Influence of entrance channels on the formation of superheavy nuclei in massive fusion reactions

Within the framework of the dinuclear system (DNS) model, the production cross sections of superheavy nuclei Hs (Z=108) and Z=112 combined with different reaction systems are analyzed systematically. It is found that the mass asymmetries and the reaction Q values of the projectile target combinations play a very important role on the formation cross sections of the evaporation residues. Both methods to obtain the fusion probability by nucleon transfer by solving a set of microscopically derived master equations along the mass asymmetry degree of freedom (ID) and distinguishing protons and neutrons of fragments (2D) are compared with each other and also with the available experimental data. (C) 2010 Elsevier B.V. All rights reserved.

On the Interaction between Data Aggregation and Topology Control in Wireless Sensor Networks

Wireless sensor networks are characterized by limited energy resources. To conserve energy, application-specific aggregation (fusion) of data reports from multiple sensors can be beneficial in reducing the amount of data flowing over the network. Furthermore, controlling the topology by scheduling the activity of nodes between active and sleep modes has often been used to uniformly distribute the energy consumption among all nodes by de-synchronizing their activities. We present an integrated analytical model to study the joint performance of in-network aggregation and topology control. We define performance metrics that capture the tradeoffs among delay, energy, and fidelity of the aggregation. Our results indicate that to achieve high fidelity levels under medium to high event reporting load, shorter and fatter aggregation/routing trees (toward the sink) offer the best delay-energy tradeoff as long as topology control is well coordinated with routing.

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Application of proton radiography in experiments of relevance to inertial confinement fusion

Multi-Mev proton beams generated by target normal sheath acceleration (TNSA) during the interaction of an ultra intense laser beam (Ia parts per thousand yen10(19) W/cm(2)) with a thin metallic foil (thickness of the order of a few tens of microns) are particularly suited as a particle probe for laser plasma experiments. The proton imaging technique employs a laser-driven proton beam in a point-projection imaging scheme as a diagnostic tool for the detection of electric fields in such experiments. The proton probing technique has been applied in experiments of relevance to inertial confinement fusion (ICF) such as laser heated gasbags and laser-hohlraum experiments. The data provides direct information on the onset of laser beam filamentation and on the plasma expansion in the hohlraum's interior, and confirms the suitability and usefulness of this technique as an ICF diagnostic.

Regulation of OPA1-mediated mitochondrial fusion by leucine zipper/EF-hand-containing transmembrane protein-1 plays a role in apoptosis

Carboxyl-terminal modulator protein (CTMP) is a tumor suppressor-like binding partner of Protein kinase B (PKB/Akt) that negative regulates this kinase. In the course of our recent work, we identified that CTMP is consistently associated with leucine zipper/EF-hand-containing transmembrane-1 (LETM1). Here, we report that adenovirus-LETM1 increased the sensitivity of HeLa cells to apoptosis, induced by either staurosporine or actinomycin D. As shown previously, LETM1 localized to the inner mitochondrial membrane. Electron-microscopy analysis of adenovirus-LETM1 transduced cells revealed that mitochondrial cristae were swollen in these cells, a phenotype similar to that observed in optic atrophy type-1 (OPA1)-ablated cells. OPA1 cleavage was increased in LETM1-overexpressing cells, and this phenotype was reversed by overexpression of OPA1 variant-7, a cleavage resistant form of OPA1. Taken together, these data suggest that LETM1 is a novel binding partner for CTMP that may play an important role in mitochondrial fragmentation via OPA1-cleavage. (C) 2009 Elsevier Inc. All rights reserved

Burkholderia cenocepacia requires RpoE for growth under stress conditions and delay of phagolysosomal fusion in macrophages

