Internal Cluster Research Articles

Evaluation of the Effectiveness of Financing Industrial Clusters

The article is solving an actual problem — development of a system of indicators to evaluate the effectiveness of financing industrial clusters in Russia. The article analyzes the cluster models of Russian and foreign authors, identifies their strengths and weaknesses. A universal information model of the cluster was developed, reflecting the interaction of the participants among themselves and with external stakeholders of the cluster development. The developed model has three control loops: internal cluster stakeholders, cluster, cluster's region. Each has the specificity of the movement of inventory and cash flows, information interaction in the implementation of cluster policy, and reflects the interests of various stakeholders of industrial clusters. The model lays the groundwork to justify a three-tier system of indicators to evaluate the effectiveness of financing industrial clusters. The subsystems of the indicators of the impact of the industrial cluster on the regional economy, of the indicators of the industrial cluster development and the subsystem of the indicators of the financial condition of enterprises participating in the industrial cluster are highlighted in the proposed system. The study used the methods of bibliographic and logical analysis, synthesis and systems approach, mathematical methods of statistical data processing. The developed system of indicators for assessing the effectiveness of financing industrial clusters can be used to conduct current and subsequent monitoring of financing the implementation of cluster programs, to prepare decisions on the allocation of budgetary funds by state and municipal authorities, and to potential investors to determine the most promising investment instruments.

An explanation for dark matter and dark energy consistent with the standard model of particle physics and General Relativity

Analyses of internal galaxy and cluster dynamics typically employ Newton’s law of gravity, which neglects the field self-interaction effects of General Relativity. This may be why dark matter seems necessary. The universe evolution, on the other hand, is treated with the full theory, General Relativity. However, the approximations of isotropy and homogeneity, normally used to derive and solve the universe evolution equations, effectively suppress General Relativity’s field self-interaction effects and this may introduce the need for dark energy. Calculations have shown that field self-interaction increases the binding of matter inside massive systems, which may account for galaxy and cluster dynamics without invoking dark matter. In turn, energy conservation dictates that the increased binding must be balanced by an effectively decreased gravitational interaction outside the massive system. In this article, such suppression is estimated and its consequence for the Universe’s evolution is discussed. Observations are reproduced without need for dark energy.

Determining Optimal Coarse-Grained Representation for Biomolecules Using Internal Cluster Validation Indexes.

The development of ultracoarse-grained models for large biomolecules needs to derive the optimal number of coarse-grained (CG) sites to represent the targets. In this work, we propose to use the statistical internal cluster validation indexes to determine the optimal number of CG sites that are optimized based on the essential dynamics coarse-graining method. The calculated curves of Calinski-Harabasz and Silhouette Coefficient indexes exhibit the extrema corresponding to the similar CG numbers. The calculated ratios of the optimal CG numbers to the residue numbers of fine-grained models are in the range from 4 to 2. The comparison of the stability of index results indicates that Calinski-Harabasz index is the better choice to determine the optimal CG representation in coarse-graining. © 2019 Wiley Periodicals, Inc.

Crystallographic and Theoretical Investigations of Er2 @C2 n (2 n=82, 84, 86): Indication of Distance-Dependent Metal-Metal Bonding Nature.

Successful isolation and characterization of a series of Er-based dimetallofullerenes present valuable insights into the realm of metal-metal bonding. These species are crystallographically identified as Er2 @Cs (6)-C82 , Er2 @C3v (8)-C82 , Er2 @C1 (12)-C84 , and Er2 @C2v (9)-C86 , in which the structure of the C1 (12)-C84 cage is unambiguously characterized for the first time by single-crystal X-ray diffraction. Interestingly, natural bond orbital analysis demonstrates that the two Er atoms in Er2 @Cs (6)-C82 , Er2 @C3v (8)-C82 , and Er2 @C2v (9)-C86 form a two-electron-two-center Er-Er bond. However, for Er2 @C1 (12)-C84 , with the longest Er⋅⋅⋅Er distance, a one-electron-two-center Er-Er bond may exist. Thus, the difference in the Er⋅⋅⋅Er separation indicates distinct metal bonding natures, suggesting a distance-dependent bonding behavior for the internal dimetallic cluster. Additionally, electrochemical studies suggest that Er2 @C82-86 are good electron donors instead of electron acceptors. Hence, this finding initiates a connection between metal-metal bonding chemistry and fullerene chemistry.

