942 resultados para Geospatial Data Model
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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Dissertação para a obtenção de Grau de Mestre em Engenharia e Gestão Industrial
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In-network storage of data in wireless sensor networks contributes to reduce the communications inside the network and to favor data aggregation. In this paper, we consider the use of n out of m codes and data dispersal in combination to in-network storage. In particular, we provide an abstract model of in-network storage to show how n out of m codes can be used, and we discuss how this can be achieved in five cases of study. We also define a model aimed at evaluating the probability of correct data encoding and decoding, we exploit this model and simulations to show how, in the cases of study, the parameters of the n out of m codes and the network should be configured in order to achieve correct data coding and decoding with high probability.
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Accepted in 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia 2015), Amsterdam, Netherlands.
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Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Terminologia e Gestão da Informação de Especialidade
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BACKGROUND: Furniture companies can analyze their safety status using quantitative measures. However, the data needed are not always available and the number of accidents is under-reported. Safety climate scales may be an alternative. However, there are no validated Portuguese scales that account for the specific attributes of the furniture sector. OBJECTIVE: The current study aims to develop and validate an instrument that uses a multilevel structure to measure the safety climate of the Portuguese furniture industry. METHODS: The Safety Climate in Wood Industries (SCWI) model was developed and applied to the safety climate analysis using three different scales: organizational, group and individual. A multilevel exploratory factor analysis was performed to analyze the factorial structure. The studied companies’ safety conditions were also analyzed. RESULTS: Different factorial structures were found between and within levels. In general, the results show the presence of a group-level safety climate. The scores of safety climates are directly and positively related to companies’ safety conditions; the organizational scale is the one that best reflects the actual safety conditions. CONCLUSIONS: The SCWI instrument allows for the identification of different safety climates in groups that comprise the same furniture company and it seems to reflect those groups’ safety conditions. The study also demonstrates the need for a multilevel analysis of the studied instrument.
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New arguments proving that successive (repeated) measurements have a memory and actually remember each other are presented. The recognition of this peculiarity can change essentially the existing paradigm associated with conventional observation in behavior of different complex systems and lead towards the application of an intermediate model (IM). This IM can provide a very accurate fit of the measured data in terms of the Prony's decomposition. This decomposition, in turn, contains a small set of the fitting parameters relatively to the number of initial data points and allows comparing the measured data in cases where the “best fit” model based on some specific physical principles is absent. As an example, we consider two X-ray diffractometers (defined in paper as A- (“cheap”) and B- (“expensive”) that are used after their proper calibration for the measuring of the same substance (corundum a-Al2O3). The amplitude-frequency response (AFR) obtained in the frame of the Prony's decomposition can be used for comparison of the spectra recorded from (A) and (B) - X-ray diffractometers (XRDs) for calibration and other practical purposes. We prove also that the Fourier decomposition can be adapted to “ideal” experiment without memory while the Prony's decomposition corresponds to real measurement and can be fitted in the frame of the IM in this case. New statistical parameters describing the properties of experimental equipment (irrespective to their internal “filling”) are found. The suggested approach is rather general and can be used for calibration and comparison of different complex dynamical systems in practical purposes.
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BACKGROUND: The detection of psychosocial distress is a significant communication problem in Southern Europe and other countries. Work in this area is hampered by a lack of data. Because not much is known about training aimed at improving the recognition of psychosocial disorders in cancer patients, we developed a basic course model for medical oncology professionals. METHODS: A specific educational and experiential model (12 hours divided into 2 modules) involving formal teaching (ie, journal articles, large-group presentations), practice in small groups (ie, small-group exercises and role playing), and discussion in large groups was developed with the aim of improving the ability of oncologists to detect emotional disturbances in cancer patients (ie, depression, anxiety, and adjustment disorders). RESULTS: A total of 30 oncologists from 3 Southern European countries (Italy, Portugal, and Spain) participated in the workshop. The training course was well accepted by most participants who expressed general satisfaction and a positive subjective perception of the utility of the course for clinical practice. Of the total participants, 28 physicians (93.3%) thought that had they been exposed to this material sooner, they would have incorporated the techniques received in the workshop into their practices; 2 participants stated they would likely have done so. Half of the doctors (n = 15) believed that their clinical communication techniques were improved by participating in the workshop, and the remaining half thought that their abilities to communicate with cancer patients had improved. CONCLUSIONS: This model is a feasible approach for oncologists and is easily applicable to various oncology settings. Further studies will demonstrate the effectiveness of this method for improving oncologists skills in recognizing emotional disorders in their patients with cancer.
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OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.