Heat transfer device

A heat transfer device is characterized by an hermetically sealed tubular housing including a tubular shell terminating in spaced end plates, and a tubular mesh wick concentrically arranged and operatively supported within said housing. The invention provides an improved wicking restraint formed as an elongated and radially expanded tubular helix concentrically related to the wick and adapted to be axially foreshortened and radially expanded into engagement with the wick in response to an axially applied compressive load. The wick is continuously supported in a contiguous relationship with the internal surfaces of the shell

Acquired versus Non-Acquired Subsidiaries - Which Entry Mode do Parent Firms Prefer

Despite the economic importance of international foreign direct investment (FDI) flows, investment decisions of multinational firms are not well understood. A multinational firm can establish a subsidiary in a foreign country through greenfield investment or through acquiring an existing firm in the target country. The goal of this paper is to shed some light on the determinants of foreign market entry modes. In particular to analyze the systematic variation in the mode choice of FDI, namely acquisition versus non-acquisition (greenfield) investments. We propose a transparent and general applicable method to construct a data base. This database includes information about parent firms and their majority owned affiliates in foreign countries. A particular feature is the construction of a variable which allows to differentiate the establishment mode of parent firms into foreign markets. For this purpose two databases from the Bureau van Dijk are interlinked: Osiris and Zephyr. We provide evidence that firm heterogeneity is important for U.S. multinational firms in determining their entry mode choice. However, this is not a distinguishing feature for European multinational firms. For both sets of parent firms the host country characteristics play an important role in deciding on the entry mode. Higher institutional quality increases the likelihood of acquisitions versus greenfield investments.Acquisition, Greenfield, Subsidiaries, Mode Choice, FDI, Institutions;

A machine learning based material homogenization technique for masonry structures

