Complex-valued b-spline neural networks for modelling and inverse of wiener systems

Communication signal processing applications often involve complex-valued (CV) functional representations for signals and systems. CV artificial neural networks have been studied theoretically and applied widely in nonlinear signal and data processing [1–11]. Note that most artificial neural networks cannot be automatically extended from the real-valued (RV) domain to the CV domain because the resulting model would in general violate Cauchy-Riemann conditions, and this means that the training algorithms become unusable. A number of analytic functions were introduced for the fully CV multilayer perceptrons (MLP) [4]. A fully CV radial basis function (RBF) nework was introduced in [8] for regression and classification applications. Alternatively, the problem can be avoided by using two RV artificial neural networks, one processing the real part and the other processing the imaginary part of the CV signal/system. A even more challenging problem is the inverse of a CV

Modelling capability and affordance as properties of human/machine resource systems

Understanding how and why the capability of one set of business resources, its structural arrangements and mechanisms compared to another works can provide competitive advantage in terms of new business processes and product and service development. However, most business models of capability are descriptive and lack formal modelling language to qualitatively and quantifiably compare capabilities, Gibson’s theory of affordance, the potential for action, provides a formal basis for a more robust and quantitative model, but most formal affordance models are complex and abstract and lack support for real-world applications. We aim to understand the ‘how’ and ‘why’ of business capability, by developing a quantitative and qualitative model that underpins earlier work on Capability-Affordance Modelling – CAM. This paper integrates an affordance based capability model and the formalism of Coloured Petri Nets to develop a simulation model. Using the model, we show how capability depends on the space time path of interacting resources, the mechanism of transition and specific critical affordance factors relating to the values of the variables for resources, people and physical objects. We show how the model can identify the capabilities of resources to enable the capability to inject a drug and anaesthetise a patient.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

MODELLING OF HIGH-PRESSURE PHASE EQUILIBRIUM IN SYSTEMS OF INTEREST IN THE FOOD ENGINEERING FIELD USING THE PENG-ROBINSON EQUATION OF STATE WITH TWO DIFFERENT MIXING RULES

In this work, thermodynamic models for fitting the phase equilibrium of binary systems were applied, aiming to predict the high pressure phase equilibrium of multicomponent systems of interest in the food engineering field, comparing the results generated by the models with new experimental data and with those from the literature. Two mixing rules were used with the Peng-Robinson equation of state, one with the mixing rule of van der Waals and the other with the composition-dependent mixing rule of Mathias et al. The systems chosen are of fundamental importance in food industries, such as the binary systems CO(2)-limonene, CO(2)-citral and CO(2)-linalool, and the ternary systems CO(2)-Limonene-Citral and CO(2)-Limonene-Linalool, where high pressure phase equilibrium knowledge is important to extract and fractionate citrus fruit essential oils. For the CO(2)-limonene system, some experimental data were also measured in this work. The results showed the high capability of the model using the composition-dependent mixing rule to model the phase equilibrium behavior of these systems.

Modelling the Provenance of Data in Autonomous Systems

Determining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are textitreactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or textitagents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example.

Modelling and Administration of Contract-Based Systems

Mirroring the paper versions exchanged between businesses today, electronic contracts offer the possibility of dynamic, automatic creation and enforcement of restrictions and compulsions on agent behaviour that are designed to ensure business objectives are met. However, where there are many contracts within a particular application, it can be difficult to determine whether the system can reliably fulfil them all; computer-parsable electronic contracts may allow such verification to be automated. In this paper, we describe a conceptual framework and architecture specification in which normative business contracts can be electronically represented, verified, established, renewed, etc. In particular, we aim to allow systems containing multiple contracts to be checked for conflicts and violations of business objectives. We illustrate the framework and architecture with an aerospace example.

Effects of Self-Etching Adhesive Systems Used in the Dental Modelling Technique on the Cohesive Strength of Composite Resin

This study evaluated the cohesive strength of composite using self-etching adhesive systems (SE) in the lubrication of instruments between layers of composite. The specimens were made by using a Teflon (R) device. SE were used at the interface to lubricate the instruments: Group 1(G1) - control group, no lubricant was used; Group 2(G2) -Futurabond (R) M; Group 3(G3) - Optibond (R) All-In-One; Group 4(G4) - Clearfil (R) SE Bond; Group 5(G5) - Futurabond (R) NR; Group 6(G6) - Adper (R) SE Plus; Group 7(G7) - One Up Bond (R) F. Specimens were submitted to the tensile test to evaluate the cohesive strength. Data were submitted to the ANOVA and Tukey tests. ANOVA showed a value of p = 0.00. The average means (SD): G2 = 11.33(+/-3.44) a, G3 = 15.36(+/-4.06) ab, G4 = 18.9(+/-4.72) bc, G7 = 19.62(+/-4.46) bc, G5 = 21.02(+/-5.09) bc, G6 = 23.39(+/-4.17) cd, and G1 = 28.49(+/-2.89) d. All SE decreased the cohesive strength of the composite, except for Adper (R) SE Plus.

