Stochastic modeling for computational warranty analysis

We examine two key stochastic processes of interest for warranty modeling: (1) remaining total warranty coverage time exposure and (2) warranty load (total items under warranty at time t). Integral equations suitable for numerical computation are developed to yield probability law for these warranty measures. These two warranty measures permit warranty managers to better understand time‐dependent warranty behavior, and thus better manage warranty cash reserves. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Equity in genetic newborn screening

State‐level newborn screening allows for early treatment of genetic disorders, which can substantially improve health outcomes for newborns. As the cost of genetic testing decreases, it is becoming an essential part of newborn screening. A genetic disorder can be caused by many mutation variants; therefore, an important decision is to determine which variants to search for (ie, the panel design), under a testing budget. The frequency of variants that cause a disorder and the incidence of the disorder vary by racial/ethnic group. Consequently, it is important to consider equity issues in panel design, so as to reduce disparities among different groups. We study the panel design problem using cystic fibrosis (CF) as a model disorder, considering the trade‐offs between equity and accuracy, under a limited budget. Most states use a genetic test in their CF screening protocol, but panel designs vary, and, due to cost, no state's panel includes all CF‐causing variants. We develop models that design equitable genetic testing panels, and compare them with panels that maximize sensitivity in the general population. Our case study, based on realistic CF data, highlights the value of equitable panels and provides important insight for newborn screening practices.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

A higher order coupled frequency characteristics study of smartmagneto-electro-elastic composite plates with cut-outs using finite element methods

This article deals with investigating the effect of cut-outs on the natural frequencies of magneto-electro-elastic (MEE) plates incorporating finite element methods based on higher order shear deformation theory (HSDT). In order to consider the influence of cut-out, the energy of the cut-out domain is sub-tracted from the total energy of the entire plate. The governing equations of motions are derived through incorporating Hamilton's principle and the solution is obtained using condensation technique. The proposed numerical formulation is verified with the results of previously published literature as well as the numerical software. In addition, this research focuses on evaluating the effect of geometrical skewness and boundary conditions on the frequency response. The influence of cut-outs on the degree of coupling between magnetic, electric and elasticfields is also investigated.     

A platform for initial testing of multiple camouflage patterns

The human visual system is still an important factor in military warfare; military personnel receive training on effective search strategies, and camouflage that can effectively conceal objects and personnel is a key component of a successful integrated survivability strategy. Previous methods of camouflage assessment have, amongst others, used psychophysics to generate distinctiveness metrics. However, the population from which the human observers are drawn is often not well defined, or necessarily appropriate. In this experiment we designed a new platform for testing multiple patterns based on a camouflaged object detection task, and investigate whether trained military observers perform better than civilians. We use a two-alternative forced choice paradigm, with participants searching images of woodland for a replica military helmet displaying Olive Green, Multi Terrain Pattern, US Marine Pattern or, as a conspicuous control, UN Peacekeeper Blue. Our data show that there is no difference in detection performance between the two observer groups but that there are clear differences in the effectiveness of the different helmet colour patterns in a temperate woodland environment. We conclude that when tasks involve very short stimulus presentation times, task-specific training has little effect on the success of target detection and thus this paradigm is particularly suitable for robust estimates of camouflage efficacy.     

Improvement of VIS and IR camouflage properties by impregnating cotton fabric with PVB/IF-WS2

In order to examine the possibility to improve its camouflage properties standard cotton fabric with camouflage print was impregnated with poly(vinyl butyral), PVB and fullerene-like nanoparticles of tungsten disulfide, PVB/IF-WS₂. FTIR analysis excluded any possible chemical interaction of IF-WS₂ with PVB and the fabric. The camouflage behavior of the impregnated fabric has been examined firstly in the VIS part of the spectrum. Diffuse reflection, specular gloss and color coordinates were measured for three different shades (black, brown and dark green). Thermal imaging was applied to examine the camouflage abilities of this impregnation in IR part of the spectrum. The obtained results show that PVB/IF-WS₂ impregnation system induced enhacement of the materials camouflage properties, i.e. that IF-WS₂ have a positive effect on spectrophotometric characteristics of the fabric.     

Transient analysis of Markov models of fault‐tolerant systems with deferred repair using split regenerative randomization

The (standard) randomization method is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well‐controlled computation error, and ability to specify the computation error in advance. However, the fact that the method can be computationally very expensive limits its applicability. In this paper, we develop a new method called split regenerative randomization, which, having the same good properties as standard randomization, can be significantly more efficient. The method covers reliability‐like models with a particular but quite general structure and requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, model class C₂, including typical failure/repair reliability‐like models with exponential failure and repair time distributions and deferred repair, natural selections are available for both the subset of states and the regenerative state and, for those natural selections, theoretical results are available assessing the efficiency of the method in terms of “visible” model characteristics. Those results can be used to anticipate when the method can be expected to be competitive. We illustrate the application of the method using a large class C₂ model and show that for models in that class the method can indeed be significantly more efficient than previously available randomization‐based methods. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

ECONOMIC GROWTH AND VIOLENT INTERNATIONAL CONFLICT: 1875–1999

This paper extends the joint product model of military alliances to apply to the new strategic doctrine adopted by NATO in the 1990s. In particular, a choice must be made between protecting one's own territory and pooling forces for an alliancewide rapid reaction force. This new model accounts for a host of externalities and their implications for burden sharing, full financing, and allocative efficiency. The Pigouvian taxes that adjust for force thinning and attack deflection are shown to finance optimal border‐protecting forces under a variety of circumstances. Second‐best considerations arise owing to the pure publicness of rapid reaction forces. The ideal toll arrangement does not currently characterize NATO financing, nor is it likely to do so.     

NEGOTIATING NONPROLIFERATION: Scholarship,Pedagogy, and Nuclear Weapons Policy

In nuclear nonproliferation negotiations, many governments pursue multiple objectives, and changes in policy can occur rapidly—and often unexpectedly. For these reasons, understanding nonproliferation requires empathy and imagination rather than just historical fact. This article considers one teaching tool to encourage such insight—simulations—and demonstrates how teaching and scholarship can interact to improve our understanding of the complex decisions and negotiations involved in nuclear nonproliferation. The article consists of five parts: first, it explains the benefits of simulations as both a policy development tool in Washington and as a teaching tool in universities; second, it describes the pedagogical strategy of the Stanford University simulation program; third, it shows how the simulations have identified and highlighted theoretical and substantive insights that are often neglected in scholarly studies of nonproliferation; and fourth, it describes how students are tested to enhance the learning experience from the simulation. Fifth and finally, the article provides concluding observations about how using simulations in the classroom can help scholars develop insights that improve their understanding of real-world nuclear negotiation dynamics and outcomes.