Model Development Guidelines for UML-RT: Conventions, Patterns and Antipatterns

Software development guidelines are a set of rules which can help improve the quality of software. These rules are defined on the basis of experience gained by the software development community over time. This paper discusses a set of design guidelines for model-based development of complex real-time embedded software systems. To be precise, we propose nine design conventions, three design patterns and thirteen antipatterns for developing UML-RT models. These guidelines have been identified based on our analysis of around 100 UML-RT models from industry and academia. Most of the guidelines are explained with the help of examples, and standard templates from the current state of the art are used for documenting the design rules.

Multidimensional Intrusion Detection System for IEC 61850 based SCADA Networks

Emerging cybersecurity vulnerabilities in supervisory control and data acquisition (SCADA) systems are becoming urgent engineering issues for modern substations. This paper proposes a novel intrusion detection system (IDS) tailored for cybersecurity of IEC 61850 based substations. The proposed IDS integrates physical knowledge, protocol specifications and logical behaviours to provide a comprehensive and effective solution that is able to mitigate various cyberattacks. The proposed approach comprises access control detection, protocol whitelisting, model-based detection, and multi-parameter based detection. This SCADA-specific IDS is implemented and validated using a comprehensive and realistic cyber-physical test-bed and data from a real 500kV smart substation.

Étude et décontamination du transcriptome de novo du nématode doré Globodera rostochiensis

Le nématode doré, Globodera rostochiensis, est un nématode phytoparasite qui peut infecter des plantes agricoles telles la pomme de terre, la tomate et l’aubergine. En raison des pertes de rendement considérables associées à cet organisme, il est justifiable de quarantaine dans plusieurs pays, dont le Canada. Les kystes du nématode doré protègent les œufs qu’ils contiennent, leur permettant de survivre (en état de dormance) jusqu’à 20 ans dans le sol. L’éclosion des œufs n’aura lieu qu’en présence d’exsudats racinaires d’une plante hôte compatible à proximité. Malheureusement, très peu de connaissances sont disponibles sur les mécanismes moléculaires liés à cette étape-clé du cycle vital du nématode doré. Dans cet ouvrage, nous avons utilisé la technique RNA-seq pour séquencer tous les ARNm d’un échantillon de kystes du nématode doré afin d’assembler un transcriptome de novo (sans référence) et d’identifier des gènes jouant un rôle dans les mécanismes de survie et d’éclosion. Cette méthode nous a permis de constater que les processus d’éclosion et de parasitisme sont étroitement reliés. Plusieurs effecteurs impliqués dans le mouvement vers la plante hôte et la pénétration de la racine sont induits dès que le kyste est hydraté (avant même le déclenchement de l’éclosion). Avec l’aide du génome de référence du nématode doré, nous avons pu constater que la majorité des transcrits du transcriptome ne provenaient pas du nématode doré. En effet, les kystes échantillonnés au champ peuvent contenir des contaminants (bactéries, champignons, etc.) sur leur paroi et même à l’intérieur du kyste. Ces contaminants seront donc séquencés et assemblés avec le transcriptome de novo. Ces transcrits augmentent la taille du transcriptome et induisent des erreurs lors des analyses post-assemblages. Les méthodes de décontamination actuelles utilisent des alignements sur des bases de données d’organismes connus pour identifier ces séquences provenant de contaminants. Ces méthodes sont efficaces lorsque le ou les contaminants sont connus (possède un génome de référence) comme la contamination humaine. Par contre, lorsque le ou les contaminants sont inconnus, ces méthodes deviennent insuffisantes pour produire un transcriptome décontaminé de qualité. Nous avons donc conçu une méthode qui utilise un algorithme de regroupement hiérarchique des séquences. Cette méthode produit, de façon récursive, des sous-groupes de séquences homogènes en fonction des patrons fréquents présents dans les séquences. Une fois les groupes créés, ils sont étiquetés comme contaminants ou non en fonction des résultats d’alignements du sous-groupe. Les séquences ambiguës ayant aucun ou plusieurs alignements différents sont donc facilement classées en fonction de l’étiquette de leur groupe. Notre méthode a été efficace pour décontaminer le transcriptome du nématode doré ainsi que d’autres cas de contamination. Cette méthode fonctionne pour décontaminer un transcriptome, mais nous avons aussi démontré qu’elle a le potentiel de décontaminer de courtes séquences brutes. Décontaminer directement les séquences brutes serait la méthode de décontamination optimale, car elle minimiserait les erreurs d’assemblage.

A long-term energy efficiency prediction model for the Brazilian automotive industry

According to law number 12.715/2012, Brazilian government instituted guidelines for a program named Inovar-Auto. In this context, energy efficiency is a survival requirement for Brazilian automotive industry from September 2016. As proposed by law, energy efficiency is not going to be calculated by models only. It is going to be calculated by the whole universe of new vehicles registered. In this scenario, the composition of vehicles sold in market will be a key factor on profits of each automaker. Energy efficiency and its consequences should be taken into consideration in all of its aspects. In this scenario, emerges the following question: which is the efficiency curve of one automaker for long term, allowing them to adequate to rules, keep balancing on investment in technologies, increasing energy efficiency without affecting competitiveness of product lineup? Among several variables to be considered, one can highlight the analysis of manufacturing costs, customer value perception and market share, which characterizes this problem as a multi-criteria decision-making. To tackle the energy efficiency problem required by legislation, this paper proposes a framework of multi-criteria decision-making. The proposed framework combines Delphi group and Analytic Hierarchy Process to identify suitable alternatives for automakers to incorporate in main Brazilian vehicle segments. A forecast model based on artificial neural networks was used to estimate vehicle sales demand to validate expected results. This approach is demonstrated with a real case study using public vehicles sales data of Brazilian automakers and public energy efficiency data.

