Performance analysis of software run-time behaviour using 3D-visualisation

Monimutkaisen tietokonejärjestelmän suorituskykyoptimointi edellyttää järjestelmän ajonaikaisen käyttäytymisen ymmärtämistä. Ohjelmiston koon ja monimutkaisuuden kasvun myötä suorituskykyoptimointi tulee yhä tärkeämmäksi osaksi tuotekehitysprosessia. Tehokkaampien prosessorien käytön myötä myös energiankulutus ja lämmöntuotto ovat nousseet yhä suuremmiksi ongelmiksi, erityisesti pienissä, kannettavissa laitteissa. Lämpö- ja energiaongelmien rajoittamiseksi on kehitetty suorituskyvyn skaalausmenetelmiä, jotka edelleen lisäävät järjestelmän kompleksisuutta ja suorituskykyoptimoinnin tarvetta. Tässä työssä kehitettiin visualisointi- ja analysointityökalu ajonaikaisen käyttäytymisen ymmärtämisen helpottamiseksi. Lisäksi kehitettiin suorituskyvyn mitta, joka mahdollistaa erilaisten skaalausmenetelmien vertailun ja arvioimisen suoritusympäristöstä riippumatta, perustuen joko suoritustallenteen tai teoreettiseen analyysiin. Työkalu esittää ajonaikaisesti kerätyn tallenteen helposti ymmärrettävällä tavalla. Se näyttää mm. prosessit, prosessorikuorman, skaalausmenetelmien toiminnan sekä energiankulutuksen kolmiulotteista grafiikkaa käyttäen. Työkalu tuottaa myös käyttäjän valitsemasta osasta suorituskuvaa numeerista tietoa, joka sisältää useita oleellisia suorituskykyarvoja ja tilastotietoa. Työkalun sovellettavuutta tarkasteltiin todellisesta laitteesta saatua suoritustallennetta sekä suorituskyvyn skaalauksen simulointia analysoimalla. Skaalausmekanismin parametrien vaikutus simuloidun laitteen suorituskykyyn analysoitiin.

Sputum containing zinc enhances carbapenem resistance, biofilm formation and virulence of Pseudomonas aeruginosa.

Pseudomonas aeruginosa chronic lung infections are the leading cause of mortality in cystic fibrosis patients, a serious problem which is notably due to the numerous P. aeruginosa virulence factors, to its ability to form biofilms and to resist the effects of most antibiotics. Production of virulence factors and biofilm formation by P. aeruginosa is highly coordinated through complex regulatory systems. We recently found that CzcRS, the zinc and cadmium-specific two-component system is not only involved in metal resistance, but also in virulence and carbapenem antibiotic resistance in P. aeruginosa. Interestingly, zinc has been shown to be enriched in the lung secretions of cystic fibrosis patients. In this study, we investigated whether zinc might favor P. aeruginosa pathogenicity using an artificial sputum medium to mimic the cystic fibrosis lung environment. Our results show that zinc supplementation triggers a dual P. aeruginosa response: (i) it exacerbates pathogenicity by a CzcRS two-component system-dependent mechanism and (ii) it stimulates biofilm formation by a CzcRS-independent mechanism. Furthermore, P. aeruginosa cells embedded in these biofilms exhibited increased resistance to carbapenems. We identified a novel Zn-sensitive regulatory circuit controlling the expression of the OprD porin and modifying the carbapenem resistance profile. Altogether our data demonstrated that zinc levels in the sputum of cystic fibrosis patients might aggravate P. aeruginosa infection. Targeting zinc levels in sputum would be a valuable strategy to curb the increasing burden of P. aeruginosa infections in cystic fibrosis patients.

Formalizing defeasible argumentation using a labeled deductive system

In the last years there has been an increasing demand of a variety of logical systems, prompted mostly by applications of logic in AI, logic programming and other related areas. Labeled Deductive Systems (LDS) were developed as a flexible methodology to formalize such a kind of complex logical systems. In the last decade, defeasible argumentation has proven to be a confluence point for many approaches to formalizing commonsense reasoning. Different formalisms have been developed, many of them sharing common features. This paper presents a formalization of an LDS for defensible argumentation, in which the main issues concerning defeasible argumentation are captured within a unified logical framework. The proposed framework is defined in two stages. First, defeasible inference will be formalized by characterizing an argumentative LDS. That system will be then extended in order to capture conflict among arguments using a dialectical approach. We also present some logical properties emerging from the proposed framework, discussing also its semantical characterization.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

