A Step towards Valuing Utility the Marginal and Cardinal Way

Income has a direct impact on our utility as well as an indirect impact through the goods, services and life events it allows us to purchase. The indirect effect of income is not properly accounted for in existing research that uses measures of cardinal utility for economic analysis. We propose a new approach for appropriately attributing the full effects of income on utility and we show the implications of our approach using a longitudinal dataset that contains reports of subjective wellbeing (SWB). We show that income has a much greater effect on SWB when indirect effects are considered. These results have important implications for how we value the marginal benefits of non-market goods and we explore some of these issues in the papersubjective well-being, utility, happiness, multicollinearity, income, non-market goods

Push-Pull PDV Analysis

Author Institution: Sandia National LaboratoriesSlides presented at the 2nd Annual Photonic Doppler Velocimetry (PDV) Workshop held at Lawrence Livermore National Laboratory, Livermore, California, August 16-17, 2007

The Swarm Computer, an Analog Cellular-Swarm Hybrid Architecture

The “killer apps” of cellular and swarm computing are image processing and optimization, respectively; however, applying these platforms to general-purpose computing remains impractical. Designing systems within the restrictive framework of cellular automata is extremely difficult, though often very efficient and scalable. On the other hand, swarm networks are very powerful but difficult to implement in hardware. Here we introduce a hybrid model, the Swarm Computer, which is both practical to program and efficient to implement. Applications in astrophysics and image processing are considered

Window Characterization at 1550 nm

Author Institution: Los Alamos National LaboratoryAuthor Institution: Sandia National LaboratoriesSlides presented at the Heterodyne Velocimeter Workshop held at Lawrence Livermore National Laboratory, Livermore, California, July 20-21, 2006

Phase II: Chulitna River Bridge Structurally Health Monitoring

This study is phase 2 of a two phase research project. In Phase 1 a structural health monitoring system (SHMS) was installed on the Chulitna River Bridge. This bridge is 790 feet long, 42 foot 2 inches wide and has 5 spans. As part of that effort, three loaded dump trucks were used to conduct seventeen static and dynamic loadings on the structure. In addition to studying the bridge using SHMS, two ambient free vibration tests were conducted a year apart by. In 1993, the deck on this 1970 five span bridge was widened from 34-feet to a 42 foot 2 inch concrete deck. Increased load was accounted for by strengthening two variable depth exterior girders and converting interior stringers to interior truss girders. Construction documents for the upgrade called for stage construction. At the time of this study, the bridge had five bearings that were not in contact with the superstructure. Feasibility of using Structural Health Monitoring Systems (SHMS) for Alaska Highway Bridges was examined. Also, SHMS data for the load tests of Phase 1 were used to calibrate a three-dimensional model (FEM) to predict response and conduct a 2014 Operating Load Rating.LIST OF FIGURES ....................................................................................................................... iv LIST OF TABLES ........................................................................................................................ vii DISCLAIMER .............................................................................................................................. ix EXECUTIVE SUMMARY............................................................................................................. 1 CHAPTER 1.0 INTRODUCTION................................................................................................. 3 1.1 History .............................................................................................................................. 3 1.2 Bridge Details ................................................................................................................... 3 1.3 Phase 1 Research Study.................................................................................................... 5 1.4 Phase 2 Research Study.................................................................................................... 5 CHAPTER 2.0 LOAD RATING.................................................................................................... 7 2.1 General ............................................................................................................................. 7 2.2 Operating Load Rating ................................................................................................... 10 2.2.1 Investigation with updated calibrated finite element model, FEM (as-is condition) ............................................................................................................................. 11 2.2.2 Model 1 – Four members (A, B, C, and D) removed ............................................. 11 2.2.3 Model 2 – Five members (A, B, C, D, and E) removed ......................................... 11 2.2.4 Other alternative operating load ratings. ................................................................. 12 CHAPTER 3.0 CALIBRATED FINITE ELEMENT MODEL ................................................... 32 CHAPTER 4.0 PROPOSED ALASKA BRIDGE MONITORING SYSTEM ............................ 34 4.1 General ........................................................................................................................... 34 4.2 Selecting SHMS for Alaska ........................................................................................... 35 4.3 New Bridges (Proposed Monitoring Systems) ............................................................... 36 4.4 Existing Bridges (Proposed Monitoring Systems) ......................................................... 36 4.5 All Bridges (Proposed Monitoring Systems) ................................................................. 36 CHAPTER 5.0 CONCLUSIONS................................................................................................. 39 5.1 Phase 1 (Previous Study)................................................................................................ 39 5.1.1 Gravity load testing ................................................................................................. 39 5.1.2 Ambient testing (2012 tests were Phase 1; 2013 tests were Phase 2) ..................... 40 5.2 Phase 2 (Current Study) ................................................................................................. 40 5.2.1 Outcome 1 – Finite element model ......................................................................... 41 5.2.2 Outcome 2 – Structural evaluation and load rating ................................................ 41 5.2.3 Outcome 3 – LRFR HL-93 live load stresses for the critical members .................. 41 APPENDIX A – SIMPLE ACCURACY TEST............................................................................ 44 APPENDIX B – LONGITUDINAL BEHAVIOR TEST ............................................................. 47 APPENDIX C – MODEL IMPROVEMENTS (LONGITUDINAL DIRECTION) .................... 50 APPENDIX D – TRANSVERSE BEHAVIOR PRIOR TO MODEL MODIFICATIONS.......... 52 APPENDIX E – MODEL IMPROVEMENTS (TRANSVERSE DIRECTION)......................... 57 APPENDIX F – CORRELATION BETWEEN CALIBRATED MODEL AND EXPERIMENTAL DATA............................................................................................................. 61 APPENDIX G – CALIBRATED FINITE ELEMENT MODEL ................................................. 63 APPENDIX H – SENSOR LAYOUT .......................................................................................... 66 APPENDIX I – LOAD TESTING................................................................................................ 69 APPENDIX J – A FUTURISTIC APPROACH TO CALIBRATING A FINITE ELEMENT MODEL ........................................................................................................................................ 8

