Active Thermal Sensor for Improved Distributed Temperature Sensing in Haptic Arrays

The efficacy of integrating temperature sensors into compliant pressure sensing technologies, such as haptic sensing arrays, is limited by thermal losses into the substrate. A solution is proposed here whereby an active heat sink is incorporated into the sensor to mitigate these losses, while still permitting the use of common VLSI manufacturing methods and materials to be used in sensor fabrication. This active sink is capable of responding to unknown fluctuations in external temperature, that is, the temperature that is to be measured, and directly compensates in real time for the thermal power loss into the substrate by supplying an equivalent amount of power back into the thermal sensor. In this paper, the thermoelectric effects of the active heat sink/thermal sensor system are described and used to reduce the complexity of the system to a simple one-dimensional numerical model. This model is incorporated into a feedback system used to control the active heat sink and monitor the sensor output. A fabrication strategy is also described to show how such a technology can be incorporated into a common bonded silicon-on-insulator- (BSOI-) based capacitive pressure sensor array such as that used in some haptic sensing systems

Location, Location, Location: An MCMC Approach to Modeling the Spatial Context of War and Peace

This article demonstrates how spatially dependent data with a categorical response variable can be addressed in a statistical model. We introduce the idea of an autologistic model where the response for one observation is dependent on the value of the response among adjacent observations. The autologistic model has likelihood function that is mathematically intractable, since the observations are conditionally dependent upon one another. We review alternative techniques for estimating this model, with special emphasis on recent advances using Markov chain Monte Carlo (MCMC) techniques. We evaluate a highly simplified autologistic model of conflict where the likelihood of war involvement for each nation is conditional on the war involvement of proximate states. We estimate this autologistic model for a single year (1988) via maximum pseudolikelihood and MCMC maximum likelihood methods. Our results indicate that the autologistic model fits the data much better than an unconditional model and that the MCMC estimates generally dominate the pseudolikelihood estimates. The autologistic model generates predicted probabilities greater than 0.5 and has relatively good predictive abilities in an out-of-sample forecast for the subsequent decade (1989 to 1998), correctly identifying not only ongoing conflicts, but also new ones.</jats:p

Automated precision alignment of optical components for hydroxide catalysis bonding

We describe an interferometric system that can measure the alignment and separation of a polished face of a optical component and an adjacent polished surface. Accuracies achieved are ∼ 1μrad for the relative angles in two orthogonal directions and ∼ 30μm in separation. We describe the use of this readout system to automate the process of hydroxide catalysis bonding of a fused-silica component to a fused-silica baseplate. The complete alignment and bonding sequence was typically achieved in a timescale of a few minutes, followed by an initial cure of 10 minutes. A series of bonds were performed using two fluids - a simple sodium hydroxide solution and a sodium hydroxide solution with some sodium silicate solution added. In each case we achieved final bonded component angular alignment within 10 μrad and position in the critical direction within 4 μm of the planned targets. The small movements of the component during the initial bonding and curing phases were monitored. The bonds made using the sodium silicate mixture achieved their final bonded alignment over a period of ∼ 15 hours. Bonds using the simple sodium hydroxide solution achieved their final alignment in a much shorter time of a few minutes. The automated system promises to speed the manufacture of precision-aligned assemblies using hydroxide catalysis bonding by more than an order of magnitude over the more manual approach used to build the optical interferometer at the heart of the recent ESA LISA Pathfinder technology demonstrator mission. This novel approach will be key to the time-efficient and low-risk manufacture of the complex optical systems needed for the forthcoming ESA spaceborne gravitational waves observatory mission, provisionally named LISA

Multiple Imputation Using Gaussian Copulas

Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use method for generating multiple imputations using a Gaussian copula. The Gaussian copula for multiple imputation (Hoff, 2007) allows scholars to attain estimation results that have good coverage and small bias. The use of copulas to model the dependence among variables will enable researchers to construct valid joint distributions of the data, even without knowledge of the actual underlying marginal distributions. Multiple imputations are then generated by drawing observations from the resulting posterior joint distribution and replacing the missing values. Using simulated and observational data from published social science research, we compare imputation via Gaussian copulas with two other widely used imputation methods: MICE and Amelia II. Our results suggest that the Gaussian copula approach has a slightly smaller bias, higher coverage rates, and narrower confidence intervals compared to the other methods. This is especially true when the variables with missing data are not normally distributed. These results, combined with theoretical guarantees and ease-of-use suggest that the approach examined provides an attractive alternative for applied researchers undertaking multiple imputations

Rapid Effective Trace-Back Capability Value in Reducing the Cost of a Foot and Mouth Disease Event

This study evaluates how the availability of animal tracing affects the cost of a hypothetical Foot and Mouth Disease (FMD) outbreak in the Texas High Plains using alternative tracing scenarios. To accomplish this objective, the AusSpread epidemic disease spread model (Ward et al., 2006) is used to simulate a High Plains FMD outbreak under different animal tracing possibilities. A simple economic costing module (Elbakidze, 2008) is used to determine the savings in terms of animal disease mitigation costs from rapid, effective trace-back. The savings from increased traceability are then be compared to the cost of a functional National Animal Identification System (NAIS). Initial results indicate that rapid, effective tracing reduces the overall cost of disease outbreaks and that the benefits per animal in terms of reduced cost of an outbreak more than outweigh the annualized cost per animal of implementing a NAIS. A value of time related to controlling an outbreak is estimated to have increased benefits from an identification system that incorporates a rapid response capability. We also find the level of benefits vary depending on the location of initial infection and whether or not welfare slaughter occurs.Traceability, Foot and Mouth Disease, Economics, Agricultural and Food Policy, Livestock Production/Industries,