Robust control of robot manipulators using hybrid H∞/adaptive controller

A robust hybrid control method for robot manipulators is proposed which integrates an H∞ controller and an adaptive controller. The H∞ controller is used to minimize the effect of parameter uncertainties of the robot model on the tracking performance, while the adaptive controller continuously adjusts the model parameters to reduce the model error. Simulations show that disturbances generated from the model error will be quickly compensated and so small tracking errors can be achieved.published_or_final_versio

Mixed integer programming in production planning with backlogging and setup carryover : modeling and algorithms

This paper proposes a mixed integer programming formulation for modeling the capacitated multi-level lot sizing problem with both backlogging and setup carryover. Based on the model formulation, a progressive time-oriented decomposition heuristic framework is then proposed, where improvement and construction heuristics are effectively combined, therefore efficiently avoiding the weaknesses associated with the one-time decisions made by other classical time-oriented decomposition algorithms. Computational results show that the proposed optimization framework provides competitive solutions within a reasonable time

Rabies screen reveals GPe control of cocaine-triggered plasticity.

Identification of neural circuit changes that contribute to behavioural plasticity has routinely been conducted on candidate circuits that were preselected on the basis of previous results. Here we present an unbiased method for identifying experience-triggered circuit-level changes in neuronal ensembles in mice. Using rabies virus monosynaptic tracing, we mapped cocaine-induced global changes in inputs onto neurons in the ventral tegmental area. Cocaine increased rabies-labelled inputs from the globus pallidus externus (GPe), a basal ganglia nucleus not previously known to participate in behavioural plasticity triggered by drugs of abuse. We demonstrated that cocaine increased GPe neuron activity, which accounted for the increase in GPe labelling. Inhibition of GPe activity revealed that it contributes to two forms of cocaine-triggered behavioural plasticity, at least in part by disinhibiting dopamine neurons in the ventral tegmental area. These results suggest that rabies-based unbiased screening of changes in input populations can identify previously unappreciated circuit elements that critically support behavioural adaptations

Inflammatory cytokines and biofilm production sustain Staphylococcus aureus outgrowth and persistence: A pivotal interplay in the pathogenesis of Atopic Dermatitis

Individuals with Atopic dermatitis (AD) are highly susceptible to Staphylococcus aureus colonization. However, the mechanisms driving this process as well as the impact of S. aureus in AD pathogenesis are still incompletely understood. In this study, we analysed the role of biofilm in sustaining S. aureus chronic persistence and its impact on AD severity. Further we explored whether key inflammatory cytokines overexpressed in AD might provide a selective advantage to S. aureus. Results show that the strength of biofilm production by S. aureus correlated with the severity of the skin lesion, being significantly higher (P < 0.01) in patients with a more severe form of the disease as compared to those individuals with mild AD. Additionally, interleukin (IL)-β and interferon γ (IFN-γ), but not interleukin (IL)-6, induced a concentration-dependent increase of S. aureus growth. This effect was not observed with coagulase-negative staphylococci isolated from the skin of AD patients. These findings indicate that inflammatory cytokines such as IL1-β and IFN-γ, can selectively promote S. aureus outgrowth, thus subverting the composition of the healthy skin microbiome. Moreover, biofilm production by S. aureus plays a relevant role in further supporting chronic colonization and disease severity, while providing an increased tolerance to antimicrobials

Propagation of the Madden–Julian Oscillation and scale interaction with the diurnal cycle in a high-resolution GCM

The Madden–Julian Oscillation (MJO) is the chief source of tropical intra-seasonal variability, but is simulated poorly by most state-of-the-art GCMs. Common errors include a lack of eastward propagation at the correct frequency and zonal extent, and too small a ratio of eastward- to westward-propagating variability. Here it is shown that HiGEM, a high-resolution GCM, simulates a very realistic MJO with approximately the correct spatial and temporal scale. Many MJO studies in GCMs are limited to diagnostics which average over a latitude band around the equator, allowing an analysis of the MJO’s structure in time and longitude only. In this study a wider range of diagnostics is applied. It is argued that such an approach is necessary for a comprehensive analysis of a model’s MJO. The standard analysis of Wheeler and Hendon (Mon Wea Rev 132(8):1917–1932, 2004; WH04) is applied to produce composites, which show a realistic spatial structure in the MJO envelopes but for the timing of the peak precipitation in the inter-tropical convergence zone, which bifurcates the MJO signal. Further diagnostics are developed to analyse the MJO’s episodic nature and the “MJO inertia” (the tendency to remain in the same WH04 phase from one day to the next). HiGEM favours phases 2, 3, 6 and 7; has too much MJO inertia; and dies out too frequently in phase 3. Recent research has shown that a key feature of the MJO is its interaction with the diurnal cycle over the Maritime Continent. This interaction is present in HiGEM but is unrealistically weak

Using Wavelet Entropy To Demonstrate How Mindfulness Practice Increases Coordination Between Irregular Cerebral And Cardiac Activities

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, statistical parametric mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard mindfulness-based stress reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities

Applying a markov chain model in quality function deployment

[[abstract]]The relationships between customer requirements and technical measures are typically resolved by a cross-functional team with the assumption that the relationships are able to be identified objectively. However, due to the limited knowledge and experiences, determining the appropriate relationship could be difficult since the decision makers might not have enough information to evaluate the actual relationship. Moreover, the importance of technical measures is typically expressed in the current time period. It would be of interest to trace the future trends of technical measures since customer needs are fulfilled by technical measures. Under such circumstances, a Markov chain model could be an approach to model the relationship and monitor the trends of technical measures from probabilities viewpoints. With the needed probabilities, the dynamic relationships as well as the trends of technical measures can be performed by different time periods. Finally, the relationships and future trends of technical measures can be updated when the new information is available

The development of a confidence interval-based importance-performance analysis by considering variability in analyzing service quality

[[abstract]]The traditional importance-performance analysis (IPA) uses the mean ratings of importance and performance to construct a two-dimensional grid by identifying improvement opportunities and guiding strategic planning efforts. The point estimates of importance and performance vary from sample to sample such that the numerical analyses are different based upon different samples. Thus, using point estimates for items might lead the management to make false decisions. This study integrates confidence intervals and IPA to reduce the variability which enables the decision maker much easier to identify the strengths and weaknesses based upon the sample of size used. Moreover, the assumptions of equal and unequal population variances for constructing confidence intervals are discussed. (c) 2008 Elsevier Ltd. All rights reserved

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/