Non pre-emptive scheduling of messages on SMTV token-passing networks

Fieldbus communication networks aim to interconnect sensors, actuators and controllers within distributed computer-controlled systems. Therefore, they constitute the foundation upon which real-time applications are to be implemented. A specific class of fieldbus communication networks is based on a simplified version of token-passing protocols, where each station may transfer, at most, a single message per token visit (SMTV). In this paper, we establish an analogy between non-preemptive task scheduling in single processors and the scheduling of messages on SMTV token-passing networks. Moreover, we clearly show that concepts such as blocking and interference in non-preemptive task scheduling have their counterparts in the scheduling of messages on SMTV token-passing networks. Based on this task/message scheduling analogy, we provide pre-run-time schedulability conditions for supporting real-time messages with SMTV token-passing networks. We provide both utilisation-based and response time tests to perform the pre-run-time schedulability analysis of real-time messages on SMTV token-passing networks, considering RM/DM (rate monotonic/deadline monotonic) and EDF (earliest deadline first) priority assignment schemes

Constraint reasoning for differential models

The basic motivation of this work was the integration of biophysical models within the interval constraints framework for decision support. Comparing the major features of biophysical models with the expressive power of the existing interval constraints framework, it was clear that the most important inadequacy was related with the representation of differential equations. System dynamics is often modelled through differential equations but there was no way of expressing a differential equation as a constraint and integrate it within the constraints framework. Consequently, the goal of this work is focussed on the integration of ordinary differential equations within the interval constraints framework, which for this purpose is extended with the new formalism of Constraint Satisfaction Differential Problems. Such framework allows the specification of ordinary differential equations, together with related information, by means of constraints, and provides efficient propagation techniques for pruning the domains of their variables. This enabled the integration of all such information in a single constraint whose variables may subsequently be used in other constraints of the model. The specific method used for pruning its variable domains can then be combined with the pruning methods associated with the other constraints in an overall propagation algorithm for reducing the bounds of all model variables. The application of the constraint propagation algorithm for pruning the variable domains, that is, the enforcement of local-consistency, turned out to be insufficient to support decision in practical problems that include differential equations. The domain pruning achieved is not, in general, sufficient to allow safe decisions and the main reason derives from the non-linearity of the differential equations. Consequently, a complementary goal of this work proposes a new strong consistency criterion, Global Hull-consistency, particularly suited to decision support with differential models, by presenting an adequate trade-of between domain pruning and computational effort. Several alternative algorithms are proposed for enforcing Global Hull-consistency and, due to their complexity, an effort was made to provide implementations able to supply any-time pruning results. Since the consistency criterion is dependent on the existence of canonical solutions, it is proposed a local search approach that can be integrated with constraint propagation in continuous domains and, in particular, with the enforcing algorithms for anticipating the finding of canonical solutions. The last goal of this work is the validation of the approach as an important contribution for the integration of biophysical models within decision support. Consequently, a prototype application that integrated all the proposed extensions to the interval constraints framework is developed and used for solving problems in different biophysical domains.

Preemption delay analysis for floating non-preemptive region scheduling

In real-time systems, there are two distinct trends for scheduling task sets on unicore systems: non-preemptive and preemptive scheduling. Non-preemptive scheduling is obviously not subject to any preemption delay but its schedulability may be quite poor, whereas fully preemptive scheduling is subject to preemption delay, but benefits from a higher flexibility in the scheduling decisions. The time-delay involved by task preemptions is a major source of pessimism in the analysis of the task Worst-Case Execution Time (WCET) in real-time systems. Preemptive scheduling policies including non-preemptive regions are a hybrid solution between non-preemptive and fully preemptive scheduling paradigms, which enables to conjugate both world's benefits. In this paper, we exploit the connection between the progression of a task in its operations, and the knowledge of the preemption delays as a function of its progression. The pessimism in the preemption delay estimation is then reduced in comparison to state of the art methods, due to the increase in information available in the analysis.

