Vocabulary and language model adaptation using information retrieval

International audience

Healthcare Network Modeling and Analysis

Part 20: Health and Care Networks

Some features of the law and order model in Spain

This research is based on the hypothesis that law and order model is displacing the procedura justice system in Spain. After a thorough review of the international literature, one can observe that the traditional structure of the penal system does not seem to be capable of containing the new forms of crime. The new penal model assumes that public opinion is alarmed and unwilling to understand rational approaches to crime, so it will be likely to accept measures aimed at calming the fear of crime, through extensive control policies and penal tools to manage uncivil behavior. Objectives and methodology A measuring instrument has been developed to confirm this hypothesis, consisting of ten features that characterize the law and order model. This instrument has been used to identify examples of its ten features in the rules and practices developed at each phase of the Spanish criminal justice system. The analysis has focused specifically on public discourse about delinquency, criminal policy decisions, legislative processes, police routines, judicial dynamics, and prison system practices. Main results The investigation has shown that there are many processes and practices indicating that the law and order model is consolidating itself in the Spanish penal system. Nevertheless this process has a different intensity at each phase, being stronger at the legislative stage and softer in the penitentiary enforcement phase. One of the main conclusions is, therefore, that the designed instrument is ideal for measuring the degree of penetration of the model throughout the system. Some of the most striking results of the reasearch will be presented at the conference. Finally, proposals arise that could prevent the new model is fully seated in our criminal justice system, finding that the trend toward more severe penalties shown already unsustainable.

Hierarchical spatial segregation of two Mediterranean vole species: the role of patch‑network structure and matrix composition

According to ecological theory, the coexistence of competitors in patchy environments may be facilitated by hierarchical spatial segregation along axes of environmental variation, but empirical evidence is limited. Cabrera and water voles show a metapopulation-like structure in Mediterranean farmland, where they are known to segregate along space, habitat, and time axes within habitat patches. Here, we assess whether segregation also occurs among and within landscapes, and how this is influenced by patch-network and matrix composition. We surveyed 75 landscapes, each covering 78 ha, where we mapped all habitat patches potentially suitable for Cabrera and water voles, and the area effectively occupied by each species (extent of occupancy). The relatively large water vole tended to be the sole occupant of landscapes with high habitat amount but relatively low patch density (i.e., with a few large patches), and with a predominantly agricultural matrix, whereas landscapes with high patch density (i.e.,many small patches) and low agricultural cover, tended to be occupied exclusively by the small Cabrera vole. The two species tended to co-occur in landscapes with intermediate patch-network and matrix characteristics, though their extents of occurrence were negatively correlated after controlling for environmental effects. In combination with our previous studies on the Cabrera-water vole system, these findings illustrated empirically the occurrence of hierarchical spatial segregation, ranging from withinpatches to among-landscapes. Overall, our study suggests that recognizing the hierarchical nature of spatial segregation patterns and their major environmental drivers should enhance our understanding of species coexistence in patchy environments.

Modeling Alzheimer disease with iPSCs and mouse model for the identification of risk factors, new targets, and potential therapeutical strategies

Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly. Donepezil is the first-line drug used for AD. In section one, the experimental activity was oriented to evaluate and characterize molecular and cellular mechanisms that contribute to neurodegeneration induced by the Aβ1-42 oligomers (Aβ1-42O) and potential neuroprotective effects of the hybrids feruloyl-donepezil compound called PQM130. The effects of PQM130 were compared to donepezil in a murine AD model, obtained by intracerebroventricular (i.c.v.) injection of Aβ1-42O. The intraperitoneal administration of PQM130 (0.5-1 mg/kg) after i.c.v. Aβ1-42O injection improved learning and memory, protecting mice against spatial cognition decline. Moreover, it reduced oxidative stress, neuroinflammation and neuronal apoptosis, induced cell survival and protein synthesis in mice hippocampus. PQM130 modulated different pathways than donepezil, and it is more effective in counteracting Aβ1-42O damage. The section two of the experimental activity was focused on studying a loss of function variants of ABCA7. GWA studies identified mutations in the ABCA7 gene as a risk factor for AD. The mechanism through which ABCA7 contributes to AD is not clear. ABCA7 regulates lipid metabolism and critically controls phagocytic function. To investigate ABCA7 functions, CRISPR/Cas9 technology was used to engineer human iPSCs and to carry the genetic variant Y622*, which results in a premature stop codon, causing ABCA7 loss-of-function. From iPSCs, astrocytes were generated. This study revealed the effects of ABCA7 loss in astrocytes. ABCA7 Y622* mutation induced dysfunctional endocytic trafficking, impairing Aβ clearance, lipid dysregulation and cell homeostasis disruption, alterations that could contribute to AD. Though further studies are needed to confirm the PQM130 neuroprotective role and ABCA7 function in AD, the provided results showed a better understanding of AD pathophysiology, a new therapeutic approach to treat AD, and illustrated an innovative methodology for studying the disease.

