FIRM EFFICIENCY AND INFORMATION TECHNOLOGY USE: EVIDENCE FROM U.S. CASH GRAIN FARMS

We implement stochastic frontier analysis techniques to show the effects of information technology use on firm efficiency. Results from a sample of 1,865 U.S. cash grain farms reveals that information technology use within the farm business moved farms significantly towards the efficiency frontier. Also moving farms towards the efficiency frontier were the use of written long-term plans, advanced input acquisition strategies, and increased farm labor hours relative to total labor hours. In contrast, an increase in the debt to asset ratio was associated with movements away from the efficiency frontier.Crop Production/Industries,

Entangled photon apparatus for the undergraduate laboratory

We present detailed instructions for constructing and operating an apparatus to produce and detect polarization-entangled photons. The source operates by type-I spontaneous parametric downconversion in a two-crystal geometry. Photons are detected in coincidence by single-photon counting modules and show strong angular and polarization correlations. We observe more than 100 entangled photon pairs per second. A test of a Bell inequality can be performed in an afternoon.Comment: 6 pages, 9 figure

Development of mathematical models of environmental physiology

Selected articles concerned with mathematical or simulation models of human thermoregulation are presented. The articles presented include: (1) development and use of simulation models in medicine, (2) model of cardio-vascular adjustments during exercise, (3) effective temperature scale based on simple model of human physiological regulatory response, (4) behavioral approach to thermoregulatory set point during exercise, and (5) importance of skin temperature in sweat regulation

Regulating Highly Automated Robot Ecologies: Insights from Three User Studies

Highly automated robot ecologies (HARE), or societies of independent autonomous robots or agents, are rapidly becoming an important part of much of the world's critical infrastructure. As with human societies, regulation, wherein a governing body designs rules and processes for the society, plays an important role in ensuring that HARE meet societal objectives. However, to date, a careful study of interactions between a regulator and HARE is lacking. In this paper, we report on three user studies which give insights into how to design systems that allow people, acting as the regulatory authority, to effectively interact with HARE. As in the study of political systems in which governments regulate human societies, our studies analyze how interactions between HARE and regulators are impacted by regulatory power and individual (robot or agent) autonomy. Our results show that regulator power, decision support, and adaptive autonomy can each diminish the social welfare of HARE, and hint at how these seemingly desirable mechanisms can be designed so that they become part of successful HARE.Comment: 10 pages, 7 figures, to appear in the 5th International Conference on Human Agent Interaction (HAI-2017), Bielefeld, German

Classical dispersion-cancellation interferometry

Even-order dispersion cancellation, an effect previously identified with frequency-entangled photons, is demonstrated experimentally for the first time with a linear, classical interferometer. A combination of a broad bandwidth laser and a high resolution spectrometer was used to measure the intensity correlations between anti-correlated optical frequencies. Only 14% broadening of the correlation signal is observed when significant material dispersion, enough to broaden the regular interferogram by 4250%, is introduced into one arm of the interferometer.Comment: 4 pages, 3 figure

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454