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Procoagulant control strategies for the human blood clotting process
No full textThis paper describes the comparison between two drug control strategies to hemophilia A. To emulate blood clotting and the pathological condition of hemophilia, a mathematical model composed by 14 ordinary differential equations is considered. We adopt a variable structure non-linear PID approach and a Model Predictive Control in order to control the dosage of procoagulant factor used in the treatment of hemophiliac patient. The two control actions are sampled for a practical application. Finally, we discuss and compare the results of the two control approaches, introducing a suited control index (eINR)
COMT Genetic Reduction Produces Sexually Divergent Effects on Cortical Anatomy and Working Memory in Mice and Humans
No full textGenetic variations in catechol-O-methyltransferase (COMT) that modulate cortical dopamine have been associated with pleiotropic behavioral effects in humans and mice. Recent data suggest that some of these effects may vary among sexes. However, the specific brain substrates underlying COMT sexual dimorphisms remain unknown. Here, we report that genetically driven reduction in COMT enzyme activity increased cortical thickness in the prefrontal cortex (PFC) and postero-parieto-temporal cortex of male, but not female adult mice and humans. Dichotomous changes in PFC cytoarchitecture were also observed: reduced COMT increased a measure of neuronal density in males, while reducing it in female mice. Consistent with the neuroanatomical findings, COMT-dependent sex-specific morphological brain changes were paralleled by divergent effects on PFC-dependent working memory in both mice and humans. These findings emphasize a specific sex–gene interaction that can modulate brain morphological substrates with influence on behavioral outcomes in healthy subjects and, potentially, in neuropsychiatric populations
Software Engineering for Collective Autonomic Systems: The ASCENS Approach
No full textThe ASCENS project deals with designing systems as ensembles of adaptive components. Among the outputs of the ASCENS project are multiple tools that address particular issues in designing the ensembles, ranging from support for early stage formal modeling to runtime environment for executing and monitoring ensemble implementations. The goal of this chapter is to provide a compact description of the individual tools, which is supplemented by additional downloadable material on the project website
A White Box Perspective on Behavioural Adaptation
No full textWe present a white-box conceptual framework for adaptation developed in the context of the EU Project ASCENS coordinated by Martin Wirsing. We called it CoDa, for Control Data Adaptation, since it is based on the notion of control data. CoDa promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation, ranging from programming languages and paradigms to computational models and architectural solutions
A SOM-based Chan–Vese model for unsupervised image segmentation
No full textActive Contour Models (ACMs) constitute an efficient energy-based image segmentation framework. They usually deal with the segmentation problem as an optimization problem, formulated in terms of a suitable functional, constructed in such a way that its minimum is achieved in correspondence with a contour that is a close approximation of the actual object boundary. However, for existing ACMs, handling images that contain objects characterized by many different intensities still represents a challenge. In this paper, we propose a novel ACM that combines—in a global and unsupervised way—the advantages of the Self-Organizing Map (SOM) within the level set framework of a state-of-the-art unsupervised global ACM, the Chan–Vese (C–V) model. We term our proposed model SOM-based Chan–Vese (SOMCV) active contour model. It works by explicitly integrating the global information coming from the weights (prototypes) of the neurons in a trained SOM to help choosing whether to shrink or expand the current contour during the optimization process, which is performed in an iterative way. The proposed model can handle images that contain objects characterized by complex intensity distributions, and is at the same time robust to the additive noise. Experimental results show the high accuracy of the segmentation results obtained by the SOMCV model on several synthetic and real images, when compared to the Chan–Vese model and other image segmentation models
Boundary integral formulation for interfacial cracks in thermodiffusive bimaterials
No full textAn original boundary integral formulation is proposed for the problem of a semi-infinite crack at the interface between two dissimilar elastic materials in the presence of heat flows and mass diffusion. Symmetric and skew-symmetric weight function matrices are used together with a generalized Betti’s reciprocity theorem in order to derive a system of integral equations that relate the applied loading, the temperature and mass concentration fields, the heat and mass fluxes on the fracture surfaces and the resulting crack opening. The obtained integral identities can have many relevant applications, such as for the modelling of crack and damage processes at the interface between different components in electrochemical energy devices characterized by multi-layered structures (solid oxide fuel cells and lithium ions batteries)