Mashup-alustan soveltuvuus tilannekuvan luontiin verkostopuolustusympäristössä

Verkostosodankäynti on suuren huomion kohteena useiden maiden puolustusvoimien järjestelmäkehityshankkeissa. Verkostosodankäynnin tavoitteena on kytkeä kaikki taistelukentän komponentit yhteen nopean tiedonsiirtoverkon avulla. Tällä pyritään tehokkaampaan tiedonjakoon ja edelleen resurssien tehokkaampaan käyttöön. Keskeisessä osassa verkostosodankäynnin tavoitteiden saavuttamisessa on palvelukeskeinen arkkitehtuuri (SOA). Tarve yhä monimutkaisemmille tietojärjestelmille pakottaa myös sotilasympäristön toimijat etsimään ratkaisuja valmiista kaupallisista toteutuksista. Verkottunut toiminta tuottaa valtavasti erilaista tilannetietoa. Tilannetiedon pohjalta muodostetaan erilaisia tilannekuvia, joita johtajat käyttävät päätöksentekonsa tukena. Työssä tutkitaan kaupallisen mashup-alustan käyttöä tilannekuvan luomiseen. Mashup-alusta on tietojärjestelmä, jolla voidaan helposti ja nopeasti integroida useista lähteistä saatavaa informaatiota. Mashup-alusta mahdollistaa niin kutsuttujen käyttäjämääriteltyjen tilannekuvien luomisen. Työn tuloksena mashup-alustan soveltuvuus tähän käyttöön on hyvä ja se soveltuu hyvin erityisesti tilanteisiin, joissa vaaditaan nopeita ratkaisuja. Jatkotutkimusta aiheesta tarvitaan, koska mashupalustan käyttöä sotilaallisissa tietojärjestelmissä ei ole juurikaan tutkittu ja aihe on suhteellisen uusi myös tiedeyhteisössä.

Integração numérica de leis de velocidade diferenciais com o uso do SCILAB

In this work, we applied the free open source SCILAB software for the numerical integration of differential rate law equations to obtain the concentration profiles of chemical species involved in the kinetics of some complex reactions. An automated method was applied to construct the system of ordinary differential equations (ODE) from the postulated chemical models. The solutions of the ODEs were obtained numerically by standard SCILAB functions. We successfully simulated even complex chemical systems such as pH oscillators. This communication opens up the possibility of using SCILAB in simulations and modeling by our chemistry undergraduate students.

Uskonnolliset herätykset ja kulttuurinen sopeutuminen

Kirjallisuusarvostelu

Challenges in managing large-scale agile software development transformation

Adapting and scaling up agile concepts, which are characterized by iterative, self-directed, customer value focused methods, may not be a simple endeavor. This thesis concentrates on studying challenges in a large-scale agile software development transformation in order to enhance understanding and bring insight into the underlying factors for such emerging challenges. This topic is approached through understanding the concepts of agility and different methods compared to traditional plan-driven processes, complex adaptive theory and the impact of organizational culture on agile transformational efforts. The empirical part was conducted by a qualitative case study approach. The internationally operating software development case organization had a year of experience of an agile transformation effort during it had also undergone organizational realignment efforts. The primary data collection was conducted through semi-structured interviews supported by participatory observation. As a result the identified challenges were categorized under four broad themes: organizational, management, team dynamics and process related. The identified challenges indicate that agility is a multifaceted concept. Agile practices may bring visibility in issues of which many are embedded in the organizational culture or in the management style. Viewing software development as a complex adaptive system could facilitate understanding of the underpinning philosophy and eventually solving the issues: interactions are more important than processes and solving a complex problem, such a novel software development, requires constant feedback and adaptation to changing requirements. Furthermore, an agile implementation seems to be unique in nature, and agents engaged in the interaction are the pivotal part of the success of achieving agility. In case agility is not a strategic choice for whole organization, it seems additional issues may arise due to different ways of working in different parts of an organization. Lastly, detailed suggestions to mitigate the challenges of the case organization are provided.

Network connectance and autonomy analyses of the photosynthetic apparatus in tropical tree species from different successional groups under contrasting irradiance conditions

Biological systems are complex dynamical systems whose relationships with environment have strong implications on their regulation and survival. From the interactions between plant and environment can emerge a quite complex network of plant responses rarely observed through classical analytical approaches. The objective of this current study was to test the hypothesis that photosynthetic responses of different tree species to increasing irradiance are related to changes in network connectances of gas exchange and photochemical apparatus, and alterations in plant autonomy in relation to the environment. The heat dissipative capacity through daily changes in leaf temperature was also evaluated. It indicated that the early successional species (Citharexylum myrianthum Cham. and Rhamnidium elaeocarpum Reiss.) were more efficient as dissipative structures than the late successional one (Cariniana legalis (Mart.) Kuntze), suggesting that the parameter deltaT (T ºCair - T ºCleaf) could be a simple tool in order to help the classification of successional classes of tropical trees. Our results indicated a pattern of network responses and autonomy changes under high irradiance. Considering the maintenance of daily CO2 assimilation, the tolerant species (C. myrianthum and R. elaeocarpum) to high irradiance trended to maintain stable the level of gas exchange network connectance and to increase the autonomy in relation to the environment. On the other hand, the late successional species (C. legalis) trended to lose autonomy, decreasing the network connectance of gas exchange. All species showed lower autonomy and higher network connectance of the photochemical apparatus under high irradiance.

