A Methodology Based on Pulse-Velocity Measurements to Quantify the Chemical Degradation Levels in Thin Mortar Specimens

In this research, ultrasonic pulse echo measurements are used to quantify through thickness chemical degradation in thin mortar specimens. The degradation level is predicted using the time of travel of the acoustic wave through the thickness of the structure. The front and back wall interaction reflections are used to obtain additional information from very early stage degradation. The pulse-velocity of sound waves as a function of the thickness of the layers within the structure is described. With knowledge of the pulse-velocity in pristine and fully degraded conditions, it is possible to determine the complete range of degradation length over the layer thickness. The method is applicable for leaching of calcium and acidic attack. The acoustic measurements were verified with destructive testing. The correlation between the acoustic and non-acoustic experiments agree with the described pulse-velocity and degraded depth function. The method based on ultrasonic measurements can be implemented in other thin-layered structures.     

A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis

This paper proposes a view-invariant gait recognition algorithm, which builds a unique view invariant model taking advantage of the dimensionality reduction provided by the Direct Linear Discriminant Analysis (DLDA). Proposed scheme is able to reduce the under-sampling problem (USP) that appears usually when the number of training samples is much smaller than the dimension of the feature space. Proposed approach uses the Gait Energy Images (GEIs) and DLDA to create a view invariant model that is able to determine with high accuracy the identity of the person under analysis independently of incoming angles. Evaluation results show that the proposed scheme provides a recognition performance quite independent of the view angles and higher accuracy compared with other previously proposed gait recognition methods, in terms of computational complexity and recognition accuracy.     

A sound and complete algorithm for distributed commerce transactions

Summary. In a multi-party transaction (also called a distributed commerce transaction) agents face risks from dealing with untrusted agents. These risks are compounded in the face of deadlines, e.g., an agent may fail to deliver purchased goods by the time the goods are needed. We characterize the risks, and present a distributed algorithm that mitigates these risks, by using pairwise exchanges and trusted intermediaries. The algorithm generates a safe sequence of actions that completes a commerce transaction without risk, if such a sequence exists. We show that the algorithm is sound (produces only safe multi-agent action sequences) and complete (finds a safe sequence whenever one exists). The initial restriction of guaranteeing safety even when none of the principals trusts another can be relaxed in some cases, so we show how to handle principals that do trust each other and interact directly rather than through a trusted intermediary. Received: September 1997 / Accepted: December 1998     

Determination of the eutectic solubility lines of the ternary system NaHCO3–Na2CO3–H2O

A crystallizer was built and a procedure developed to accurately measure the eutectic solubility lines where ice and salt coexist in equilibrium with the solution, for potential application of Eutectic Freeze Crystallization. The eutectic solubility lines of the ternary system NaHCO₃–Na₂CO₃–H₂O were determined experimentally and calculated with the extended UNIQUAC model. The extended UNIQUAC model describes the experimental data quite well. Anhydrous NaHCO₃ and Na₂CO₃·10H₂O were the only two types of crystals present in equilibrium with ice crystals in the ternary system. At the quadruple point NaHCO₃ and Na₂CO₃·10H₂O are in equilibrium with a solution of about 4.34 wt% of Na₂CO₃ and 4.77 wt% of NaHCO₃ at −3.32 °C. The anhydrous NaHCO₃ crystals were needle shaped with lengths between 5 and 10 μm, that were agglomerated into particles of about 100–300 μm, while the Na₂CO₃·10H₂O crystals were hexagonally shaped with sizes between 100 and 500 μm.     

Helping counselor trainees learn to respond consistently to anger and depression.

50 graduate students were randomly divided into 5 groups that were exposed to different kinds of videotaped counselor training procedures. The group of main interest was exposed to a training procedure that enabled the Ss 1st to verbally practice responding to client negative affect and then to observe a model counselor's response. When these Ss counseled either angry or depressed role-playing clients, they responded more consistently to client feelings than did Ss exposed to other training procedures. (12 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

APPLIED COMPUTATIONAL INTELLIGENCE FOR FINANCE AND ECONOMICS

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.     

Goal Change in the Situation Calculus

Although there has been much discussion of belief change (e.g.[4, 21]), goal change has not received much attention. In thispaper, we propose a method for goal change in the frameworkof Reiter's; [12] theory of action in the situation calculus[8, 10], and investigate its properties. We extend the frameworkdeveloped by Shapiro et al. [17] and Shapiro and Lespérance[16], where goals and goal expansion were modelled, but goalcontraction was not.     

Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on health

Generalized additive models (GAMs) have become the standard tool for the analysis of short-term effects of air pollution on human health. Usually, the confounding effect of seasonality and long-term trend is described by flexible parametric or non-parametric functions of calendar time. Two different modeling strategies, i.e. GAM with penalized regression splines and GAM with regression splines, were compared by means of a simulation study, addressing attention to the inference on air pollutant effect. Simulation results indicated that GAM with regression splines provides negligibly biased estimates of air pollutant effect and it is robust to misspecification of the degrees of freedom of the spline. GAM with penalized regression splines requires a certain amount of undersmoothing in order to reduce the bias of the estimates and to improve the coverage of confidence intervals. These findings agree with asymptotic results developed in the context of partially splined models.     

Early bankruptcy prediction using ENPC

Bankruptcy prediction has long time been an active research field in finance. One of the main approaches to this issue is dealing with it as a classification problem. Among the range of instruments available, we focus our attention on the Evolutionary Nearest Neighbor Classifier (ENPC). In this work we assess the performance of the ENPC comparing it to six alternatives. The results suggest that this algorithm might be considered a good choice.