208 research outputs found
Does Hospital Competition Improve Efficiency? An Analysis of the Recent Market-Based Reforms to the English NHS
This paper uses a difference-in-difference estimator to test whether the introduction of patient choice and hospital competition in the English NHS in January 2006 has prompted hospitals to become more efficient. Efficiency was measured using hospitals' average length of stay (LOS) for patients undergoing elective hip replacement. LOS was broken down into its two key components: the time from a patient's admission until their surgery and the time from their surgery until their discharge. Our results illustrate that hospitals exposed to competition after a wave of market-based reforms took steps to shorten the time patients were in the hospital prior to their surgery, which resulted in a decrease in overall LOS. We find that hospitals shortened patients' LOS without compromising patient outcomes or by operating on healthier, wealthier or younger patients. Our results suggest that hospital competition within markets with fixed prices can increase hospital efficiency.Hospital Competition, Market Structure, Prospective Payment, Incentive Structure
Extrinisic Calibration of a Camera-Arm System Through Rotation Identification
Determining extrinsic calibration parameters is a necessity in any robotic
system composed of actuators and cameras. Once a system is outside the lab
environment, parameters must be determined without relying on outside artifacts
such as calibration targets. We propose a method that relies on structured
motion of an observed arm to recover extrinsic calibration parameters. Our
method combines known arm kinematics with observations of conics in the image
plane to calculate maximum-likelihood estimates for calibration extrinsics.
This method is validated in simulation and tested against a real-world model,
yielding results consistent with ruler-based estimates. Our method shows
promise for estimating the pose of a camera relative to an articulated arm's
end effector without requiring tedious measurements or external artifacts.
Index Terms: robotics, hand-eye problem, self-calibration, structure from
motio
Is There a DemocracyâCivil Society Paradox in Global Environmental Governance?
Civil society is commonly assumed to have a positive effect on international cooperation. This paper sheds light on one important facet of this assumption: we examine the impact of environmental non-governmental organizations (ENGOs) on ratification behavior of countries vis-Ă -vis international environmental agreements (IEAs). The main argument of the paper focuses on a âdemocracy-civil society paradoxâ: although ENGOs have a positive effect on ratification of IEAs on average, this effect decreases with increasing levels of democracy. This argument is counter-intuitive and appears paradoxical because democracy is generally associated both with a more active civil society and more international cooperation. The reasons for this hypothesized effect pertain to public demand for environmental public goods provision, government incentives, and problems of collective action among ENGOs. To test the net effect of ENGOs on countries' ratification behavior, the paper uses a new dataset on ENGOs in the time-period 1973â2006. The results offer strong support for the presumed democracyâcivil society paradox. </jats:p
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Autonomous On-Line Learning of Assistant Selection Policies For Fault Recovery
As robotic space exploration has expanded, increasing the autonomy of planetary exploration is necessary to overcome limitations of human-centric robotic operations. Although autonomous systems are designed to be robust, an exploration environment offers unknowns by its very definition; planning for failure handling must be an important part of mission design. This work considers the problem of how robots in space operations can learn to choose appropriate sources of assistance to recover from failures. Current assistant selection methods for failure handling are based on manually specified fixed rules, which are not responsive to dynamic environments or uncertainty in human performance. First, a novel and highly flexible reinforcement-learning based assistant selection framework is described that uses contextual multi-arm bandit algorithms. The contextual bandits exploit information from observations about their environment and potential assistants to efficiently learn selection rules, called policies, under a wide set of uncertain operating conditions and unknown/dynamically constrained assistant capabilities. The framework is first assessed for usefulness within simulation by comparing with other state of the art assistant selection approaches. Having discovered that the contextual bandit approach outperforms conventional static policies and non-contextual learning approaches with favorable robustness and scaling properties, a human factors study is designed and executed to gather real-world human performance data. On real humans, contextual bandit achieves better performance compared to baseline approaches.While useful, the dataset was not designed to capture effects of long-term learning in humans. In simulation, dynamics associated with human learning are used to assess the robustness of several contextual multi-arm bandit algorithms to situations occuring with real humans.Finally, two follow on experiments are described to bring adaptive assistant selection closer to a real-life mission deployment. As a result of this work, autonomous systems can learn to request the best assistance available.</p
Initial Incidence of White Matter Hyperintensities on MRI in Astronauts
Introduction: Previous literature has described the increase in white matter hyperintensity (WMH) burden associated with hypobaric exposure in the U-2 and altitude chamber operating personnel. Although astronauts have similar hypobaric exposure pressures to the U2 pilot population, astronauts have far fewer exposures and each exposure would be associated with a much lower level of decompression stress due to rigorous countermeasures to prevent decompression sickness. Therefore, we postulated that the WMH burden in the astronaut population would be less than in U2 pilots. Methods: Twenty-one post-flight de-identified astronaut MRIs (5 mm slice thickness FLAIR sequences) were evaluated for WMH count and volume. The only additional data provided was an age range of the astronauts (43-57) and if they had ever performed an EVA (13 yes, 8 no). Results: WMH count in these 21 astronaut MRI was 21.0 +/- 24.8 (mean+/- SD) and volume was 0.382 +/- 0.602 ml, which was significantly higher than previously published results for the U2 pilots. No significant differences between EVA and no EVA groups existed. Age range of astronaut population is not directly comparable to the U2 population. Discussion: With significantly less frequent (sometimes none) and less stressful hypobaric exposures, yet a much higher incidence of increased WMH, this indicates the possibility of additional mechanisms beyond hypobaric exposure. This increase unlikely to be attributable just to the differences in age between astronauts and U2 pilots. Forward work includes continuing review of post-flight MRI and evaluation of pre to post flight MRI changes if available. Data mining for potential WMH risk factors includes collection of age, sex, spaceflight experience, EVA hours, other hypobaric exposures, hyperoxic exposures, radiation, high performance aircraft experience and past medical history. Finally, neurocognitive and vision/eye results will be evaluated for any evidence of impairment linked to increased WMH
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