face significant challenges when applied to short-path networks and ego networks, resulting in poor performance. This issue represents a critical area of concern that our work seeks to address. This paper introduces the Node Centrality and Similarity-based Parameterised Model (NCSM), a novel method for link prediction tasks. NCSM uniquely integrates node centrality and similarity measures as edge features in a customised Graph Neural Network (GNN) layer, effectively leveraging the topological information of large networks. This model represents the first parameterised GNN-based link prediction model that considers topological information. The proposed model was evaluated on five benchmark graph datasets, each comprising thousands of nodes and edges. Experimental results highlight NCSM's superiority over existing state-of-the-art models like Graph Convolutional Networks and Variational Graph Autoencoder, as it outperforms them across various metrics and datasets. This exceptional performance can be attributed to NCSM's innovative integration of node centrality, similarity measures, and its efficient use of topological information. • Introduced Node Centrality and Similarity based Parameterised Model (NCSM) for link prediction. • NCSM uses node centrality and similarity as edge features in a custom Graph Neural Network. • Evaluated NCSM on five benchmark datasets, surpassing models like Graph Convolutional Networks and Variational Graph Autoencoder. • NCSM excels by uniquely combining node measures and effectively utilising network topology. [ABSTRACT FROM AUTHOR]

In 1983 Adler [1] pointed out that if a tetrahedron is nearly equilateral (edge lengths within 5% of each other) and the first and second longest edges are opposite, then the iterative Longest Edge Bisection (LEB) method produces ≤37 similarity classes. The importance of nearly equilateral tetrahedra is that they generate a finite number of similarity classes during the iterative LEB, a desirable property in Finite Element computations. We prove the conjecture given by Adler and improve the bound of 5% to 22.47%. A new algorithm is introduced for the computation of similarity classes in the iterative Longest Edge Bisection (SCLEB) of tetrahedra using a compact and efficient edge-based data structure. • The study of similarity classes is relevant in the LEB for Finite Element Method. • There exists a family of nearly equilateral tetrahedra that generates a finite number of similarity classes. • A new algorithm is introduced for the computation of similarity classes in the iterative (SCLEB). • We prove that the SCLEB of nearly equilateral tetrahedra introduces at most 37 similarity classes. • We prove the conjecture given by Adler and improve the bound for the convergence of similarity classes of 5% to 22.47%. [ABSTRACT FROM AUTHOR]

The definition of the structural basis of the conformational preferences of the genetically encoded amino acid residues is an important yet unresolved issue of structural biology. In order to gain insights into this intricate topic, we here determined and compared the amino acid propensity scales for different (φ, ψ) regions of the Ramachandran plot and for different secondary structure elements. These propensities were calculated using the Chou–Fasman approach on a database of non-redundant protein chains retrieved from the Protein Data Bank. Similarities between propensity scales were evaluated by linear regression analyses. One of the most striking and unexpected findings is that distant regions of the Ramachandran plot may exhibit significantly similar propensity scales. On the other hand, contiguous regions of the Ramachandran plot may present anticorrelated propensities. In order to provide an interpretative background to these results, we evaluated the role that the local variability of protein backbone geometry plays in this context. Our analysis indicates that (dis)similarities of propensity scales between different regions of the Ramachandran plot are coupled with (dis)similarities in the local geometry. The concept that similarities of the propensity scales are dictated by the similarity of the NCαC angle and not necessarily by the similarity of the (φ, ψ) conformation may have far-reaching implications in the field. [ABSTRACT FROM AUTHOR]

Recently, the graph regularized low-rank representation (GLRR) has been introduced in Hyperspectral Image (HSI) to explore global structures information by exploring the lowest-rank representation of all the data jointly and the local geometrical structure by the graph regularization. However, the traditional graph models are mostly based on a simple intrinsic structure. In this paper, to represent the complex intrinsic band information and further enhance the low rank of the matrix, we propose a soft hypergraph regularized weighted low-rank subspace clustering (HGWLRSC) method for HSI band selection. On the one hand, considering the complex correlation between adjacent bands, hypergraph technique is introduced, which take advantage of the band similarity properties to extract more valuable information and reveals the intrinsic multiple relationships of HSI band sets. On the other hand, the weighted low-rank subspace clustering model is introduced to not only capture the global structure information for the learned representation coefficient matrix but also to consider the importance of different rank components. The proposed algorithm was tested on three widely used hyperspectral data sets, and the experimental results indicate that the proposed HGWLRSC algorithm outperforms the other state-of-the art methods and achieves a very competitive band selection performance for HSI. [ABSTRACT FROM AUTHOR]

