Investigations on Structural response of Water Backed Perforated plates

The study envisaged herein contains the numerical investigations on Perforated Plate (PP) as well as numerical and experimental investigations on Perforated Plate with Lining (PPL) which has a variety of applications in underwater engineering especially related to defence applications. Finite element method has been adopted as the tool for analysis of PP and PPL. The commercial software ANSYS has been used for static and free vibration response evaluation, whereas ANSYS LS-DYNA has been used for shock analysis. SHELL63, SHELL93, SOLID45, SOLSH190, BEAM188 and FLUID30 finite elements available in the ANSYS library as well as SHELL193 and SOLID194 available in the ANSYS LS-DYNA library have been made use of. Unit cell of the PP and PPL which is a miniature of the original plate with 16 perforations have been used. Based upon the convergence characteristics, the utility of SHELL63 element for the analysis of PP and PPL, and the required mesh density are brought out. The effect of perforation, geometry and orientation of perforation, boundary conditions and lining plate are investigated for various configurations. Stress concentration and deflection factor are also studied. Based on these investigations, stadium geometry perforation with horizontal orientation is recommended for further analysis.Linear and nonlinear static analysis of PP and PPL subjected to unit normal pressure has been carried out besides the free vibration analysis. Shock analysis has also been carried out on these structural components. The analytical model measures 0.9m x 0.9m with stiffener of 0.3m interval. The influence of finite element, boundary conditions, and lining plate on linear static response has been estimated and presented. Comparison of behavior of PP and PPL in the nonlinear strain regime has been made using geometric nonlinear analysis. Free vibration analysis of the PP and PPL has been carried out ‘in vacuum’ condition and in water backed condition, and the influence of water backed condition and effect of perforation on natural frequency have been investigated.Based upon the studies on the vibration characteristics of NPP, PP and PPL in water backed condition and ‘in vacuum’ condition, the reduction in the natural frequency of the plate in immersed condition has been rightly brought out. The necessity to introduce the effect of water medium in the analysis of water backed underwater structure has been highlighted.Shock analysis of PP and PPL for three explosives viz., PEK, TNT and C4 has been carried out and deflection and stresses on plate as well as free field pressure have been estimated using ANSYS LS-DYNA. The effect of perforations and the effect of lining plate have been predicted. Experimental investigations of the measurement of free field pressure using PPL have been conducted in a shock tank. Free field pressure has been measured and has been validated with finite element analysis results. Besides, an experiment has been carried out on PPL, for the comparison of the static deflection predicted by finite element analysis.The distribution of the free field pressure and the estimation of differential pressure from experimentation and the provision for treating the differential pressure as the resistance, as a part of the design load for PPL, has been brought out.

Measurement and simulation of anisotropy in the Infrared and Raman spectra of beta-FeSi2 single crystals

In this paper we show that the orthorhombic phase of FeSi2 (stable at room temperature) displays a sizable anisotropy in the infrared spectra, with minor effects in the Raman data too. This fact is not trivial at all, since the crystal structure corresponds to a moderate distortion of the fluorite symmetry. Our analysis is carried out on small single crystals grown by flux transport, through polarization-resolved far-infrared reflectivity and Raman measurements. Their interpretation has been obtained by means of the simulated spectra with tight-binding molecular dynamics.

Application of ocean colour data to study the oceanographic and atmospheric features off the southwest coast of India with special reference to upwelling

Satellite remote sensing is being effectively used in monitoring the ocean surface and its overlying atmosphere. Technical growth in the field of satellite sensors has made satellite measurement an inevitable part of oceanographic and atmospheric research. Among the ocean observing sensors, ocean colour sensors make use of visible band of electromagnetic spectrum (shorter wavelength). The use of shorter wavelength ensures fine spatial resolution of these parameters to depict oceanographic and atmospheric characteristics of any region having significant spaio-temporal variability. Off the southwest coast of India is such an area showing very significant spatio-temporal oceanographic and atmospheric variability due to the seasonally reversing surface winds and currents. Consequently, the region is enriched with features like upwelling, sinking, eddies, fronts, etc. Among them, upwelling brings nutrient-rich waters from subsurface layers to surface layers. During this process primary production enhances, which is measured in ocean colour sensors as high values of Chl a. Vertical attenuation depth of incident solar radiation (Kd) and Aerosol Optical Depth (AOD) are another two parameters provided by ocean colour sensors. Kd is also susceptible to undergo significant seasonal variability due to the changes in the content of Chl a in the water column. Moreover, Kd is affected by sediment transport in the upper layers as the region experiences land drainage resulting from copious rainfall. The wide range of variability of wind speed and direction may also influence the aerosol source / transport and consequently AOD. The present doctoral thesis concentrates on the utility of Chl a, Kd and AODprovided by satellite ocean colour sensors to understand oceanographic and atmospheric variability off the southwest coast of India. The thesis is divided into six Chapters with further subdivisions

Characterization of human and instrument related measurement errors

Measurement is the act or the result of a quantitative comparison between a given quantity and a quantity of the same kind chosen as a unit. It is generally agreed that all measurements contain errors. In a measuring system where both a measuring instrument and a human being taking the measurement using a preset process, the measurement error could be due to the instrument, the process or the human being involved. The first part of the study is devoted to understanding the human errors in measurement. For that, selected person related and selected work related factors that could affect measurement errors have been identified. Though these are well known, the exact extent of the error and the extent of effect of different factors on human errors in measurement are less reported. Characterization of human errors in measurement is done by conducting an experimental study using different subjects, where the factors were changed one at a time and the measurements made by them recorded. From the pre‐experiment survey research studies, it is observed that the respondents could not give the correct answers to questions related to the correct values [extent] of human related measurement errors. This confirmed the fears expressed regarding lack of knowledge about the extent of human related measurement errors among professionals associated with quality. But in postexperiment phase of survey study, it is observed that the answers regarding the extent of human related measurement errors has improved significantly since the answer choices were provided based on the experimental study. It is hoped that this work will help users of measurement in practice to better understand and manage the phenomena of human related errors in measurement.

