The interconnection of the magnetic fields of the Earth and the sun

Magnetic reconnection facilitates the transfer of mass, energy, and momentum from the solar wind, through the Earth's magnetosphere and into the upper atmosphere. Recently, combined observations using both ground-based and satellite instruments have revealed much about how reconnection takes place. This new understanding has great signficance for systems which exploit, or operate within, the Earth's plasma environment, as well as for a wide variety of scientific studies.

Solvothermal synthesis of novel antimony sulphides containing [Sb4S7](2-) units

Two new antimony sulphides have been prepared solvothermally and characterised by single-crystal X-ray diffraction. [Co(en)(3)][Sb4S7] (1) was prepared at 140 degreesC from COS, Sb2S3 and S in the presence of ethylenediamine, whilst heating a mixture of Sb2S3, Co and S in tris(2aminoethyl)amine, N(CH2CH2NH2)(3), at 180 degreesC fegults in the formation of [C6H20N4][Sb4S7] (2). Both materials contain [Sb4S7](2-) chains formed from linkage of cyclic Sb3S63- units by SbS33- pyramids. In (1), the [Sb4S7] chains are linked by secondary Sb-S interactions to form sheets, between which the. charge balancing [Co(en)(3)](2+) cations reside. The structure of (2) involves interconnection of pairs of [Sb4S7](2-) chains through Sb2S2 rings to form isolated [Sb4S7](2-) double chains which are interleaved by protonated template molecules. (C) 2004 Elsevier B.V. All rights resereved.

On the maximal connected component of hypercube with faulty vertices

In evaluating an interconnection network, it is indispensable to estimate the size of the maximal connected components of the underlying graph when the network begins to lose processors. Hypercube is one of the most popular interconnection networks. This article addresses the maximal connected components of an n -dimensional cube with faulty processors. We first prove that an n -cube with a set F of at most 2n - 3 failing processors has a component of size greater than or equal to2(n) - \F\ - 1. We then prove that an n -cube with a set F of at most 3n - 6 missing processors has a component of size greater than or equal to2(n) - \F\ - 2.

Generalized honeycomb torus is Hamiltonian

Generalized honeycomb torus is a candidate for interconnection network architectures, which includes honeycomb torus, honeycomb rectangular torus, and honeycomb parallelogramic torus as special cases. Existence of Hamiltonian cycle is a basic requirement for interconnection networks since it helps map a "token ring" parallel algorithm onto the associated network in an efficient way. Cho and Hsu [Inform. Process. Lett. 86 (4) (2003) 185-190] speculated that every generalized honeycomb torus is Hamiltonian. In this paper, we have proved this conjecture. (C) 2004 Elsevier B.V. All rights reserved.

On the maximal connected component of hypercube with faulty vertices (II)

evaluating the fault tolerance of an interconnection network, it is essential to estimate the size of a maximal connected component of the network at the presence of faulty processors. Hypercube is one of the most popular interconnection networks. In this paper, we prove that for ngreater than or equal to6, an n-dimensional cube with a set F of at most (4n-10) failing processors has a component of size greater than or equal to2"-\F-3. This result demonstrates the superiority of hypercube in terms of the fault tolerance.

Locally twisted cubes are 4-pancyclic

The locally twisted cube is a newly introduced interconnection network for parallel computing. Ring embedding is an important issue for evaluating the performance of an interconnection network. In this paper, we investigate the problem of embedding rings into a locally twisted cube. Our main contribution is to find that, for each integer l is an element of (4,5,...,2(n)}, a ring of length I can be embedded into an n-dimensional locally twisted cube so that both the dilation and the load factor are one. As a result, a locally twisted cube is Hamiltonian. We conclude that a locally twisted cube is superior to a hypercube in terms of ring embedding capability. (C) 2004 Elsevier Ltd. All rights reserved.

A comparison-based diagnosis algorithm tailored for crossed cube multiprocessor systems

Comparison-based diagnosis is an effective approach to system-level fault diagnosis. Under the Maeng-Malek comparison model (NM* model), Sengupta and Dahbura proposed an O(N-5) diagnosis algorithm for general diagnosable systems with N nodes. Thanks to lower diameter and better graph embedding capability as compared with a hypercube of the same size, the crossed cube has been a promising candidate for interconnection networks. In this paper, we propose a fault diagnosis algorithm tailored for crossed cube connected multicomputer systems under the MM* model. By introducing appropriate data structures, this algorithm runs in O(Nlog(2)(2) N) time, which is linear in the size of the input. As a result, this algorithm is significantly superior to the Sengupta-Dahbura's algorithm when applied to crossed cube systems. (C) 2004 Elsevier B.V. All rights reserved.

Pancyclicity of Mobius cubes with faulty nodes

An interconnection network with n nodes is four-pancyclic if it contains a cycle of length l for each integer l with 4 <= l <= n. An interconnection network is fault-tolerant four-pancyclic if the surviving network is four-pancyclic in the presence of faults. The fault-tolerant four-pancyclicity of interconnection networks is a desired property because many classical parallel algorithms can be mapped onto such networks in a communication-efficient fashion, even in the presence of failing nodes or edges. Due to some attractive properties as compared with its hypercube counterpart of the same size, the Mobius cube has been proposed as a promising candidate for interconnection topology. Hsieh and Chen [S.Y. Hsieh, C.H. Chen, Pancyclicity on Mobius cubes with maximal edge faults, Parallel Computing, 30(3) (2004) 407-421.] showed that an n-dimensional Mobius cube is four-pancyclic in the presence of up to n-2 faulty edges. In this paper, we show that an n-dimensional Mobius cube is four-pancyclic in the presence of up to n-2 faulty nodes. The obtained result is optimal in that, if n-1 nodes are removed, the surviving network may not be four-pancyclic. (C) 2005 Elsevier B.V. All rights reserved.

