Population shift bias in tests of space–time interaction

Common to all tests of space–time interaction is the assumption that the population underlying the events of interest exhibits a trajectory of growth that is consistent through time and across space. In practice, however, this assumption is often untenable and, when violated, can introduce population shift bias into the results of these tests. While this problem is widely recognized, more work remains to compare its effect across tests and to determine the extent to which it is a problem for study short periods. This paper quantifies and compares the population shift bias present in the results of the Knox, Mantel, and Jacquez tests of space–time interaction. A simulation study is carried out which quantifies the bias present in each test across a variety of population movement scenarios. Results show a positive relationship between population shift bias and the heterogeneity in population growth across all the tests. They also demonstrate variability in the size of the bias across the three tests for space–time interaction considered. Finally, the results illustrate that population shift bias can be a serious problem for short study periods. Collectively, these findings suggest that an unbiased approach to assessing the significance of space–time interaction test results is needed whenever spatially heterogeneous population change is identified within a study area.     

Micro-engineered first wall tungsten armor for high average power laser fusion energy systems

The high average power laser program is developing an inertial fusion energy demonstration power reactor with a solid first wall chamber. The first wall (FW) will be subject to high energy density radiation and high doses of high energy helium implantation. Tungsten has been identified as the candidate material for a FW armor. The fundamental concern is long term thermo-mechanical survivability of the armor against the effects of high temperature pulsed operation and exfoliation due to the retention of implanted helium. Even if a solid tungsten armor coating would survive the high temperature cyclic operation with minimal failure, the high helium implantation and retention would result in unacceptable material loss rates. Micro-engineered materials, such as castellated structures, plasma sprayed nano-porous coatings and refractory foams are suggested as a first wall armor material to address these fundamental concerns. A micro-engineered FW armor would have to be designed with specific geometric features that tolerate high cyclic heating loads and recycle most of the implanted helium without any significant failure. Micro-engineered materials are briefly reviewed. In particular, plasma-sprayed nano-porous tungsten and tungsten foams are assessed for their potential to accommodate inertial fusion specific loads. Tests show that nano-porous plasma spray coatings can be manufactured with high permeability to helium gas, while retaining relatively high thermal conductivities. Tungsten foams where shown to be able to overcome thermo-mechanical loads by cell rotation and deformation. Helium implantation tests have shown, that pulsed implantation and heating releases significant levels of implanted helium. Helium implantation and release from tungsten was modeled using an expanded kinetic rate theory, to include the effects of pulsed implantations and thermal cycles. Although, significant challenges remain micro-engineered materials are shown to constitute potential candidate FW armor materials.     

Electronic Commerce Customer Relationship Management: A Research Agenda

In this paper, we approach electronic commerce Customer Relationship Management (e-CRM) from the perspective of five research areas. Our purpose is to define a conceptual framework to examine the relationships among and between these five research areas within e-CRM and to propose how they might be integrated to further research this area. We begin with a discussion of each of the research areas through brief reviews of relevant literature for each and a discussion of the theoretical and strategic implications associated with some CRM technologies and research areas. Next we present our framework, which focuses on e-CRM from the five research perspectives. We then present a theoretical framework for e-CRM in terms of the five research areas and how they affect one another, as well as e-CRM processes and both performance and non-performance outcomes.     

Scheduling and Lot Streaming in Flowshops with No-Wait in Process

Lot streaming involves splitting a production lot into a number of sublots, in order to allow the overlapping of successive operations, in multi-machine manufacturing systems. In no-wait flowshop scheduling, sublots are necessarily consistent, that is, they remain the same over all machines. The benefits of lot streaming include reductions in lead times and work-in-process, and increases in machine utilization rates. We study the problem of minimizing the makespan in no-wait flowshops producing multiple products with attached setup times, using lot streaming. Our study of the single product problem resolves an open question from the lot streaming literature. The intractable multiple product problem requires finding the optimal number of sublots, sublot sizes, and a product sequence for each machine. We develop a dynamic programming algorithm to generate all the nondominated schedule profiles for each product that are required to formulate the flowshop problem as a generalized traveling salesman problem. This problem is equivalent to a classical traveling salesman problem with a pseudopolynomial number of cities. We develop and computationally test an efficient heuristic for this problem. Our results indicate that solutions can quickly be found for flowshops with up to 10 machines and 50 products. Moreover, the solutions found by our heuristic provide a substantial improvement over previously published results.     

