COMPASS: A general purpose computer aided scheduling tool

COMPASS is a generic scheduling system developed by McDonnell Douglas under the direction of the Software Technology Branch at JSC. COMPASS is intended to illustrate the latest advances in scheduling technology and provide a basis from which custom scheduling systems can be built. COMPASS was written in Ada to promote readability and to conform to potential NASA Space Station Freedom standards. COMPASS has some unique characteristics that distinguishes it from commercial products. These characteristics are discussed and used to illustrate some differences between scheduling tools

HANDWRITING: A QUALITATIVE STUDY EXPLORING ELEMENTARY TEACHERS’ BELIEFS, KNOWLEDGE, PREPARATION, PRACTICE AND INFLUENCING FACTORS IN HANDWRITING INSTRUCTION

Handwriting instruction is often seen as less important in the curriculum, particularly due to pressures of passing high states assessments and the need to develop technology competencies, as technology in education and society has become commonplace. Current research shows that literacy skills are supported through the direct instruction of handwriting. Handwriting has also been associated with academic success, autoactivating the memory and processing portion of the brain, and is an important component in preparing the brain for phonics and reading acquisition. This has created a problem of a significant disconnect between research-based recommendations and current classroom practices in handwriting instruction. The teachers’ beliefs, knowledge, and instructional practice are key components of successful handwriting implementation as studies have found that teachers who receive researched-based training deliver quality instruction while those who do not receive this training seem to avoid teaching handwriting. With the instructor and quality of instruction being a strong indicator of student performance, it is important to understand teachers’ beliefs about handwriting and perceptions of their knowledge and skills concerning handwriting instruction, as teachers’ beliefs affect how they teach which in turn affects student achievement. This qualitative study explored elementary teachers\u27 beliefs, knowledge, preparation, and practice of handwriting instruction. Interviews were conducted with K-4 grade level teachers from three school districts in the upper Midwest. The qualitative analysis consisted of identifying themes from a semi-structured interviews with ten participants, two teachers from each K-4 grade levels from three upper Midwestern school districts. Conclusions from the study showed teachers believe handwriting is a fundamental skill important for literacy and academic success but aren’t familiar with the research to support their belief. Teachers are concerned about their level of preparation and whether their current practice is ‘best practice.’ Finding time to teach handwriting in busy schedules was identified as a challenge and there was inconsistency in the length and frequency of handwriting instructional time across participants. Strong leadership, conversations around effective practices in handwriting, and more training about handwriting instruction were identified as ways improve practice

Glucosamine hydrochloride for the treatment of osteoarthritis symptoms

Osteoarthritis is the most common arthritis in the world. It affects millions of people with age being the greatest risk factor for developing the disease. The burden of disease will worsen with the aging of the world’s population. The disease causes pain and functional disability. The direct costs of osteoarthritis include hospital and physician visits, medications, and assistive services. The indirect costs include work absences and lost wages. Many studies have sought to find a therapy to relieve pain and reduce disability. Glucosamine hydrochloride (HCl) is one of these therapies. There are limited studies of glucosamine HCl in humans. Although some subjects do report statistically significant improvement in pain and function from products combining glucosamine HCl and other agents, glucosamine HCl by itself appears to offer little benefit to those suffering from osteoarthritis

Goal Directed Learning: Early Assessment And Individualized Education Plans for Family Medicine Interns

Articulate rationale for early assessment Describe process of assessment and feedback sessions List benefits of process based on: Objective data Opinion of interns over past 2 year

Glucosamine and Chondroitin for Osteoarthritis

Glucosamine reduces pain and improves function in patients with knee or hip osteoarthritis. (Strength of recommendation: B, based on systematic reviews and a meta-analysis) Glucosamine may be beneficial in other forms of osteoarthritis as well. (Strength of recommendation: B, based on a randomized controlled trial [RCT]) Chondroitin has not consistently been found to improve pain or functional status. (Strength of recommendation: B, based on a systematic review and a meta-analysis