Coupled dynamics of long-range and cluster-internal spin order in the cluster Mott insulator Cu2OSeO3

Cu$_4$ triplet clusters form the relevant spin entity for the formation of long-range magnetic order in the cluster magnet Cu$_2$OSeO$_3$. Using time-resolved Raman spectroscopy, we probed photoinduced spin and lattice dynamics in this Mott insulator. Multiple ps-decade spin-lattice relaxation dynamics is observed, evidencing a separation of the order parameter dynamics into disordering of long-range and internal spin cluster order. Our study exemplifies the double order parameter dynamics of generalized molecular crystals of charge, spin, and orbital nature.

Baryon clustering at the critical line and near the hypothetical critical point in heavy-ion collisions

We study clustering of baryons at the freeze-out point of relativistic heavy-ion collisions. Using a Walecka-Serot model for the nucleon-nucleon (NN) interaction we analyze how the modified/critical $\sigma$ mode---responsible for the NN attraction---allows for clustering of nucleons when the system is close to a possible critical point of QCD. We investigate clusters of few nucleons, and also the internal cluster configuration when the system is long lived. For realistic heavy-ion collisions we study to how extend such clusters can be formed in a finite time, and perform the statistical analysis of cumulants and higher-order moments (skewness and kurtosis) for collisions at the Beam Energy Scan of RHIC.

Integration Processes in the Agro-Industrial Complex of the Regions: Development and Environmental Problems

This paper is devoted to the methodology for assessing the development of extra- and intra-integration structures. This methodology consists of three levels of economic assessment of the agro-industrial sector: the regional level, the economic sector level, and the enterprise level. The agro-industrial complex of the Altai Republic has positive business dynamics due to the development of the livestock industry. Through assessing the business outlook for agro-industrial integration using the three levels, it has been determined that the livestock industry has the greatest potential for creating large integrated structures in the Altai Republic.
 The study justifies the need for creating an internal cluster (intra-cluster) in the maral deer breeding sphere of the Altai Republic, and an external cluster (extra-cluster) in the sphere of meat-and-dairy cattle breeding in the Altai Republic and the Altai Territory. The authors propose a ‘business-as-usual’ and a target scenario for the development of the agro-industrial complex of the Altai Republic until 2025.
 The target scenario reflects the effectiveness of the proposed measures for the development of the agro-industrial complex of the Altai Republic. According to this scenario, the formation of intra-clusters and extra-clusters in 2025 will lead to an increase in total profits of the livestock industry enterprises under analysis by 451 million rubles and of budget allocations by 194.3 million rubles. The goal of this study is to develop theoretical and methodological guidelines for shaping and developing integrated structures in the agro-industrial complex.

External prior learning and internal mean sparse coding for image denoising

Image prior and sparse coding learning methods have important uses in image denoising. Many denoising methods learn priors either from the noisy image itself or an external clean image dataset. But using only these as priors does not always reconstruct the image effectively. In addition, when the image is corrupted by noise, the local sparse coding coefficient obtained from a noisy image patch is inaccurate, restricting denoising performance. We present a noise removal framework based on external prior learning and an internal mean sparse coding method, making use of the innate sparsity and nonlocal self-similarity (NSS) of natural images. Specifically, we first obtain external priors from a clean natural image dataset by Gaussian mixture model. The external priors are applied to guide the subspace clustering of internal noisy image patches, and a compact dictionary is generated for each internal noisy patch cluster. Then an internal mean sparse coding strategy based on NSS is introduced into the sparse representation model, whose regularization parameters then are deduced through a Bayesian framework. An iterative shrinkage method is employed to solve the l1-optimization problem in the sparse representation model. Application of the noise removal model to 16 test images demonstrates denoising performance exceeding other competing methods.