An improved preemption delay upper bound for floating non-preemptive region

In embedded systems, the timing behaviour of the control mechanisms are sometimes of critical importance for the operational safety. These high criticality systems require strict compliance with the offline predicted task execution time. The execution of a task when subject to preemption may vary significantly in comparison to its non-preemptive execution. Hence, when preemptive scheduling is required to operate the workload, preemption delay estimation is of paramount importance. In this paper a preemption delay estimation method for floating non-preemptive scheduling policies is presented. This work builds on [1], extending the model and optimising it considerably. The preemption delay function is subject to a major tightness improvement, considering the WCET analysis context. Moreover more information is provided as well in the form of an extrinsic cache misses function, which enables the method to provide a solution in situations where the non-preemptive regions sizes are small. Finally experimental results from the implementation of the proposed solutions in Heptane are provided for real benchmarks which validate the significance of this work.

The utilization bound of non-preemptive rate-monotonic scheduling in controller area networks is 25%

Consider a distributed computer system comprising many computer nodes, each interconnected with a controller area network (CAN) bus. We prove that if priorities to message streams are assigned using rate-monotonic (RM) and if the requested capacity of the CAN bus does not exceed 25% then all deadlines are met.

Non invasive ventilation during exercise in COPD patients: a systematic review with meta-analysis

COPD is a major cause of morbidity and mortality worldwide, representing a major public health problem due to the high health and economic resource consumption. Pulmonary rehabilitation is a standard care recommendation for these patients, in order to control the symptoms and optimize the functional capacity, reducing health care costs associated with exacerbations and activity limitations and participation. However, in patients with severe COPD exercise performance can be difficult, due to extreme dyspnea, decreased muscle strength and fatigue. In addition, hypoxemia and dyspnea during efforts and daily activities may occur, limiting their quality of life. Thus, NIV have been used as adjunct to exercise, in order to improve exercise capacity in these patients. However, there is no consensus for this technique recommendation. Our objective was to verify whether the use of NIV during exercise is effective than exercise without NIV in dyspnea, walked distance, blood gases and health status in COPD patients, through a systematic review and meta-analysis.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Non-adherence to telemedicine interventions for drug users: systematic review

OBJECTIVE To estimate rates of non-adherence to telemedicine strategies aimed at treating drug addiction. METHODS A systematic review was conducted of randomized controlled trials investigating different telemedicine treatment methods for drug addiction. The following databases were consulted between May 18, 2012 and June 21, 2012: PubMed, PsycINFO, SciELO, Wiley (The Cochrane Library), Embase, Clinical trials and Google Scholar. The Grading of Recommendations Assessment, Development and Evaluation was used to evaluate the quality of the studies. The criteria evaluated were: appropriate sequence of data generation, allocation concealment, blinding, description of losses and exclusions and analysis by intention to treat. There were 274 studies selected, of which 20 were analyzed. RESULTS Non-adherence rates varied between 15.0% and 70.0%. The interventions evaluated were of at least three months duration and, although they all used telemedicine as support, treatment methods differed. Regarding the quality of the studies, the values also varied from very poor to high quality. High quality studies showed better adherence rates, as did those using more than one technique of intervention and a limited treatment time. Mono-user studies showed better adherence rates than poly-user studies. CONCLUSIONS Rates of non-adherence to treatment involving telemedicine on the part of users of psycho-active substances differed considerably, depending on the country, the intervention method, follow-up time and substances used. Using more than one technique of intervention, short duration of treatment and the type of substance used by patients appear to facilitate adherence.

Visualizing non-linear control system performance by means of multidimensional scaling

This paper addresses the use of multidimensional scaling in the evaluation of controller performance. Several nonlinear systems are analyzed based on the closed loop time response under the action of a reference step input signal. Three alternative performance indices, based on the time response, Fourier analysis, and mutual information, are tested. The numerical experiments demonstrate the feasibility of the proposed methodology and motivate its extension for other performance measures and new classes of nonlinearities.