Quantitative refinement of reaction-based biomodels

In the field of molecular biology, scientists adopted for decades a reductionist perspective in their inquiries, being predominantly concerned with the intricate mechanistic details of subcellular regulatory systems. However, integrative thinking was still applied at a smaller scale in molecular biology to understand the underlying processes of cellular behaviour for at least half a century. It was not until the genomic revolution at the end of the previous century that we required model building to account for systemic properties of cellular activity. Our system-level understanding of cellular function is to this day hindered by drastic limitations in our capability of predicting cellular behaviour to reflect system dynamics and system structures. To this end, systems biology aims for a system-level understanding of functional intraand inter-cellular activity. Modern biology brings about a high volume of data, whose comprehension we cannot even aim for in the absence of computational support. Computational modelling, hence, bridges modern biology to computer science, enabling a number of assets, which prove to be invaluable in the analysis of complex biological systems, such as: a rigorous characterization of the system structure, simulation techniques, perturbations analysis, etc. Computational biomodels augmented in size considerably in the past years, major contributions being made towards the simulation and analysis of large-scale models, starting with signalling pathways and culminating with whole-cell models, tissue-level models, organ models and full-scale patient models. The simulation and analysis of models of such complexity very often requires, in fact, the integration of various sub-models, entwined at different levels of resolution and whose organization spans over several levels of hierarchy. This thesis revolves around the concept of quantitative model refinement in relation to the process of model building in computational systems biology. The thesis proposes a sound computational framework for the stepwise augmentation of a biomodel. One starts with an abstract, high-level representation of a biological phenomenon, which is materialised into an initial model that is validated against a set of existing data. Consequently, the model is refined to include more details regarding its species and/or reactions. The framework is employed in the development of two models, one for the heat shock response in eukaryotes and the second for the ErbB signalling pathway. The thesis spans over several formalisms used in computational systems biology, inherently quantitative: reaction-network models, rule-based models and Petri net models, as well as a recent formalism intrinsically qualitative: reaction systems. The choice of modelling formalism is, however, determined by the nature of the question the modeler aims to answer. Quantitative model refinement turns out to be not only essential in the model development cycle, but also beneficial for the compilation of large-scale models, whose development requires the integration of several sub-models across various levels of resolution and underlying formal representations.

Implementation of Quantitative Reaction-based Model Refinement

Building a computational model for complex biological systems is an iterative process. It starts from an abstraction of the process and then incorporates more details regarding the specific biochemical reactions which results in the change of the model fit. Meanwhile, the model’s numerical properties such as its numerical fit and validation should be preserved. However, refitting the model after each refinement iteration is computationally expensive resource-wise. There is an alternative approach which ensures the model fit preservation without the need to refit the model after each refinement iteration. And this approach is known as quantitative model refinement. The aim of this thesis is to develop and implement a tool called ModelRef which does the quantitative model refinement automatically. It is both implemented as a stand-alone Java application and as one of Anduril framework components. ModelRef performs data refinement of a model and generates the results in two different well known formats (SBML and CPS formats). The development of this tool successfully reduces the time and resource needed and the errors generated as well by traditional reiteration of the whole model to perform the fitting procedure.

Impacts potentiels d’un changement climatique sur le pergélisol dans le nord canadien

Les fichiers video (d'animation) sont dans un format Windows Media (.wmv)

Étude des interactions supramoléculaires par modélisation moléculaire

L’avancée des infrastructures informatiques a permis l’émergence de la modélisation moléculaire. À cet effet, une multitude de modèles mathématiques sont aujourd’hui disponibles pour simuler différents systèmes chimiques. À l’aide de la modélisation moléculaire, différents types d’interactions chimiques ont été observés. À partir des systèmes les plus simples permettant l’utilisation de modèles quantiques rigoureux, une série d’approximations a été considérée pour rendre envisageable la simulation de systèmes moléculaires de plus en plus complexes. En premier lieu, la théorie de la fonctionnelle de densité dépendante du temps a été utilisée pour simuler les énergies d’excitation de molécules photoactives. De manière similaire, la DFT indépendante du temps a permis la simulation du pont hydrogène intramoléculaire de structures analogues au 1,3,5-triazapentadiène et la rationalisation de la stabilité des états de transition. Par la suite, la dynamique moléculaire et la mécanique moléculaire ont permis de simuler les interactions d’un trimère d’acide cholique et d’un pyrène dans différents solvants. Cette même méthodologie a été utilisée pour simuler les interactions d’un rotaxane-parapluie à l’interface d’un système biphasique. Finalement, l’arrimage moléculaire et les fonctions de score ont été utilisés pour simuler les interactions intermoléculaires entre une protéine et des milliers de candidats moléculaires. Les résultats ont permis de mettre en place une stratégie de développement d’un nouvel inhibiteur enzymatique.