Colloidal gel elasticity arises from the packing of locally glassy clusters

Colloidal gels formed by arrested phase separation are found widely in agriculture, biotechnology, and advanced manufacturing; yet, the emergence of elasticity and the nature of the arrested state in these abundant materials remains unresolved. Here, the quantitative agreement between integrated experimental, computational, and graph theoretic approaches are used to understand the arrested state and the origins of the gel elastic response. The micro-structural source of elasticity is identified by the l-balanced graph partition of the gels into minimally interconnected clusters that act as rigid, load bearing units. The number density of cluster-cluster connections grows with increasing attraction, and explains the emergence of elasticity in the network through the classic Cauchy-Born theory. Clusters are amorphous and iso-static. The internal cluster concentration maps onto the known attractive glass line of sticky colloids at low attraction strengths and extends it to higher strengths and lower particle volume fractions.

The Visual SuperTree: similarity-based multi-scale visualization

Similarity-based exploration of multi-dimensional data sets is a difficult task, in which most techniques do not perform well with large data sets, particularly in handling clutter that invariably happens as data sets grow larger. In this paper, we introduce the Visual SuperTree (VST), a method to build a multi-scale similarity tree that can deal with large data sets at interactive rates, maintaining most of the accuracy and the data organization capabilities of other available methods. The VST is built on top of a clustered multi-level configuration of the data that allows the user to quickly explore data sets by similarity. The method is shown to be useful for both unlabeled and labeled data, and it is capable of revealing external and internal cluster structures. We demonstrate its application on artificial and real data sets, showing additional advantages of the approach when exploring data that can be summarized meaningfully.

Wide-area photometric and astrometric (Gaia DR2) study of the young cluster NGC 6530

Context. NGC 6530 is a young cluster, with a complex morphology and star-formation history. We present a statistical study of its global properties, using a new, large list of candidate members down to masses of 0.2−0.4 M⊙ and Gaia DR2 astrometry. Aims. We consider a larger sky region compared to previous studies, to investigate the entire cluster until its periphery, including any diffuse population all around the main cluster. We study the distribution of extinction and age across the different regions, and obtain constraints on the star-formation history. We also study the dynamics of cluster members. Methods. Cluster membership was determined on the basis of literature X-ray data, Hα emission, near-IR and UV excesses from the VPHAS+ and UKIDSS photometric surveys and published near-IR catalogs, and Gaia DR2 astrometry; moreover, we used a method for photometric selection of M-type pre-main-sequence cluster members, which we recently developed and used for other star-formation regions. The list of candidates includes nearly 3700 stars, of which we estimate approximately 2700 to be genuine NGC 6530 members. Results. Using Gaia parallaxes, the cluster distance is found to be 1325 pc, with errors of 0.5% (statistical) and 8.5% (systematic), in agreement with previous determinations. The cluster morphology and boundaries are established with great confidence, from the agreement between the subsamples of members selected using different criteria. There is no diffuse population of members around the cluster, but there are minor condensations of true members in addition to the two main groups in the cluster core and in the Hourglass nebula. Two such subgroups are spatially associated with the stars 7 Sgr (F2II-III) and HD 164536 (O7.5V). There is a definite pattern of sequential star formation across the cluster, within an age range from less than 0.5 Myr to ∼5 Myr. Extinction is spatially non-uniform, with part of the population still embedded or obscured by thick dust. The precise Gaia proper motion data indicate that the NGC 6530 parent cloud collided with the Galactic plane around 4 Myr ago, and we suggest that event as the trigger of the bulk of star formation in NGC 6530. The internal cluster dynamics is also partially resolved by the Gaia data, indicating expansion of the main cluster population with respect to its center.

