Over-reassurance and undersupport after a 'false alarm': a systematic review of the impact on subsequent cancer symptom attribution and help seeking

This literature review examined research into the impact of a previous 'all-clear' or non-cancer diagnosis following symptomatic presentation ('false alarm') on symptom attribution and delays in help seeking for subsequent possible cancer symptoms

Determination of impact sensitivity of materials at high pressures

Compact device is used to determine impact sensitivity of material in static, high pressure, gaseous environment. It can also be instrumented to monitor and record pressure, temperature, and striker impact force. Device is used in conjunction with commercially available liquid oxygen impact tester which provides impact energy

How distorting are direct payments?

Agricultural and Food Policy,

The impact of vacant, tax-delinquent, and foreclosed property on sales prices of neighboring homes

In this empirical analysis, we estimate the impact of vacancy, neglect associated with property-tax delinquency, and foreclosures on the value of neighboring homes using parcel-level observations. Numerous studies have estimated the impact of foreclosures on neighboring properties, and these papers theorize that the foreclosure impact works partially through creating vacant and neglected homes. To our knowledge, this is only the second attempt to estimate the impact of vacancy itself and the first to estimate the impact of tax-delinquent properties on neighboring home sales. We link vacancy observations from Postal Service data with property-tax delinquency and sales data from Cuyahoga County (the county encompassing Cleveland, Ohio). We estimate hedonic price models with corrections for spatial autocorrelation. We find that an additional property within 500 feet that is vacant, delinquent, or both reduces the home’s selling price by at least 1.3 percent. In low-poverty areas, tax-current foreclosed homes have large negative impacts of 4.6 percent. In high-poverty areas, we observe positive correlations of sale prices with tax-current foreclosures and negative correlations with tax-delinquent foreclosures. This may reflect selective foreclosing on better-maintained properties or better maintenance by tax-paying foreclosure auction winners. The marginal medium-poverty census tracts display the largest negative responses to vacancy and delinquency in nearby nonforeclosed homes.Foreclosure ; Housing - Prices

Functional evidence for cone-specific connectivity in the human retina

NoPhysiological studies of colour vision have not yet resolved the controversial issue of how chromatic opponency is constructed at a neuronal level. Two competing theories, the cone-selective hypothesis and the random-wiring hypothesis, are currently equivocal to the architecture of the primate retina. In central vision, both schemes are capable of producing colour opponency due to the fact that receptive field centres receive input from a single bipolar cell ¿ the so called `private line arrangement¿. However, in peripheral vision this single-cone input to the receptive field centre is lost, so that any random cone connectivity would result in a predictable reduction in the quality of colour vision. Behavioural studies thus far have indeed suggested a selective loss of chromatic sensitivity in peripheral vision. We investigated chromatic sensitivity as a function of eccentricity for the cardinal chromatic (L/M and S/(L + M)) and achromatic (L + M) pathways, adopting stimulus size as the critical variable. Results show that performance can be equated across the visual field simply by a change of scale (size). In other words, there exists no qualitative loss of chromatic sensitivity across the visual field. Critically, however, the quantitative nature of size dependency for each of the cardinal chromatic and achromatic mechanisms is very specific, reinforcing their independence in terms of anatomy and genetics. Our data provide clear evidence for a physiological model of primate colour vision that retains chromatic quality in peripheral vision, thus supporting the cone-selective hypothesis

Help seeking for cancer 'alarm' symptoms: a qualitative interview study of primary care patients in the UK.

Delay in help seeking for cancer 'alarm' symptoms has been identified as a contributor to delayed diagnosis

Making financial markets safer for consumers: lessons from consumer goods markets and beyond

In the wake of the mortgage meltdown, policymakers are discussing how best to protect consumers in financial product markets.Consumer protection ; Financial markets

ON-ICE DETECTION, CLASSIFICATION, LOCALIZATION AND TRACKING OF ANTHROPOGENIC ACOUSTIC SOURCES WITH MACHINE LEARNING

Arctic acoustics have been of concern in recent years for the US navy. First-year ice is now the prevalent factor in ice coverage in the Arctic, which changes the previously understood acoustic properties. Due to the ice melting each year, anthropogenic sources in the Arctic region are more common: military exercises, shipping, and tourism. For the navy, it is of interest to detect, classify, localize, and track these sources to have situational awareness of these surroundings. Because the sources are on-water or on-ice, acoustic radiation propagates at a longer distance and so acoustics are the method by which the sources are detected, classified, localized, and tracked. These methods are all part of sound navigation and ranging (SONAR). This dissertation describes algorithms which will better SONAR results without modification of the sensors or the environment and the process by which to arrive to this point. The focus is to use supervised machine learning algorithms to facilitate such technological enhancements. Specifically, neural networks analyze labeled experimental data from a first-year, shore-fast, shallow and narrow water environment. The experiments were conducted over the span of three years from 2019 to 2022, mostly during the months from January to March where ice formed over the Keweenaw Waterway at the Michigan Technological University. All experiments were conducted to analyze a passive acoustic source; that is, the source was non-cooperative and did not send any localizing pings for active SONAR. The experiments were recorded using an underwater pa-type acoustic vector sensor (AVS). The data and analysis were done intermittently to update any upcoming experiments with discrepancies found in the analysis to create a more generalized algorithm. The work in this dissertation focuses on two topics for passive SONAR: localization and classification. Because of the ``black box nature in machine learning, tracking the target source is an extension of localization and thought of as the same goal within machine learning. To introduce and verify the complexity of the testing environment, an underwater acoustic simulation is shown with Ray tracing and bathymetry data to compare with the experimental results used in machine learning. The focus of the algorithms is to produce the best results for the experiments and compare the results with traditional methods, such as a simulation or a linear Gaussian localization with a Kalman filter. Experiments studying neural network types have shown that the Vision Transformer (ViT) produces excellent results. The ViT is capable of analyzing acoustic intensity azimuthal spectrogram (azigram) data and localizing a moving target at high accuracy, and the ViT is capable of classifying multiple acoustic sources with the acoustic intensity magnitude spectrogram at high accuracy as well