USING THE RANDOM PARAMETERS LOGIT MODEL TO COMBINE REVEALED AND STATED PREFERENCE DATA

Recent literature has combined Revealed (RP) and Stated Preference (SP) data in the Multinomial Logit Model (MNL) to estimate the value of environmental goods. However, emerging research has identified that a limitation of the MNL is the assumption of Independently and Identically Distributed (IID) errors, resulting in inaccurate model predictions and inconsistent utility parameters. Our analysis applies an alternative method to combine RP and SP data that takes into account the heterogeneity in both the observable and unobservable components of utility. This allows us to test whether such heterogeneity has an important effect on predicting behavioral choices.Revealed and Stated Preference Data, Scale Factor, Environmental Economics and Policy,

Estimating the Nonmarket Value of Green Technologies Using Partial Data Enrichment Techniques

Recent studies have suggested that green technologies may be a cost effective way to manage urban runoff. Literature has also suggests that there needs to be a greater empirical basis to estimate the benefits associated with social values associated with urban trees; we therefore estimate ecosystem benefits of green technologies using emerging data enrichment valuation methods.Environmental Economics and Policy,

Evaluation of Differential Blood Stain for Detection of Enterovirus D68

Enterovirus D68 (EV-D68) is a small, non-enveloped RNA virus, and is suspected to be the cause of respiratory and neurological disease in young children.1 The most concerning clinical sign is an acute flaccid paralysis of the lower limbs. At the Institute for Antiviral Research, and under the direction of Dr. Bart Tarbet, we have developed an animal model for studying EV-D68 infection. With this model, we will pursue gaining a better understanding of the neuropathogenesis of EV-D68. It has been shown, in in-vitro studies, that EV-68 is able to produce infectious progeny in leukocyte cell lines.2 We will, therefore, compare the white blood cell counts of infected and non-infected mice from our animal model. We will be taking blood samples from AG129 mice pre- and post-infection. With the blood collected, we will use a differential stain kit to stain for percentages of the different cell types in the blood. With the results from the stain, we will look at Complete blood count (CBC) from the collected blood. This will then be analyzed against a baseline CBC for AG129 mice. We hope that this information will provide an extra parameter when examining EV-D68 infection in our mouse model. If we identify a specific cell type associated with EV-D68, then further research may include examining the pathway of the infected cells throughout the body

Automated model parameter extraction for noise coupling analysis in silicon substrates

An automated method, requiring the fabrication of a small set of test structures, efficiently extracts the coefficients of Z-parameter based macromodels. The extraction approach has been validated for both heavily and lightly doped substrates and can be applied to a variety of technologies. After the parameters of a macromodel have been extracted, the model can be used to quickly and accurately calculate the equivalent substrate network connecting an arbitrary number of contacts. This automated extraction process has been integrated into the Cadence DFII environment to provide a seamless flow for substrate noise analysis.Keywords: coupling, model, substrate, nois

Dynamic Coalition Formation Under Uncertainty

Coalition formation algorithms are generally not applicable to real-world robotic collectives since they lack mechanisms to handle uncertainty. Those mechanisms that do address uncertainty either deflect it by soliciting information from others or apply reinforcement learning to select an agent type from within a set. This paper presents a coalition formation mechanism that directly addresses uncertainty while allowing the agent types to fall outside of a known set. The agent types are captured through a novel agent modeling technique that handles uncertainty through a belief-based evaluation mechanism. This technique allows for uncertainty in environmental data, agent type, coalition value, and agent cost. An investigation of both the effects of adding agents on processing time and of model quality on the convergence rate of initial agent models (and thereby coalition quality) is provided. This approach handles uncertainty on a larger scale than previous work and provides a mechanism readily applied to a dynamic collective of real-world robots. Abstract © IEEE

Characterization of health care utilization in patients receiving implantable cardioverter-defibrillator therapies: An analysis of the managed ventricular pacing trial.

BACKGROUND: Implantable cardioverter-defibrillators (ICDs) are effective in terminating lethal arrhythmias, but little is known about the degree of health care utilization (HCU) after ICD therapies. OBJECTIVE: Using data from the managed ventricular pacing trial, we sought to identify the incidence and types of HCU in ICD patients after receiving ICD therapy (shocks or antitachycardia pacing [ATP]). METHODS: We analyzed HCU events (ventricular tachyarrhythmia [VTA]-related, heart failure-related, ICD implant procedure-related, ICD system-related, or other) and their association with ICD therapies (shocked ventricular tachycardia episode, ATP-terminated ventricular tachycardia episode, and inappropriately shocked episode). RESULTS: A total of 1879 HCUs occurred in 695 of 1030 subjects (80% primary prevention) and were classified as follows: 133 (7%) VTA-related, 373 (20%) heart failure-related, 97 (5%) implant procedure-related, 115 (6%) system-related, and 1160 (62%) other. Of 2113 treated VTA episodes, 1680 (80%) received ATP only and 433 (20%) received shocks. Stratifying VTA-related HCUs on the basis of the type of ICD therapy delivered, there were 25 HCUs per 100 shocked VTA episodes compared with 1 HCU per 100 ATP-terminated episodes. Inappropriate ICD shocks occurred in 8.7% of the subjects and were associated with 115 HCUs. The majority of HCUs (52%) began in the emergency department, and 66% of all HCUs resulted in hospitalization. CONCLUSION: For VTA-related HCUs, shocks are associated with a 25-fold increase in HCUs compared to VTAs treated by ATP only. Application of evidence-based strategies and automated device-based algorithms to reduce ICD shocks (higher rate cutoffs, use of ATP, and arrhythmia detection) may help reduce HCUs

Determining Solution Space Characteristics for Real-Time Strategy Games and Characterizing Winning Strategies

The underlying goal of a competing agent in a discrete real-time strategy (RTS) game is to defeat an adversary. Strategic agents or participants must define an a priori plan to maneuver their resources in order to destroy the adversary and the adversary\u27s resources as well as secure physical regions of the environment. This a priori plan can be generated by leveraging collected historical knowledge about the environment. This knowledge is then employed in the generation of a classification model for real-time decision-making in the RTS domain. The best way to generate a classification model for a complex problem domain depends on the characteristics of the solution space. An experimental method to determine solution space (search landscape) characteristics is through analysis of historical algorithm performance for solving the specific problem. We select a deterministic search technique and a stochastic search method for a priori classification model generation. These approaches are designed, implemented, and tested for a specific complex RTS game, Bos Wars. Their performance allows us to draw various conclusions about applying a competing agent in complex search landscapes associated with RTS games