Agent-mediated knowledge sharing for intelligent services management

For service-oriented architectures that span multiple businesses, organizations must transfer information back-and-forth about their available services. Because of the potential large volume, it is unreasonable and impractical to expect human practitioners to handle the scale of interactions desired and/or required on a continual basis. Intelligent agents offer the adaptability and flexibility to handle the knowledge transfer that must occur in order to share service offerings. Effectively transferring service-oriented information in this domain requires autonomous systems that adapt to heterogeneous environments. This work introduces an architecture and specialized communication procedures designed for this sort of knowledge sharing environment. We show that these procedures perform reasonably when evaluated with current agent communication technologies.

Screening of serum samples from Wegener's granulomatosis patients using antibody microarrays

Wegener's Granulomatosis (WG) is an idiopathic granulomatosis autoimmune vasculitis that primarily affects small vessels and is associated with glomerulonephritis and pulmonary granulomatous vasculitis. Anti‐neutrophil cytoplasmic auto‐antibodies (cANCA) against proteinase‐3 are used to identify WG, but ANCA titers are not present in some patients with the localized disease. The objective of this study was to develop an antibody array to help identify protein expression patterns in serum from patients with WG as compared to normals. The arrays were tested for limits of detection, background, and cross reactivity using standard proteins. The arrays were hybridized with either normal patient serum (n = 30) or with serum samples from a population of WG patients (n = 26) that were age and sex matched. Data analysis and curve fitting of the standard dilution series calculated r² values and determined a sensitivity of <50 pg/mL for the majority of proteins. A total of 24 proteins were assessed. Several statistically significant increases (p<0.05) were seen in the expression of: angiotensin converting enzyme‐I, IFN‐γ, IL‐8, s‐ICAM‐1 and s‐VCAM in WG patients as compared to controls. Utilizing the antibody microarray technology has led to the identification of potential biomarkers of vascular injury in the serum of WG patients.     

Determination of the Fe content of embedded Cu-rich particles in ferritic alloys using energy-filtered TEM

A novel technique for the quantification of the iron content of copper precipitates in ferritic steels is presented. Energy-filtered (EF) imaging has been used to extract elemental maps with high spatial resolution. These maps contain enough information to attempt the quantification of the signal produced by the precipitates when either a line profile is measured across them or the whole image signal is integrated. Assumptions such as sphericity of the precipitates and composition variations are discussed. Special attention to the assessment of drift on the information extracted from EF images has been taken. Minimum detectability and optimum acquisition conditions are discussed.     

Intermittent microclimate cooling during exercise-heat stress in US army chemical protective clothing

The effectiveness of intermittent, microclimate cooling for men who worked in US Army chemical protective clothing (modified mission-oriented protective posture level 3; MOPP 3) was examined. The hypothesis was that intermittent cooling on a 2 min on-off schedule using a liquid cooling garment (LCG) covering 72% of the body surface area would reduce heat strain comparably to constant cooling. Four male subjects completed three experiments at 30 degrees C, 30% relative humidity wearing the LCG under the MOPP 3 during 80 min of treadmill walking at 224 +/- 5 W . m(-2). Water temperature to the LCG was held constant at 21 degrees C. The experiments were; 1) constant cooling (CC); 2) intermittent cooling at 2-min intervals (IC); 3) no cooling (NC). Core temperature increased (1.6 +/- 0.2 degrees C) in NC, which was greater than IC (0.5 +/- 0.2 degrees C) and CC (0.5 +/- 0.3 degrees C) ( p < 0.05). Mean skin temperature was higher during NC (36.1 +/- 0.4 degrees C) than IC (33.7 +/- 0.6 degrees C) and CC (32.6 +/- 0.6 degrees C) and mean skin temperature was higher during IC than CC ( p < 0.05). Mean heart rate during NC (139 +/- 9 b . min(-1)) was greater than IC (110 +/- 10 b . min(-1)) and CC (107 +/- 9 b . min(-1)) ( p < 0.05). Cooling by conduction (K) during NC (94 +/- 4 W . m(-2)) was lower than IC (142 +/- 7 W . m(-2)) and CC (146 +/- 4 W . m(-2)) ( p < 0.05). These findings suggest that IC provided a favourable skin to LCG gradient for heat dissipation by conduction and reduced heat strain comparable to CC during exercise-heat stress in chemical protective clothing.     

Atom probe tomography characterization of solute segregation to dislocations

The extent and level of solute segregation to individual dislocations may be quantified by atom probe tomography. The technique is best applied to materials with high dislocation densities, such as cold worked, mechanically alloyed, or neutron-irradiated materials. Dislocations may be observed in field ion images by a change of the normal concentric atom terraces at crystallographic poles to spirals. Solute segregation is evident in field ion images by brightly imaging atoms near the core of the dislocation. Dislocations are evident in atom maps in the three-dimensional atom probe by linear regions of enhanced solute concentration. The maximum separation envelope and tracer methods may be used to quantify the levels of solute at the dislocation at the subnanometer scale. Examples of interstitial and substitutional element segregation in a mechanically alloyed, oxide dispersion strengthened ferrite steel and phosphorus segregation to dislocations in neutron-irradiated pressure vessel steels are presented.     

Using SAS to conduct nonparametric residual bootstrap multilevel modeling with a small number of groups

In multilevel modeling, researchers often encounter data with a relatively small number of units at the higher levels. As a result, of this and/or non-normality of the residuals, model parameter estimates, particularly the variance components and standard errors of parameter estimates at the group level, may be biased, thus the corresponding statistical inferences may not be trustworthy. This problem can be addressed by using bootstrap methods to estimate the standard errors of the parameter estimates for significance testing. This study illustrates how to use statistical analysis system (SAS) to conduct nonparametric residual bootstrap multilevel modeling. Specific SAS programs for such modeling are provided.     

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.     

Profiles of Random Trees: Limit Theorems for Random Recursive Trees and Binary Search Trees

We prove convergence in distribution for the profile (the number of nodes at each level), normalized by its mean, of random recursive trees when the limit ratio α of the level and the logarithm of tree size lies in [0,e). Convergence of all moments is shown to hold only for α ∈ [0,1] (with only convergence of finite moments when α ∈ (1,e)). When the limit ratio is 0 or 1 for which the limit laws are both constant, we prove asymptotic normality for α = 0 and a "quicksort type" limit law for α = 1, the latter case having additionally a small range where there is no fixed limit law. Our tools are based on the contraction method and method of moments. Similar phenomena also hold for other classes of trees; we apply our tools to binary search trees and give a complete characterization of the profile. The profiles of these random trees represent concrete examples for which the range of convergence in distribution differs from that of convergence of all moments.     

Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia

The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and executive functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia, and amygdala, which together form an actor-critic architecture. The critic system learns which prefrontal representations are task relevant and trains the actor, which in turn provides a dynamic gating mechanism for controlling working memory updating. Computationally, the learning mechanism is designed to simultaneously solve the temporal and structural credit assignment problems. The model's performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task and other benchmark working memory tasks.