Évaluation de la performance du score de propension à hautes dimensions dans le cadre d’études observationnelles québécoises

Les scores de propension (PS) sont fréquemment utilisés dans l’ajustement pour des facteurs confondants liés au biais d’indication. Cependant, ils sont limités par le fait qu’ils permettent uniquement l’ajustement pour des facteurs confondants connus et mesurés. Les scores de propension à hautes dimensions (hdPS), une variante des PS, utilisent un algorithme standardisé afin de sélectionner les covariables pour lesquelles ils vont ajuster. L’utilisation de cet algorithme pourrait permettre l’ajustement de tous les types de facteurs confondants. Cette thèse a pour but d’évaluer la performance de l’hdPS vis-à-vis le biais d’indication dans le contexte d’une étude observationnelle examinant l’effet diabétogénique potentiel des statines. Dans un premier temps, nous avons examiné si l’exposition aux statines était associée au risque de diabète. Les résultats de ce premier article suggèrent que l’exposition aux statines est associée avec une augmentation du risque de diabète et que cette relation est dose-dépendante et réversible dans le temps. Suite à l’identification de cette association, nous avons examiné dans un deuxième article si l’hdPS permettait un meilleur ajustement pour le biais d’indication que le PS; cette évaluation fut entreprise grâce à deux approches: 1) en fonction des mesures d’association ajustées et 2) en fonction de la capacité du PS et de l’hdPS à sélectionner des sous-cohortes appariées de patients présentant des caractéristiques similaires vis-à-vis 19 caractéristiques lorsqu’ils sont utilisés comme critère d’appariement. Selon les résultats présentés dans le cadre du deuxième article, nous avons démontré que l’évaluation de la performance en fonction de la première approche était non concluante, mais que l’évaluation en fonction de la deuxième approche favorisait l’hdPS dans son ajustement pour le biais d’indication. Le dernier article de cette thèse a cherché à examiner la performance de l’hdPS lorsque des facteurs confondants connus et mesurés sont masqués à l’algorithme de sélection. Les résultats de ce dernier article indiquent que l’hdPS pourrait, au moins partiellement, ajuster pour des facteurs confondants masqués et qu’il pourrait donc potentiellement ajuster pour des facteurs confondants non mesurés. Ensemble ces résultats indiquent que l’hdPS serait supérieur au PS dans l’ajustement pour le biais d’indication et supportent son utilisation lors de futures études observationnelles basées sur des données médico-administratives.

Composing idiomatically for specific performers : collaboration in the creation of electroacoustic music

Cette thèse examine l'impact de la collaboration avec des instrumentistes particuliers sur la composition de quatre œuvres électroacoustiques. Assumant un rôle plus important que celui de consultant ou conseiller, les interprètes ont influencé les décisions de l'auteur / compositeur dans le cadre de multiples ateliers et d'enregistrements de ceux-ci. Cette thèse examine ainsi comment les outils médiatiques de la musique électroacoustique affectent et enrichissent les relations personnelles : ces outils favorisent la transcription et la traduction, qui à la fois soulignent et transforment la spécificité du son. Le dialogue de la collaboration permet par la suite non seulement une réconciliation plus facile entre les éléments médiatisés et directs dans une oeuvre, mais aussi l'ouverture de son potentiel d'interprétation. En se servant d'une méthodologie qui fait appel à une pratique d'auto-réflexion et récursivité, cette thèse explore des sujets tels que : l'analyse du style personnel dans un cadre linguistique; l'importance du contact physique dans la collaboration et sa traduction incomplète sur support; et les défis de la préservation de la musique électroacoustique pour média ou interprète particulier. Des exemples de la création collaborative de quatre œuvres, racontés de manière personnelle, sont tressés parmi le récit plus théorique de cette thèse, imitant le va-et-vient de la recherche-création.

Plan d’intervention visant à soutenir le processus décisionnel concernant l’implantation d’un dispositif d’assistance ventriculaire auprès des patients vivant avec l’insuffisance cardiaque avancée et leur famille

Rapport de stage présenté à la Faculté des sciences infirmières en vue de l'obtention du grade de Maître ès sciences (M.Sc.) en sciences infirmières option expertise-conseil en soins infirmiers

A heuristic algorithm for the Capacitated Vehicle Routing Problem with Synchronized Pick-ups and Drop-offs : a case study for medications delivery and supervision in DR Congo

Dans des contextes de post-urgence tels que le vit la partie occidentale de la République Démocratique du Congo (RDC), l’un des défis cruciaux auxquels font face les hôpitaux ruraux est de maintenir un niveau de médicaments essentiels dans la pharmacie. Sans ces médicaments pour traiter les maladies graves, l’impact sur la santé de la population est significatif. Les hôpitaux encourent également des pertes financières dues à la péremption lorsque trop de médicaments sont commandés. De plus, les coûts du transport des médicaments ainsi que du superviseur sont très élevés pour les hôpitaux isolés ; les coûts du transport peuvent à eux seuls dépasser ceux des médicaments. En utilisant la province du Bandundu, RDC pour une étude de cas, notre recherche tente de déterminer la faisabilité (en termes et de la complexité du problème et des économies potentielles) d’un problème de routage synchronisé pour la livraison de médicaments et pour les visites de supervision. Nous proposons une formulation du problème de tournées de véhicules avec capacité limitée qui gère plusieurs exigences nouvelles, soit la synchronisation des activités, la préséance et deux fréquences d’activités. Nous mettons en œuvre une heuristique « cluster first, route second » avec une base de données géospatiales qui permet de résoudre le problème. Nous présentons également un outil Internet qui permet de visualiser les solutions sur des cartes. Les résultats préliminaires de notre étude suggèrent qu’une solution synchronisée pourrait offrir la possibilité aux hôpitaux ruraux d’augmenter l’accessibilité des services médicaux aux populations rurales avec une augmentation modique du coût de transport actuel.

