Dynamic Clustering

Reactions between 6Li and 6Li were studied in the laboratory energy range of 2-16 MeV. Angular distributions and excitation functions were measured for various proton, deuteron, triton and e-channels. In addition excitation functions were obtained for five 7transitions in ~~ and the ground-state transitions in both 7Li and VBe. Detailed statistical model calculations were performed under various assumptions concerning the formation of the compound nucleus. It is concluded that a smooth cutoff of the entrance channel angular momenta yields the only consistent description for both angular distributions and excitation functions. Only a few proton channels are dominated by compound nucleus decay. All other channels reflect the complex structure of 6Li which causes a variety of direct reaction modes. Nuclear Reactions: 6Li + 6Li at 2 < Ela b ~ 16 MeV; measured angular distributions and excitation functions of p, d, t, e-channels and y-transitions in 7Li, 7Be, l~ statistical model calculations; deduced fusion cross section; reaction mechanism inferred.

How to process the big data generated in large IoT networks is still challenging current techniques. To date, a lot of network clustering approaches have been proposed to improve the performance of data aggregation in IoT. However, most of them focus on partitioning networks with static topologies, and thus they are not optimal on handling the case with moving objects in the networks. Moreover, as the best of knowledge, none of them has ever considered the performance of following computing in edge servers. To improve these problems, in this work, we propose a highly efficient IoT network dynamic clustering solution in edge computing using deep reinforcement learning (DRL). Our approach can both fulfill the data communication requirements from IoT networks and load-balancing requirements from edge servers, and thus provide a great opportunity for future high performance IoT data analytics. We implement our approach by Deep Q-learning Network (DQN) model, and our preliminary experimental results show that the DQN solution can achieve higher score in cluster partitioning compared with the current static benchmark solution.

Background: This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle. Despite rapid advancements in statistical methods for gene expression microarray analysis, much more work is needed for multiple source heterogeneous genomic data, such as multiple organisms/tissues, multiple platforms, multiple species and even more from transcriptome, genome, to proteome in order to develop valid and dependable methods that are mainly applicable to microarray data. The congruency of these different data sources needs a unified framework for combining the multiple

Host–parasitoid systems with evolving mutation rates are studied. By increasing the growth rate of hosts, the diversity of both species is maintained dynamically. For the lower growth rate, diversity is brought about by mere parasitism. The average mutation rate for parasites is elevated to a high value, while that for hosts is suppressed at a low level. For the higher growth rate, the mutation rates for both hosts and parasites are elevated to form a symbiotic cluster connected by on-going mutation. This symbiotic state is sustained through a chaotic oscillation keeping some coherency among species. For a flat landscape for hosts, dynamical clustering of oscillation is observed. Lyapunov spectra of such oscillations show that high dimensional chaos with small positive exponents underlies in the symbiotic state. This weak high dimensional chaos, termed ‘‘homeochaos,’’ is essential to the maintenance of symbiosis in ecosystems.

In this paper we present a new algorithm for document clustering called Condensed Star (ACONS). This algorithm is a natural evolution of the Star algorithm proposed by Aslam et al., and improved by them and other researchers. In this method, we introduced a new concept of star allowing a different star-shaped form; in this way we retain the strengths of previous algorithms as well as address previous shortcomings. The evaluation experiments on standard document collections show that the proposed algorithm outperforms previously defined methods and obtains a smaller number of clusters. Since the ACONS algorithm is relatively simple to implement and is also efficient, we advocate its use for tasks that require clustering, such as information organization, browsing, topic tracking, and new topic detection.

PurposeThe purpose of this paper is to explore the factors influencing entrepreneurial intensity. More specifically the study addresses the following objectives: propose a way to measure entrepreneurial intensity within the community context in order to determine entrepreneurial activity over a period of ten years, detect the factors influencing the entrepreneurial intensity, and finally locate Kibbutz communities on the entrepreneurial grid.Design/methodology/approachKibbutz communities are the level of analysis. Using a comprehensive questionnaire, a sample of 60 Kibbutzim – constituting 22 percent of the population of Kibbutz communities in Israel – was investigated over a period of ten years. The same questionnaire was administered to the same sample Kibbutzim (Kibbutzim is the plural of Kibbutz) in 1994, 1997 and 2004. Collected data include number and types of enterprises, economic strength, organizational size and age, and features of organizational structure and culture.Find...

Interoperability plays an important role for a variety of applications. One of them are Peer Data Management Systems, where autonomous data sources (peers) interact with each other based on semantic mappings between their schemas. The building blocks that enable interoperability and thus the main challenges in such systems are mapping representation, query rewriting, and efficient query processing. While most approaches regard these aspects in separate this paper presents a comprehensive study of the interactions between these blocks. Our considerations try to provide a holistic view on semantic interoperability in distributed environments such as PDMS. We discuss techniques for distributed query processing and rewriting that consider high-level query operators such as top-N and skyline. Furthermore, we discuss how to increase efficiency by applying routing indexes and relaxation of result completeness/correctness.