Cluster formation in copper vapor jet expanding into vacuum: the direct simulation Monte Carlo

Expansion of the condensing copper vapor into vacuum from a high-temperature source is studied for transient and near-continuum flow regimes within the direct simulation Monte Carlo method. Kinetic approach, which assumes cluster formation/decay description via monomer-cluster reactions with corresponding cross-sections, is employed. Clusters observed in calculations are of subnanoscale size. Impact of condensation on gas-dynamic jet parameters is significant for the regimes for which the terminal mole cluster fraction in the jet exceeds 1%. For such regimes both velocities and translation temperatures of clusters and monomers, are close to each other, while cluster internal temperature is higher than the translational vapor temperature. Characteristic clusterization modes are revealed depending on the intensity of the cluster formation process in the source domain and in the jet. Obtained values of the internal dimer temperature and average cluster size in the jet far field are in a good agreement with the experimental data and known scaling laws.

Chameleon 2

Traditional clustering algorithms fail to produce human-like results when confronted with data of variable density, complex distributions, or in the presence of noise. We propose an improved graph-based clustering algorithm called Chameleon 2, which overcomes several drawbacks of state-of-the-art clustering approaches. We modified the internal cluster quality measure and added an extra step to ensure algorithm robustness. Our results reveal a significant positive impact on the clustering quality measured by Normalized Mutual Information on 32 artificial datasets used in the clustering literature. This significant improvement is also confirmed on real-world datasets. The performance of clustering algorithms such as DBSCAN is extremely parameter sensitive, and exhaustive manual parameter tuning is necessary to obtain a meaningful result. All hierarchical clustering methods are very sensitive to cutoff selection, and a human expert is often required to find the true cutoff for each clustering result. We present an automated cutoff selection method that enables the Chameleon 2 algorithm to generate high-quality clustering in autonomous mode.

A kernel semi-supervised distance metric learning with relative distance: Integration with a MOO approach

Metric learning, which aims to determine an appropriate distance function to measure the similarity and dissimilarity between data points accurately, is one of the most popular methods to enhance the performance of many machine learning methods such as K-means clustering and K nearest neighbor classifier algorithms. These algorithms may not perform well because of the use of normal Euclidean distance function that ignores any statistical regularities that might be estimated from a large training set of labeled examples. In many real-world applications, the Euclidean distance may not be fit to capture the intrinsic similarity and dissimilarity between the data points. Compared to existing metric learning algorithms, which use large amount of labeled data in the form of must-link (ML) and cannot-link constraints as side information where the granularity of true clustering is unknown, our proposed approach uses few labeled data in the form of relative-distance constraints such as equality constraints, Ceq, and inequality constraints, Cneq. For satisfying such constraints, we need to project the initial Euclidean distance matrix by using Bregman projection on the convex subset of constraints in such a way that all the constraints are satisfied. Since Bregman projection is not orthogonal, means while satisfying the current constraint previously satisfied constraints may get unsatisfied, we need to select a proper subset of constraints for learning better distance function. The multi-objective framework is utilized for selecting a good subset of constraints which can help in getting the proper labeling of the data set. The selected subset of constraints is used for adjusting the initial kernel-matrix. K-means clustering technique is applied to the adjusted kernel matrix to label the data set. In order to evaluate the quality of obtained labeling, different external and internal cluster validity indices are deployed. The values of these indices are simultaneously optimized using the search capability of MOO with the aim of selecting the appropriate subset of constraints. The proposed approach is evaluated on UCI Human Activity Recognition using Smartphone Dataset v1.0 along with nine other popular data sets. Results show that our approach outperforms the state of the art semi-supervised metric learning algorithms with respect to different internal and external cluster validity indices.

Crystallographic characterization of Lu2C2n (2n= 76-90): cluster selection by cage size.