Isotropic Energies, Filters and Splines for Vector Field Regularization

The aim of this paper is to propose new regularization and filtering techniques for dense and sparse vector fields, and to focus on their application to non-rigid registration. Indeed, most of the regularization energies used in non-rigid registration operate independently on each coordinate of the transformation. The only common exception is the linear elastic energy, which enables cross-effects between coordinates. Cross-effects are yet essential to give realistic deformations in the uniform parts of the image, where displacements are interpolated.In this paper, we propose to find isotropic quadratic differential forms operating on a vector field, using a known theorem on isotropic tensors, and we give results for differentials of order 1 and 2. The quadratic approximation induced by these energies yields a new class of vectorial filters, applied numerically in the Fourier domain. We also propose a class of separable isotropic filters generalizing Gaussian filtering to vector fields, which enables fast smoothing in the spatial domain. Then we deduce splines in the context of interpolation or approximation of sparse displacements. These splines generalize scalar Laplacian splines, such as thin-plate splines, to vector interpolation. Finally, we propose to solve the problem of approximating a dense and a sparse displacement field at the same time. This last formulation enables us to introduce sparse geometrical constraints in intensity based non-rigid registration algorithms, illustrated here on intersubject brain registration.     

Stochastic inverse heat conduction using a spectral approach

An adjoint‐based functional optimization technique in conjunction with the spectral stochastic finite element method is proposed for the solution of an inverse heat conduction problem in the presence of uncertainties in material data, process conditions and measurement noise. The ill‐posed stochastic inverse problem is restated as a conditionally well‐posed L₂ optimization problem. The gradient of the objective function is obtained in a distributional sense by defining an appropriate stochastic adjoint field. The L₂ optimization problem is solved using a conjugate‐gradient approach. Accuracy and effectiveness of the proposed approach is appraised with the solution of several stochastic inverse heat conduction problems. Copyright © 2004 John Wiley & Sons, Ltd.     

A stabilized volume‐averaging finite element method for flow in porous media and binary alloy solidification processes

A stabilized equal‐order velocity–pressure finite element algorithm is presented for the analysis of flow in porous media and in the solidification of binary alloys. The adopted governing macroscopic conservation equations of momentum, energy and species transport are derived from their microscopic counterparts using the volume‐averaging method. The analysis is performed in a single domain with a fixed numerical grid. The fluid flow scheme developed includes SUPG (streamline‐upwind/Petrov–Galerkin), PSPG (pressure stabilizing/Petrov–Galerkin) and DSPG (Darcy stabilizing/Petrov–Galerkin) stabilization terms in a variable porosity medium. For the energy and species equations a classical SUPG‐based finite element method is employed. The developed algorithms were tested extensively with bilinear elements and were shown to perform stably and with nearly quadratic convergence in high Rayleigh number flows in varying porosity media. Examples are shown in natural and double diffusive convection in porous media and in the directional solidification of a binary‐alloy. Copyright © 2004 John Wiley & Sons, Ltd.     

Sensitivity Analysis for Scheduling Problems

This paper represents a first attempt at a systematic study of sensitivity analysis for scheduling problems. Because schedules contain both combinatorial and temporal structures, scheduling problems present unique issues for sensitivity analysis. Some of the issues that we discuss have not been considered before. Others, while studied before, have not been explored in the context of scheduling. The applicability of these issues is illustrated using well-known scheduling models. We provide fast methods to determine when a previously optimal schedule remains optimal. Other methods restore an optimal schedule after a parameter change. The value of studying the sensitivity of an optimal sequence instead of the sensitivity of an optimal schedule is demonstrated. We show that, for some problems, sensitivity analysis results depend on the positions of jobs with changed parameters. We identify scheduling problems where performing additional or different computations during optimization facilitates sensitivity analysis. To improve the robustness of an optimal schedule, selection among multiple optimal schedules is considered. We discuss which types of sensitivity analysis questions are intractable because the scheduling problem itself is intractable. We also study how heuristic error bounds vary when the data of a scheduling problem is continuously modified. Although we focus on scheduling problems, several of the issues we discuss and our classification scheme can be extended to other optimization problems.     

Cluster randomized trials with treatment noncompliance.

Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)