Contemporary computers collect databases that can be too large for classical methods to handle. The present work takes data whose observations are distribution functions (rather than the single numerical point value of classical data) and presents the computational statistical approach of a new methodology to group the distributions into classes. The clustering method links the searched partition to the decomposition of mixture densities, through the notions of a function of distributions and of multidimensional copulas. The new clustering technique is illustrated by ascertaining distinct temperature and humidity regions for a global climate dataset and shows that the results compare favorably with those obtained from the standard EM algorithm method.

It is shown that the nucleotide sequences in DNA molecules have clusterscaling properties (discovered for the first time in turbulent processes: Sreenivasan and Bershadskii, 2006, J. Stat. Phys., 125, 1141-1153.). These properties are relevant to both types of nucleotide pair-bases interactions: hydrogen bonds and stacking interactions. It is shown that taking into account the cluster-scaling properties can help to improve heterogeneous models of the DNA dynamics. Two human genes: BRCA2 and NRXN1, have been considered as examples.

In wireless sensor networks, failures occur due to energy depletion, environmental hazards, hardware failure, communication link errors, etc. These failures could prevent them to accomplish their tasks. Moreover, most routing protocols are designed for ideal environment such as LEACH. Hence, if nodes fail the performance of these protocols degrade. In this context, we propose two improved versions of LEACH so that it becomes a fault-tolerant protocol. In the first version, we propose a clustered architecture for LEACH in which there are two cluster-heads in each cluster: one is primary (CHp) and the other is secondary (CHs). In the second version, we propose to use the checkpoint technique. Finally, we conducted several simulations to illustrate the performance our contribution and compared obtained results to LEACH protocol in a realistic environment.

This paper discusses processes of technological change in the tile cluster in the village Karanggeneng in Central Java, Indonesia. A growing number of producers in this cluster have switched from traditional kiln to so-called handpress production. We will analyze the processes of innovation adoption of the handpress technology in the cluster. Adoption of the handpress technology encompasses certain indivisibilities which require collaboration among producers to render innovation adoption profitable. We will discuss how pioneer adopters share information and stimulate social learning and adoption on the part of their neighbours, because simultaneous adoption of the handpress technology by groups of producers is needed in order to tackle the indivisibilities which pioneers are facing.

This paper gives purpose to identify the factors of the constitution of "the Tunisian cluster" in an environment open to competition. It is a contribution to the debate on the importance of clusters for competitiveness of small and medium enterprises (SMEs) to make more innovative and competitive regions and to promote strategically important sectors in technology. Approaches based on the knowledge economy grew by better integrating forms of proximity, organizational, institutional and geographical, (Torre and Rallet 2005) and relational (Boshma, 2005). An empirical study was conducted on a sample of Information and Communication Technologies ICTs'companies. The results show that the lack of attractiveness of ICT Tunisian companies to form clusters is not due to a lack of suitable infrastructure but to the absence of relations involved in a partnership approach or localized nature between higher education, research centers, industry training and organization, enabling to carry out scientific and technical projects.

The study of possible associations between social and health factors was one of the purposes of the multicentric Italian survey of perinatal preventive medicine. To get a concise and coherent social indicator, which could be useful to this study, we explored the socioeconomic information systematically collected on the parents of 12,058 babies born in 1973-1975 in four centres (Trieste, Milan, Parma and Bari). which reflect some of the heterogeneous aspects of Italy. The Correspondence analysis indicated that the seven considered indexes. concerning both parents' education levels and occupations, dwelling quality and the length of father's residence in the area in which the baby was born. were highly and similarly correlated in each centre. The Dynamic cluster analysis enabled us to bring together the socioeconomic profiles of the families into six classes. on the basis of their similarities, which depend only upon the multivariate distribution of the seven categorical variables under study. Therefore, the attained classification not only is independent of whatever score is assigned to the socioeconomic categories but also allows for the strong interactions between the variables and exploits all the available information. Our six classes were found to be rather similar to the six socioeconomic groups (S.E.G.) of the U.K. Registrar General and to show some resemblance to certain social groups defined by other classifications adopted in obstetric and pediatric field. The socioeconomic indicator constructed here should enable us to find out how much, in different areas of Italy, the social classes may account for inequalities in health conditions and care of the mother and her baby.