Understanding the elastic properties of materials is critical for their safe incorporation and predictable performance. Current methods of bulk elastic characterization often have notable limitations for in situ structural applications, with usage restricted to simple geometries and material distributions. To address these existing issues, this study sought to expand the capabilities of resonant ultrasound spectroscopy (RUS), an established nondestructive evaluation method, to include the characterization of isotropic multi-material samples. In this work, finite-element-based RUS analysis consisted of numerical simulations and experimental testing of composite samples comprised of material pairs with varying elasticity and density contrasts. Utilizing genetic algorithm inversion and mode matching, our results demonstrate that elastic properties of multi-material samples can be reliably identified within several percent of known or nominal values using a minimum number of identified resonance modes, given sample mass is held consistent. The accurate recovery of material properties for composite samples of varying material similarity and geometry expands the pool of viable samples for RUS and advances the method towards in situ inspection and evaluation. [ABSTRACT FROM AUTHOR]

Trust relation, as defined in Social Network Analysis (SNA), is one of the recent notions considered in decision making. This inspired our integration of trust relation in constructing a similarity–trust network. Similarity of experts' preferences is analyzed inclusively with trust relation by defining a new combination function of both attributes. The agglomerative hierarchical clustering approach is applied to group experts into subclusters based on the constructed similarity—trust degrees. The centrality concept from SNA is then used to determine the expert's similarity–trust centrality (STC) index, which is the basis for the construction of a new aggregation operator, STC‐induced ordered weighted averaging operator, to fuse the individual experts' preferences into a collective one, from which the consensus solution is derived. An analysis of results with different levels of trust degree is carried out. We show that this new idea is promising and relevant to be used in solving certain consensus group decision‐making problems. [ABSTRACT FROM AUTHOR]

The concept of similarity measure is fundamental for evaluating the consistency between intuitionistic fuzzy sets (IFSs). Several researchers have developed various intuitionistic fuzzy similarity measures (IFSimMs) utilizing intuitionistic fuzzy distance measures (IFDisMs) or combinations of membership and non-membership degrees of IFSs. Nevertheless, there is still ample room for the improvement, as some of these measures do not satisfy the axiomatic properties of IFSimMs and produce unreasonable results. In this paper, we first establish a dual relationship between IFDisMs and IFSimMs based on fuzzy negations, enabling the construction of an infinite number of IFSimMs from a given IFDisM. We then propose two novel IFSimMs by directly manipulating the membership and non-membership degrees of IFSs and prove that they satisfy the axiomatic properties of strict IFSimMs (SIFSimMs). Furthermore, a comparative analysis reveals that the proposed SIFSimMs do not yield any unreasonable results in various scenarios. Finally, we apply these SIFSimMs to pattern recognition, medical diagnosis, and clustering analysis problems, demonstrating their superior performance compared to some existing measures. • Show the duality between intuitionistic fuzzy distance and similarity measures. • Identify the limitations of some existing intuitionistic fuzzy similarity measures. • The properties of strict intuitionistic fuzzy similarity measure are investigated. • Pattern recognition, medical diagnosis, and clustering analysis are discussed. • Superiority of the proposed approaches is explained. [ABSTRACT FROM AUTHOR]

This study presents a novel approach of hybrid modified Jaya and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) optimization (MJTO) for efficiently determining the optimal frequency of low-frequency HVac lines. The proposed technique takes frequency constraints and static synchronous compensator (STATCOM) equations into the account. The objective functions in this study are the minimization of power losses, voltage deviation, and voltage stability index of transmission system. This study examines two types of objective functions: the traditional quadratic function and the enhanced quadratic function. These functions are used to incorporate more accurate modeling that considers low frequency. The MJTO is employed for global searching and gradient-free optimization to determine optimal control settings. The Modified Newton-Raphson method is utilized to minimize the mismatch power between power flow (PF) and STATCOM equations. The proposed method utilizes a hybrid technique to handle inequality constraints by preserving feasible solutions while keeping the original objective function intact. The suggested methodology is successfully implemented in extensive benchmark testing of IEEE systems on the 39 Bus and 14 Bus. The optimal solutions obtained are validated by comparing them to recent algorithms as mentioned in literature. The analytical and Statistical results indicates that the proposed optimization strategy is more optimistic for the LF-HVac system. • A novel frequency-dependent OPF model using frequency as an optimization parameter. • Increase in transmission capacity for lines. • STATCOM, simple NR load flow is combined with TOPSIS approach. • Various topologies and operating points affect the best frequency selection. [ABSTRACT FROM AUTHOR]