Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application

Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented.Peer Reviewe

Response to Gutstein Generalized- A Philosophical Debate

It is a pleasure, challenge, and an honor to respond to the thoughtful and innovative debate started by Braver, Micklus, Bradley, van Spronsen, Allen, & Campbell on teaching mathematics for social justice. They take seriously the issues in, and raise many interesting views about, my article, Teaching and Learning Mathematics for Social Justice in an Urban Latino School (JRME, January, 2003). I would like to respond to (connected) two points in particular: the relationship of functional to critical literacies, and the relationship of “critical thinking” in mathematics to learning mathematics for social justice. However, I would first like to clarify certain points about my article

Information extraction from primary care visits to support patient-provider interactions

The extent of electronic health record systems usage in clinical settings has affected the dynamic between clinicians and patients and has thus been connected to physician morale and the quality of care patients receive. Recent research has also uncovered a correlation between physician burnout and negative physician attitudes electronic health record systems. In order to begin exploring the nature of the relationship between electronic health record usage, physician burnout, and patient care, it is necessary to first analyze patient-provider interactions within the context of verbal features such as turn-taking and non-verbal features such as eye-contact. While previous works have sought to annotate non-verbal and verbal features via manual coding techniques and then analyze their impacts, we seek to automate the process of annotation in order to create a more robust system of analysis in less time-consuming fashion. This research thesis focuses upon physician gaze and speaking annotations, as these are non-verbal and verbal components of the interaction which can be connected to eye-contact and turn-taking, respectively, which are themselves features that have linked in certain research to patient outcomes. Previously published work from within this project has demonstrated the viability of extracting image features in the form of YOLO-based person positioning coordinates and optical flow summary statistics to inform the learning of physician gaze for two physicians and six patients with over 80% minimum accuracy. The work described in this thesis expands upon the previous findings by increasing the number of patients and physicians in the realm of analysis; by diversifying the classifiers to be more robust to new data; and by incorporating automatically extracted audio information in the form of mel frequency cepstral coefficients and its derivatives, as well as an additional optical flow summary statistic, in order to make predictions regarding physician gaze and speaking annotations on a frame by frame basis. We thus illustrate a process of developing and implementing an automated system for multiple video labeling of physician-patient interactions. In so doing, we demonstrate that a combination of audio and visual features can be combined to inform the predictions of physician gaze and speaking annotations in both testing and sequential validation data. While our approach focuses upon learning physician gaze and speaking annotations, the methodologies introduced can be extended to capture other aspects of the interaction as well as connect these interactions to patient ratings of clinical interactions, physician usage of electronic health record systems, and measures of physician burnout. Ultimately, the approaches presented in this paper can aid the creation of an interactive system providing instantaneous feedback to providers during clinician visits, which will be created with the intention of improving clinical care within the context of electronic health care so as to enhance care, improve patient outcomes, and reduce instances of physician burnout

The Political Context of the National Mathematics Advisory Panel

The National Mathematics Advisory Panel needs to be situated in its broader political context to more fully understand it. Who created it, for what purpose, and who will (and will not) benefit from it are key questions I address in this article. My argument is that the NMAP, as part of a larger initiative undertaken by the Bush Administration and US financial/corporate elites, serves capital’s efforts to shore up the US’s weakening economic global position and does not benefit the majority of the US people—particularly marginalized and excluded students of color and low-income students

Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Adolescents and Suicide: Restoring the Kin Network

Hogg Foundation for Mental Healt