The paper shows that regional differences in labour productivity can be decomposed into a sector structure, a cluster economies and a residual regional component. The decomposition is applied to Dutch regional productivity levels and growth rates for 1990-2001. Sector structure accounts for about one-quarter of the differences in both the level and growth of productivity. Cluster economies account for one-quarter of the differences in levels and one-third of the differences in growth rates. Static cluster economies are predominantly positive, whereas positive and negative dynamic cluster economies balance. Econometric analysis shows both components to be statistically highly significant. Labour productivity Sectoral structure Agglomeration economies The Netherlands OOSTERHAVEN J. et BROERSMA L. (2007) La structure sectorielle et les économies en grappes, Regional Studies 41, 639-659. Cet article cherche à démontrer que l'on peut décomposer les écarts régionaux de la productivité du travail en une structure sectorielle, des économies en grappes et un résiduel régional. On applique la décomposition aux niveaux de productivité et aux taux de croissance régionaux en Hollande pour la période allant de 1990 jusqu'à 2001. La structure sectorielle explique à peu près un quart des écarts du niveau et de la croissance de la productivité. Les économies en grappes expliquent un quart des écarts des niveaux et un tiers des écarts des taux de croissance. Les économies en grappes statiques sont largement positives, tandis que les économies en grappes positives et négatives dynamiques ont tendance à s'équilibrer. A partir de l'analyse économétrique, il est évident que les deux composantes sont statistiquement importantes. Productivitédu travail Structure sectorielle Economies d'agglomération Hollande OOSTERHAVEN J. und BROERSMA L. (2007) Sektorenstruktur und Cluster-Wirtschaften: eine Aufgliederung der regionalen Arbeitsproduktivität, Regional Studies 41, 639-659. Es wird gezeigt, dass sich regionale Unterschiede bei der Arbeitsproduktivität in drei Komponenten aufgliedern lassen: Sektorenstruktur, Clusterwirtschaft und übrige regionale Faktoren. Diese Gliederung wird auf das regionale Produktivitätsniveau der Niederlande und auf die Wachstumsraten zwischen 1990 und 2001 angewandt. Auf die Sektorenstruktur entfällt ungefähr ein Viertel der Unterschiede beim Niveau und Wachstum der Produktivität. Auf die Clusterwirtschaft entfallen ungefähr ein Viertel der Unterschiede beim Produktivitätsniveau sowie ein Drittel der Unterschiede beim Produktivitätswachstum. Statische Cluster-Wirtschaften sind überwiegend positiv, während bei dynamischen Cluster-Wirtschaften positive und negative Komponenten gleich stark ausgeprägt sind. Bei einer ökonometrischen Analyse zeigt sich, dass beide Komponenten statistisch hochgradig signifikant sind. Arbeitsproduktivität Sektorenstruktur Agglomerationswirtschaften Niederlande OOSTERHAVEN J. y BROERSMA L. (2007) Estructura sectorial y las economías de aglomeración: descomposición de la productividad laboral regional, Regional Studies 41, 639-659. En este artículo mostramos que las diferencias regionales en la productividad laboral pueden descomponerse en componentes de estructura sectorial, de economías de aglomeración y de aspectos regionales residuales. Esta descomposición se aplica a los niveles de productividad regional en Holanda y las tasas de crecimiento para 1990-2001. La estructura sectorial es responsable de aproximadamente un cuarto de las diferencias tanto en los niveles como en el crecimiento de la productividad. Las economías de aglomeración son responsables de un cuarto de los niveles y un tercio de las diferencias en las tasas de crecimiento. Las economías estáticas de aglomeración son predominantemente positivas mientras que se equilibran las economías dinámicas de aglomeración positivas y negativas. Los análisis econométricos muestran que ambos componentes son estadísticamente muy significativos. Productividad laboral Estructura sectorial Economías de aglomeración Países Bajos JEL classifications: D24, J24, O18, R11

In this paper we introduce a new unsupervised segmentation algorithm for textured sonar images. A Dynamic Self-Organizing Maps (DSOM) algorithm capable of incremental learning has been developed to automatically cluster the input data into relevant classes of seabed. DSOM algorithm is an extension of classical Self-Organizing Maps (SOM) algorithm combined with Adaptive Resonance Theory (ART) technique. The proposed approach is based on growing map size during learning processes. Starting with a minimal number of neurons, the map size increases dynamically and the growth is controlled by the vigilance threshold of the ART network. To assess the consistency of the proposed approach, the DSOM algorithm is first tested on simulated data sets and then applied on real sidescan sonar images. The results obtained using the proposed approach demonstrate its capability to successfully cluster sonar images into their relevant seabed classes, very close to those resulting from human expert interpretation.

In this paper we introduce a new unsupervised segmentation algorithm for textured sonar images. A Dynamic Self-Organizing Maps (DSOM) algorithm capable of incremental learning has been developed to automatically cluster the input data into relevant classes of seabed. DSOM algorithm is an extension of classical Self-Organizing Maps (SOM) algorithm combined with Adaptive Resonance Theory (ART) technique. The proposed approach is based on growing map size during learning processes. Starting with a minimal number of neurons, the map size increases dynamically and the growth is controlled by the vigilance threshold of the ART network. To assess the consistency of the proposed approach, the DSOM algorithm is first tested on simulated data sets and then applied on real sidescan sonar images. The results obtained using the proposed approach demonstrate its capability to successfully cluster sonar images into their relevant seabed classes, very close to those resulting from human expert interpretation.