Waste prevention: policy instruments and business model innovation

Waste prevention (WP) is a strategy which helps societies and individuals to strive for sufficiency in resource consumption within planetary boundaries alongside sustainable and equitable well-being and to decouple the concepts of well-being and life satisfaction from materialism. Within this dissertation, some instruments to promote WP are analysed, by adopting two perspectives: firstly, the one of policymakers, at different governance levels, and secondly, the one of business in the electrical and electronic equipment (EEE) sector. At a national level, the role of WP programmes and market-based instruments (extended producer responsibility, pay-as-you-throw schemes, deposit-refund systems, environmental taxes) in boosting prevention of municipal solid waste is investigated. Then, focusing on the Emilia-Romagna Region (Italy), the performances of the waste management system are assessed over a long period, including some years before and after an institutional reform of the waste management governance regime. The impact of a centralisation (at a regional level) of both planning and economic regulation of the waste services on waste generation and WP is analysed. Finally, to support the regional decision-makers in the prioritisation of publicly funded projects for WP, a framework for the sustainability assessment, the evaluation of success, and the prioritisation of WP measures was applied to some projects implemented by Municipalities in the Region. Trying to close the research gap between engineering and business, WP strategies are discussed as drivers for business model (BM) innovation in EEE sector. Firstly, an innovative approach to a digital tracking solution for professional EEE management is analysed. New BMs which facilitate repair, reuse, remanufacturing, and recycling are created and discussed. Secondly, the impact of BMs based on servitisation and on producer ownership on the extension of equipment lifetime is analysed, by performing a review of real cases of organizations in the EEE sector applying result- and use-oriented BMs.

Hybrid ground-aerial mesh networks for IoT monitoring applications: network design and software platform development

The Internet of Things (IoT) has grown rapidly in recent years, leading to an increased need for efficient and secure communication between connected devices. Wireless Sensor Networks (WSNs) are composed of small, low-power devices that are capable of sensing and exchanging data, and are often used in IoT applications. In addition, Mesh WSNs involve intermediate nodes forwarding data to ensure more robust communication. The integration of Unmanned Aerial Vehicles (UAVs) in Mesh WSNs has emerged as a promising solution for increasing the effectiveness of data collection, as UAVs can act as mobile relays, providing extended communication range and reducing energy consumption. However, the integration of UAVs and Mesh WSNs still poses new challenges, such as the design of efficient control and communication strategies. This thesis explores the networking capabilities of WSNs and investigates how the integration of UAVs can enhance their performance. The research focuses on three main objectives: (1) Ground Wireless Mesh Sensor Networks, (2) Aerial Wireless Mesh Sensor Networks, and (3) Ground/Aerial WMSN integration. For the first objective, we investigate the use of the Bluetooth Mesh standard for IoT monitoring in different environments. The second objective focuses on deploying aerial nodes to maximize data collection effectiveness and QoS of UAV-to-UAV links while maintaining the aerial mesh connectivity. The third objective investigates hybrid WMSN scenarios with air-to-ground communication links. One of the main contribution of the thesis consists in the design and implementation of a software framework called "Uhura", which enables the creation of Hybrid Wireless Mesh Sensor Networks and abstracts and handles multiple M2M communication stacks on both ground and aerial links. The operations of Uhura have been validated through simulations and small-scale testbeds involving ground and aerial devices.