Non-uniform microstrip antenna array for RX DSRC communications

The urgent need to mitigate traffic problems such as accidents, road hazards, pollution and traffic jam have strongly driven the development of vehicular communications. DSRC (Dedicated Short Range Communications) is the technology of choice in vehicular communications, enabling real time information exchange among vehicles V2V (Vehicle-to-Vehicle) and between vehicles and infrastructure V2I (Vehicle-Infrastructure). This paper presents a receiving antenna for a single lane DSRC control unit. The antenna is a non-uniform array with five microstrip patches. The obtained beam width, bandwidth and circular polarization quality, among other characteristics, are compatible with the DSRC standards, making this antenna suitable for this application. © 2014 IEEE.

The impact of different power dividers used in a non-uniform planar antenna array

Wireless communications are widely used for various applications, requiring antennas with different features. Often, to achieve the desired radiation pattern, is necessary to employ antenna arrays, using non-uniform excitation on its elements. Power dividers can be used and the best known are the T-junction and the Wilkinson power divider, whose main advantage is the isolation between output ports. In this paper the impact of this isolation on the overall performance of a circularly polarized planar antenna array using non-uniform excitation is investigated. Results show a huge decrease of the array bandwidths either in terms of return loss or in polarization, without resistors. © 2014 IEEE.

Evidence for non-coaxiality of ferrimagnetic and paramagnetic fabrics, developed during magma flow and cooling in a thick mafic dyke

A detailed analysis of fabrics of the chilled margin of a thick dolerite dyke (Foum Zguid dyke, Southern Morocco) was performed in order to better understand the development of sub-fabrics during dyke emplacement and cooling. AMS data were complemented with measurements of paramagnetic and ferrimagnetic fabrics (measured with high field torque magnetometer), neutron texture and microstructural analyses. The ferrimagnetic and AMS fabrics are similar, indicating that the ferrimagnetic minerals dominate the AMS signal. The paramagnetic fabric is different from the previous ones. Based on the crystallization timing of the different mineralogical phases, the paramagnetic fabric appears related to the upward flow, while the ferrimagnetic fabric rather reflects the late-stage of dyke emplacement and cooling stresses. (C) 2014 Elsevier B.V. All rights reserved.

Wrong sign and symmetric limits and non-decoupling in 2HDMs

We analyse the possibility that, in two Higgs doublet models, one or more of the Higgs couplings to fermions or to gauge bosons change sign, relative to the respective Higgs Standard Model couplings. Possible sign changes in the coupling of a neutral scalar to charged ones are also discussed. These wrong signs can have important physical consequences, manifesting themselves in Higgs production via gluon fusion or Higgs decay into two gluons or into two photons. We consider all possible wrong sign scenarios, and also the symmetric limit, in all possible Yukawa implementations of the two Higgs doublet model, in two different possibilities: the observed Higgs boson is the lightest CP-even scalar, or the heaviest one. We also analyse thoroughly the impact of the currently available LHC data on such scenarios. With all 8 TeV data analysed, all wrong sign scenarios are allowed in all Yukawa types, even at the 1 sigma level. However, we will show that B-physics constraints are crucial in excluding the possibility of wrong sign scenarios in the case where tan beta is below 1. We will also discuss the future prospects for probing the wrong sign scenarios at the next LHC run. Finally we will present a scenario where the alignment limit could be excluded due to non-decoupling in the case where the heavy CP-even Higgs is the one discovered at the LHC.

Equilibrium distributions of discrete non-autonomous graphs

We introduce the notions of equilibrium distribution and time of convergence in discrete non-autonomous graphs. Under some conditions we give an estimate to the convergence time to the equilibrium distribution using the second largest eigenvalue of some matrices associated with the system.

Comments on ‘‘Modeling fractional stochastic systems as non-random fractional dynamics driven Brownian motions

Applied Mathematical Modelling, Vol.33