A model is presented that simulates the process of neuronal synchronization, formation of coherent activity clusters and their dynamic reorganization in the olivo-cerebellar system. Three coupled 2D lattices dealing with the main cellular groups in this neuronal circuit are used to model the dynamics of the excitatory feedforward loop linking the inferior olive (IO) neurons to the cerebellar nuclei (CN) via collateral axons that also proceed to terminate as climbing fiber afferents to Purkinje cells (PC). Inhibitory feedback from the CN-lattice fosters decoupling of units in a vicinity of a given IO neuron. It is shown that noise-sustained oscillations in the IO-lattice are capable to synchronize and generate coherent firing clusters in the layer accounting for the excitable collateral axons. The model also provides phase resetting of the oscillations in the IO-lattices with transient silent behavior. It is also shown that the CN-IO feedback leads to transient patterns of couplings in the IO and to a dynamic control of the size of clusters.

The unstable heavier nuclei release energy spontaneously by emitting radiations and particles, the process called radioactivity, namely, alpha, beta and gamma radioactive decays. In addition to these three types of decays, there exists fourth type of radioactivity known as cluster radioactivity (CR), defined as the spontaneous emission of fragments or clusters (A ≥ 4) heavier than alpha particle but less than a fission fragment. The decays of number of radioactive nuclei in trans-lead region, which lead to doubly magic Pb (Z=82, N=126) or neighboring daughter nuclei through emission of various clusters have been explored in many experimental as well as theoretical studies during last 3-4 decades. The shell closure in the atomic nucleus has now been established as the prominent cause of the process of CR.

This work is devoted to to the research into the possibilities of improving Proof-of-Work blockchain technology based on dynamic clustering of nodes to reduce transaction time.To eliminate the problem with the Proof-of-Work mechanism, which is considered in this work, it is necessary to solve the problem of reducing the amount of time spent on a transaction. It is proposed to implement this by dividing the system into subnets: when the consensus is not accepted by the entire community, but it is accepted by groups separately-thus minimizing the transaction time in the Proof-of-State algorithm. There is no ready-made solution for the Proof-of-Work dynamic consensus mechanism that would be successfully applied in blockchain technologies. All existing algorithms for dividing the blockchain network into subgroups are used only for static algorithms, but Proof-of-Work is dynamic and has certain features: there is no scope; the user does not see the list of nodes. These features greatly complicate the implementation of clustering for the Proof-of-Work consensus mechanism. The task of this study is the formulation of hypotheses and the verification of the formulated hypotheses, which are aimed at increasing the speed of the transaction. For verification, it is proposed to simulate a blockchain network to conduct experiments and test hypotheses that can potentially solve the Proof-of-Work problem. To develop a way for improving the Proof-of-Work consensus mechanism by implementing dynamic clustering of network nodes, flexibility and scalability, minimal impact on the existing blockchain protocol are taken into account, security issues are also important, management of the process of accepting new nodes to avoid possible attacks and ensure integrity and network security. Existing methods of improving Proof-of-Work technology, clustering methods that can be applied to the network are analyzed; problems that arise when developing a new technique are identified. A blockchain network modeling system has been developed and implemented, with the help of which the approach of dynamic grouping of nodes of the blockchain network, in which the system is divided into subsystems, is implemented. The results of the study allow us to conclude: the cluster system gives improved values of the number of transactions per second (by two hundredths transactions), average transaction time (by one and sixty-seven hundredthsseconds), throughput (by two tenthstransactions), transaction delay (by one and six hundred sixty-seven thousandths seconds) and significantly reduces the total energy consumption of the system (a difference of five thousand, one hundred twenty-two units). This indicates the potential of the proposed method in various practical applications.

The FORUM project aims at extending existing data integration techniques in order to facilitate the development of mediation systems in large and dynamic environments. It is well known from the literature that a crucial point that hampers the development and wide adoption of mediation systems lies in the high entry and maintenance costs of such systems. To overcome these barriers,

In Libya, from time to time, the National Electricity Grid is directed by the National Electricity Company to conduct load shedding to alleviate pressure on supply at times of peak demand. This involves hours’ of power outages in the area covered by this study, namely, the Southern Electrical Grid of Libya (SEGL). The proposed approach focuses on a set of region categories to mitigate the effects of the load shedding problem by considering the demand priorities on the operation of the power system during emergencies. The classification of these region categories is solved by using a relatively new algorithm, the harmony search (HS) algorithm, which is applied to a randomly generated list of electricity deficits and region categories to identify those regions that should not experience a power outage at times of peak load. The proposed approach is applied to 50 simulated cases and compared to actual SEGL cases. The obtained results show that the proposed algorithm has a high level of...