Burkholderia cenocepacia is an opportunistic pathogen causing serious infections in patients with cystic fibrosis. The widespread distribution of this bacterium in the environment suggests that it must adapt to stress to be able to survive. We identified in B. cenocepacia K56-2 a gene predicted to encode RpoE, the extra-cytoplasmic stress response regulator. The rpoE gene is the first gene of a predicted operon encoding proteins homologous to RseA, RseB, MucD and a protein of unknown function. The genomic organization and the co-transcription of these genes were confirmed by PCR and RT-PCR. The mucD and rpoE genes were mutated, giving rise to B. cenocepacia RSF24 and RSF25, respectively. While mutant RSF24 did not demonstrate any growth defects under the conditions tested, RSF25 was compromised for growth under temperature (44 degrees C) and osmotic stress (426 mM NaCl). Expression of RpoE in trans could complement the osmotic growth defect but exacerbated temperature sensitivity in both RSF25 and wild-type K56-2. Inactivation of rpoE altered the bacterial cell surface, as indicated by increased binding of the fluorescent dye calcofluor white and by an altered outer-membrane protein profile. These cell surface changes were restored by complementation with a plasmid encoding rpoE. Macrophage infections in which bacterial colocalization with fluorescent dextran was examined demonstrated that the rpoE mutant could not delay the fusion of B. cenocepacia-containing vacuoles with lysosomes, in contrast to the parental strain K56-2. These data show that B. cenocepacia rpoE is required for bacterial growth under certain stress conditions and for the ability of intracellular bacteria to delay phagolysosomal fusion in macrophages.

The Ties That Bind Us: Ritual, Fusion, and Identification

Most social scientists endorse some version of the claim that participating in collective rituals promotes social cohesion. The systematic testing and evaluation of this claim, however, has been prevented by a lack of precision regarding the nature of both ‘ritual’and ‘social cohesion’ as well as a lack of integration between the theories and findings of the social and evolutionary sciences. By directly addressing these challenges, we argue that a systematic investigation and evaluation of the claim that ritual promotes social cohesion is achievable.

We present a general and testable theory of the relationship between ritual, cohesion, and cooperation that more precisely connects particular elements of ‘ritual,’ such as causal opacity and emotional arousal, to two particular forms
of ‘social cohesion’: group identification and identity fusion. Further, we ground this theory in an evolutionary account of why particular modes of ritual practice would be adaptive for societies with particular resource acquisition strategies. In setting out our conceptual framework we report numerous ongoing investigations that test our hypotheses against data from controlled psychological experiments as well as from theethnographic, archaeological, and historical records.

A Data Model for Content Modelling of Temporal Media

This paper describes a data model for content representation of temporal media in an IP based sensor network. The model is formed by introducing the idea of semantic-role from linguistics into the underlying concepts of formal event representation with the aim of developing a common event model. The architecture of a prototype system for a multi camera surveillance system, based on the proposed model is described. The important aspects of the proposed model are its expressiveness, its ability to model content of temporal media, and its suitability for use with a natural language interface. It also provides a platform for temporal information fusion, as well as organizing sensor annotations by help of ontologies.

Robust Multimodal Person Identification With Limited Training Data

This paper presents a novel method of audio-visual feature-level fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there are limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature representation and a modified cosine similarity are introduced to combine and compare bimodal features with limited training data, as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal dataset created from the SPIDRE speaker recognition database and AR face recognition database with variable noise corruption of speech and occlusion in the face images. The system's speaker identification performance on the SPIDRE database, and facial identification performance on the AR database, is comparable with the literature. Combining both modalities using the new method of multimodal fusion leads to significantly improved accuracy over the unimodal systems, even when both modalities have been corrupted. The new method also shows improved identification accuracy compared with the bimodal systems based on multicondition model training or missing-feature decoding alone.

Fault Location on a Distribution Network Using a Decentralised Analysis Process

A reduction in the time required to locate and restore faults on a utility's distribution network improves the customer minutes lost (CML) measurement and hence brings direct cost savings to the operating company. The traditional approach to fault location involves fault impedance determination from high volume waveform files dispatched across a communications channel to a central location for processing and analysis. This paper examines an alternative scheme where data processing is undertaken locally within a recording instrument thus reducing the volume of data to be transmitted. Processed event fault reports may be emailed to relevant operational staff for the timely repair and restoration of the line.

Assessment of Atomic Data: Problems and Solutions

For the reliable analysis and modeling of astrophysical, laser-produced, and fusion plasmas, atomic data are required for a number of parameters, including energy levels, radiative rates, and electron impact excitation rates. Such data are desired for a range of elements (H to W) and their many ions. However, measurements of atomic data, mainly for radiative and excitation rates, are not feasible for many species, and therefore, calculations are needed. For some ions (such as of C, Fe, and Kr), there is a variety of calculations available in the literature, but often, they differ significantly from one another. Therefore, there is a great demand from the user community to have data "assessed" for accuracy so that they can be confidently applied to the modeling of plasmas. In this paper we highlight the difficulties in assessing atomic data and offer some solutions for improving the accuracy of calculated results.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Robust bimodal person identification using face and speech with limited training data and corruption of both modalities

This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.