Artificial neural networks for the automated detection of trichloroethylene by passive Fourier transform infrared spectrometry

Artificial neural networks are applied to the automated classification of trichloroethylene (TCE) signatures from passive Fourier transform infrared remote sensing interferogram data. Through the use of three data collection methods, a combination of laboratory and field data is acquired that allows the methodology to be evaluated under a variety of infrared background conditions and in the presence of potentially interfering compounds such as sulfur hexafluoride, methyl ethyl ketone, acetone, carbon tetrachloride, and ammonia. To maximize the computational efficiency of the network optimization, experimental design techniques are employed to develop a training protocol for the network that takes into account the relationships among five variables that are related either to the network architecture or to the training process. This protocol is implemented for the case of a back-propagation neural network (BNN) and is used to develop an optimized network for the detection of TCE. The classification performance of the network is assessed by comparing both TCE detection capabilities and false detection rates to similar classification results obtained with the technique of piecewise linear discriminant analysis (PLDA). When applied to prediction data withheld from the optimization of both the BNN and PLDA algorithms, the BNN method is observed to outperform PLDA overall, with TCE detection rates in excess of 99% and false detection rates less than 0.5%.     

Relationship between peptide selectivities of human transporters associated with antigen processing and HLA class I molecules

Efficiency of presentation of a peptide epitope by a MHC class I molecule depends on two parameters: its binding to the MHC molecule and its generation by intracellular Ag processing. In contrast to the former parameter, the mechanisms underlying peptide selection in Ag processing are poorly understood. Peptide translocation by the TAP transporter is required for presentation of most epitopes and may modulate peptide supply to MHC class I molecules. To study the role of human TAP for peptide presentation by individual HLA class I molecules, we generated artificial neural networks capable of predicting the affinity of TAP for random sequence 9-mer peptides. Using neural network-based predictions of TAP affinity, we found that peptides eluted from three different HLA class I molecules had higher TAP affinities than control peptides with equal binding affinities for the same HLA class I molecules, suggesting that human TAP may contribute to epitope selection. In simulated TAP binding experiments with 408 HLA class I binding peptides, HLA class I molecules differed significantly with respect to TAP affinities of their ligands. As a result, some class I molecules, especially HLA-B27, may be particularly efficient in presentation of cytosolic peptides with low concentrations, while most class I molecules may predominantly present abundant cytosolic peptides.     

A Review of the U.S. Department of Energy's Inertial Fusion Energy Program

This is the final report of a panel set up by the U.S. Department of Energy (DOE) Fusion Energy Sciences Advisory Committee (FESAC) in response to a charge letter from Dr. Ray Orbach (Appendix A). In that letter, Dr. Orbach asked FESAC for an assessment of the present status of inertial fusion energy (IFE) research carried out in contributing programs. These programs include the heavy ion (HI) beam, the high average power laser (HAPL), and Z-Pinch drivers and associated technologies, including fast ignition (FI). This report, presented to FESAC on March 29, 2004, and subsequently approved by them (Appendix B), presents FESAC's response to that charge.     

Mechanical properties of young concrete: Part II: Determination of model parameters and test program proposals

The paper concerns testing and modelling of the mechanical properties required as input to calculation programs made for crack risk estimation of hardening concrete structures. The results from several test series on mechanical properties of young concrete as described in Part I of this paper, are further evaluated. Model parameters for the modified CEB 1990 Model Code-equations, are determined for six concrete mixes, all having a w/b-ratio on 0.40, for compressive strength, tensile strength and E-modulus. To make the models applicable for young concrete, at ₀-parameter is introduced to fix the time at which significant mechanical properties are present. A test program to determine the model parameters is proposed, based on the experience that compressive strength tests have the smallest statistical scatter and that they are simplest to carry out.     

High gain feedback,tolerance, and stabilization of linear systems

The problem of stabilizing linear continuous-time systems is revisited with the objective of investigating the tolerance allowed in the implementation of stabilizing feedback controllers. It is shown that, for high gain feedback, this tolerance can be described by a cone in the feedback parameter space, called the “tolerance cone”. The tolerance cone describes fractional (percentage) errors the feedback controller can tolerate without jeopardizing the internal stability of the controlled system. The larger the vertex angle of this cone, the more tolerance is available when implementing the controller. The vertex angle of the tolerance cone is determined by the proximity to singularity of a certain matrix derived from the controlled system.     

Generalization ability of folding networks

The information theoretical learnability of folding networks, a very successful approach capable of dealing with tree structured inputs, is examined. We find bounds on the VC, pseudo-, and fat shattering dimension of folding networks with various activation functions. As a consequence, valid generalization of folding networks can be guaranteed. However, distribution independent bounds on the generalization error cannot exist in principle. We propose two approaches which take the specific distribution into account and allow us to derive explicit bounds on the deviation of the empirical error from the real error of a learning algorithm. The first approach requires the probability of large trees to be limited a priori and the second approach deals with situations where the maximum input height in a concrete learning example is restricted     

Sabbatical leave: Who gains and how much?

A rigorous quasi-experiment tested the ameliorative effects of a sabbatical leave, a special case of respite from routine work. We hypothesized that (a) respite increases resource level and well-being and (b) individual differences and respite features moderate respite effects. A sample of 129 faculty members on sabbatical and 129 matched controls completed measures of resource gain, resource loss, and well-being before, during, and after the sabbatical. Among the sabbatees, resource loss declined and resource gain and well-being rose during the sabbatical. The comparison group showed no change. Moderation analysis revealed that those who reported higher respite self-efficacy and greater control, were more detached, had a more positive sabbatical experience, and spent their sabbatical outside their home country enjoyed more enhanced well-being than others. (PsycINFO Database Record (c) 2010 APA, all rights reserved)