Panic onset and major events in the lives of agoraphobics: A test of contiguity.

Major life events were reported in greater numbers and by a higher percentage of 50 index agoraphobics during a time period around panic onset than during either a within-subjects or a between-subjects control period. These differences were found for analyses of life events in general, events that preceded panic, and foreseeable events that occurred shortly after panic onset. Though many events involved separation or interpersonal conflict, other types of events were frequently reported. Results provide more convincing evidence than prior studies of a contiguous relationship between life events and onset of panic attacks associated with agoraphobia. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The relationship of local and distant failure from endometrial cancer: defining a clinical paradigm

Water-soluble amyloid beta-peptides (sA beta), ending at residue 42, precede amyloid plaques in Down's syndrome (DS). Here we report that sA beta consists of the full-length A beta(1-42) and peptides truncated and modified by cyclization of the N-terminal glutamates, A beta[3(pE)-42] and A beta[11(pE)-42]. The A beta[3(pE)-42] peptide is the most abundant form of sA beta in Alzheimer's disease (AD) brains. In DS, sA beta[3(pE)-42] concentration increases with age and the peptide becomes a dominant species in the presence of plaques. Both pyroglutamate-modified peptides and the full-length A beta form a stable aggregate that is water soluble. The findings point to a crucial role of the aggregated and modified sA beta in the plaque formation and pathogenesis of AD.     

Wide-field, surface-sensitive four-wave mixing microscopy of nanostructures

We describe a wide-field four-wave mixing (FWM) microscope with imaging characteristics optimized for examining nanostructures. The microscope employs surface-plasmon polariton (SPP) excitation in a gold film to achieve surface-sensitive imaging conditions. The SPP surface fields boost the FWM efficiency by 2 orders of magnitude relative to the excitation efficiency of the evanescent fields at a bare glass surface. We demonstrate two excitation geometries that completely suppress the electronic FWM response of the metal film while allowing the far-field detection of FWM radiation from nanostructures at the interface. We obtained wide-field FWM images from individual carbon nanotubes and nanoclusters of neocyanine molecules at image acquisition times of 1 s, demonstrating the potential for background free, surface-enhanced FWM imaging of nanomaterials.     

GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

ContextIn software industry, project managers usually rely on their previous experience to estimate the number men/hours required for each software project. The accuracy of such estimates is a key factor for the efficient application of human resources. Machine learning techniques such as radial basis function (RBF) neural networks, multi-layer perceptron (MLP) neural networks, support vector regression (SVR), bagging predictors and regression-based trees have recently been applied for estimating software development effort. Some works have demonstrated that the level of accuracy in software effort estimates strongly depends on the values of the parameters of these methods. In addition, it has been shown that the selection of the input features may also have an important influence on estimation accuracy.ObjectiveThis paper proposes and investigates the use of a genetic algorithm method for simultaneously (1) select an optimal input feature subset and (2) optimize the parameters of machine learning methods, aiming at a higher accuracy level for the software effort estimates.MethodSimulations are carried out using six benchmark data sets of software projects, namely, Desharnais, NASA, COCOMO, Albrecht, Kemerer and Koten and Gray. The results are compared to those obtained by methods proposed in the literature using neural networks, support vector machines, multiple additive regression trees, bagging, and Bayesian statistical models.ResultsIn all data sets, the simulations have shown that the proposed GA-based method was able to improve the performance of the machine learning methods. The simulations have also demonstrated that the proposed method outperforms some recent methods reported in the recent literature for software effort estimation. Furthermore, the use of GA for feature selection considerably reduced the number of input features for five of the data sets used in our analysis.ConclusionsThe combination of input features selection and parameters optimization of machine learning methods improves the accuracy of software development effort. In addition, this reduces model complexity, which may help understanding the relevance of each input feature. Therefore, some input parameters can be ignored without loss of accuracy in the estimations.     

PCJ River Basins’ Water Availability Caused by Water Diversion Scenarios to Supply Metropolitan Areas of São Paulo

A dynamic systems simulation model of water resources was developed as a tool to help analyze alternatives to water resources management for the Piracicaba, Capivari and Jundiaí River Water Basins (RB-PCJ), and used to run six 50-year simulations from 2004 to 2054. The model estimates water supply and demand, as well as contamination load by several consumers. Six runs were performed using a constant mean precipitation value, changing water supply and demand and different volumes diverted from RB-PCJ to RB-Alto Tietê. For the Business as Usual scenario, the Sustainability Index went from 0.44 in 2004 to 0.20 by 2054. The Water Sustainability Index changed from 74% in 2004 to 131% by 2054. The Falkenmark Index changed from 1,403 m³ person^− 1 year^− 1 in 2004 to 734 m³ person^− 1 year^− 1 by 2054. We concluded that sanitation is one of the major problems for the PCJ River Basins.     

A production planning system to continuously integrate the characteristics of reconfigurable manufacturing systems

Operation in a dynamic environment requires companies to constantly adapt their manufacturing systems to stay competitive. One approach to manufacturing is the use of reconfigurable manufacturing systems (RMS). Current production planning approaches cannot model and realize the production-side adaptions available in RMS. In this article a novel planning system for modeling that continuously integrates the key characteristics of RMS in production planning and control (PPC) is presented. First, the challenges for production planning using RMS are defined. A feasible planning system is then presented, based on modeling and specification approaches, followed by a planning method to realize capacity scalability and functionality changes in planning processes. Finally, a prototypical application is outlined and the planning system is evaluated with the help of different production scenarios. The application results demonstrate the feasibility of the planning system as well as the potential improvement of typical key performance indicators in manufacturing.     

Friedel-Crafts acylations with modified clays as catalysts

Friedel-Crafts acylations are very effectively catalyzed by clays modified through exchange of the interlamellar cations or through impregnation of metal chlorides. The acidic K10 montmorillonite exchanged with iron(III) gives outstanding results in quantitative (98–100%) acylation of anisole (15 min), mesitylene (15 min), and p-xylene (3 h) at 140–160 °C. Clays impregnated with zinc chloride, such as the K10 montmorillonite, or Japanese acidic clay show near-equivalent catalytic performance.     

Bilinear Models for Spatio-Temporal Point Distribution Analysis

In this work we describe the usage of bilinear statistical models as a means of factoring the shape variability into two components attributed to inter-subject variation and to the intrinsic dynamics of the human heart. We show that it is feasible to reconstruct the shape of the heart at discrete points in the cardiac cycle. Provided we are given a small number of shape instances representing the same heart at different points in the same cycle, we can use the bilinear model to establish this. Using a temporal and a spatial alignment step in the preprocessing of the shapes, around half of the reconstruction errors were on the order of the axial image resolution of 2 mm, and over 90% was within 3.5 mm. From this, we conclude that the dynamics were indeed separated from the inter-subject variability in our dataset.