In this paper, we present a novel dynamic clustering algorithm that efficiently deals with data streams and achieves several important properties which are not generally found together in the same algorithm. The dynamic clustering algorithm operates online in two different timescale stages, a fast distancebased stage that generates micro-clusters and a density-based stage that groups the micro-clusters according to their density and generates the final clusters. The algorithm achieves novelty detection and concept drift thanks to a forgetting function that allows micro-clusters and final clusters to appear, drift, merge, split or disappear. The outlier identification is made in a natural way using micro-clusters density. This algorithm has been designed to be able to detect complex patterns even in multi-density distributions and making no assumption of cluster convexity. The performance of the dynamic clustering algorithm is assessed theoretically through complexity analysis and empirically through a set of experiments that compare the algorithm with other algorithms of the literature on popular data sets that have interesting characteristics regarding the emergence, disappearance and movement of clusters as well as multi-density, non-convexity and noise.

This paper presents a mobile agent information system designed as the basic tool for creating dynamic clusters. It is aimed at mobile and heterogeneous environments where needs for complex resources and services may arise from stationary or mobile devices of different computing capabilities. MAIS is part of a larger project that will offer users necessary tools for using in a simple manner aggregated heterogeneous and mobile resources and services. Experimental evaluations, reported in this paper, prove the effectiveness and flexibility of the MAIS system.

Considering the Indian context as the basis of enquiry into industrial clustering three important issues that largely determine the performance and strategy of firms to survive compete and grow are identified. These three issues relate to the characteristics of the existing and potential ...

Context. Observational and instrumental difficulties observing small bodies below 0.5 μm make this wavelength range poorly studied compared with the visible and near-infrared. Furthermore, the suitability of many commonly used solar analogues, essential in the computation of asteroid reflectances, is usually assessed only in visible wavelengths, while some of these objects show spectra that are quite different from the spectrum of the Sun at wavelengths below 0.55 μm. Stars HD 28099 (Hyades 64) and HD 186427 (16 Cyg B) are two well-studied solar analogues that instead present spectra that are also very similar to the spectrum of the Sun in the wavelength region between 0.36 and 0.55 μm. Aims. We aim to assess the suitability in the near-ultraviolet (NUV) region of the solar analogues selected by the team responsible for the asteroid reflectance included in Gaia Data Release 3 (DR3) and to suggest a correction (in the form of multiplicative factors) to be applied to the Gaia DR3 aste...

Research in sensor networks has focused on development of energy efficient and secure infrastructures. In this article, we introduce a new approach to organize sensor networks in clusters in order to reduce energy dissipation. Our contribution is an heuristic to define the number of clusters and also an efficient manner to choose cluster heads by minimizing the distance between the cluster heads and its cluster nodes. Inspired from LEACH, a well-known TDMA cluster-based sensor network architecture, we introduce a new method for building and maintaining clusters using the paradigm of a soccer team. In this work, a new algorithm called OH-Kmeans, based on the Kmeans algorithm, is used to find dynamically the number of clusters and form them guaranteeing direct transmission between the cluster heads and cluster nodes.

The coefficients of the successiveh 2 orders of the extended Thomas-Fermi expansion of the kinetic energy density are obtained to the leading order in the density inhomogeneities. This is done by comparing the corresponding static polarizability with the exact value in two cases: a non-interacting nuclear infinite system and symmetric nuclear matter with a Skyrme interaction.

we propose a new perspective on the identification of linear dynamic system using structural similarity. The proposal consists in the meaningful exploration of each model, specifically behavior of the state variable. The decomposition of the behavior of a state variable in different modes of behavior of a system, each one has a different set of weights and shows different patterns of behavior. These weights are more significant than eigenvalue to develop a new technique for identifying linear system and invariants over time. We use two methods based on different areas of knowledge such as linear algebra and statistics. This paper is a conceptual proof that enriches the implementation and validity not only from point of view algorithmic likewise physic mathematical.

This paper addresses dynamic data clustering as an optimization problem and propose techniques for finding optimal (number of) clusters in a multi-dimensional data or feature space. In order to accomplish this objective we first propose two novel techniques, which successfully address several major problems in the field of Particle Swarm Optimization (PSO) and promise a significant breakthrough over complex, multi-modal optimization problems at high dimensions. The first one, so-called Multi-Dimensional (MD) PSO, reforms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. To address this problem we propose Fractional Global Best Formation (FGBF) technique, which basically collects all promising dimensional components and fractionally creates an artificial global-best particle (aGB) that has the potential to be a better "guide" than the PSO's native gbest particle. We investigated both individual and mutual applications of the proposed techniques and demonstrated that the best clustering performance can be achieved by their mutual operation. In order to test and evaluate their clustering performance in terms of accuracy, robustness and scalability, a synthetic data-set, which contains ground-truth clusters and offers a broad range of complexity levels is used. An extensive set of experiments demonstrate that the proposed dynamic clustering technique based on MD PSO with FGBF can extract the optimal (number of) clusters by converging to the global optimum of the validity index function at the true dimension.