The successful isolation and unambiguous crystallographic assignment of a series of lutetium-containing endohedral metallofullerenes (EMFs), Lu2C2n (2n = 76, 78, 80, 84, 86, 88, 90), reveal an unrecognized decisive effect of the cage size on the configuration of the encapsulated clusters. The molecular structures of these compounds are unambiguously assigned as Lu2@T d(2)-C76, Lu2@D 3h(5)-C78, Lu2@C 2v(5)-C80, Lu2@C 2v(7)-C84, Lu2@C s(8)-C86, Lu2@C s(15)-C86, Lu2@C 1(26)-C88, Lu2C2@C 2v(9)-C86, Lu2C2@C s(32)-C88 and Lu2C2@D 2(35)-C88. Specifically, when the cage is relatively small, Lu2@C2n (2n = 76-86) are all dimetallofullerenes (di-EMFs) and a Lu-Lu single bond could be formed between the two lutetium ions inside the cages. However, when the cage expands further, the valence electrons forming the possible Lu-Lu bond donate to a readily inserted C2-unit, resulting in the formation of carbide EMFs, Lu2C2@C2n (2n = 86, 88). Consistently, our theoretical results reveal that all these EMFs are thermodynamically favorable isomers. Thus the comprehensive characterization of the series of Lu2C76-90 isomers and the overall agreement between the experimental and theoretical results reveal for the first time that the exact configuration of the internal metallic cluster is determined by the cage size, taking a solid step towards the controlled synthesis of novel hybrid molecules which may have potential applications as building blocks of single molecule devices.

Automatic Scientific Document Clustering Using Self-organized Multi-objective Differential Evolution

Document clustering is the partitioning of a given collection of documents into various K- groups based on some similarity/dissimilarity criterion. This task has applications in scope detection of journals/conferences, development of some automated peer-review support systems, topic-modeling, latest cognitive-inspired works on text summarization, and classification of documents based on semantics, etc. In the current paper, a cognitive-inspired multi-objective automatic document clustering technique is proposed which is a fusion of self-organizing map (SOM) and multi-objective differential evolution approach. The variable number of cluster centers are encoded in different solutions of the population to determine the number of clusters from a data set in an automated way. These solutions undergo various genetic operations during evolution. The concept of SOM is utilized in designing new genetic operators for the proposed clustering technique. In order to measure the goodness of a clustering solution, two cluster validity indices, Pakhira-Bandyopadhyay-Maulik index, and Silhouette index, are optimized simultaneously. The effectiveness of the proposed approach, namely self-organizing map based multi-objective document clustering technique (SMODoc_clust) is shown in automatic classification of some scientific articles and web-documents. Different representation schemas including tf, tf-idf and word-embedding are employed to convert articles in vector-forms. Comparative results with respect to internal cluster validity indices, namely, Dunn index and Davies-Bouldin index, are shown against several state-of-the-art clustering techniques including three multi-objective clustering techniques namely MOCK, VAMOSA, NSGA-II-Clust, single objective genetic algorithm (SOGA) based clustering technique, K-means, and single-linkage clustering. Results obtained clearly show that our approach is better than existing approaches. The validation of the obtained results is also shown using statistical significant t tests.

Exploring the Role of Mountain Origin and Autochthonous Breed on Urban Consumers’ Acceptability

In the last decades, the European Union (EU) agricultural policy has encouraged extensive livestock farming systems as a sustainable way of production, while more recently, voluntary certification schemes on mountain origin and autochthonous breeds have been put in place to further contribute to the preservation of biodiversity and economic activity in mountainous areas. The aim of this study is twofold: first, to investigate the role of the mountain origin and local autochthonous breed certification on urban consumer’s acceptability; and second, to compare consumer’s overall acceptability at central location versus home-based. These two specific objectives are designed to better ascertain the suitability of these voluntary certifications as a marketing tool that helps to bridge the gap between urban consumers’ beef choices and rural sustainability. Main methods of analysis included internal preference mapping and cluster analysis. Findings suggested that both claims, mountain origin (Pyrenean in particular) and autochthonous breed conveyed relevant information to consumers at the moment of testing, while the former played a stronger role on hedonic valuations. We have found that the environmental setting significantly influences acceptability with higher ratings obtained in the home test. A small niche market for Pyrenean cattle breeders was also detected, while reaching the larger and more product involved segment of urban consumers would require further marketing actions.