Bayesian nonparametric learning of complex dynamical phenomena

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 257-270).The complexity of many dynamical phenomena precludes the use of linear models for which exact analytic techniques are available. However, inference on standard nonlinear models quickly becomes intractable. In some cases, Markov switching processes, with switches between a set of simpler models, are employed to describe the observed dynamics. Such models typically rely on pre-specifying the number of Markov modes. In this thesis, we instead take a Bayesian nonparametric approach in defining a prior on the model parameters that allows for flexibility in the complexity of the learned model and for development of efficient inference algorithms. We start by considering dynamical phenomena that can be well-modeled as a hidden discrete Markov process, but in which there is uncertainty about the cardinality of the state space. The standard finite state hidden Markov model (HMM) has been widely applied in speech recognition, digital communications, and bioinformatics, amongst other fields. Through the use of the hierarchical Dirichlet process (HDP), one can examine an HMM with an unbounded number of possible states. We revisit this HDPHMM and develop a generalization of the model, the sticky HDP-HMM, that allows more robust learning of smoothly varying state dynamics through a learned bias towards self-transitions. We show that this sticky HDP-HMM not only better segments data according to the underlying state sequence, but also improves the predictive performance of the learned model. Additionally, the sticky HDP-HMM enables learning more complex, multimodal emission distributions.(cont.) We demonstrate the utility of the sticky HDP-HMM on the NIST speaker diarization database, segmenting audio files into speaker labels while simultaneously identifying the number of speakers present. Although the HDP-HMM and its sticky extension are very flexible time series models, they make a strong Markovian assumption that observations are conditionally independent given the discrete HMM state. This assumption is often insufficient for capturing the temporal dependencies of the observations in real data. To address this issue, we develop extensions of the sticky HDP-HMM for learning two classes of switching dynamical processes: the switching linear dynamical system (SLDS) and the switching vector autoregressive (SVAR) process. These conditionally linear dynamical models can describe a wide range of complex dynamical phenomena from the stochastic volatility of financial time series to the dance of honey bees, two examples we use to show the power and flexibility of our Bayesian nonparametric approach. For all of the presented models, we develop efficient Gibbs sampling algorithms employing a truncated approximation to the HDP that allows incorporation of dynamic programming techniques, greatly improving mixing rates. In many applications, one would like to discover and model dynamical behaviors which are shared among several related time series. By jointly modeling such sequences, we may more robustly estimate representative dynamic models, and also uncover interesting relationships among activities.(cont.) In the latter part of this thesis, we consider a Bayesian nonparametric approach to this problem by harnessing the beta process to allow each time series to have infinitely many potential behaviors, while encouraging sharing of behaviors amongst the time series. For this model, we develop an efficient and exact Markov chain Monte Carlo (MCMC) inference algorithm. In particular, we exploit the finite dynamical system induced by a fixed set of behaviors to efficiently compute acceptance probabilities, and reversible jump birth and death proposals to explore new behaviors. We present results on unsupervised segmentation of data from the CMU motion capture database.by Emily B. Fox.Ph.D

Detection and localization of aerosol releases from sparse sensor measurements

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 111-114).In this thesis we focus on addressing two aspects pertinent to biological release detection. The first is that of detecting and localizing an aerosolized particle release using a sparse array of sensors. The problem is challenging for several reasons. It is often the case that sensors are costly and consequently only a sparse deployment is possible. Additionally, while dynamic models can be formulated in many environmental conditions, the underlying model parameters may not be precisely known. The combination of these two issues impacts the effectiveness of inference approaches. We restrict ourselves to propagation models consisting of diffusion plus transport according to a Gaussian puff model. We derive optimal inference algorithms utilizing sparse sensor measurements, provided the model parameterization is known precisely. The primary assumptions are that the mean wind field is deterministically known and that the Gaussian puff model is valid. Under these assumptions, we characterize the change in performance of detection, time-to-detection and localization as a function of the number of sensors. We then examine some performance impacts when the underlying dynamical model deviates from the assumed model. In addition to detecting an abrupt change in particles in an environment, it is also important to be able to classify the releases as not all contaminants are of interest. For this reason, the second aspect of addressed is feature extraction, a stage where sensor measurements are reduced to a set of pertinent features that can be used as an input to the classifier.(cont.) Shift invariance of the feature set is critical and thus the Dual Tree Complex Wavelet Transform (DT CWT) is proposed as the wavelet feature domain.by Emily Beth Fox.M.Eng

The Importance of Computing Education Research

Interest in computer science is growing. As a result, computer science (CS) and related departments are experiencing an explosive increase in undergraduate enrollments and unprecedented demand from other disciplines for learning computing. According to the 2014 CRA Taulbee Survey, the number of undergraduates declaring a computing major at Ph.D. granting departments in the US has increased 60% from 2011-2014 and the number of degrees granted has increased by 34% from 2008-2013

The Importance of Computing Education Research

Interest in computer science is growing. As a result, computer science (CS) and related departments are experiencing an explosive increase in undergraduate enrollments and unprecedented demand from other disciplines for learning computing. According to the 2014 CRA Taulbee Survey, the number of undergraduates declaring a computing major at Ph.D. granting departments in the US has increased 60% from 2011-2014 and the number of degrees granted has increased by 34% from 2008-2013