A class of multivariate skew-normal models

The existing model for multivariate skew normal data does not cohere with the joint distribution of a random sample from a univariate skew normal distribution. This incoherence causes awkward interpretation for data analysis in practice, especially in the development of the sampling distribution theory. In this paper, we propose a refined model that is coherent with the joint distribution of the univariate skew normal random sample, for multivariate skew normal data. The proposed model extends and strengthens the multivariate skew model described in Azzalini (1985,Scandinavian Journal of Statistics,12, 171–178). We present a stochastic representation for the newly proposed model, and discuss a bivariate setting, which confirms that the newly proposed model is more plausible than the one given by Azzalini and Dalla Valle (1996,Biometrika,83, 715–726).     

A RANDOM FIXED POINT ITERATION FOR THREE RANDOM OPERATORS ON UNIFORMLY CONVEX BANACH SPACES

In the present paper we introduce a random iteration scheme for three random operators defined on a closed and convex subset of a uniformly convex Banach space and prove its convergence to a common fixed point of three random operators. The result is also an extersion of a known theorem in the corresponding non-random case.     

Synchronization in supply chains: implications for design and management

Supply chain management literature calls for coordination between the different members of the chain. Materials should be moved from one supplier to the next according to a just-in-time schedule. In this paper, we show that for many supply chain configurations, complete synchronization will result in some members of the chain being ‘losers’ in terms of cost. We develop an algorithm for optimal synchronization of supply chains and provide some guidelines for incentive alignment along the supply chain. In developing the model, we use the economic delivery and scheduling problem model and analyze supply chains dealing with single and multiple components. For single-component supply chains, we derive a closed-form expression for the optimal synchronized cycle time. For multi-component supply chains, we develop an algorithm for finding the optimal synchronized cycle time. We test the performance of the algorithm and show that it provides optimal solutions for a wide range of problems. We illustrate the models with numerical examples.     

Lower Bounds for the Height and Size of the Ideal Class Group in CM-Fields

 We prove that, under the assumption of the Generalized Riemann Hypothesis, the exponent of the ideal class group of a CM-field goes to infinity with its absolute discriminant. This gives a positive answer to a question raised by Louboutin and Okazaki [4]. Received September 10, 2001; in revised form April 5, 2002     

Flow-injection spectrophotometric determination of sulfadiazine and sulfamethoxazole in pharmaceuticals and urine.

A rapid and sensitive flow-injection spectrophotometric method is proposed for the determination of sulfadiazine and sulfamethoxazole. This method is based on the diazotization of sulfonamide with sodium nitrite, and a coupling reaction of the diazo-compound with alpha-naphthylamine. The optimum experimental conditions are obtained by using the controlled and weighted centroid simplex method. The linear ranges for the determination of sulfadiazine and sulfamethoxazole are 0.2-20 microg ml(-1) and 0.1-20 microg ml(-1), and their detection limits are 0.06 microg ml(-1) and 0.05 microg ml(-1), respectively, and the sampling frequency is 130 samples per hour. The method has been used to determine sulfadiazine and sulfamethoxazole in pharmaceuticals and urine without separation. The results are in agreement with those obtained by a high-performance liquid chromatograph technique at the 95% confidence level.     

A new joint model for longitudinal and survival data with a cure fraction

We develop a new joint cure rate model for longitudinal and survival data. The model allows for multiple longitudinal markers as well as a cure structure for the survival component based on the promotion time cure rate model, as described in Ibrahim et al. (Bayesian Survival Analysis, Springer, New York, 2001). Several characteristics and properties of the new model are discussed and examined. A real dataset from a melanoma clinical trial is given to demonstrate the methodology.     

Positive periodic solution of two-species ratio-dependent predator–prey system with time delay in two-patch environment

In this paper, we are concerned with the existence of positive periodic solution to a class of two-species ratio-dependent predator–prey diffusion model with time delay. By using the continuation theorem of coincidence degree theory, we transform this problem into a problem of calculating the topological degree of a continuous mapping, and then some sufficient conditions of the existence of positive periodic solution is established for the system.     

Cesàro means of double Walsh-Fourier series

We investigate approximation properties of Cesàro (C; −α, −β)-means of double Walsh-Fourier series with α, β ∈ (0, 1). As an application, we obtain a sufficient condition for the convergence of the means σ _n,m ^/−α,−β (f; x, y) to f(x,y) in the L ^p -metric, p ∈ [1, ∞]. We also show that this sufficient condition cannot be improved.     

A Branch & Cut algorithm for a four-index assignment problem

In this paper, we examine the orthogonal Latin squares (OLS) problem from an integer programming perspective. The OLS problem has a long history and its significance arises from both theoretical aspects and practical applications. The problem is formulated as a four-index assignment problem whose solutions correspond to OLS. This relationship is exploited by various routines (preliminary variable fixing, branching, etc) of the Branch & Cut algorithm we present. Clique, odd-hole and antiweb inequalities implement the ‘Cut’ component of the algorithm. For each cut type a polynomial-time separation algorithm is implemented. Extensive computational analysis examines multiple aspects concerning the design of our algorithm. The results illustrate clearly the improvement achieved over simple Branch & Bound.     

Parallel discrete event simulation algorithm for manufacturing supply chains

Parallel discrete event simulation (PDES) is concerned with the distributed execution of large-scale system models on multiple processors. It is an enabler in the implementation of the virtual enterprise concept, integrating semi-autonomous models of production cells, factories, or units of a supply chain. The key issue in PDES is to maintain causality relationships between system events, while maximizing parallelism in their execution. Events can be executed conservatively only when it is safe to do so, sacrificing the extent to which potential parallelism of the system can be exploited. Alternatively, they can be processed optimistically without guarantee of correctness, but incurring the overhead of a rollback to an earlier saved state when causality error is detected. The paper proposes a modified optimistic scheme for distributed simulation of constituent models of a supply chain in manufacturing, which exploits the inherent operating characteristics of its domain.