Electron transport properties of Co71−xFexCr7Si8B14 (x=0, 2, 3.2, 4, 6,8 and 12 at%) amorphous alloys during devitrification

The investigation addresses the electron transport properties of Co_71−xFe_xCr₇Si₈B₁₄ (x=0, 2, 3.2, 4, 6, 8 and 12 at%) amorphous alloys. The variation in electrical resistivity of as-cast amorphous materials with thermal scanning from room temperature to 1000 K was measured. The CoFe-based alloys revealed an initial decrease in temperature coefficient of resistivity (TCR), a characteristic of spin-wave phenomena in glassy metallic systems. This behaviour in the present alloys was in a sharp contrast to the Co-based amorphous materials that indicate the drop in resistivity much below room temperature. In the studied alloys, the variation in initial TCR values and the full-width at half-maxima determined from X-ray diffraction of as-quenched materials exhibited a similar trend with increasing Fe content, indicating the compositional effect of near neighbouring atoms. After the initial decrease in resistivity, all the alloys indicated a subsequent increase at T_min. The Curie temperature (T_C), which was measured from thermal variation of ac susceptibility showed non-monotonic change with Fe content. In the temperature range between T_min and T_C the relative scattering by electron-magnon and electron-phonon resulted in the non-monotonic change in Curie temperature. At crystallization onset (T_X1) all the alloys except there with X=6, showed a sharp decrease in electrical resistivity which was attributed to ordering phenomena. In contrast to this resistivity decrease, X=6 alloy exhibited a drastic increase in resistivity around T_X1 observed during amorphous to nanocrystalline transformation. Such nanocrystalline state was observed by Transmission electron microscopy.     

A model for strategic planning under uncertainty

Summary In this paper the multi-period strategic planning problem for a consumer sumer product manufacturing chain is considered. Our discussion is focused on investment decisions which, are economically optimal over the whole planning horizonT, while meeting customer demands and conforming to technological requirements. In strategic planning, time and uncertainty play important roles. The uncertainties in the model are due to different levels of forecast demands, cost estimates and equipment behaviour. The main aim of this paper is to develop and analyse a multiperiod stochastic model representing the entire manufacturing chain, from the acquisitions of raw material to the delivering of final products. The resulting optimization problem is computationally intractable because of the enormous, and sometimes unrealistic, number of scenarios that must be considered in order to identify the optimal planning strategy. We propose two different solution approaches; firstly, we apply a scenario risk analysis giving the related results of experiments on a particular real data set. We then describe and investigate an Integer Stochastic Programming formulation of the problem and propose, as a solution technique, a variation of Benders decomposition method, namely theL-shaped method.     

Characterisation of PMMA microfluidic channels and devices fabricated by hot embossing and sealed by direct bonding

In this study we fabricated a silicon-based stamp with various microchannel arrays, and demonstrated successful replication of the stamp micro-structure on poly methyl methacrylate (PMMA) substrates. We used maskless UV lithography for the production of the micro-structured stamp. Thermal imprint lithography was used to fabricate microfeatured fluidic platforms on PMMA substrates, as well as to bond PMMA lids on the fluidic platforms. The microfeature in the silicon-based (silicon wafer coated with SU-8) stamp includes microchannel arrays of approximately 30 μm in depth and 5 mm in width. We produced various channels without pillars, as well as with SU-8 pillars in the range of 50–100 μm wide and 6 μm in height. PMMA discs of 1 mm thickness were utilized as the molding substrate. We found 10 kN applied force and 100 °C embossing temperature were optimum for transferring the micro-structure to the PMMA substrate.     

Nuclear orbiting and anomalies in nuclear reactions

In this paper, we report our measurements of back-angle oxygen and carbon particle yields from ¹⁶O+⁸⁹Y, ¹²C+⁹³Nb reactions forming the same compound nucleus ¹⁰⁵Ag at the same excitation energy and spin distribution. We find anomalously large oxygen yield and entrance channel dependence at high excitation energies from ¹⁶O+⁸⁹Y reaction implying formation of a dinuclear orbiting complex. Possible connection between nuclear orbiting and fast fission is also discussed.     