Modélisation des émotions de l’apprenant et interventions implicites pour les systèmes tutoriels intelligents

La modélisation de l’expérience de l’utilisateur dans les Interactions Homme-Machine est un enjeu important pour la conception et le développement des systèmes adaptatifs intelligents. Dans ce contexte, une attention particulière est portée sur les réactions émotionnelles de l’utilisateur, car elles ont une influence capitale sur ses aptitudes cognitives, comme la perception et la prise de décision. La modélisation des émotions est particulièrement pertinente pour les Systèmes Tutoriels Émotionnellement Intelligents (STEI). Ces systèmes cherchent à identifier les émotions de l’apprenant lors des sessions d’apprentissage, et à optimiser son expérience d’interaction en recourant à diverses stratégies d’interventions. Cette thèse vise à améliorer les méthodes de modélisation des émotions et les stratégies émotionnelles utilisées actuellement par les STEI pour agir sur les émotions de l’apprenant. Plus précisément, notre premier objectif a été de proposer une nouvelle méthode pour détecter l’état émotionnel de l’apprenant, en utilisant différentes sources d’informations qui permettent de mesurer les émotions de façon précise, tout en tenant compte des variables individuelles qui peuvent avoir un impact sur la manifestation des émotions. Pour ce faire, nous avons développé une approche multimodale combinant plusieurs mesures physiologiques (activité cérébrale, réactions galvaniques et rythme cardiaque) avec des variables individuelles, pour détecter une émotion très fréquemment observée lors des sessions d’apprentissage, à savoir l’incertitude. Dans un premier lieu, nous avons identifié les indicateurs physiologiques clés qui sont associés à cet état, ainsi que les caractéristiques individuelles qui contribuent à sa manifestation. Puis, nous avons développé des modèles prédictifs permettant de détecter automatiquement cet état à partir des différentes variables analysées, à travers l’entrainement d’algorithmes d’apprentissage machine. Notre deuxième objectif a été de proposer une approche unifiée pour reconnaître simultanément une combinaison de plusieurs émotions, et évaluer explicitement l’impact de ces émotions sur l’expérience d’interaction de l’apprenant. Pour cela, nous avons développé une plateforme hiérarchique, probabiliste et dynamique permettant de suivre les changements émotionnels de l'apprenant au fil du temps, et d’inférer automatiquement la tendance générale qui caractérise son expérience d’interaction à savoir : l’immersion, le blocage ou le décrochage. L’immersion correspond à une expérience optimale : un état dans lequel l'apprenant est complètement concentré et impliqué dans l’activité d’apprentissage. L’état de blocage correspond à une tendance d’interaction non optimale où l'apprenant a de la difficulté à se concentrer. Finalement, le décrochage correspond à un état extrêmement défavorable où l’apprenant n’est plus du tout impliqué dans l’activité d’apprentissage. La plateforme proposée intègre trois modalités de variables diagnostiques permettant d’évaluer l’expérience de l’apprenant à savoir : des variables physiologiques, des variables comportementales, et des mesures de performance, en combinaison avec des variables prédictives qui représentent le contexte courant de l’interaction et les caractéristiques personnelles de l'apprenant. Une étude a été réalisée pour valider notre approche à travers un protocole expérimental permettant de provoquer délibérément les trois tendances ciblées durant l’interaction des apprenants avec différents environnements d’apprentissage. Enfin, notre troisième objectif a été de proposer de nouvelles stratégies pour influencer positivement l’état émotionnel de l’apprenant, sans interrompre la dynamique de la session d’apprentissage. Nous avons à cette fin introduit le concept de stratégies émotionnelles implicites : une nouvelle approche pour agir subtilement sur les émotions de l’apprenant, dans le but d’améliorer son expérience d’apprentissage. Ces stratégies utilisent la perception subliminale, et plus précisément une technique connue sous le nom d’amorçage affectif. Cette technique permet de solliciter inconsciemment les émotions de l’apprenant, à travers la projection d’amorces comportant certaines connotations affectives. Nous avons mis en œuvre une stratégie émotionnelle implicite utilisant une forme particulière d’amorçage affectif à savoir : le conditionnement évaluatif, qui est destiné à améliorer de façon inconsciente l’estime de soi. Une étude expérimentale a été réalisée afin d’évaluer l’impact de cette stratégie sur les réactions émotionnelles et les performances des apprenants.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.