Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

Random perturbations of stochastic processes with unbounded variable length memory

We consider binary infinite order stochastic chains perturbed by a random noise. This means that at each time step, the value assumed by the chain can be randomly and independently flipped with a small fixed probability. We show that the transition probabilities of the perturbed chain are uniformly close to the corresponding transition probabilities of the original chain. As a consequence, in the case of stochastic chains with unbounded but otherwise finite variable length memory, we show that it is possible to recover the context tree of the original chain, using a suitable version of the algorithm Context, provided that the noise is small enough.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Role of kinin B1 and B2 receptors in memory consolidation during the aging process of mice

Under physiological conditions, elderly people present memory deficit associated with neuronal loss. This pattern is also associated with Alzheimer`s disease but, in this case, in a dramatically intensified level. Kinin receptors have been involved in neurodegeneration and increase of amyloid-beta concentration, associated with Alzheimer`s disease (AD). Considering these findings, this work evaluated the role of kinin receptors in memory consolidation during the aging process. Male C57BI/6 (wt), knock-out B1 (koB1) or B2 (koB2) mice (3, 6, 12 and 18-month-old - mo; n = 10 per group) were submitted to an acquisition session, reinforcement to learning (24 h later: test 1) and final test (7 days later: test 2), in an active avoidance apparatus, to evaluate memory. Conditioned avoidance responses (CAR, % of 50 trials) were registered. In acquisition sessions, similar CAR were obtained among age matched animals from all strains. However, a significant decrease in CAR was observed throughout the aging process (3mo: 8.8 +/- 2.3%; 6mo: 4.1 +/- 0.6%; 12mo: 2.2 +/- 0.6%, 18mo: 3.6 +/- 0.6%, P < 0.01), indicating a reduction in the learning process. In test 1, as expected, memory retention increased significantly (P < 0.05) in all 3- and 6-month-old animals as well as in 12-month-old-wt and 12-month-old-koB1 (P < 0.01), compared to the training session. However, 12-month-old-koB2 and all 18-month-old animals did not show an increase in memory retention. In test 2, 3- and 6-month-old wt and koB1 mice of all ages showed a significant improvement in memory (P < 0.05) compared to test 1. However, 12-month-old wt and koB2 mice of all ages showed no difference in memory retention. We suggest that, during the aging process, the B1 receptor could be involved in neurodegeneration and memory loss. Nevertheless, the B2 receptor is apparently acting as a neuroprotective factor. (C) 2009 Elsevier Ltd. All rights reserved.

Participation of kinin receptors on memory impairment after chronic infusion of human amyloid-beta 1-40 peptide in mice

Chronic infusion of human amyloid-beta 1-40 (A beta) in the lateral ventricle (LV) of rats is associated with memory impairment and increase of kinin receptors in cortical and hippocampal areas. Deletion of kinin B1 or B2 receptors abolished memory impairment caused by an acute single injection of A beta in the LV. As brain tissue and kinin receptors could unlikely react to acute or chronic administration of a similar quantity of A beta, we evaluated the participation of B1 or B2 receptors in memory impairment after chronic infusion of A beta. Male C57BI/6 J (wt), knock-out B1 (koB1) or B2 (koB2) mice (12 weeks of age) previously trained in a two-way shuttle-box and achieving conditioned avoidance responses (CAR, % of 50 trials) were infused with AB (550 pmol, 0.12 mu L/h, 28 days) or vehicle in the LV using a mini-osmotic pump. They were tested before the surgery (TO), 7 and 35 days after the infusion started (T7; T35). In T0, no difference was observed between CAR of the control (Cwt = 59.7 +/- 6.7%; CkoB1 = 46.7 +/- 4.0%; CkoB2 = 64.4 +/- 5.8%) and A beta (A beta wt = 66.0 +/- 3.0%; A beta koB1 = 66.8 +/- 8.2%; A beta koB2 = 58.7 +/- 5.9%) groups. In T7, A beta koB2 showed a significant decrease in CAR (41.0 +/- 8.6%) compared to the control-koB2 (72.8 +/- 2.2%, P <0.05). In T35, a significant decrease (P <0.05) was observed in A beta wt (40.7 +/- 3.3%) and A beta koB2 (41.2 +/- 10.7%) but not in the A beta koB1 (64.0 +/- 14.0%) compared to their control groups. No changes were observed in the controls at T35. We suggest that in chronic infusion of BA, B1 receptors could playan important role in the neurodegenerative process. Conversely, the premature memory impairment of koB2 suggests that it may be a protective factor. (C) 2009 Elsevier Ltd. All rights reserved.