Modelling and simulating in systems biology: an approach based on multi-agent systems

Systems Biology is an innovative way of doing biology recently raised in bio-informatics contexts, characterised by the study of biological systems as complex systems with a strong focus on the system level and on the interaction dimension. In other words, the objective is to understand biological systems as a whole, putting on the foreground not only the study of the individual parts as standalone parts, but also of their interaction and of the global properties that emerge at the system level by means of the interaction among the parts. This thesis focuses on the adoption of multi-agent systems (MAS) as a suitable paradigm for Systems Biology, for developing models and simulation of complex biological systems. Multi-agent system have been recently introduced in informatics context as a suitabe paradigm for modelling and engineering complex systems. Roughly speaking, a MAS can be conceived as a set of autonomous and interacting entities, called agents, situated in some kind of nvironment, where they fruitfully interact and coordinate so as to obtain a coherent global system behaviour. The claim of this work is that the general properties of MAS make them an effective approach for modelling and building simulations of complex biological systems, following the methodological principles identified by Systems Biology. In particular, the thesis focuses on cell populations as biological systems. In order to support the claim, the thesis introduces and describes (i) a MAS-based model conceived for modelling the dynamics of systems of cells interacting inside cell environment called niches. (ii) a computational tool, developed for implementing the models and executing the simulations. The tool is meant to work as a kind of virtual laboratory, on top of which kinds of virtual experiments can be performed, characterised by the definition and execution of specific models implemented as MASs, so as to support the validation, falsification and improvement of the models through the observation and analysis of the simulations. A hematopoietic stem cell system is taken as reference case study for formulating a specific model and executing virtual experiments.

Territorial Intelligence of Vulnerability Systems 2- Sustainable Modelling of Globalization Challenge

Globalization as progress of economic development has increased population socioeconomical vulnerability when unequal wealth distribution within economic development process constitutes the main rule, with widening the gap between rich and poors by environmental pricing. Econological vulnerability is therefore increasing too, as dangerous substance and techniques should produce polluted effluents and industrial or climatic risk increasing (Woloszyn, Quenault, Faburel, 2012). To illustrate and model this process, we propose to introduce an analogical induction-model to describe both vulnerability situations and associated resilience procedures. At this aim, we first develop a well-known late 80?s model of socio-economic crack-up, known as 'Silent Weapons for Quiet Wars', which presents economics as a social extension of natural energy systems. This last, also named 'E-model', is constituted by three passive components, potential energy, kinetic energy, and energy dissipation, thus allowing economical data to be treated as a thermodynamical system. To extend this model to social and ecological sustainability pillars, we propose to built an extended E(Economic)-S(Social)-O(Organic) model, based on the three previous components, as an open model considering feedbacks as evolution sources. An applicative illustration of this model will then be described, through this summer's american severe drought event analysis

Territorial Intelligence of Vulnerability Systems 2- Sustainable Modelling of Globalization Challenge

Globalization as progress of economic development has increased population socioeconomical vulnerability when unequal wealth distribution within economic development process constitutes the main rule, with widening the gap between rich and poors by environmental pricing. Econological vulnerability is therefore increasing too, as dangerous substance and techniques should produce polluted effluents and industrial or climatic risk increasing (Woloszyn, Quenault, Faburel, 2012). To illustrate and model this process, we propose to introduce an analogical induction-model to describe both vulnerability situations and associated resilience procedures. At this aim, we first develop a well-known late 80?s model of socio-economic crack-up, known as 'Silent Weapons for Quiet Wars', which presents economics as a social extension of natural energy systems. This last, also named 'E-model', is constituted by three passive components, potential energy, kinetic energy, and energy dissipation, thus allowing economical data to be treated as a thermodynamical system. To extend this model to social and ecological sustainability pillars, we propose to built an extended E(Economic)-S(Social)-O(Organic) model, based on the three previous components, as an open model considering feedbacks as evolution sources. An applicative illustration of this model will then be described, through this summer's american severe drought event analysis

Territorial Intelligence of Vulnerability Systems 2- Sustainable Modelling of Globalization Challenge

Globalization as progress of economic development has increased population socioeconomical vulnerability when unequal wealth distribution within economic development process constitutes the main rule, with widening the gap between rich and poors by environmental pricing. Econological vulnerability is therefore increasing too, as dangerous substance and techniques should produce polluted effluents and industrial or climatic risk increasing (Woloszyn, Quenault, Faburel, 2012). To illustrate and model this process, we propose to introduce an analogical induction-model to describe both vulnerability situations and associated resilience procedures. At this aim, we first develop a well-known late 80?s model of socio-economic crack-up, known as 'Silent Weapons for Quiet Wars', which presents economics as a social extension of natural energy systems. This last, also named 'E-model', is constituted by three passive components, potential energy, kinetic energy, and energy dissipation, thus allowing economical data to be treated as a thermodynamical system. To extend this model to social and ecological sustainability pillars, we propose to built an extended E(Economic)-S(Social)-O(Organic) model, based on the three previous components, as an open model considering feedbacks as evolution sources. An applicative illustration of this model will then be described, through this summer's american severe drought event analysis

A modelling language for the resilience assessment of networked systems of systems

Systems of Systems (SoS) present challenging features and existing tools result often inadequate for their analysis, especially for heteregeneous networked infrastructures. Most accident scenarios in networked systems cannot be addressed by a simplistic black or white (i.e. functioning or failed) approach. Slow deviations from nominal operation conditions may cause degraded behaviours that suddenly end up into unexpected malfunctioning, with large portions of the network affected. In this paper,we present a language for modelling networked SoS. The language makes it possible to represent interdependencies of various natures, e.g. technical, organizational and human. The representation of interdependencies is based on control relationships that exchange physical quantities and related information. The language also makes it possible the identification of accident scenarios, by representing the propagation of failure events throughout the network. The results can be used for assessing the effectiveness of those mechanisms and measures that contribute to the overall resilience, both in qualitative and quantitative terms. The presented modelling methodology is general enough to be applied in combination with already existing system analysis techniques, such as risk assessment, dependability and performance evaluation