Les Réseaux de la Pénombre:Typologie de l'aide reçue par les Personnes Âgées

Nous nous sommes intéressés à l’analyse et à la mise à jour d’une typologie de l’aide reçue par les personnes âgées de 65 ans et plus vivant à domicile. Cette étude secondaire s’est basée sur les données recueuillies dans deux milieux francophones, Hochelaga-Maisonneuve (HM) et Moncton (MCT). La collecte de données avait été faite par l’entremise d’un questionnaire administré par entrevue face à face. Les deux objectifs, de cette thèse sont : 1) Établir une typologie des réseaux d’aide, résultant de la combinaison des sources d’aide et des tâches accomplies ; 2) Identifier les principaux déterminants d’appartenance aux réseaux. La typologie obtenue met en relation les ressources, formelles ou informelles, utilisées par les personnes âgées et l’aide instrumentale reçue. La capacité ou l’incapacité à effectuer neuf activités de la vie quotidienne et huit de la vie domestique ont servi à évaluer l’aide reçue. Six ressources formelles et dix informelles ont été examinées selon qu’elles étaient les 1ères, 2ièmes ou 3ièmes sources d’aide utilisées par les personnes âgées. L’approche privilégiée s’est inspirée de celle des réseaux sociaux et du modèle de Pescosolido. C’est l’influence des caractéristiques sociodémographiques des personnes âgées, de leurs états de santé, de leurs habitudes de vie sur leurs réseaux qui nous ont intéressés. Les résultats sont présentés à chaque fois pour nos deux milieux séparément. Nous commençons par un descriptif des sources d’aide utilisées et des aides reçues. Puis les profils des sources d’aide utilisées et des activités accomplies sont exposés pour l’ensemble des personnes âgées. Ces profils servent de base pour obtenir notre typologie. Elle comprend cinq catégories. Ces catégories sont toutes composées de personnes âgées faisant appel à de l’aide formelle, informelle ou mixte pour accomplir des tâches uniques ou multiples. La première catégorie « Transitoire », comprend 39% (HM) et 46% (MCT) des personnes âgées qui débute un processus d’incapacité. Elles font appel à des ressources informelles pour accomplir une tâche unique. La deuxième catégorie « Personnes âgées seules » en rassemble 14% (HM et MCT), majoritairement des femmes, avec peu d’incapacités. Ces dernières utilisent de l’aide formelle pour une tâche unique. La troisième catégorie « Familiale » regroupe 12% (HM et MCT) des personnes âgées bien entourées qui ont plusieurs incapacités. Ces gens font appel à des sources d’aide informelles pour réaliser des tâches multiples. La quatrième catégorie « Très fragile » rassemble 30% (HM) et 25% (MCT) des personnes âgées peu entourées ayant beaucoup d’incapacités. Elles utilisent des ressources d’aide mixtes pour effectuer des tâches multiples. La cinquième catégorie « Pré institutionnel » comprend 4% (HM et MCT) des personnes âgées qui ont le plus d’incapacités et qui sont seules. Ces gens font appel à de l’aide formelle pour des tâches multiples. Les déterminants d’appartenance à ces catégories proviennent des blocs sociodémographiques, état de santé et réseaux sociaux de notre modèle théorique. Une des contributions importantes de cette thèse a été de pouvoir identifier cinq catégories bien distinctes composant une typologie de l’aide reçue, indépendamment du milieu, par des personnes âgées vivant à domicile. MOTS CLÉS : Typologie, réseaux sociaux, personnes âgées, services de soins, formels, informels, aides reçues, sources d’aide, incapacités, déterminants d’appartenance, fragilité

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Decomposition Methods and Network Design Problems

Decomposition based approaches are recalled from primal and dual point of view. The possibility of building partially disaggregated reduced master problems is investigated. This extends the idea of aggregated-versus-disaggregated formulation to a gradual choice of alternative level of aggregation. Partial aggregation is applied to the linear multicommodity minimum cost flow problem. The possibility of having only partially aggregated bundles opens a wide range of alternatives with different trade-offs between the number of iterations and the required computation for solving it. This trade-off is explored for several sets of instances and the results are compared with the ones obtained by directly solving the natural node-arc formulation. An iterative solution process to the route assignment problem is proposed, based on the well-known Frank Wolfe algorithm. In order to provide a first feasible solution to the Frank Wolfe algorithm, a linear multicommodity min-cost flow problem is solved to optimality by using the decomposition techniques mentioned above. Solutions of this problem are useful for network orientation and design, especially in relation with public transportation systems as the Personal Rapid Transit. A single-commodity robust network design problem is addressed. In this, an undirected graph with edge costs is given together with a discrete set of balance matrices, representing different supply/demand scenarios. The goal is to determine the minimum cost installation of capacities on the edges such that the flow exchange is feasible for every scenario. A set of new instances that are computationally hard for the natural flow formulation are solved by means of a new heuristic algorithm. Finally, an efficient decomposition-based heuristic approach for a large scale stochastic unit commitment problem is presented. The addressed real-world stochastic problem employs at its core a deterministic unit commitment planning model developed by the California Independent System Operator (ISO).

Mechanism and model of the oscillatory electro-oxidation of methanol

A mechanism for the kinetic instabilities observed in the galvanostatic electro-oxidation of methanol is suggested and a model developed. The model is investigated using stoichiometric network analysis as well as concepts from algebraic geometry (polynomial rings and ideal theory) revealing the occurrence of a Hopf and a saddle-node bifurcation. These analytical solutions are confirmed by numerical integration of the system of differential equations. (C) 2010 American Institute of Physics

A model for heterogeneous networks management and Performance evaluation

In general, modern networks are analysed by taking several Key Performance Indicators (KPIs) into account, their proper balance being required in order to guarantee a desired Quality of Service (QoS), particularly, cellular wireless heterogeneous networks. A model to integrate a set of KPIs into a single one is presented, by using a Cost Function that includes these KPIs, providing for each network node a single evaluation parameter as output, and reflecting network conditions and common radio resource management strategies performance. The proposed model enables the implementation of different network management policies, by manipulating KPIs according to users' or operators' perspectives, allowing for a better QoS. Results show that different policies can in fact be established, with a different impact on the network, e.g., with median values ranging by a factor higher than two.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.