Analysis of dissipation of a burst-type martensite transformation in a Fe-Mn alloy by internal friction measurements

Recently, we have proposed a theory to analyze the first-order phase transition (FOPT) in solids. In order to test the concept of the physics of dissipation during FOPT in solids, it is necessary to test the theory with different FOPT system. We study here a burst-type martensite transformation in a Fe-18.8% Mn alloy sample for this purpose. We investigate the characteristics of γ(fcc)⇌ɛ(hcp) transformation in this alloy and measure the dependence of internal friction (IF) during γ/ɛ transformation in varying rate of temperature Ṫ and vibration frequency ω. For free oscillations, the IF was defined to be Qδ-1=δ/π where δ is the logarithmic decrement. For general (forced) oscillations, IF is usually defined to be Qw-1=(1/2π)(ΔW/W), where ΔW is the dissipation over one cycle, while W is the maximum stored energy. During our analysis, the relation between Qδ-1 and Qw-1 is deduced. The parameter l (coupling factor between phase interface and oscillating stress) takes a small value (0.015–0.035) during PT, but takes a large value (0.86) during static state. The parameter n (exponent of rate for effective PT driving force) takes a large value 0.33 during heating and 0.47 during cooling. The physical meaning of n and l is discussed. The methodology introduced here appears to be an effective way of studying FOPT in solids. © 1996 The American Physical Society.published_or_final_versio

Characterization of the thermoelastic martensitic transformation in a NiTi alloy driven by temperature variation and external stress

In order to test the concept of the physics of dissipation during first-order phase transitions in solids, we measured the internal friction (Q-1) and the relative shear modulus (μ) during a thermoelastic martensitic transformation in a NiTi alloy. We adopted two approaches: temperature variation and application of external stress. This investigation of internal friction was carried out with various vibration frequencies ω, temperature variation rates Ṫ, and strain variation rates ɛ̇. The index l (coupling factor between phase interface and oscillating stress) and index n (rate exponent for the effective phase transformation driving force) have been calculated from the experimental data for each case and the values of l and n are about the same in the two (doped) NiTi samples, irrespective of whether the phase transition is driven by a temperature variation or stress induced process. We compare the values of n and l for the NiTi samples with that of the other samples (VO2 ceramics and FeMn alloys), reinforcing the previous physical interpretations of these indices. We believe the indices n and l are indeed fingerprints of first-order phase transitions in solids.published_or_final_versio

Efficiently computing Top-K shortest path join

© 2015, Copyright is with the authors. Driven by many applications, in this paper we study the problem of computing the top-k shortest paths from one set of target nodes to another set of target nodes in a graph, namely the top-k shortest path join (KPJ) between two sets of target nodes. While KPJ is an extension of the problem of computing the top-k shortest paths (KSP) between two target nodes, the existing technique by converting KPJ to KSP has several deficiencies in conducting the computation. To resolve these, we propose to use the best-first paradigm to recursively divide search subspaces into smaller subspaces, and to compute the shortest path in each of the subspaces in a prioritized order based on their lower bounds. Consequently, we only compute shortest paths in subspaces whose lower bounds are larger than the length of the current k-th shortest path. To improve the efficiency, we further propose an iteratively bounding approach to tightening lower bounds of subspaces. Moreover, we propose two index structures which can be used to reduce the exploration area of a graph dramatically; these greatly speed up the computation. Extensive performance studies based on real road networks demonstrate the scalability of our approaches and that our approaches outperform the existing approach by several orders of magnitude. Furthermore, our approaches can be immediately used to compute KSP. Our experiment also demonstrates that our techniques outperform the state-of-the-art algorithm for KSP by several orders of magnitude

I/O efficient Core Graph Decomposition at web scale.

Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are big and may not reside in memory. In the literature, there is only one work for I/O efficient core decomposition that avoids loading the whole graph in memory. However, this approach is not scalable to handle big graphs because it cannot bound the memory size and may load most parts of the graph in memory. In addition, this approach can hardly handle graph updates. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. This model works well in many web-scale graphs. We propose a semi-external algorithm and two optimized algorithms for I/O efficient core decomposition using very simple structures and data access model. To handle dynamic graph updates, we show that our algorithm can be naturally extended to handle edge deletion. We also propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost by investigating some new graph properties. We conduct extensive experiments on 12 real large graphs. Our optimal algorithm significantly outperform the existing I/O efficient algorithm in terms of both processing time and memory consumption. In many memory-resident graphs, our algorithms for both core decomposition and maintenance can even outperform the in-memory algorithm due to the simple structures and data access model used. Our algorithms are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory

Scalable supergraph search in large graph databases

© 2016 IEEE. Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prunes false answers based on features in the pruning phase and performs subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationship to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a verification-free algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. Finally, we conduct extensive performance studies on two real large datasets to demonstrate the high scalability of our algorithms

Optimal Enumeration: Efficient Top-k Tree Matching

Driven by many real applications, graph pattern matching has attracted a great deal of attention recently. Consider that a twigpattern matching may result in an extremely large number ofmatches in a graph; this may not only confuse users by providing too many results but also lead to high computational costs. In this paper, we study the problem of top-k tree pattern matching; that is, given a rooted tree T, compute its top-k matches in a directed graph G based on the twig-pattern matching semantics. We firstly present a novel and optimal enumeration paradigm based on the principle of Lawler's procedure. We show that our enumeration algorithm runs in O(nT + log k) time in each round where nT is the number of nodes in T. Considering that the time complexity to output a match of T is O(nT) and nT = log k in practice, our enumeration technique is optimal. Moreover, the cost of generating top-1 match of T in our algorithm is O(mR) where mR is the number of edges in the transitive closure of a data graph G involving all relevant nodes to T. O(mR) is also optimal in the worst case without preknowledge of G. Consequently, our algorithm is optimal with the running time O(mR +k(nT +log k)) in contrast to the time complexity O(mR log k+knT (log k+dT)) of the existing technique where dT is the maximal node degree in T. Secondly, a novel priority based access technique is proposed, which greatly reduces the number of edges accessed and results in a significant performance improvement. Finally, we apply our techniques to the general form of top-k graph pattern matching problem (i.e., query is a graph) to improve the existing techniques. Comprehensive empirical studies demonstrate that our techniques may improve the existing techniques by orders of magnitude

Rice protein radicals: growth and stability under microwave treatment

published_or_final_versio

Characterisation of thermo-elastic Martensitic transformation in NiTi and FeMn alloys driven by temperature variation and external stress

Abstract no. D41.109published_or_final_versio

Caspase-2 is upregulated after sciatic nerve transection and its inhibition protects dorsal root ganglion neurons from Apoptosis after serum withdrawal

Sciatic nerve (SN) transection-induced apoptosis of dorsal root ganglion neurons (DRGN) is one factor determining the efficacy of peripheral axonal regeneration and the return of sensation. Here, we tested the hypothesis that caspase-2(CASP2) orchestrates apoptosis of axotomised DRGN both in vivo and in vitro by disrupting the local neurotrophic supply to DRGN. We observed significantly elevated levels of cleaved CASP2 (C-CASP2), compared to cleaved caspase-3 (C-CASP3), within TUNEL+DRGN and DRG glia (satellite and Schwann cells) after SN transection. A serum withdrawal cell culture model, which induced 40% apoptotic death in DRGN and 60% in glia, was used to model DRGN loss after neurotrophic factor withdrawal. Elevated C-CASP2 and TUNEL were observed in both DRGN and DRG glia, with C-CASP2 localisation shifting from the cytosol to the nucleus, a required step for induction of direct CASP2-mediated apoptosis. Furthermore, siRNAmediated downregulation of CASP2 protected 50% of DRGN from apoptosis after serum withdrawal, while downregulation of CASP3 had no effect on DRGN or DRG glia survival. We conclude that CASP2 orchestrates the death of SN-axotomised DRGN directly and also indirectly through loss of DRG glia and their local neurotrophic factor support. Accordingly, inhibiting CASP2 expression is a potential therapy for improving both the SN regeneration response and peripheral sensory recovery