In Libya, from time to time, the National Electricity Grid is directed by the National Electricity Company to conduct load shedding to alleviate pressure on supply at times of peak demand. This involves hours’ of power outages in the area covered by this study, namely, the Southern Electrical Grid of Libya (SEGL). The proposed approach focuses on a set of region categories to mitigate the effects of the load shedding problem by considering the demand priorities on the operation of the power system during emergencies. The classification of these region categories is solved by using a relatively new algorithm, the harmony search (HS) algorithm, which is applied to a randomly generated list of electricity deficits and region categories to identify those regions that should not experience a power outage at times of peak load. The proposed approach is applied to 50 simulated cases and compared to actual SEGL cases. The obtained results show that the proposed algorithm has a high level of...

The dynamics. of the diffusion-limited model of cluster-cluster aggregation is investigated in two and three dimensions by studying the temporal evolution of the cluster-size distribution n, (t), which is the number of clusters of size s at time t. In a recent study it was shown that the results of the two-dimensional simulations for mass-independent diffusivity can be well represented by a dynamic-scaling function of the form n, (t)-s f(s/t'), where f(x) is a scaling function with a power-law behavior for small x, namely f (x)-xs for x « 1 aud f (x) « 1 for x » 1. In this paper we extend the calculations of the cluster-size distribution to three dimensions and to the case of the cluster diffusivity depending on the size of the clusters. The diffusion constant of a cluster of size s is assumed to be proportional to s~. The overall behavior of n, (t) and the exponents 5 and z have been determined for a set of values of y. We find that the results are consistent with the scaling theory, and the exponents in n, (t) depend continuously on y. Moreover, there is a critical value of y [y, (d =2)=-", , y, (d =3)=z ] at which the shape of the cluster-size distribution crosses over from a monotonically decreasing function to a bell-shaped curve which can be described by the above scaling form for n, (t), but with a scaling function f(x) different from f (x).

Clustering is an important data mining task for exploration and visualization of different data types like news stories, scientific publications, weblogs, etc. Due to the evolving nature of these data, evolutionary clustering, also known as dynamic clustering, has recently emerged to cope with the challenges of mining temporally smooth clusters over time. A good evolutionary clustering algorithm should be able to fit the data well at each time epoch, and at the same time results in a smooth cluster evolution that provides the data analyst with a coherent and easily interpretable model. In this paper we introduce the temporal Dirichlet process mixture model (TDPM) as a framework for evolutionary clustering. TDPM is a generalization of the DPM framework for clustering that automatically grows the number of clusters with the data. In our framework, the data is divided into epochs; all data points inside the same epoch are assumed to be fully exchangeable, whereas the temporal order is maintained across epochs. Moreover, The number of clusters in each epoch is unbounded: the clusters can retain, die out or emerge over time, and the actual parameterization of each cluster can also evolve over time in a Markovian fashion. We give a detailed and intuitive construction of this framework using the recurrent Chinese restaurant process (RCRP) metaphor, as well as a Gibbs sampling algorithm to carry out posterior inference in order to determine the optimal cluster evolution. We demonstrate our model over simulated data by using it to build an infinite dynamic mixture of Gaussian factors, and over real dataset by using it to build a simple non-parametric dynamic clustering-topic model and apply it to analyze the NIPS12 document collection.

The clustering effects in light mass alpha conjugate 20 Ne, 28 Si and non-alpha conjugate 21 Ne, 22 Ne nuclear systems have been investigated comparatively, within the collective clusterization approach of dynamical cluster-decay model. The evolution of cluster structure with temperature at three different temperatures corresponding to ground state, Ikeda's threshold energy and experimentally available energy has been made by taking into account the modified pairing strength in liquid drop energies. The results present that in addition to xα-type clusters (x is an integer) and xn-xα type clusters in alpha conjugate and non-alpha conjugate nuclear systems, respectively, np-xα type (n-neutron, p-proton) clusters are also preformed significantly at higher experimental excitation energy due to decreased pairing strength in liquid drop energies with rising temperature. Further, the fragmentation analysis has been undertaken at higher excitation energies to explore the reaction mechanism in the decay of different Z = 5, 6, 7 (or complementary Z = 3, 4, 5) fragments/clusters in reference to experimentally available Z-distribution data. The analysis depicts the presence of competing mechanisms of fusion-fission (FF) and deep inelastic orbiting (DIO) in the decay path, where the contribution of DIO cross-section σ DIO has been evaluated empirically. The higher contribution of σ DIO has been found for C-yield in all three 20 Ne * , 21 Ne * and 22 Ne * nuclear systems and the experimental verification is called for the same. The DCM calculated total cross-sections for different Z-fragments are in agreement with the experimental data.

Tabla de contenidos 2. Introducción 3. ¿Qué es una red de sensores inalámbrica? 4. Aplicaciones de las redes de sensores inalámbricas 4.1. Aplicaciones militares 4.2. Aplicaciones medioambientales 4.3. Aplicaciones sanitarias 4.4. Otras aplicaciones comerciales 5.