An integrated GIS platform architecture for spatiotemporal big data

With the increase in smart devices, spatiotemporal data has grown exponentially. To deal with challenges caused by an increase data requires a scalable and efficient architecture that can store, query, analyze, and visualize spatiotemporal big data. This paper describes a Cloud-terminal integrated GIS platform architecture designed to meet the requirements of processing and analyzing spatiotemporal big data. Cloud-terminal Integration GIS is developed according to the architecture. Extensive experiments deployed on the internal organization cluster using real-time datasets showed that the SuperMap GIS spatiotemporal big data engine achieved excellent performance.

Isolation and Crystallographic Characterization of Lu3 N@C2n (2n=80-88): Cage Selection by Cluster Size.

The small Sc3 N cluster has only been found in such small cages as C2n (2n=68, 78, 80, 82), whereas the large M3 N (M=Y, Gd, Tb, Tm) clusters choose those larger cages C2n (2n=82-88). Herein, concrete experimental evidence is presented to establish the size effect of the internal metallic cluster on selecting the outer cage of endohedral metallofullerenes (EMFs) by using a medium-sized metal, lutetium, which possesses an ionic radius between Sc and Gd. A series of lutetium-containing EMFs have been obtained and their structures are unambiguously determined as Lu3 N@Ih (7)-C80 , Lu3 N@D5h (6)-C80 , Lu3 N@C2v (9)-C82 , Lu3 N@Cs (51365)-C84 , Lu3 N@D3 (17)-C86 , and Lu3 N@D2 (35)-C88 by single-crystal X-ray diffraction crystallography. It was confirmed that the encaged Lu3 N cluster always adopts a planar geometry in Lu3 N@C80-88 isomers to ensure substantial metal-cage/metal-nitrogen interactions. As a result, the Lu3 N cluster selects the C2v (9)-C82 cage, which also encapsulates Sc3 N, instead of the Cs (39663)-C82 cage which is more suitable for M3 N (M=Y, Gd, Tb, Tm). However, different from Sc3 N, Lu3 N can also template the C84-88 cages which are absent for Sc3 N-containing EMFs, confirming clearly the size effect of the internal cluster on selecting the outer cage.

Application of a density based clustering technique on biomedical datasets

The detection of the number of clusters in a biomedical dataset is very important for generating high quality clusters from the biomedical dataset. In this paper, we aim to evaluate the performance of a density based K-Means clustering technique called DenClust on biomedical datasets. DenClust produces the number of clusters and the high quality initial seeds from a dataset through a density based seed selection approach without requiring an user input on the number of clusters and the radius of the clusters. The high quality initial seeds for K-Means results in high quality clusters from a dataset. The performance of DenClust is compared with six other existing clustering techniques namely CRUDAW-F, CRUDAW-H, AGCUK, GAGR, K-Means, and K-Means++ on the twenty biomedical datasets in terms of two external cluster evaluation criteria namely Entropy and Purity and one internal cluster evaluation criteria called Sum of Squared Error (SSE). We also perform a statistical non-parametric sign test on the cluster evaluation results of the techniques. Both the cluster evaluation results and statistical non-parametric sign test results indicate the superiority of DenClust over the existing techniques on the biomedical datasets. The complexity of DenClust is O(n2) but the overall execution time of DenClust on the datasets is less than the execution time of AGCUK and GAGR having O(n) complexity.