Surface roughness noise prediction for silent aircraft experimental design SAX-40

Surface roughness noise is a potentially important contributor to airframe noise. In this paper, noise assessment due to surface roughness is performed for a conceptual Silent Aircraft design SAX-40 by means of a prediction model developed in previous theoretical work and validated experimentally. Estimates of three idealized test cases show that surface roughness could produce a significant noise level above that due to the trailing edge at high frequencies. Roughness height and roughness density are the two most significant parameters influencing surface roughness noise, with roughness height having the dominant effect. The ratio of roughness height to boundary-layer thickness is the relevant non-dimensional parameter and this decreases in the streamwise direction. The candidate surface roughness is selected for SAX-40 to meet an aggressive noise target and keep surface roughness noise at a negligible level. Copyright © 2008 by Yu Liu and Ann P. Dowling.

Frame:for soprano sax, bass clarinet and tape

written for and first performed by Julian Siegel (sax) and Simon Atkinson (bass clarinet)

Is SAX J1808.4-3658 a Strange Star?

The possibility of strange stars is one of the most important issues in the study of compact objects. Here we use the observations of the newly discovered millisecond x-ray pulsar SAX J1808.4-3658 to constrain the radius of the compact star. Comparing the mass-radius relation of SAX J1808.4-3658 with theoretical models for both neutron stars and strange stars, we argue that a strange star model could be more consistent with SAX J1808.4-3658, and suggest that it is a likely strange star candidate. Our results are useful in constraining microscopic chiral symmetry restoration parameters in the quantum chromodynamics (QCD) modeling of strange matter.

Uta Sax als Madanika Kasantasena, Gisela Stein als Bagadere Kasantasena, Rollenbild, Doppelbildnis, in: Ferdinand Bruckner: Das irdene Wägelchen

Signatur des Originals: S 36/F11107

Estación de Sax [Material gráfico]

Texto impreso al verso

Quaestiones scaenicae. Halis Sax. 1919.

Halle, Phil. Diss. v. 3. Febr. 1920, Ref. Robert.

For old acquaintance; prints from the etchings of E. T.Hurley, with comment by Sara Sax.

Mode of access: Internet.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Verbal instruction abolishes fear conditioned to racial out-group faces

Previous research has shown resistance to extinction of fear conditioned to racial out-group faces, suggesting that these stimuli may be subject to prepared fear learning. The current study replicated and extended previous research by using a different racial out-group, and testing the prediction that prepared fear learning is unaffected by verbal instructions. Four groups of Caucasian participants were trained with male in-group (Caucasian) or out-group (Chinese) faces as conditional stimuli; one paired with an electro-tactile shock (CS+) and one presented alone (CS). Before extinction, half the participants were instructed that no more shocks would be presented. Fear conditioning, indexed by larger electrodermal responses to, and blink startle modulation during the CS+, occurred during acquisition in all groups. Resistance to extinction of fear learning was found only in the racial out-group, no instruction condition. Fear conditioned to a racial out-group face was reduced following verbal instructions, contrary to predictions for the nature of prepared fear learning.

A estandardização dos contratos internacionais e o modelo FIDIC - Silver Book para contratos EPC

No presente trabalho acadêmico se apresentarão as principais características do processo de globalização, passando-se à exposição da relevância do Direito Comparado, até se chegar à análise da adoção de modelos internacionais de contratos como esforço uniformizador, de modo a reduzir riscos e a minorar custos de transação. Na sequência, investigar-se-á o modelo contratual EPC (acrônimo das palavras inglesas Engineering, Procurement and Construction - Engenharia, Gestão de Compra e Construção), originário da prática anglo-saxã, focando-se na análise e na qualificação tipológica do Contrato EPC, comparando-o com institutos existentes na legislação brasileira. Delinear-se-á o contexto de disseminação do Contrato EPC no exterior e sua consolidação no Brasil, amparadas, em larga medida, na necessidade de que - em vultosos projetos de infraestrutura, sobretudo em áreas de investimento em relação às quais os empreendedores desconheçam o ambiente regulatório e a realidade socioeconômica se tenha estatuto privado a transladar ao construtor a maior parte dos riscos atinentes a serviços complexos de engenharia. Enfrentar-se-ão as características essenciais deste modelo contratual, tomando-se como padrão o Conditions of Contract for EPC Turnkey Projects - general conditions, guidance for the preparation of particular conditions, forms of letter of tender, contract agreement and dispute adjudication agreement, recomendado pela Fédération Internationale des Ingénieurs Conseils - FIDIC. Apresentar-se-á como o direito estrangeiro equilibra a assunção dos riscos pelo construtor (Contractor ou Builder), inclusive aqueles, referentes a eventos não antecipáveis (unforeseen risks), inobstante a preservação pelo contratante (Owner) de lato poder de monitoramento e fiscalização (overseeing attributions, key personnel and contract manager approval, step-in rights).