A wavelet filtering based estimation of output gap

Accurate estimation of output gap is an important and challenging problem for any economy. We consider here the problem of estimating the output gap for the Indian economy. The estimation of output gap involves a hypothetical variable, the potential output of the economy. In this paper, we propose a wavelet filtering based technique for estimation of output gap using monthly the Index of Industrial Production (IIP) series. We compare the results obtained using the proposed wavelet based technique with the widely used Hodrick-Prescott filtering and Seasonal ARIMA modeling based techniques. In order to compare the output gap estimated using the proposed technique with the other techniques, we explore the causal relationship between the estimates of output gap and the growth rate of an equity market indicator. It is observed that the wavelet filtering technique gives better results than the more popular econometric and standard time-series modeling techniques.     

Efficient Computation of Rectilinear Geodesic Voronoi Neighbor in the Presence of Obstacles

In this paper we present an algorithm to compute the rectilinear geodesic voronoi neighbor of an arbitrary query pointqamong a setSofmpoints in the presence of a set ofnvertical line segment obstacles inside a rectangular floor. The distance between a pair of points α and β is the shortest rectilinear distance avoiding the obstacles in and is denoted by δ(α, β). The rectilinear geodesic voronoi neighbor of an arbitrary query pointq,RGVN(q) is the pointp_i ∈ Ssuch that δ(q, p_i) is minimum. The algorithm suggests a preprocessing of the elements of the setsSand inO((m + n)log(m + n)) time such that for an arbitrary query pointq, theRGVNquery can be answered inO(log(m + n)) time. The space required for storing the preprocessed information isO(n + m log m). If the points inSare placed on the boundary of the rectangular floor, a different technique is adopted to decrease the space complexity toO(m + n). This technique works even if the obstacles are rectangles instead of line segments. Finally, the parallelization of the preprocessing steps for the latter algorithm is suggested, which takesO(log³(m + n)) time, usingO((m + n)^1.5/log²(m + n)) processors andO(log(m + n)) query time.     

Detecting Embedded Networks in LP Using GUB Structures and Independent Set Algorithms

In this paper, we present an alternative multi-stage generalized upper bounds (GUB) based approach for detecting an embedded pure network structure in an LP problem. In order to identify a GUB structure, we use two different approaches; the first is based on the notion of Markowitz merit count and the second exploits independent sets in the corresponding graphs. Our computational experiments show that the multi-stage GUB algorithm based on these approaches performs favourably when compared with other well known algorithms.     

An application of Lagrangian relaxation to a capacity planning problem under uncertainty

A supply chain network-planning problem is presented as a two-stage resource allocation model with 0-1 discrete variables. In contrast to the deterministic mathematical programming approach, we use scenarios, to represent the uncertainties in demand. This formulation leads to a very large scale mixed integer-programming problem which is intractable. We apply Lagrangian relaxation and its corresponding decomposition of the initial problem in a novel way, whereby the Lagrangian relaxation is reinterpreted as a column generator and the integer feasible solutions are used to approximate the given problem. This approach addresses two closely related problems of scenario analysis and two-stage stochastic programs. Computational solutions for large data instances of these problems are carried out successfully and their solutions analysed and reported. The model and the solution system have been applied to study supply chain capacity investment and planning.     

A simple test of exponentiality against NWBUE family of life distributions

In this paper we propose a simple procedure to test the null hypothesis of exponentiality against the alternative that it belongs to the new worse then better than used in expectation (NWBUE) family. The test is shown to be consistent and the asymptotic distribution of the test statistic has been obtained. The performance of the test against various classes of alternatives has been studied by means of simulation. Copyright © 2005 John Wiley & Sons, Ltd.     

Multiple outlier detection in multivariate data using self-organizing maps title

Summary  The problem of detection of multidimensional outliers is a fundamental and important problem in applied statistics. The unreliability of multivariate outlier detection techniques such as Mahalanobis distance and hat matrix leverage has led to development of techniques which have been known in the statistical community for well over a decade. The literature on this subject is vast and growing. In this paper, we propose to use the artificial intelligence technique ofself-organizing map (SOM) for detecting multiple outliers in multidimensional datasets. SOM, which produces a topology-preserving mapping of the multidimensional data cloud onto lower dimensional visualizable plane, provides an easy way of detection of multidimensional outliers in the data, at respective levels of leverage. The proposed SOM based method for outlier detection not only identifies the multidimensional outliers, it actually provides information about the entire outlier neighbourhood. Being an artificial intelligence technique, SOM based outlier detection technique is non-parametric and can be used to detect outliers from very large multidimensional datasets. The method is applied to detect outliers from varied types of simulated multivariate datasets, a benchmark dataset and also to real life cheque processing dataset. The results show that SOM can effectively be used as a useful technique for multidimensional outlier detection.