Array pattern nulling by element position perturbations using a genetic algorithm

A genetic algorithm has been used for null steering in phased and adaptive arrays . It has been shown that it is possible to steer the array null s precisely to the required interference directions and to achieve any prescribed null depths . A comparison with the results obtained from the analytic solution shows the advantages of using the genetic algorithm for null steering in linear array patterns

Creation and Annihilation of Fluxons in ac‐driven Semi-annular Josephson Junction

A new geometry (semiannular) for Josephson junction has been proposed and theoretical studies have shown that the new geometry is useful for electronic applications [1, 2]. In this work we study the voltage‐current response of the junction with a periodic modulation. The fluxon experiences an oscillating potential in the presence of the ac‐bias which increases the depinning current value. We show that in a system with periodic boundary conditions, average progressive motion of fluxon commences after the amplitude of the ac drive exceeds a certain threshold value. The analytic studies are justified by simulating the equation using finite‐difference method. We observe creation and annihilation of fluxons in semiannular Josephson junction with an ac‐bias in the presence of an external magnetic field.

An Error-Localization, Validation and Optimization Tool for Embedded Code Augmentation: an Architecture Oriented Approach

Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.This dissertation contributes to an architecture oriented code validation, error localization and optimization technique assisting the embedded system designer in software debugging, to make it more effective at early detection of software bugs that are otherwise hard to detect, using the static analysis of machine codes. The focus of this work is to develop methods that automatically localize faults as well as optimize the code and thus improve the debugging process as well as quality of the code.Validation is done with the help of rules of inferences formulated for the target processor. The rules govern the occurrence of illegitimate/out of place instructions and code sequences for executing the computational and integrated peripheral functions. The stipulated rules are encoded in propositional logic formulae and their compliance is tested individually in all possible execution paths of the application programs. An incorrect sequence of machine code pattern is identified using slicing techniques on the control flow graph generated from the machine code.An algorithm to assist the compiler to eliminate the redundant bank switching codes and decide on optimum data allocation to banked memory resulting in minimum number of bank switching codes in embedded system software is proposed. A relation matrix and a state transition diagram formed for the active memory bank state transition corresponding to each bank selection instruction is used for the detection of redundant codes. Instances of code redundancy based on the stipulated rules for the target processor are identified.This validation and optimization tool can be integrated to the system development environment. It is a novel approach independent of compiler/assembler, applicable to a wide range of processors once appropriate rules are formulated. Program states are identified mainly with machine code pattern, which drastically reduces the state space creation contributing to an improved state-of-the-art model checking. Though the technique described is general, the implementation is architecture oriented, and hence the feasibility study is conducted on PIC16F87X microcontrollers. The proposed tool will be very useful in steering novices towards correct use of difficult microcontroller features in developing embedded systems.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

An adaptive cluster based routing scheme for mobile wireless sensor networks

Clustering schemes improve energy efficiency of wireless sensor networks. The inclusion of mobility as a new criterion for the cluster creation and maintenance adds new challenges for these clustering schemes. Cluster formation and cluster head selection is done on a stochastic basis for most of the algorithms. In this paper we introduce a cluster formation and routing algorithm based on a mobility factor. The proposed algorithm is compared with LEACH-M protocol based on metrics viz. number of cluster head transitions, average residual energy, number of alive nodes and number of messages lost

Fixed Point Decimal Multiplication Using RPS Algorithm

Decimal multiplication is an integral part of financial, commercial, and internet-based computations. A novel design for single digit decimal multiplication that reduces the critical path delay and area for an iterative multiplier is proposed in this research. The partial products are generated using single digit multipliers, and are accumulated based on a novel RPS algorithm. This design uses n single digit multipliers for an n × n multiplication. The latency for the multiplication of two n-digit Binary Coded Decimal (BCD) operands is (n + 1) cycles and a new multiplication can begin every n cycle. The accumulation of final partial products and the first iteration of partial product generation for next set of inputs are done simultaneously. This iterative decimal multiplier offers low latency and high throughput, and can be extended for decimal floating-point multiplication.

IR: Knowledge Creation, Knowledge Management and Knowledge Dissemination

Open access iiiovemerit and open source software movement plays an important role in creation of knowledge, knowledge management and knowledge dissemination. Scholarly communication and publishing are increasingly taking place in the electronic environment. With a growing proportion of the scholarly record now existing only in digital format, serious issues regarding access and preservation are being raised that are central to future scholarship. Institutional Repositories provide access to past. present and future scholarly literature and research documentation; ensures its preservation; assists users in discovery and use; and offers educational programs to enable users to develop lifelong literacy. This paper explores these aspects on how IR of Cochin University of Science & Technology supports scientific community for knowledge creation. knowledge Management, and knowledge dissemination.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Parallel Genetic Algorithm for Document Image Compression Optimization

This work proposes a parallel genetic algorithm for compressing scanned document images. A fitness function is designed with Hausdorff distance which determines the terminating condition. The algorithm helps to locate the text lines. A greater compression ratio has achieved with lesser distortion

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.