MEMORY TRAINING FOR OLDER ADULTS WITH LOW EDUCATION: MENTAL IMAGES VERSUS CATEGORIZATION

This study aimed to describe the benefits of memory training for older adults with low education. Twenty-nine healthy older adults with zero to two years of formal education participated. Sixteen participants received training based on categorization (categorization group = CATG) and 13 received training based on mental images (imagery group = IMG). One group served as control for the other because they trained with different strategies. Training was offered in eight sessions of 90 minutes. The participants were evaluated pre- and posttraining. IMG improved performance in episodic memory tests and had reduced depressive symptoms. CATG increased the use of categorization but did not increase performance in episodic memory tests. Results suggest that the strategy based on the creation of mental images was more effective for older adults with low formal education.

Relationship Between Cortisol Levels and Memory Performance may be Modulated by the Presence or Absence of Cognitive Impairment: Evidence from Healthy Elderly, Mild Cognitive Impairment and Alzheimer`s Disease Subjects

An inverted U-shape function between cortisol levels and memory performance has been reported in studies on both young animals and humans. Yet little is known about this relationship in normal aging or in older subjects with cognitive impairment. This issue is particularly significant since increased levels of cortisol have been reported in Alzheimer`s disease (AD). The present study examined the association between cortisol levels and visual memory performance in healthy subjects as well as in individuals presenting mild cognitive impairment (MCI) or AD. Salivary cortisol was measured in 40 healthy elderly subjects, 31 individuals with amnestic MCI, and 40 subjects with mild probable AD. Memory performance was evaluated using the Brief Cognitive Screening Battery. Higher cortisol levels were associated with better memory performance in healthy elderly (p = 0.005), while higher cortisol levels were correlated with poorer memory performance in MCI subjects (p = 0.011). No correlation between cortisol and memory was found in the AD group (p > 0.05). These results suggest that the relationship between cortisol levels and memory performance in the aging process could vary according to the presence or absence of cognitive impairment.

Antimicrobial drug administration errors identified in Brazilian multicentric study

Medication administration errors (MAE) are the most frequent kind of medication errors. Errors with antimicrobial drugs (AD) are relevant because they may interfere inpatient safety and in the development of microbial resistance. The aim of this study is to analyze the AD errors detected in a Brazilian multicentric study of MAE. It was a devcriptive and explorotory study carried out in clinical units in five Brazilian teaching hospitals. The hospitals were investigated during 30 days. MAE were detected by observation technique. MAE were classified in categories: wrong route(WR), wrong patient(WP), wrong dose(WD) wrong time (WT) and unordered drug (UD). AD with MA E were classified by Anatomical-Therapeutical-Chemical Classification System. AD with narrow therapeutic index (NTI) wet-e identified A descriptive statistical analysis was performed using SPSS version 11.5 software. A total of 1500 errors were observed, 277 (18.5%) of them were error with AD. The hopes of AD error were: WT87.7%, QD 6.9%, WR 1.5%, UD 3.2% and WP 0.7%. The number of AD found was 36. The mostly ATC class were fluoroquinolones 13.9%, combinations of penicillin 13.9%, macrolides 8.3% and third-generation cephalosporines 5.6%. The parenteral drug dosage form was associated with 55.6% of AD. 16.7% of AD were NTI. 47.4% of WD and 21.8% WT were with NTI drugs. This study shows that these errors should be considered potential areas for improvement in the medication process and patient safety plus there is requirement to develop rational drug use of AD.

Offline Detection, Identification, and Correction of Branch Parameter Errors Based on Several Measurement Snapshots

This paper proposes a three-stage offline approach to detect, identify, and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V - theta state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional weighted least squares estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Quebec TransEnergie network.

Robot measuring form errors

This work presents an automated system for the measurement of form errors of mechanical components using an industrial robot. A three-probe error separation technique was employed to allow decoupling between the measured form error and errors introduced by the robotic system. A mathematical model of the measuring system was developed to provide inspection results by means of the solution of a system of linear equations. A new self-calibration procedure, which employs redundant data from several runs, minimizes the influence of probes zero-adjustment on the final result. Experimental tests applied to the measurement of straightness errors of mechanical components were accomplished and demonstrated the effectiveness of the employed methodology. (C) 2007 Elsevier Ltd. All rights reserved.