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Risk Aversion in Inventory Management

Traditional inventory models focus on risk-neutral decision makers, i.e., characterizing replenishment strategies that maximize expected total profit, or equivalently, minimize expected total cost over a planning horizon. In this paper, we propose a framework for incorporating risk aversion in multi-period inventory models as well as multi-period models that coordinate inventory and pricing strategies. In each case, we characterize the optimal policy for various measures of risk that have been commonly used in the finance literature. In particular, we show that the structure of the optimal policy for a decision maker with exponential utility functions is almost identical to the structure of the optimal risk-neutral inventory (and pricing) policies. Computational results demonstrate the importance of this approach not only to risk-averse decision makers, but also to risk-neutral decision makers with limited information on the demand distribution.

Evaluación de la ejecución mediante el modelo Many-Facet Rasch Measurement.

Resumen tomado de la publicación

Multilevel multitrait-multimethod model: application to the measurement of egocentered social networks

Our goal in this paper is to assess reliability and validity of egocentered network data using multilevel analysis (Muthen, 1989, Hox, 1993) under the multitrait-multimethod approach. The confirmatory factor analysis model for multitrait-multimethod data (Werts & Linn, 1970; Andrews, 1984) is used for our analyses. In this study we reanalyse a part of data of another study (Kogovšek et al., 2002) done on a representative sample of the inhabitants of Ljubljana. The traits used in our article are the name interpreters. We consider egocentered network data as hierarchical; therefore a multilevel analysis is required. We use Muthen's partial maximum likelihood approach, called pseudobalanced solution (Muthen, 1989, 1990, 1994) which produces estimations close to maximum likelihood for large ego sample sizes (Hox & Mass, 2001). Several analyses will be done in order to compare this multilevel analysis to classic methods of analysis such as the ones made in Kogovšek et al. (2002), who analysed the data only at group (ego) level considering averages of all alters within the ego. We show that some of the results obtained by classic methods are biased and that multilevel analysis provides more detailed information that much enriches the interpretation of reliability and validity of hierarchical data. Within and between-ego reliabilities and validities and other related quality measures are defined, computed and interpreted

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Development of a design performance measurement matrix for improving collaborative design during a design process

This is a research paper. Research presented in this paper aimed to investigate how to measure collaborative design performance and, in turn, improve the final design output during a design process, with a clear objective to develop a Design Performance Measurement (DPM) matrix to measure design project team member's design collaboration performance.

La sabiduría como competencia gerencial

Aunque el concepto de sabiduría ha sido ampliamente estudiado por expertos de áreas como la filosofía, la religión y la psicología, aún enfrenta limitaciones en cuanto a su definición y evaluación. Por esto, el presente trabajo tiene como objetivo, formular una definición del concepto de sabiduría que permita realizar una propuesta de evaluación del concepto como competencia en los gerentes. Para esto, se realizó un análisis documental de tipo cualitativo. De esta manera, se analizaron diversos textos sobre la historia, las definiciones y las metodologías para evaluar tanto la sabiduría como las competencias; diferenciando la sabiduría de otros constructos y analizando la diferencia entre las competencias generales y las gerenciales para posteriormente, definir la sabiduría como una competencia gerencial. Como resultado de este análisis se generó un prototipo de prueba denominado SAPIENS-O, a través del cuál se busca evaluar la sabiduría como competencia gerencial. Como alcances del instrumento se pueden identificar la posibilidad de medir la sabiduría como competencia en los gerentes, la posibilidad de dar un nuevo panorama a las dificultades teóricas y empíricas sobre la sabiduría y la posibilidad de facilitar el estudio de la sabiduría en ambientes reales, más específicamente en ambientes organizacionales.

Radio 33 comparado con el patrón de oro para diagnóstico de osteoporosis

Introducción: la medición de la densidad mineral ósea en el fémur y columna lumbar es considerada la prueba de oro para el diagnóstico de osteoporosis en mujeres postmenopáusicas, sin tener suficiente evidencia de la utilidad al medir de manera individual o simultanea el radio 33 con este mismo propósito. Metodología Se realizó un estudio de pruebas diagnósticas, valorando concordancia anidada para determinar el valor diagnóstico del radio 33 comparado con los resultados por dual fémur y columna, actualmente patrón de oro el en diagnóstico. Se incluyeron mujeres mayores de 50 años entre el periodo de 2008 y 2011. Resultados Se incluyeron 3241 osteodensitometrias de pacientes femeninas con columna AP, dual fémur, radio ultradistal y radio 33. Tomando como estándar de oro la medición en columna y/o femur, el radio-33 obtuvo una sensibilidad de 95.5% y especificidad de 91% (p= 0,000) para osteoporosis, y una sensibilidad de 76.5% y especificidad de 54.9% p=0,000 para el diagnóstico de osteopenia. El radio 33 permite diagnosticar un 35% adicional de casos que no son captados con la medición en fémur y columna lumbar. Discusión La osteodensitometría en los sitios usados comúnmente, predice únicamente el 40% del riesgo de fractura (1). Si se incluye la medición de radio 33, la sensibilidad de la osteodensitometría sube en un 35.66% adicional, logrando detectar más pacientes con este riesgo y generando un impacto en la oportunidad del tratamiento y quizás reducción en la morbi-mortalidad secundaria a fracturas en esta población susceptible.