Prediction of mortality in patients without angina: Use of an exercise score and exercise echocardiography

Background Exercise testing has limited efficacy for identifying coronary artery disease (CAD) in the absence of anginal. symptoms. Exercise echocardiography is more accurate than standard exercise testing, but its efficacy in this situation has not been defined. We sought to identify whether the Duke treadmill. score or exercise echocardiography (ExE) could be used to identify risk in patients without anginal symptoms. Methods We studied 1859 patients without typical or atypical angina, heart failure, or a history or ECG evidence of infarction or CAD, who were referred for ExE, of whom 1832 (age 51 15 years, 944 men) were followed for up to 10 years. The presence and extent of ischaemia and scar were interpreted by expert reviewers at the time of the original study. Results Exercise provoked significant (>0.1 mV) ST segment depression in 215 patients (12%), and wall motion abnormalities in 137 (8%). Seventy-eight patients (4%) died before revascularization, only 17 from known cardiac causes. The independent predictors of death were age (RR 1.1, p

Prediction of multilayer adsorption and capillary condensation phenomena in cylindrical mesopores

MCM-41 periodic mesoporous silicates with a high degree of structural ordering are synthesized and used as model adsorbents to study the isotherm prediction of nitrogen adsorption. The nitrogen adsorption isotherm at 77 K for a macroporous silica is measured and used in high-resolution alpha(s)-plot comparative analysis to determine the external surface area, total surface area and primary mesopore volume of the MCM-41 materials. Adsorption equilibrium data of nitrogen on the different pore size MCM-41 samples (pore diameters from 2.40 to 4.92 nm) are also obtained. Based on the Broekhoff and de Boer' thermodynamic analysis, the nitrogen adsorption isotherms for the different pore size MCM-41 samples are interpreted using a novel strategy, in which the parameters of an empirical expression, used to represent the potential of interaction between the adsorbate and adsorbent, are obtained by fitting only the multilayer region prior to capillary condensation for C-16 MCM-41. Subsequently the entire isotherm, including the phase transition, is predicted for all the different pore size MCM-41 samples without any fitting. The results show that the prediction of multilayer adsorption and total adsorbed amount are in good agreement with the experimental isotherms. The predictions of the relative pressure corresponding to capillary equilibrium (coexistence) transition agree remarkably with experimental data on the adsorption branch even for hysteretic isotherms, confirming that this is the branch appropriate for pore size distribution analysis. The impact of pore radius on the adsorption film thickness and capillary coexistence pressure is also investigated, and found to agree with the experimental data. (C) 2003 Elsevier Inc. All rights reserved.

Fatigue Prediction on Fibrin Poly-ε-caprolactone Macroporous Scaffolds

Tissue engineering applications rely on scaffolds that during its service life, either for in-vivo or in vitro applications, are under mechanical solicitations. The variation of the mechanical condition of the scaffold is strongly relevant for cell culture and has been scarcely addressed. Fatigue life cycle of poly-ε-caprolactone, PCL, scaffolds with and without fibrin as filler of the pore structure were characterized both dry and immersed in liquid water. It is observed that the there is a strong increase from 100 to 500 in the number of loading cycles before collapse in the samples tested in immersed conditions due to the more uniform stress distributions within the samples, the fibrin loading playing a minor role in the mechanical performance of the scaffolds

Prediction of genetic gain from selection indices for disease resistance in papaya hybrids

In order to select superior hybrids for the concentration of favorable alleles for resistance to papaya black spot, powdery mildew and phoma spot, 67 hybrids were evaluated in two seasons, in 2007, in a randomized block design with two replications. Genetic gains were estimated from the selection indices of Smith & Hazel, Pesek & Baker, Williams, Mulamba & Mock, with selection intensity of 22.39%, corresponding to 15 hybrids. The index of Mulamba & Mock showed gains more suitable for the five traits assessed when it was used the criterion of economic weight tentatively assigned. Together, severity of black spot on leaves and on fruits, characteristics considered most relevant to the selection of resistant materials, expressed percentage gain of -44.15%. In addition, there were gains for other characteristics, with negative predicted selective percentage gain. The results showed that the index of Mulamba & Mock is the most efficient procedure for simultaneous selection of papaya hybrid resistant to black spot, powdery mildew and phoma spot.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Assessment and prediction of asthma and its severity in the pediatric community