Cutting-edge methods in the computational analysis of structures have been developed over the last decades. Such modern tools are helpful to assess the safety of existing buildings. Two main finite element (FE) modeling approaches have been developed in the field of masonry structures, i.e. micro and macro scale. While the micro modeling distinguishes between the masonry components in order to accurately represent the typical masonry damage mechanisms in the material constituents, macro modeling considers a single continuum material with smeared properties so that large scale masonry models can be analyzed. Both techniques have demonstrated their advantages in different structural applications. However, each approach comes along with some possible disadvantages. For example, the use of micro modeling is limited to small scale structures, since the computational effort becomes too expensive for large scale applications, while macro modeling cannot take into account precisely the complex interaction among masonry components (brick units and mortar joints). Multi scale techniques have been proposed to combine the accuracy of micro modeling and the computational efficiency of macro modeling. Such procedures consider linked FE analyses at both scales, and are based on the concept of a representative volume element (RVE). The analysis of a RVE takes into account the micro structural behavior of component materials, and scales it up to the macro level. In spite of being a very accurate tool for the analysis of masonry structures, multi scale techniques still exhibit high computational cost while connecting the FE analyses at the two scales. Machine learning (ML) tools have been utilized successfully to train specific models by feeding big source data from different fields, e.g. autonomous driving, face recognition, etc. This thesis proposes the use of ML to develop a novel homogenization strategy for the in-plane analysis of masonry structures, where a continuous nonlinear material law is calibrated by considering relevant data derived from micro scale analysis. The proposed method is based on a ML tool that links the macro and micro scales of the analysis, by training a macro model smeared damage constitutive law through benchmark data from numerical tests derived from RVE micro models. In this context, numerical nonlinear tests on masonry micro models executed in a virtual laboratory provide the benchmark data for feeding the ML training procedure. The adopted ML technique allows the accurate and efficient simulation of the anisotropic behavior of masonry material by means of a tensor mapping procedure. The final stage of this novel homogenization method is the definition of a calibrated continuum constitutive model for the structural application to the masonry macro scale. The developed technique is applied to the in-plane homogenization of a Flemish bond masonry wall. Evaluation examples based on the simulation of physical laboratory tests show the accuracy of the method when compared with sophisticated micro modeling of the entire structure. Finally, an application example of the novel homogenization technique is given for the pushover analysis of a masonry heritage structure.En las últimas décadas se han desarrollado diversos métodos avanzados para el análisis computacional de estructuras. Estas herramientas modernas son también útiles para evaluar la seguridad de los edificios existentes. En el campo de las estructuras de la obra de fábrica se han desarrollado principalmente dos técnicas de modelizacón por elementos finitos (FE): la modelización en escala micro y en escala macro. Mientras que en un micromodelo se distingue entre los componentes de la obra de fábrica para representar con precisión los mecanismos de daño característicos de la misma, en un macromodelo se asignan las propiedades a un único material continuo que permite analizar modelos de obra de fábrica a gran escala. Ambas técnicas han demostrado sus ventajas en diferentes aplicaciones estructurales. Sin embargo, cada enfoque viene acompañado de algunas posibles desventajas. Por ejemplo, la micromodelización se limita a estructuras de pequeña escala, puesto que el esfuerzo computacional que requieren aumenta rápidamente con el tamaño de los modelos, mientras que la macromodelización, por su parte, es un enfoque promediado que no puede por tanto tener en cuenta precisamente la interacción compleja entre los componentes de la fábrica (unidades de ladrillo y juntas de mortero). Hasta el momento, se han propuesto algunas técnicas multiescala para combinar la precisión de la micromodelización y la eficiencia computacional de la macromodelización. Estos procedimientos aplican el análisis de FE vinculado a ambas escalas y se basan en el concepto de elemento de volumen representativo (RVE). El análisis de un RVE tiene en cuenta el comportamiento microestructural de los materiales componentes y lo escala hasta el nivel macro. A pesar de ser una herramienta muy precisa para el análisis de obra de fábrica, las técnicas multiescala siguen presentando un elevado coste computacional que se produce al conectar los análisis de FE de dos escalas. Además, diversos autores han utilizado con éxito herramientas de aprendizaje automático (machine learning (ML)) para poner a punto modelos específicos alimentados con grandes fuentes de datos de diferentes campos, por ejemplo, la conducción autónoma, el reconocimiento de caras, etc. Partiendo de los anteriores conceptos, este tesis propone el uso de ML para desarrollar una novedosa estrategia de homogeneización para el análisis en plano de estructuras de mampostería, donde se calibra una ley de materiales continua no lineal considerando datos relevantes derivados del análisis a microescala. El método propuesto se basa en una herramienta de ML que vincula las escalas macro y micro del análisis mediante la puesta a punto de una ley constitutiva para el modelo macro a través de datos producidos en ensayos numéricos de un RVE micro modelo. En este contexto, los ensayos numéricos no lineales sobre micro modelos de mampostería ejecutados en un laboratorio virtual proporcionan los datos de referencia para alimentar el procedimiento de entrenamiento del ML. La técnica de ML adoptada permite la simulación precisa y eficiente del comportamiento anisotrópico del material de mampostería mediante un procedimiento de mapeo tensorial. La etapa final de este novedoso método de homogeneización es la definición de un modelo constitutivo continuo calibrado para la aplicación estructural a la macroescala de mampostería. La técnica desarrollada se aplica a la homogeneización en el plano de un muro de obra de fábrica construido con aparejo flamenco. Ejemplos de evaluación basados en la simulación de pruebas físicas de laboratorio muestran la precisión del método en comparación con una sofisticada micro modelización de toda la estructura. Por último, se ofrece un ejemplo de aplicación de la novedosa técnica de homogeneización para el análisis pushover de una estructura patrimonial de obra de fábrica.Postprint (published version

THE RELATIVE IMPORTANCE OF SIX CLASSES OF SCHOOL-READINESS VARIABLES WITH ACADEMIC ACHIEVEMENT IN ELEMENTARY- SCHOOL STUDENTS: A GROWTH ANALYSIS OF THE ECLS-K:2011

