Designing a Data-Tracking System for a Private Therapeutic Day School

The Children\u27s Institute on Mercer Island (CHILD) is a private therapeutic day school in the Seattle area serving students in elementary and secondary education. Their stated mission is to provide innovative school programs and therapies that promote social, emotional and academic development for children with special needs. In the fall of 2012 they engaged in a program evaluation that in many respects resembles a needs assessment in order to explore and improve aspects of their functioning. Through preliminary evaluation processes, including dialogue with CHILD\u27s Leadership Team and a survey of internal stakeholders, an area of interest in student mental health was uncovered and an initial evaluation question emerged: How does CHILD claim expertise, particularly in the area of mental health? Historically, evidence of CHILD\u27s impact in this regard has been largely anecdotal. Aside from a limited collection of behavioral data pertaining largely to IEP goals and objectives, CHILD does not track mental health, or long-term student outcomes. As a program interested in its own claims to expertise, members of the Leadership Team and other stakeholders have called for improved data collection in this regard. This program evaluation is an attempt to understand the types of data that would be most useful to CHILD\u27s interest in expertise and then design a program for tracking this data. Bronfenbrenner\u27s (1994) ecological model was used as a framework for guiding data collection, the results of which are synthesized and integrated into a series of recommendations constituting the final results of the project. This dissertation is available in open access at AURA, http://aura.antioch.edu/ and Ohio Link ETD Center, https://etd.ohiolink.edu/et

Forest resources of Texas

Citation: Graham, J. Olin. Forest resources of Texas. Senior thesis, Kansas State Agricultural College, 1908.Introduction: Within the state of Texas the natural conditions on which depend the range and make-up of the forest are of the most diverse character. In geographical position its southernmost point lies almost in the edge of the Tropics, while its far northern portions are within the wheat and corn belt of the Middle Western States

Calibration of the TWIST high-precision drift chambers

A method for the precise measurement of drift times for the high-precision drift chambers used in the TWIST detector is described. It is based on the iterative correction of the space-time relationships by the time residuals of the track fit, resulting in a measurement of the effective drift times. The corrected drift time maps are parametrised individually for each chamber using spline functions. Biases introduced by the reconstruction itself are taken into account as well, making it necessary to apply the procedure to both data and simulation. The described calibration is shown to improve the reconstruction performance and to extend significantly the physics reach of the experiment.Comment: 8 pages, 5 figure

Reduced stereopsis due to binocular masking

Reduced stereopsis due to binocular maskin

The Interpretation of Electron Diffraction Patterns from Hydrocarbon Films

The theoretical expressions previously derived for the scattering of electrons by oriented hydrocarbon chains have been extended and applied to the calculation of characteristic diffraction patterns. These patterns are analyzed to form a basis for obtaining information about the molecular orientation. For the long chain molecules, the azimuthal direction, and the declination from the vertical may be determined independently. The orientation of the hydrocarbon chain about its own axis is less easily established, since it is determined only from the intensity distribution within the separate diffraction orders. When the declination is sufficiently large, randomness in the azimuthal directions is distinguished by the crossed‐line pattern obtained. Randomness in the declination from the vertical may be estimated from the irregular spacing of the intercepts of the crossed lines. Quantitative intensity data would permit a more precise study of the angular distribution of the declination, and also of the orientation of the hydrocarbon chain about its own axis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70890/2/JCPSA6-15-5-213-1.pd

Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making

Asthma on the job: work-related factors in new-onset asthma and in exacerbations of pre-existing asthma

AbstractOccupational asthma (OA) can be defined as variable airways narrowing causally related to exposure in the working environment to airborne dusts, gases, vapours or fumes. There are many agents in the work-place that can induce asthma or cause substantial deterioration in pre-existing asthma. It has been estimated that 5–15% of adult-onset asthma can be attributed to occupational exposures. Hence adult patients, especially those with new-onset asthma, must be investigated with regard to occupational risk factors for disease. The prognosis for OA is improved if the causal exposure is controlled either by controlling the exposure at the workplace or by moving the patient out of the workplace