A Cortical and Sub-cortical Parcellation Clustering by Intrinsic Functional Connectivity
Get PDFNetwork analysis of resting-state fMRI (rsfMRI) has been widely utilized to investigate the functional architecture of the whole brain. Such analysis can divide the brain into several discrete elements (nodes) connected by links (edges) representing the relation between two elements. The brain cortical and subcortical areas can be segmented or parcelled into several functional and/or structural regions. The connectome analysis of human-brain structure and functional connectivity provides a unique opportunity to understand the organisation of brain networks. However, such analyses require an appropriate definition of functional or structural nodes to efficiently represent cortical regions. In order to address this issue, here we propose a robust parcellation method based on resting-state fMRI, which can be generalized from the single-subject level to the multi-group one. Considering the input data of a single subject and constructing multi-resolution graph elements. We combined voting-based measurements to divide the cortical region into sub-regions in order to obtain the whole brain parcellation. Our parcellation relies on majority vote and poses spatial constraints within a hierarchical agglomerative clustering framework to define parcels that are spatially homogeneous. We used rsfMRI data collected from 40 healthy subjects and we showed that our purposed algorithm is able to compute stable and reproducible parcellations across the group of subjects at multi-resolution level. We find that, even though previous methods ensure on average larger overlap between parcels and regions in AAL atlas, the method proposed herein reduces inter-subject variability, especially when the number of parcels increases. Our high-resolution parcels seem to be functionally more consistent and reliable and can be a useful tool for future analysis that will aim to match functional and structural architecture of the brain
Forward and Backward Bisimulations for Chemical Reaction Networks
No full textWe present two quantitative behavioral equivalences over species of a chemical reaction network(CRN) with semantics based on ordinary differential equations.Forward CRN bisimulationiden-tifies a partition where each equivalence class represents the exact sum of the concentrations ofthe species belonging to that class. Backward CRN bisimulationrelates species that have theidentical solutions at all time points when starting from the same initial conditions. Both notionscan be checked using only CRN syntactical information, i.e., by inspection of the set of reactions. We provide a unified algorithm that computes the coarsest refinement up to our bisimulationsin polynomial time. Further, we give algorithms to compute quotient CRNs induced by a bisim-ulation. As an application, we find significant reductions in a number of models of biologicalprocesses from the literature. In two cases we allow the analysis of benchmark models whichwould be otherwise intractable due to their memory requirements
Systemic risk and banking regulation: some facts on the new regulatory framework
No full textThe recent financial crisis highlighted the relevant role of the systemic effects of banks’ defaults on the stability of the whole financial system. In this work we draw an organic picture of the current regulations, moving from the definitions of systemic risk to the issues concerning data availability. We show how a more detailed flow of data on traded deals might shed light on some systemic risk features taken into account only partially in the past. In particular, we analyse how the new regulatory framework allows regulators to describe OTC derivatives markets according to more detailed partitions, thus depicting a more realistic picture of the system. Finally, we suggest to study sub-markets illiquidity conditions to consider possible spill over effects which might lead to a worsening for the entire system
Learning as Constraint Reactions
No full textA theory of learning is proposed,which extends naturally the classic regularization framework of kernelmachines to the case in which the agent interacts with a richer environment, compactly described by the notion of constraint. Variational calculus is exploited to derive general representer theorems that give a description of the structure of the solution to the learning problem. It is shown that such solution can be represented in terms of constraint reactions, which remind the corresponding notion in analytic mechanics. In particular, the derived representer theorems clearly show the extension of the classic kernel expansion on support vectors to the expansion on support constraints. As an application of the proposed theory three examples are given, which illustrate the dimensional collapse to a finite-dimensional space of parameters. The constraint reactions are calculated for the classic collection of supervised examples, for the case of box constraints, and for the case of hard holonomic linear constraints mixed with supervised examples. Interestingly, this leads to representer theorems for which we can re-use the kernel machine mathematical and algorithmic apparatus