An oblivious shortest-path routing algorithm for fully connected cubic networks

Fully connected cubic networks (FCCNs) are a class of newly proposed hierarchical interconnection networks for multicomputer systems, which enjoy the strengths of constant node degree and good expandability. The shortest path routing in FCCNs is an open problem. In this paper, we present an oblivious routing algorithm for n-level FCCN with N = 8(n) nodes, and prove that this algorithm creates a shortest path from the source to the destination. At the costs of both an O(N)-parallel-step off-line preprocessing phase and a list of size N stored at each node, the proposed algorithm is carried out at each related node in O(n) time. In some cases the proposed algorithm is superior to the one proposed by Chang and Wang in terms of the length of the routing path. This justifies the utility of our routing strategy. (C) 2006 Elsevier Inc. All rights reserved.

Diameter of parallelogramic honeycomb torus

The determination of the diameter of an interconnection network is essential in evaluating the performance of the network. Parallelogramic honeycomb torus is an attractive alternative to classical torus network due to smaller vertex degree, and hence, lower implementation cost. In this paper, we present the expression for the diameter of a parallelogramic, honeycomb torus, which extends a known result about rhombic: honeycomb torus. (c) 2005 Elsevier Ltd. All rights reserved.

Largest connected component of a star graph with faulty vertices

In order to make a full evaluation of an interconnection network, it is essential to estimate the minimum size of a largest connected component of this network provided the faulty vertices in the network may break its connectedness. Star graphs are recognized as promising candidates for interconnection networks. This article addresses the size of a largest connected component of a faulty star graph. We prove that, in an n-star graph (n >= 3) with up to 2n-4 faulty vertices, all fault-free vertices but at most two form a connected component. Moreover, all fault-free vertices but exactly two form a connected component if and only if the set of all faulty vertices is equal to the neighbourhood of a pair of fault-free adjacent vertices. These results show that star graphs exhibit excellent fault-tolerant abilities in the sense that there exists a large functional network in a faulty star graph.

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

Measuring significant variability characteristics: An assessment of three UK renewables

The variability of renewable energy is widely recognised as a challenge for integrating high levels of renewable generation into electricity systems. However, to explore its implications effectively, variability itself should first be clearly understood. This is particularly true for national electricity systems with high planned penetration of renewables and limited interconnection such as the UK. Variability cannot be considered as a distinct resource property with a single measurable parameter, but is a multi-faceted concept best described by a range of distinct characteristics. This paper identifies relevant characteristics of variability, and considers their implications for energy research. This is done through analysis of wind, solar and tidal current resources, with a primary focus on the Bristol Channel region in the UK. The relationship with electricity demand is considered, alongside the potential benefits of resource diversity. Analysis is presented in terms of persistence, distribution, frequency and correlation between supply and demand. Marked differences are seen between the behaviours of the individual resources, and these give rise to a range of different implications for system integration. Wind shows strong persistence and a useful seasonal pattern, but also a high spread in energy levels at timescales beyond one or two days. The solar resource is most closely correlated with electricity demand, but is undermined by night-time zero values and an even greater spread of monthly energy delivered than wind. In contrast, the tidal resource exhibits very low persistence, but also much greater consistency in energy values assessed across monthly time scales. Whilst this paper focuses primarily on the behaviour of resources, it is noted that discrete variability characteristics can be related to different system impacts. Persistence and predictability are relevant for system balancing, whereas statistical distribution is more relevant when exploring issues of asset utilisation and energy curtailment. Areas of further research are also identified, including the need to assess the value of predictability in relation to other characteristics.

Beyond arousal and valence: the importance of the biological versus social relevance of emotional stimuli

The present study addressed the hypothesis that emotional stimuli relevant to survival or reproduction (biologically emotional stimuli) automatically affect cognitive processing (e.g., attention, memory), while those relevant to social life (socially emotional stimuli) require elaborative processing to modulate attention and memory. Results of our behavioral studies showed that (1) biologically emotional images hold attention more strongly than do socially emotional images, (2) memory for biologically emotional images was enhanced even with limited cognitive resources, but (3) memory for socially emotional images was enhanced only when people had sufficient cognitive resources at encoding. Neither images’ subjective arousal nor their valence modulated these patterns. A subsequent functional magnetic resonance imaging study revealed that biologically emotional images induced stronger activity in the visual cortex and greater functional connectivity between the amygdala and visual cortex than did socially emotional images. These results suggest that the interconnection between the amygdala and visual cortex supports enhanced attention allocation to biological stimuli. In contrast, socially emotional images evoked greater activity in the medial prefrontal cortex (MPFC) and yielded stronger functional connectivity between the amygdala and MPFC than did biological images. Thus, it appears that emotional processing of social stimuli involves elaborative processing requiring frontal lobe activity.