Seventy four asthmatic children aged 7 to 11 years were examined along with controls matched by age and sex. Clinical and laboratory investigations preceded a 28-day follow-up where data about morning and evening peak expiratory flow rate (PEF), symptoms and treatment were recorded. The coefficient of variation of PEF was found to be an objective measurement of asthma severity that has statistically significant correlation with both symptoms (r s= .36) and treatment (r s= .60). Moreover, it separates mild and severe asthmatics, as confirmed by statistically significant differences (p= .008 or less) in symptoms, treatment, skin allergy and airways response to exercise. Skin allergy and airways responsiveness to exercise were found to be predictors of both disease and severity. By means of logistic regression analysis it was possible to establish the probabilities for both asthma and severe asthma when children presenting and not presenting these characteristics are compared. One single positive skin test represent a probability of 88% for the development of asthma and a probability of 70% for severe disease. A PEF reduction of 10% after an exercise test implies a probability of 73% for disease and a probability of 64% for severe disease. Increases in these variables imply geometrically increased risks and their presence together have a multiplicative effect in the final risk.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Complex question answering on semi-structured repositories: a user centric process enhanced with context

A Teia Mundial (Web) foi prevista como uma rede de documentos de hipertexto interligados de forma a criar uma espaço de informação onde humanos e máquinas poderiam comunicar. No entanto, a informação contida na Web tradicional foi/é armazenada de forma não estruturada o que leva a que apenas os humanos a possam consumir convenientemente. Consequentemente, a procura de informações na Web sintáctica é uma tarefa principalmente executada pelos humanos e nesse sentido nem sempre é fácil de concretizar. Neste contexto, tornou-se essencial a evolução para uma Web mais estruturada e mais significativa onde é dado significado bem definido à informação de forma a permitir a cooperação entre humanos e máquinas. Esta Web é usualmente referida como Web Semântica. Além disso, a Web Semântica é totalmente alcançável apenas se os dados de diferentes fontes forem ligados criando assim um repositório de Dados Abertos Ligados (LOD). Com o aparecimento de uma nova Web de Dados (Abertos) Ligados (i.e. a Web Semântica), novas oportunidades e desafios surgiram. Pergunta Resposta (QA) sobre informação semântica é actualmente uma área de investigação activa que tenta tirar vantagens do uso das tecnologias ligadas à Web Semântica para melhorar a tarefa de responder a questões. O principal objectivo do projecto World Search passa por explorar a Web Semântica para criar mecanismos que suportem os utilizadores de domínios de aplicação específicos a responder a questões complexas com base em dados oriundos de diferentes repositórios. No entanto, a avaliação feita ao estado da arte permite concluir que as aplicações existentes não suportam os utilizadores na resposta a questões complexas. Nesse sentido, o trabalho desenvolvido neste documento foca-se em estudar/desenvolver metodologias/processos que permitam ajudar os utilizadores a encontrar respostas exactas/corretas para questões complexas que não podem ser respondidas fazendo uso dos sistemas tradicionais. Tal inclui: (i) Ultrapassar a dificuldade dos utilizadores visionarem o esquema subjacente aos repositórios de conhecimento; (ii) Fazer a ponte entre a linguagem natural expressa pelos utilizadores e a linguagem (formal) entendível pelos repositórios; (iii) Processar e retornar informações relevantes que respondem apropriadamente às questões dos utilizadores. Para esse efeito, são identificadas um conjunto de funcionalidades que são consideradas necessárias para suportar o utilizador na resposta a questões complexas. É também fornecida uma descrição formal dessas funcionalidades. A proposta é materializada num protótipo que implementa as funcionalidades previamente descritas. As experiências realizadas com o protótipo desenvolvido demonstram que os utilizadores efectivamente beneficiam das funcionalidades apresentadas: ▪ Pois estas permitem que os utilizadores naveguem eficientemente sobre os repositórios de informação; ▪ O fosso entre as conceptualizações dos diferentes intervenientes é minimizado; ▪ Os utilizadores conseguem responder a questões complexas que não conseguiam responder com os sistemas tradicionais. Em suma, este documento apresenta uma proposta que comprovadamente permite, de forma orientada pelo utilizador, responder a questões complexas em repositórios semiestruturados.