Modeling, control and experimental validation of a novel actuator based on shape memory alloys

The paper presents the development of a mechanical actuator using a shape memory alloy with a cooling system based on the thermoelectric effect (Seebeck-Peltier effect). Such a method has the advantage of reduced weight and requires a simpler control strategy as compared to other forced cooling systems. A complete mathematical model of the actuator was derived, and an experimental prototype was implemented. Several experiments are used to validate the model and to identify all parameters. A robust and nonlinear controller, based on sliding-mode theory, was derived and implemented. Experiments were used to evaluate the actuator closed-loop performance, stability, and robustness properties. The results showed that the proposed cooling system and controller are able to improve the dynamic response of the actuator. (C) 2009 Elsevier Ltd. All rights reserved.

Diagnostic errors and repetitive sequential classifications in on-line process control by attributes

The procedure of on-line process control by attributes, known as Taguchi`s on-line process control, consists of inspecting the mth item (a single item) at every m produced items and deciding, at each inspection, whether the fraction of conforming items was reduced or not. If the inspected item is nonconforming, the production is stopped for adjustment. As the inspection system can be subject to diagnosis errors, one develops a probabilistic model that classifies repeatedly the examined item until a conforming or b non-conforming classification is observed. The first event that occurs (a conforming classifications or b non-conforming classifications) determines the final classification of the examined item. Proprieties of an ergodic Markov chain were used to get the expression of average cost of the system of control, which can be optimized by three parameters: the sampling interval of the inspections (m); the number of repeated conforming classifications (a); and the number of repeated non-conforming classifications (b). The optimum design is compared with two alternative approaches: the first one consists of a simple preventive policy. The production system is adjusted at every n produced items (no inspection is performed). The second classifies the examined item repeatedly r (fixed) times and considers it conforming if most classification results are conforming. Results indicate that the current proposal performs better than the procedure that fixes the number of repeated classifications and classifies the examined item as conforming if most classifications were conforming. On the other hand, the preventive policy can be averagely the most economical alternative rather than those ones that require inspection depending on the degree of errors and costs. A numerical example illustrates the proposed procedure. (C) 2009 Elsevier B. V. All rights reserved.

Effects of the preventive and corrective adjustments in economical designs for online process control for attributes with misclassification errors

The procedure for online process control by attributes consists of inspecting a single item at every m produced items. It is decided on the basis of the inspection result whether the process is in-control (the conforming fraction is stable) or out-of-control (the conforming fraction is decreased, for example). Most articles about online process control have cited the stoppage of the production process for an adjustment when the inspected item is non-conforming (then the production is restarted in-control, here denominated as corrective adjustment). Moreover, the articles related to this subject do not present semi-economical designs (which may yield high quantities of non-conforming items), as they do not include a policy of preventive adjustments (in such case no item is inspected), which can be more economical, mainly if the inspected item can be misclassified. In this article, the possibility of preventive or corrective adjustments in the process is decided at every m produced item. If a preventive adjustment is decided upon, then no item is inspected. On the contrary, the m-th item is inspected; if it conforms, the production goes on, otherwise, an adjustment takes place and the process restarts in-control. This approach is economically feasible for some practical situations and the parameters of the proposed procedure are determined minimizing an average cost function subject to some statistical restrictions (for example, to assure a minimal levelfixed in advanceof conforming items in the production process). Numerical examples illustrate the proposal.

On Building a Memory Evolutive System for Application to Learning and Cognition Modeling

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich`s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.

Coarse-grid higher order finite-difference time-domain algorithm with low dispersion errors

Higher order (2,4) FDTD schemes used for numerical solutions of Maxwell`s equations are focused on diminishing the truncation errors caused by the Taylor series expansion of the spatial derivatives. These schemes use a larger computational stencil, which generally makes use of the two constant coefficients, C-1 and C-2, for the four-point central-difference operators. In this paper we propose a novel way to diminish these truncation errors, in order to obtain more accurate numerical solutions of Maxwell`s equations. For such purpose, we present a method to individually optimize the pair of coefficients, C-1 and C-2, based on any desired grid size resolution and size of time step. Particularly, we are interested in using coarser grid discretizations to be able to simulate electrically large domains. The results of our optimization algorithm show a significant reduction in dispersion error and numerical anisotropy for all modeled grid size resolutions. Numerical simulations of free-space propagation verifies the very promising theoretical results. The model is also shown to perform well in more complex, realistic scenarios.