IUPUI mechanical engineering technology senior assessment

This paper discusses the methods and analysis of 6-semesters of senior assessment examination data identifying the courses and subject material students found the most difficult to solve in the MET program Senior Assessment Examination. The analysis results indicate that MET 111 (Applied Statics), MET 213 (Dynamics), and MET 348 (Engineering Materials) are courses in need of potential improvement. Furthermore, subject areas such as the calculation of entropy change, the calculation of pressure drop flow through a pipe, and Hooke's Law are subject material that poses greatest problems for senior students. For the past 12 years, the Mechanical Engineering Technology (MET) Program faculty at IUPUI require all seniors to take a MET Senior Assessment Examination that is similar in content to the Fundamentals of Engineering (FE) examination. This paper discusses the methods used to provide insightful and actionable inputs for the IUPUI MET program process improvements plan. The raw data consists of test scores from 123 senior students who took the examination from 2014 through 2016. The Accreditation Board for Engineering & Technology (ABET) is an organization that ensures universities and institutions like IUPUI meet certain accreditation requirements and requires that each program develops a continuous improvement plan. The improvement plan typically consists of a compilation of student materials, employer surveys, and course evaluations used to ensure continuous improvement within a program. In 2004 IUPUI, MET program faculty decided that a standardized senior examination would be part of the program process improvement process, [1]

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Causas de reingreso de los pacientes de hospitalizaci\uf3n a la unidad de trauma shock del Hospital Universitario de Pediatr\ueda Dr. "Agust\uedn Zubillaga" Barquisimeto, Estado Lara

El presente estudio se realiz\uf3 con el prop\uf3sito de determinar las causas de reingreso de los pacientes de hospitalizaci\uf3n a la Unidad de Trauma Shock del Hospital Universitario de Pediatr\ueda Dr. "Agust\uedn Zubillaga" Barquisimeto Estado Lara. Es una investigaci\uf3n de campo no experimental descriptiva de cohorte transversal con paradigma cuantitativo, se aplic\uf3 la encuesta como instrumento contentivo a 90 enfermeras de las diferentes \ue1reas de hospitalizaci\uf3n, con el cual se obtuvo informaci\uf3n acerca de; nivel acad\ue9mico del personal de Enfermer\ueda que labora en las unidades de hospitalizaci\uf3n, la jerarquizaci\uf3n de sus cuidados, los recursos m\ue9dicos\uadquirurgicos donde ingresan esos pacientes as\ued como los factores de reingreso cl\uednico\uadepidemiol\uf3gico del mismo. Entre los resultados se destacan: la mayor\ueda de las enfermeras son licenciadas con 75.5%, encontr\ue1ndose que jerarquizan sus cuidados dependiendo de las condiciones cl\uednicas del paciente con un 48.9%, en cuanto al material m\ue9dico-quir\ufargico necesarios para brindar cuidados a los pacientes egresados de la unidad de trauma shock presentan porcentajes relativamente bajos con las tomas de ox\uedgeno para cada paciente nunca est\ue1n presentes con 46.6% contando siempre en las \ue1reas con inyectadoras con 64.4% y casi siempre con mascarillas descartables en 47,7%. Los datos cl\uednicos epidemiol\uf3gicos del paciente arrojaron en su mayor\ueda que son lactantes en 66.7% con buen estado nutricional en 55.5%, reingresando en un tiempo mayor a 5 d\uedas con 55.6% de procedencia hospitalaria de la unidad de agudos en un 77.8% y regresando de nuevo a la unidad de trauma shock con afecciones respiratorias con un 88.9%

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu