986 resultados para degenerate test set


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Stone masonry is one of the oldest and most worldwide used building techniques. Nevertheless, the structural response of masonry structures is complex and the effective knowledge about their mechanical behaviour is still limited. This fact is particularly notorious when dealing with the description of their out-of-plane behaviour under horizontal loadings, as is the case of the earthquake action. In this context, this paper describes an experimental program, conducted in laboratory environment, aiming at characterizing the out-of-plane behaviour of traditional unreinforced stone masonry walls. In the scope of this campaign, six full-scale sacco stone masonry specimens were fully characterised regarding their most important mechanic, geometric and dynamic features and were tested resorting to two different loading techniques under three distinct vertical pre-compression states; three of the specimens were subjected to an out-of-plane surface load by means of a system of airbags and the remaining were subjected to an out-of-plane horizontal line-load at the top. From the experiments it was possible to observe that both test setups were able to globally mobilize the out-of-plane response of the walls, which presented substantial displacement capacity, with ratios of ultimate displacement to the wall thickness ranging between 26 and 45 %, as well as good energy dissipation capacity. Finally, very interesting results were also obtained from a simple analytical model used herein to compute a set of experimental-based ratios, namely between the maximum stability displacement and the wall thickness for which a mean value of about 60 % was found.

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

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In this work I propose an additional test to be implemented in EDP’s residential electricity use feedback trials, under InovCity’s project scope. The proposed product to be tested consists of an interface between the smart meter and the television, through a set-top box. I provide a theoretical framework of the importance of feedback, an analysis of results from past studies involving smart metering, and a detailed description of my proposal. The results of a self-developed questionnaire related to the proposal and segmentation issues are also analyzed. Finally, general conclusions are drawn and potential future improvements and challenges are presented.

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IP networks are currently the major communication infrastructure used by an increasing number of applications and heterogeneous services, including voice services. In this context, the Session Initiation Protocol (SIP) is a signaling protocol widely used for controlling multimedia communication sessions such as voice or video calls over IP networks, thus performing vital functions in an extensive set of public and enter- prise solutions. However, the SIP protocol dissemination also entails some challenges, such as the complexity associated with the testing/validation processes of IMS/SIP networks. As a consequence, manual IMS/SIP testing solutions are inherently costly and time consuming tasks, being crucial to develop automated approaches in this specific area. In this perspective, this article presents an experimental approach for automated testing/validation of SIP scenarios in IMS networks. For that purpose, an automation framework is proposed allowing to replicate the configuration of SIP equipment from the pro- duction network and submit such equipment to a battery of tests in the testing network. The proposed solution allows to drastically reduce the test and validation times when compared with traditional manual approaches, also allowing to enhance testing reliability and coverage. The automation framework comprises of some freely available tools which are conveniently integrated with other specific modules implemented within the context of this work. In order to illustrate the advantages of the proposed automated framework, a real case study taken from a PT Inovação customer is presented comparing the time required to perform a manual SIP testing approach with the one time required when using the proposed auto- mated framework. The presented results clearly corroborate the advantages of using the presented framework.

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Entre los factores que contribuyen a predecir el rendimiento académico se pueden destacar aquellos que reflejan capacidades cognitivas (inteligencia, por ejemplo), y aquellas diferencias individuales consideradas como no-cognitivas (rasgos de personalidad, por ejemplo). En los últimos años, también se considera al Conocimiento General (CG) como un criterio para el éxito académico (ver Ackerman, 1997), ya que se ha evidenciado que el conocimiento previo ayuda en la adquisición de nuevo conocimiento (Hambrick & Engle, 2001). Uno de los objetivos de la psicología educacional consiste en identificar las principales variables que explican el rendimiento académico, como también proponer modelos teóricos que expliquen las relaciones existentes entre estas variables. El modelo teórico PPIK (Inteligencia-como-Proceso, Personalidad, Intereses e Inteligencia-como-Conocimiento) propuesto por Ackerman (1996) propone que el conocimiento y las destrezas adquiridas en un dominio en particular son el resultado de la dedicación de recursos cognitivos que una persona realiza durante un prolongado período de tiempo. Este modelo propone que los rasgos de personalidad, intereses individuales/vocacionales y aspectos motivacionales están integrados como rasgos complejos que determinan la dirección y la intensidad de la dedicación de recursos cognitivos sobre el aprendizaje que realiza una persona (Ackerman, 2003). En nuestro medio (Córdoba, Argentina), un grupo de investigadores ha desarrollado una serie de recursos técnicos necesarios para la evaluación de algunos de los constructos propuesto por este modelo. Sin embargo, por el momento no contamos con una medida de Conocimiento General. Por lo tanto, en el presente proyecto se propone la construcción de un instrumento para medir Conocimiento General (CG), indispensable para poder contar con una herramienta que permita establecer parámetros sobre el nivel de conocimiento de la población universitaria y para en próximos trabajos poner a prueba los postulados de la teoría PPIK (Ackerman, 1996). Between the factors that contribute to predict the academic achievement, may be featured those who reflect cognitive capacities (i.g. intelligence) and those who reflect individual differences that are considered like non-cognitive (i.g. personality traits). In the last years, also the General Knowledge has been considered like a criterion for the academic successfully (see Ackerman, 1997), since it has been shown that the previous knowledge helps in the acquisition of the new knowledge (Hambrick & Engle, 2001). An interesting theoretical model that has proposed an explanation for the academic achievement, is the PPIK (intelligence like a process, interests and inteligence like knowledge) proposed by Ackerman (1996), who argues that knowledge and the acquired skills in a particular domain are the result of the dedication of cognitive resources that a person perform during a long period of time. This model proposes that personality traits, individuals interests and motivational aspects are integrated as complex traits that determine the direction and the intensity of the dedication of cognitive resources on the learning that a person make (Ackerman, 2003). In our context, (Córdoba, Argentina), a group of researcher has developed a series of necessary technical resoures for the assesment of some of the theoretical constructs proposed by this model. However, by the moment, we do not have an instrument for evaluate the General Knowledge. Therefore, this project aims the construction of an instrument to asess General Knowledge, essential to set parameters on the knowledge level of the university population and for in next works test the PPIK theory postulates.

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This paper develops a new test of true versus spurious long memory, based on log-periodogram estimation of the long memory parameter using skip-sampled data. A correction factor is derived to overcome the bias in this estimator due to aliasing. The procedure is designed to be used in the context of a conventional test of significance of the long memory parameter, and composite test procedure described that has the properties of known asymptotic size and consistency. The test is implemented using the bootstrap, with the distribution under the null hypothesis being approximated using a dependent-sample bootstrap technique to approximate short-run dependence following fractional differencing. The properties of the test are investigated in a set of Monte Carlo experiments. The procedure is illustrated by applications to exchange rate volatility and dividend growth series.

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Cannula design is of prime importance for venous drainage during cardiopulmonary bypass (CPB). To evaluate cannulas intended for CPB, an in vitro circuit was set up with silicone tubing between the test cannula encased in a movable preload reservoir and another static reservoir. The pressure-drop (DeltaP) value (P-drainage - P-preload) was measured using Millar pressure transducers. Flow rate (Q) was measured using an ultrasound flowmeter. Data display and data recording were controlled using a LabView application, custom made particularly for our experiments. Our results demonstrated that DeltaP, Q, and cannula resistance (DeltaP/Q) values were significantly decreased when the cannula diameter was increased for Smart and Medtronic cannulas. Smartcanula showed 36% and 43% less resistance compared to Medtronic venous and Medtronic femoral cannulas, respectively. The cannula shape (straight- or curved-tips) did not affect the DLP cannula resistance. Out of five cannulas tested, the Smartcanula outperforms the other commercially available cannulas. The mean (DeltaP/Q) values were 3.3 +/- 0.08, 4.07 +/- 0.08, 5.58 +/- 0.10, 5.74 +/- 0.15, and 6.45 +/- 0.15 for Smart, Medtronic, Edwards, Sarns, and Gambro cannulas, respectively (two-way ANOVA, p < 0.0001). In conclusion, the present assay allows discrimination between different forms of cannula with high or low lumen resistance.

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Neutrality tests in quantitative genetics provide a statistical framework for the detection of selection on polygenic traits in wild populations. However, the existing method based on comparisons of divergence at neutral markers and quantitative traits (Q(st)-F(st)) suffers from several limitations that hinder a clear interpretation of the results with typical empirical designs. In this article, we propose a multivariate extension of this neutrality test based on empirical estimates of the among-populations (D) and within-populations (G) covariance matrices by MANOVA. A simple pattern is expected under neutrality: D = 2F(st)/(1 - F(st))G, so that neutrality implies both proportionality of the two matrices and a specific value of the proportionality coefficient. This pattern is tested using Flury's framework for matrix comparison [common principal-component (CPC) analysis], a well-known tool in G matrix evolution studies. We show the importance of using a Bartlett adjustment of the test for the small sample sizes typically found in empirical studies. We propose a dual test: (i) that the proportionality coefficient is not different from its neutral expectation [2F(st)/(1 - F(st))] and (ii) that the MANOVA estimates of mean square matrices between and among populations are proportional. These two tests combined provide a more stringent test for neutrality than the classic Q(st)-F(st) comparison and avoid several statistical problems. Extensive simulations of realistic empirical designs suggest that these tests correctly detect the expected pattern under neutrality and have enough power to efficiently detect mild to strong selection (homogeneous, heterogeneous, or mixed) when it is occurring on a set of traits. This method also provides a rigorous and quantitative framework for disentangling the effects of different selection regimes and of drift on the evolution of the G matrix. We discuss practical requirements for the proper application of our test in empirical studies and potential extensions.

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There are no validated criteria for the diagnosis of sensory neuronopathy (SNN) yet. In a preliminary monocenter study a set of criteria relying on clinical and electrophysiological data showed good sensitivity and specificity for a diagnosis of probable SNN. The aim of this study was to test these criteria on a French multicenter study. 210 patients with sensory neuropathies from 15 francophone reference centers for neuromuscular diseases were included in the study with an expert diagnosis of non-SNN, SNN or suspected SNN according to the investigations performed in these centers. Diagnosis was obtained independently from the set of criteria to be tested. The expert diagnosis was taken as the reference against which the proposed SNN criteria were tested. The set relied on clinical and electrophysiological data easily obtainable with routine investigations. 9/61 (16.4 %) of non-SNN patients, 23/36 (63.9 %) of suspected SNN, and 102/113 (90.3 %) of SNN patients according to the expert diagnosis were classified as SNN by the criteria. The SNN criteria tested against the expert diagnosis in the SNN and non-SNN groups had 90.3 % (102/113) sensitivity, 85.2 % (52/61) specificity, 91.9 % (102/111) positive predictive value, and 82.5 % (52/63) negative predictive value. Discordance between the expert diagnosis and the SNN criteria occurred in 20 cases. After analysis of these cases, 11 could be reallocated to a correct diagnosis in accordance with the SNN criteria. The proposed criteria may be useful for the diagnosis of probable SNN in patients with sensory neuropathy. They can be reached with simple clinical and paraclinical investigations.

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Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the "rare species modelling paradox" and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models aren't overfitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted ensemble approach. We further propose that this ensemble forecasting is conducted within a hierarchic multi-scale framework. We present two ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales.

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Topological indices have been applied to build QSAR models for a set of 20 antimalarial cyclic peroxy cetals. In order to evaluate the reliability of the proposed linear models leave-n-out and Internal Test Sets (ITS) approaches have been considered. The proposed procedure resulted in a robust and consensued prediction equation and here it is shown why it is superior to the employed standard cross-validation algorithms involving multilinear regression models

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The paper explores the consequences that relying on different behavioral assumptions in training managers may have on their future performance. We argue that training with an emphasis on the standard assumptions used in economics (rationality and self-interest) leads future managers to rely excessively on rational and explicit safeguarding, crowding out instinctive contractual heuristics and signaling a 'bad' type to potential partners. In contrast, human assumptions used in management theories, because of their diverse, implicit and even contradictory nature, do not conflict with the innate set of cooperative tools and may provide a good training ground for such tools. We present tentative confirmatory evidence by examining how the weight given to behavioral assumptions in the core courses of the top 100 business schools influences the average salaries of their MBA graduates. Controlling for the average quality of their students and some other schools' characteristics, average salaries are significantly greater for those schools whose core MBA courses contain a higher proportion of management courses as opposed to courses based on economics or technical disciplines.

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Hierarchical clustering is a popular method for finding structure in multivariate data,resulting in a binary tree constructed on the particular objects of the study, usually samplingunits. The user faces the decision where to cut the binary tree in order to determine the numberof clusters to interpret and there are various ad hoc rules for arriving at a decision. A simplepermutation test is presented that diagnoses whether non-random levels of clustering are presentin the set of objects and, if so, indicates the specific level at which the tree can be cut. The test isvalidated against random matrices to verify the type I error probability and a power study isperformed on data sets with known clusteredness to study the type II error.

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The Proctor test is time-consuming and requires sampling of several kilograms of soil. Proctor test parameters were predicted in Mollisols, Entisols and Vertisols of the Pampean region of Argentina under different management systems. They were estimated from a minimum number of readily available soil properties (soil texture, total organic C) and management (training data set; n = 73). The results were used to generate a soil compaction susceptibility model, which was subsequently validated using a second group of independent data (test data set; n = 24). Soil maximum bulk density was estimated as follows: Maximum bulk density (Mg m-3) = 1.4756 - 0.00599 total organic C (g kg-1) + 0.0000275 sand (g kg-1) + 0.0539 management. Management was equal to 0 for uncropped and untilled soils and 1 for conventionally tilled soils. The established models predicted the Proctor test parameters reasonably well, based on readily available soil properties. Tillage systems induced changes in the maximum bulk density regardless of total organic matter content or soil texture. The lower maximum apparent bulk density values under no-tillage require a revision of the relative compaction thresholds for different no-tillage crops.

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PURPOSE: The current study tested the applicability of Jessor's problem behavior theory (PBT) in national probability samples from Georgia and Switzerland. Comparisons focused on (1) the applicability of the problem behavior syndrome (PBS) in both developmental contexts, and (2) on the applicability of employing a set of theory-driven risk and protective factors in the prediction of problem behaviors. METHODS: School-based questionnaire data were collected from n = 18,239 adolescents in Georgia (n = 9499) and Switzerland (n = 8740) following the same protocol. Participants rated five measures of problem behaviors (alcohol and drug use, problems because of alcohol and drug use, and deviance), three risk factors (future uncertainty, depression, and stress), and three protective factors (family, peer, and school attachment). Final study samples included n = 9043 Georgian youth (mean age = 15.57; 58.8% females) and n = 8348 Swiss youth (mean age = 17.95; 48.5% females). Data analyses were completed using structural equation modeling, path analyses, and post hoc z-tests for comparisons of regression coefficients. RESULTS: Findings indicated that the PBS replicated in both samples, and that theory-driven risk and protective factors accounted for 13% and 10% in Georgian and Swiss samples, respectively in the PBS, net the effects by demographic variables. Follow-up z-tests provided evidence of some differences in the magnitude, but not direction, in five of six individual paths by country. CONCLUSION: PBT and the PBS find empirical support in these Eurasian and Western European samples; thus, Jessor's theory holds value and promise in understanding the etiology of adolescent problem behaviors outside of the United States.