The fission fragment mass distribution followed by neutron emission is studied for the 208 Pb(18 O, f) reaction using the asymmetric two centre shell model. The measured mass distribution spectrum reveals new kind of systematics on shell structure and leads to an improved understanding of structure effects in nuclear fission. A detailed investigation of shell effects both in potential and cranking mass parameter has been carried out for explaining the observed fine structure dips (i.e., less probable distributions) corresponding to shell closure (Z = 50 and/or N = 82) of fission fragments and their complementary partners. The available energy states for the decay process are obtained by solving the Schrödinger equation and found that first-five eigenstates are sufficient in reproducing the observed mass distribution spectrum. An outcome of the asymmetric two centre shell model also completely favours the observed claim that "the total number of emitted neutrons between correlated pairs of fission fragments should not exceed 6". A complete observed spectrum is obtained by adding the mass distribution yields of all 6-neutron emission channels. This suggests a possible importance of extending these calculations to get new insight into an understanding of the dynamical behaviour of fragment formation in the fission process.

The structural configurations of the quasi-molecular resonance states, at energies both below and above the Coulomb barrier, are studied within the orbiting cluster model approach. A generalized moment of inetia expression for the two-centre shell-model nuclear shape, that applies also to dinuclear con~gurations, is used. Applications of the method are made to "C + "C, 160 + 12C, t60+ 'b *'Mg+"Mg and *sSi+% systems. Further experimental investigations are needed.

We study the implications and limitations of galaxy cluster surveys for constraining models of particle physics and gravity beyond the Standard Model. Flux limited cluster counts probe the history of large scale structure formation in the universe, and as such provide useful constraints on cosmological parameters. As a result of uncertainties in some aspects of cluster dynamics, cluster surveys are currently more useful for analyzing physics that would affect the formation of structure than physics that would modify the appearance of clusters. As an example we consider the ΛCDM cosmology and dimming mechanisms, such as photon-axion mixing.

The clustering effects in light mass alpha conjugate 20 Ne, 28 Si and non-alpha conjugate 21 Ne, 22 Ne nuclear systems have been investigated comparatively, within the collective clusterization approach of dynamical cluster-decay model. The evolution of cluster structure with temperature at three different temperatures corresponding to ground state, Ikeda's threshold energy and experimentally available energy has been made by taking into account the modified pairing strength in liquid drop energies. The results present that in addition to xα-type clusters (x is an integer) and xn-xα type clusters in alpha conjugate and non-alpha conjugate nuclear systems, respectively, np-xα type (n-neutron, p-proton) clusters are also preformed significantly at higher experimental excitation energy due to decreased pairing strength in liquid drop energies with rising temperature. Further, the fragmentation analysis has been undertaken at higher excitation energies to explore the reaction mechanism in the decay of different Z = 5, 6, 7 (or complementary Z = 3, 4, 5) fragments/clusters in reference to experimentally available Z-distribution data. The analysis depicts the presence of competing mechanisms of fusion-fission (FF) and deep inelastic orbiting (DIO) in the decay path, where the contribution of DIO cross-section σ DIO has been evaluated empirically. The higher contribution of σ DIO has been found for C-yield in all three 20 Ne * , 21 Ne * and 22 Ne * nuclear systems and the experimental verification is called for the same. The DCM calculated total cross-sections for different Z-fragments are in agreement with the experimental data.

This paper addresses the problem of temporal data clustering using a dynamic Gaussian mixture model whose means are considered as latent variables distributed according to random walks. Its final objective is to track the dynamic evolution of some critical railway components using data acquired through embedded sensors. The parameters of the proposed algorithm are estimated by maximum likelihood via the Expectation-Maximization algorithm. In contrast to other approaches as the maximum a posteriori estimation in which the covariance matrices of the random walks have to be fixed by the user, the results of the simulations show the ability of the proposed algorithm to correctly estimate these covariances while keeping a low clustering error rate.

We obtain the nuclear proximity potential by using semiclassical extended Thomas Fermi (ETF) approach in Skyrme energy density formalism (SEDF), and use it in the extended ℓ-summed Wong formula under frozen density approximation. This method has the advantage of allowing the use of different Skyrme forces, giving different barriers. Thus, for a given reaction, we could choose a Skyrme force with proper barrier characteristics, not-requiring extra "barrier lowering" or "barrier narrowing" for a best fit to data. For the 64 Ni+ 100 Mo reaction, the ℓ-summed Wong formula, with effects of deformations and orientations of nuclei included, fits the fusion-evaporation cross section data exactly for the force GSkI, requiring additional barrier modifications for forces SIII and SV. However, the same for other similar reactions, like 58,64 Ni+ 58,64 Ni, fits the data best for SIII force. Hence, the barrier modification effects in ℓ-summed Wong expression depends on the choice of Skyrme force in extended ETF method.