School readiness is a multi-variable construct that includes six classes of variables: (a) cognitive knowledge and skills, (b) social and emotional skills, (c) physical skills and health, (d) family structure and home environment, (e) access to community resources, and (e) early school experiences. The problem with school readiness is that the six classes have been studied separately but never together, which raises the question, what variables make children the most ready to succeed academically in school? Answering this question may help to address the achievement gap because differences in students’ academic achievement can be linked to differences in school readiness. This study examined the relationships between 13 school-readiness variables that were organized into six classes with students’ academic achievement and growth as represented by students’ reading and mathematics assessment scores over 5 years of elementary school (fall kindergarten through spring fourth grade). This study was a secondary analysis of the longitudinal data set ECLS-K:2011, a national probability sample of more than 18,000 U.S. elementary-school students, using hierarchical linear growth modeling (HLM growth modeling). Results indicated that of the six classes of variables the three with the strongest relationship to academic achievement in fall kindergarten were student’s cognitive knowledge and skills, social and emotional skills, and family structure and home environment. Within these three classes, the variables with the strongest influence on reading and mathematics academic achievement in fall kindergarten as well as on academic growth in elementary school in order of importance were kindergarten teachers’ ratings of students’ general academic knowledge, students’ working memory ability, students’ socioeconomic status (SES), students’ cognitive flexibility, and teachers’ ratings of students’ behavior. The academic starting points as measured by reading and mathematics assessment scores in fall kindergarten and the growth rates for each variable as measured by reading and mathematics assessment points in the spring semesters of grades first through fourth are provided in this study. Implications for future research include examining the relationships between students’ general academic knowledge, SES, and working memory. Implications for future practice include providing more feedback to early-childhood educators and elementary school teachers in the form of classroom observations to help them improve their teaching practice. By improving their teaching practice, early-childhood teachers can help their young students achieve greater academic success and preparedness to start elementary school, which in turn can help alleviate the school-readiness gap and ultimately the achievement gap

Spontaneous emission enhancement of a single molecule by a double-sphere nanoantenna across an interface

We report on two orders of magnitude reduction in the fluorescence lifetime when a single molecule placed in a thin film is surrounded by two gold nanospheres across the film interface. By attaching one of the gold particles to the end of a glass fiber tip, we could control the modification of the molecular fluorescence at will. We find a good agreement between our experimental data and the outcome of numerical calculations

A Practical Guide to Instrument Development and Score Validation in the Social Sciences: The MEASURE Approach

The research and practice of social scientists who work in a myriad of different specialty areas involve developing and validating scores on instruments as well as evaluating the psychometric properties of existing instrumentation for use with research participants. In this article, the author introduces The MEASURE Approach to instrument development, an acronym of seven empirically supported steps for instrument development, and initial score validation that he developed based on the recommendations of leading psychometric researchers and based on his own extensive background in instrument development. Implications for how The MEASURE Approach has utility for enhancing the assessment literacy of social scientists who work in a variety of different specialty areas are discussed

Establishing the Psychometric Properties of the Understanding Mental Health Scale: A Dissertation Study

The purpose of this dissertation study was to validate the Understanding Mental Health Scale (UMHS). The UMHS is a 50-item questionnaire that was designed to measure college students’ awareness of mental health issues. To test the psychometric properties of the UHMS, a principal axis factor (PAF) analysis with an oblique rotation was conducted using an existing data set of 350 college students. Results revealed a two-factor structure underlying college students’ understanding of mental health issues. The factors were named risk-factor awareness (familiarity with warning signs of mental health issues) and resource awareness (knowledge of resources for mental health issues). A multivariate analysis of variance (MANOVA) was conducted to investigate group differences by gender and ethnicity in students’ understanding of awareness and resource awareness for mental health issues. Statistically significant main effects emerged for gender and for ethnicity. Women scored significantly higher than men on both the risk-factor awareness factor and the protective factor subscales. In addition, participants who identified as White scored significantly higher on the risk-factor awareness scale compared to participants who identified as African American or non-White/African American. Implications for college counselors, educators, university administrators, and students are discussed. A review of the limitations and potential contributions of this study are provided

Identifying Barriers to Attendance in Counseling Among Adults in the United States: Confirming the Factor Structure of the Revised Fit, Stigma, & Value Scale

The primary aim of this study was to cross-validate the Revised Fit, Stigma, & Value (FSV) Scale, a questionnaire for measuring barriers to counseling, using a stratified random sample of adults in the United States. Researchers also investigated the percentage of adults living in the United States that had previously attended counseling and examined demographic differences in participants’ sensitivity to barriers to counseling. The results of a confirmatory factor analysis supported the factorial validity of the three-dimensional FSV model. Results also revealed that close to one-third of adults in the United States have attended counseling, with women attending counseling at higher rates (35%) than men (28%). Implications for practice, including how professional counselors, counseling agencies, and counseling professional organizations can use the FSV Scale to appraise and reduce barriers to counseling among prospective clients are discussed