Geostatistical Prediction of Ocean Outfall Plume Characteristics Based on an Autonomous Underwater Vehicle

Geostatistics has been successfully used to analyze and characterize the spatial variability of environmental properties. Besides giving estimated values at unsampled locations, it provides a measure of the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. In this work universal block kriging is novelty used to model and map the spatial distribution of salinity measurements gathered by an Autonomous Underwater Vehicle in a sea outfall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters, characterizing its spatial variability in the vicinity of the discharge and estimating dilution. The results demonstrate that geostatistical methodology can provide good estimates of the dispersion of effluents that are very valuable in assessing the environmental impact and managing sea outfalls. Moreover, since accurate measurements of the plume’s dilution are rare, these studies might be very helpful in the future to validate dispersion models.

Influence of the cohesive law parameters on the strength prediction of adhesively-bonded joints

Adhesive joints are largely employed nowadays as a fast and effective joining process. The respective techniques for strength prediction have also improved over the years. Cohesive Zone Models (CZM’s) coupled to Finite Element Method (FEM) analyses surpass the limitations of stress and fracture criteria and allow modelling damage. CZM’s require the energy release rates in tension (Gn) and shear (Gs) and respective fracture energies in tension (Gnc) and shear (Gsc). Additionally, the cohesive strengths (tn0 for tension and ts0 for shear) must also be defined. In this work, the influence of the CZM parameters of a triangular CZM used to model a thin adhesive layer is studied, to estimate their effect on the predictions. Some conclusions were drawn for the accuracy of the simulation results by variations of each one of these parameters.

Numerical prediction of delamination onset in carbon/epoxy composites drilling

Despite the fact that their physical properties make them an attractive family of materials, composites machining can cause several damage modes such as delamination, fibre pull-out, thermal degradation, and others. Minimization of axial thrust force during drilling reduces the probability of delamination onset, as it has been demonstrated by analytical models based on linear elastic fracture mechanics (LEFM). A finite element model considering solid elements of the ABAQUS® software library and interface elements including a cohesive damage model was developed in order to simulate thrust forces and delamination onset during drilling. Thrust force results for delamination onset are compared with existing analytical models.

Strength prediction of adhesively-bonded scarf repairs in composite structures under bending

This work reports on the experimental and numerical study of the bending behaviour of two-dimensional adhesively-bonded scarf repairs of carbon-epoxy laminates, bonded with the ductile adhesive Araldite 2015®. Scarf angles varying from 2 to 45º were tested. The experimental work performed was used to validate a numerical Finite Element analysis using ABAQUS® and a methodology developed by the authors to predict the strength of bonded assemblies. This methodology consists on replacing the adhesive layer by cohesive elements, including mixed-mode criteria to deal with the mixed-mode behaviour usually observed in structures. Trapezoidal laws in pure modes I and II were used to account for the ductility of the adhesive used. The cohesive laws in pure modes I and II were determined with Double Cantilever Beam and End-Notched Flexure tests, respectively, using an inverse method. Since in the experiments interlaminar and transverse intralaminar failures of the carbon-epoxy components also occurred in some regions, cohesive laws to simulate these failure modes were also obtained experimentally with a similar procedure. A good correlation with the experiments was found on the elastic stiffness, maximum load and failure mode of the repairs, showing that this methodology simulates accurately the mechanical behaviour of bonded assemblies.

Strength prediction and experimental validation of adhesive joints including polyethylene, carbon-epoxy and aluminium adherends

Polyolefins are especially difficult to bond due to their non-polar, non-porous and chemically inert surfaces. Acrylic adhesives used in industry are particularly suited to bond these materials, including many grades of polypropylene (PP) and polyethylene (PE), without special surface preparation. In this work, the tensile strength of single-lap PE and mixed joints bonded with an acrylic adhesive was investigated. The mixed joints included PE with aluminium (AL) or carbon fibre reinforced plastic (CFRP) substrates. The PE substrates were only cleaned with isopropanol, which assured cohesive failures. For the PE CFRP joints, three different surfaces preparations were employed for the CFRP substrates: cleaning with acetone, abrasion with 100 grit sand paper and peel-ply finishing. In the PE AL joints, the AL bonding surfaces were prepared by the following methods: cleaning with acetone, abrasion with 180 and 320 grit sand papers, grit blasting and chemical etching with chromic acid. After abrasion of the CFRP and AL substrates, the surfaces were always cleaned with acetone. The tensile strengths were compared with numerical results from ABAQUS® and a mixed mode (I+II) cohesive damage model. A good agreement was found between the experimental and numerical results, except for the PE AL joints, since the AL surface treatments were not found to be effective.