Representing complex objects with basic geometric primitives has long been a topic in computer vision. Primitive-based representations have the merits of compactness and computational efficiency in higher-level tasks such as physics simulation, collision checking, and robotic manipulation. Unlike previous works which extract polygonal meshes from a signed distance function (SDF), in this paper, we present a novel method, named Marching-Primitives, to obtain a primitive-based abstraction directly from an SDF. Our method grows geometric primitives (such as superquadrics) iteratively by analyzing the connectivity of voxels while marching at different levels of signed distance. For each valid connected volume of interest, we march on the scope of voxels from which a primitive is able to be extracted in a probabilistic sense and simultaneously solve for the parameters of the primitive to capture the underlying local geometry. We evaluate the performance of our method on both synthetic and real-world datasets. The results show that the proposed method outperforms the state-of-the-art in terms of accuracy, and is directly generalizable among different categories and scales. The code is open-sourced at https://github.com/ ChirikjianLab/Marching-Primitives.git.

Applying twelve different versions of nuclear potentials, the influence of static quadrupole and hexadecapole deformation of targets and its orientations on the fusion cross section have been studied. The barrier parameters for the reactions 16 O + 58 Ni, 16 O + 112 Sn and 18 O + 112 Sn have been estimated from the variations of total interaction potential with the inter-nuclear separation which is then used to determine the fusion cross section for the systems through Wong's formula. It has been found that the nucleus-nucleus potential strongly depends on the deformation parameters and orientation of the target. The experimental fusion cross-section of these reactions (wherever available) have been investigated by applying Wong's formula along with the results of a one-dimensional barrier penetration model using coupled-channel (CCFULL) code. The fusion cross-sections by Prox 10, Prox 88, Prox 00, Prox 00 DP, CW 76, Ngô 80 and AW 95 potentials have been found out to be in better agreement towards the experimental data.

Applying twelve different versions of nuclear potentials, the influence of static quadrupole and hexadecapole deformation of targets and its orientations on the fusion cross section have been studied. The barrier parameters for the reactions 16 O + 58 Ni, 16 O + 112 Sn and 18 O + 112 Sn have been estimated from the variations of total interaction potential with the inter-nuclear separation which is then used to determine the fusion cross section for the systems through Wong's formula. It has been found that the nucleus-nucleus potential strongly depends on the deformation parameters and orientation of the target. The experimental fusion cross-section of these reactions (wherever available) have been investigated by applying Wong's formula along with the results of a one-dimensional barrier penetration model using coupled-channel (CCFULL) code. The fusion cross-sections by Prox 10, Prox 88, Prox 00, Prox 00 DP, CW 76, Ngô 80 and AW 95 potentials have been found out to be in better agreement towards the experimental data.

Narrowband reflectance spectra of the saturnian satellites S VII Hyperion and S IX Phoebe were obtained across the 0.4-to 0.8-µm spectral region. The spectrum of Phoebe shows an absorption feature centered near 0.43 µm superimposed on the UV/blue intervalence charge transfer transition present in the spectrum, similar to absorptions identified in the spectra of C-class asteroids (Vilas et al. 1993b. Icarus 105, 67-78). We use a linear mixing model to separate the reflectance spectrum of the dark material on Hyperion from the icy material. A distinct absorption feature centered at 0.67 µm is present. A slight inflection suggesting an absorption feature near 0.4-0.6 µm, and change in slope near 0.73 µm suggesting the lower wavelength edge of an absorption, are also present. These absorptions are very similar to those identified in the spectrum of the dark material on the surface of Iapetus (Vilas et al. 1996. Icarus 124, 262-267), suggesting that the dark material on these two satellites is compositionally similar and has a similar origin. We attribute these absorption features to the 6 A 1 → 4 T 2 (G) and 6 A 1 → 4 T 1 (G) ferric charge transfer transitions in iron alteration minerals such as goethite and hematite that are products of the aqueous alteration of anhydrous silicates.

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We present a new approach for tracking evolving and noisy data streams by estimating clusters based on density, while taking into account the possibility of the presence of an unknown amount of outliers, the emergence of new patterns, and the forgetting of old patterns.

Iterative applications are known to run as slow as their slowest computational component. This paper introduces malleability, a new dynamic reconfiguration strategy to overcome this limitation. Malleability is the ability to dynamically change the data size and number of computational entities in an application. Malleability can be used by middleware to autonomously reconfigure an application in response to dynamic changes in resource availability in an architecture-aware manner, allowing applications to optimize the use of multiple processors and diverse memory hierarchies in heterogeneous environments. The modular Internet Operating System (IOS) was extended to reconfigure applications autonomously using malleability. Two different iterative applications were made malleable. The first is used in astronomical modeling, and representative of maximum-likelihood applications was made malleable in the SALSA programming language. The second models the diffusion of heat over a two dimensional object, and is representative of applications such as partial differential equations and some types of distributed simulations. Versions of the heat application were made malleable both in SALSA and MPI. Algorithms for concurrent data redistribution are given for each type of application. Results show that using malleability for reconfiguration is 10 to 100 times faster on the tested environments. The algorithms are