A unifying Bayesian account of contextual effects in value-based choice

Empirical evidence suggests the incentive value of an option is affected by other options available during choice and by options presented in the past. These contextual effects are hard to reconcile with classical theories and have inspired accounts where contextual influences play a crucial role. However, each account only addresses one or the other of the empirical findings and a unifying perspective has been elusive. Here, we offer a unifying theory of context effects on incentive value attribution and choice based on normative Bayesian principles. This formulation assumes that incentive value corresponds to a precision-weighted prediction error, where predictions are based upon expectations about reward. We show that this scheme explains a wide range of contextual effects, such as those elicited by other options available during choice (or within-choice context effects). These include both conditions in which choice requires an integration of multiple attributes and conditions where a multi-attribute integration is not necessary. Moreover, the same scheme explains context effects elicited by options presented in the past or between-choice context effects. Our formulation encompasses a wide range of contextual influences (comprising both within- and between-choice effects) by calling on Bayesian principles, without invoking ad-hoc assumptions. This helps clarify the contextual nature of incentive value and choice behaviour and may offer insights into psychopathologies characterized by dysfunctional decision-making, such as addiction and pathological gambling

Electrical Anomalies Observed During DC3

The primary scientific goals of DC3 involved improving our understanding of the chemical impacts of thunderstorms and their anvils. However, the Colorado domain provided opportunities to study other interesting phenomena, including the potential impacts of smoke ingestion on convection and thunderstorms, electrification processes in smoke plumes and pyrocumulonimbus clouds, and the production of sprites by unconventional thunderstorm

Polarimetric and Multi-Doppler Radar Observations of Electrified and Unelectrified Wildfire Smoke Plumes

Pyrocumulus clouds above three Colorado wildfires (Hewlett Gulch, High Park, and Waldo Canyon; all occurred during summer 2012) electrified and produced small intracloud discharges whenever the smoke plumes grew to high altitudes (over 10 km above mean sea level, or MSL). This occurred during periods of rapid wildfire growth, as indicated by the shortwave infrared channel on a geostationary satellite, as well as by incident reports. In the Hewlett Gulch case, the fire growth led to increased updrafts within the plume, as inferred by multiple Doppler radar syntheses, which led to the vertical development and subsequent electrification a life cycle as short as 30 minutes. The lightning, detected by a threedimensional lightning mapping network, was favored in highaltitude regions (~10 km MSL) containing modest reflectivities (25 dBZ and lower), ~0 dB differential reflectivity, and reduced correlation coefficient (~0.60.7). This indicated the likely presence of ice particles (crystals and aggregates, possibly rimed) mixed with ash. Though neither multipleDoppler nor polarimetric observations were available during the electrification of the High Park and Waldo Canyon plumes, their NEXRAD observations showed reflectivity structures consistent with Hewlett Gulch. In addition, polarimetric and multipleDoppler scanning of unelectrified High Park plumes indicated only irregularly shaped ash, and not ice, was present (i.e., reflectivities 5 dB, correlation < 0.4), and there was no broaching of the 10 km altitude. Based on these results, the electrification likely was caused by icebased processes that did not involve significant amounts of graupel. The results demonstrate the scientific value of multipleDoppler and polarimetric radar observations of wildfire smoke plumes including the ability to distinguish between regions of pure hydrometeors, regions of pure ash, and mixtures of both and also suggest a possible new application for lightning data in monitoring wildfires