Superconductivity in dilute Cu-Nb-Sn alloys containing Nb3Sn precipitates

The dilute Cu-Nb-Sn alloys containing small amounts of Nb and Sn less than 1 at % exhibited superconductivity after quenching from the liquid state and ageing. The best superconducting properties ( andJ _c=130 A cm^–2) in a Cu-0.30 at % Nb 0.15 at % Sn alloy were obtained when the sample was aged at 550° C for 384 h. This sample exhibited a structure of fine Nb₃Sn precipitates of 200 to 500Å diameter distributed homogeneously in the Cu matrix, and therefore it was concluded that superconductivity in these alloys resulted from the proximity effect of Nb₃Sn particles. In spite of the similar structure obtained by ageing at 800° C, the Cu-Nb-Sn alloys showed inferior superconducting properties compared to the Cu-0.4 at % Nb alloy and this would be explained qualitatively by the difference in the mean free path in the two alloys.     

Efficient blind dereverberation and echo cancellation based on independent component analysis for actual acoustic signals

This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT??= 670 ms reverberant environment.     

A high‐performance sorting algorithm for multicore single‐instruction multiple‐data processors

Many sorting algorithms have been studied in the past, but there are only a few algorithms that can effectively exploit both single‐instruction multiple‐data (SIMD) instructions and thread‐level parallelism. In this paper, we propose a new high‐performance sorting algorithm, called aligned‐access sort (AA‐sort), that exploits both the SIMD instructions and thread‐level parallelism available on today's multicore processors. Our algorithm consists of two phases, an in‐core sorting phase and an out‐of‐core merging phase. The in‐core sorting phase uses our new sorting algorithm that extends combsort to exploit SIMD instructions. The out‐of‐core algorithm is based on mergesort with our novel vectorized merging algorithm. Both phases can take advantage of SIMD instructions. The key to high performance is eliminating unaligned memory accesses that would reduce the effectiveness of SIMD instructions in both phases. We implemented and evaluated the AA‐sort on PowerPC 970MP and Cell Broadband Engine platforms. In summary, a sequential version of the AA‐sort using SIMD instructions outperformed IBM's optimized sequential sorting library by 1.8 times and bitonic mergesort using SIMD instructions by 3.3 times on PowerPC 970MP when sorting 32 million random 32‐bit integers. Also, a parallel version of AA‐sort demonstrated better scalability with increasing numbers of cores than a parallel version of bitonic mergesort on both platforms. Copyright © 2011 John Wiley & Sons, Ltd.     

A Linear-Time Algorithm for Star-Shaped Drawings of Planar Graphs with the Minimum Number of Concave Corners

A star-shaped drawing of a graph is a straight-line drawing such that each inner facial cycle is drawn as a star-shaped polygon, and the outer facial cycle is drawn as a convex polygon. In this paper, we consider the problem of finding a star-shaped drawing of a biconnected planar graph with the minimum number of concave corners. We first show new structural properties of planar graphs to derive a lower bound on the number of concave corners. Based on the lower bound, we prove that the problem can be solved in linear time by presenting a linear-time algorithm for finding a best plane embedding of a biconnected planar graph with the minimum number of concave corners. This is in spite of the fact that a biconnected planar graph may have an exponential number of different plane embeddings.     

Capability and limitation of max- and sum-type construction of Lyapunov functions for networks of iISS systems

This paper addresses the problem of verifying stability of networks whose subsystems admit dissipation inequalities of integral input-to-state stability (iISS). We focus on two ways of constructing a Lyapunov function satisfying a dissipation inequality of a given network. Their difference from one another is elucidated from the viewpoint of formulation, relation, fundamental limitation and capability. One is referred to as the max-type construction resulting in a Lipschitz continuous Lyapunov function. The other is the sum-type construction resulting in a continuously differentiable Lyapunov function. This paper presents geometrical conditions under which the Lyapunov construction is possible for a network comprising $n\ge 2$ subsystems. Although the sum-type construction for general $n>2$ has not yet been reduced to a readily computable condition, we obtain a simple condition of iISS small gain in the case of $n=2$. It is demonstrated that the max-type construction fails to offer a Lyapunov function if the network contains subsystems which are not input-to-state stable (ISS).     

Microstructure and yield strength of UDIMET 720LI alloyed with Co-16.9 Wt Pct Ti

We proposed a new method for developing Ni-base turbine disc alloy for application at temperatures above 700 °C by mixing a Ni-base superalloy U720LI with a two-phase alloy Co-16.9 wt pct Ti in various contents. The microstructure and phase stability of the alloys were analyzed using an optical microscope, a scanning electron microscope, energy-dispersive spectroscopy, and an X-ray diffractometer. The yield strength was studied by compression tests at temperatures ranging from 25 °C to 1200 °C. The results show that all the alloys had a dendritic structure. Ni₃Ti (η) phase was formed in the interdendritic region in the alloys with the addition of Co-16.9 wt pct Ti, and its volume fraction increased with the increase in the addition of Co-16.9 wt pct Ti. The results of exposure at 750 °C show that the addition of Co-16.9 wt pct Ti to U720LI had a great effect on suppressing the formation of σ phase due to the reduced Cr content in the γ matrix. Compared to U720LI, the alloys with the addition of Co-16.9 wt pct Ti possessed higher yield strength. The solid-solution strengthening of γ and γ′ and higher volume fraction of γ′ were assumed to cause this strength increase.     

Study on Magnetic Responsibility of Rare Earth Ferrite／Polyacrylamide Magnetic Microsphere

Magneticpolymermicrospherecanbeseparatedquicklyundermagneticfieldandithasgotwideappli cationinmanyfields ,suchascellseparationetc .Themicrospheremustpossesshighmagneticresponsibili ty ,sothatmagneticseparationisquickandconve nient.Themagneticresponsibilit…     

Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

Basket Analysis, which is a standard method for data mining, derives frequent itemsets from database. However, its mining ability is limited to transaction data consisting of items. In reality, there are many applications where data are described in a more structural way, e.g. chemical compounds and Web browsing history. There are a few approaches that can discover characteristic patterns from graph-structured data in the field of machine learning. However, almost all of them are not suitable for such applications that require a complete search for all frequent subgraph patterns in the data. In this paper, we propose a novel principle and its algorithm that derive the characteristic patterns which frequently appear in graph-structured data. Our algorithm can derive all frequent induced subgraphs from both directed and undirected graph structured data having loops (including self-loops) with labeled or unlabeled nodes and links. Its performance is evaluated through the applications to Web browsing pattern analysis and chemical carcinogenesis analysis.     

A Knowledge Discovery System with Support for Model Selection and Visualization

The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.     

双束强辐照对氧化物弥散强化铁素体型合金强化相稳定性影响

采用双束复合辐照装置,研究了He存在条件下,强辐照对长期时效后的ODS合金中强化相(Y2O3)的稳定性和辐照损伤特征的影响。实验结果表明:双束强辐照下,ODS合金中强化相不稳定,发生聚集长大并造成附近基体中Ti、Y浓度增高,导致空洞尺寸和空洞肿胀增加,并对这一结果从理论上进行解释。