Tile-Based Two-Dimensional Phase Unwrapping for Digital Holography Using a Modular Framework

A variety of physical and biomedical imaging techniques, such as digital holography, interferometric synthetic aperture radar (InSAR), or magnetic resonance imaging (MRI) enable measurement of the phase of a physical quantity additionally to its amplitude. However, the phase can commonly only be measured modulo 2π, as a so called wrapped phase map. Phase unwrapping is the process of obtaining the underlying physical phase map from the wrapped phase. Tile-based phase unwrapping algorithms operate by first tessellating the phase map, then unwrapping individual tiles, and finally merging them to a continuous phase map. They can be implemented computationally efficiently and are robust to noise. However, they are prone to failure in the presence of phase residues or erroneous unwraps of single tiles. We tried to overcome these shortcomings by creating novel tile unwrapping and merging algorithms as well as creating a framework that allows to combine them in modular fashion. To increase the robustness of the tile unwrapping step, we implemented a model-based algorithm that makes efficient use of linear algebra to unwrap individual tiles. Furthermore, we adapted an established pixel-based unwrapping algorithm to create a quality guided tile merger. These original algorithms as well as previously existing ones were implemented in a modular phase unwrapping C++ framework. By examining different combinations of unwrapping and merging algorithms we compared our method to existing approaches. We could show that the appropriate choice of unwrapping and merging algorithms can significantly improve the unwrapped result in the presence of phase residues and noise. Beyond that, our modular framework allows for efficient design and test of new tile-based phase unwrapping algorithms. The software developed in this study is freely available.

A study of computational methods to analyze gene expression data

The recent advent of new technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the immense amounts of genomic data is a challenging task. Therefore, effective and efficient computational techniques are needed to analyze and interpret genomic data. In this thesis, novel computational methods are proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with K-means clustering and principal component analysis (PCA), biological insights are derived such as context specific/dependent relationships and nested structures within microarray where biological replicates are encapsulated. The Bayesian mixture framework is then extended to explore posterior distributions of network space by incorporating a Markov chain Monte Carlo (MCMC) model. The extended Bayesian mixture model summarizes the sampled network structures by extracting biologically meaningful features. Finally, an Eigen-brain approach is proposed to analyze in situ hybridization data for the identification of the cell-type specific genes, which can be useful for informative blood diagnostics. Computational results with region-based clustering reveals the critical evidence for the consistency with brain anatomical structure.

A numerical model for prediction of oil, trajectory due to extraction activity in the north coasts of the Persian Gulf

This thesis considers a three- dimensional numerical model based on 3-D Navier— Stokes and continuity equations involving various wind speeds (North west), water surface levels, horizontal shier stresses, eddy viscosity, densities of oil and gas condensate- water mixture flows. The model is used to simulate the prediction of the surface movement of oil and gas condensate slicks from spill accident in the north coasts of Persian Gulf.

A unified model for the long and high jump

A simple model based on, the maximum energy that an athlete can produce in a small time interval is used to describe the high and long jump. Conservation of angular momentum is used to explain why an athlete should, run horizontally to perform a vertical jump. Our results agree with world records. (c) 2005 American Association of Physics Teachers.

Implementation of the CDM model to the analisys of the hotel sector

The main aim of this study was to determine the impact of innovation on productivity in service sector companies — especially those in the hospitality sector — that value the reduction of environmental impact as relevant to the innovation process. We used a structural analysis model based on the one developed by Crépon, Duguet, and Mairesse (1998). This model is known as the CDM model (an acronym of the authors’ surnames). These authors developed seminal studies in the field of the relationships between innovation and productivity (see Griliches 1979; Pakes and Grilliches 1980). The main advantage of the CDM model is its ability to integrate the process of innovation and business productivity from an empirical perspective.

Evaluation of the conductor-like screening model for real solvents for the prediction of the water activity coefficient at infinite dilution in ionic liquids

Ionic liquids (ILs) have attracted great attention, from both industry and academia, as alternative fluids for very different types of applications. The large number of cations and anions allow a wide range of physical and chemical characteristics to be designed. However, the exhaustive measurement of all these systems is impractical, thus requiring the use of a predictive model for their study. In this work, the predictive capability of the conductor-like screening model for real solvents (COSMO-RS), a model based on unimolecular quantum chemistry calculations, was evaluated for the prediction water activity coefficient at infinite dilution, gamma(infinity)(w), in several classes of ILs. A critical evaluation of the experimental and predicted data using COSMO-RS was carried out. The global average relative deviation was found to be 27.2%, indicating that the model presents a satisfactory prediction ability to estimate gamma(infinity)(w) in a broad range of ILs. The results also showed that the basicity of the ILs anions plays an important role in their interaction with water, and it considerably determines the enthalpic behavior of the binary mixtures composed by Its and water. Concerning the cation effect, it is possible to state that generally gamma(infinity)(w) increases with the cation size, but it is shown that the cation-anion interaction strength is also important and is strongly correlated to the anion ability to interact with water. The results here reported are relevant in the understanding of ILs-water interactions and the impact of the various structural features of its on the gamma(infinity)(w) as these allow the development of guidelines for the choice of the most suitable lLs with enhanced interaction with water.

Search for Higgs Boson Production Beyond the Standard Model Using the Razor Kinematic Variables in pp Collisions at √s=8 TeV and Optimization of Higgs Boson Identification Using a Quantum Annealer

In the first part of this thesis we search for beyond the Standard Model physics through the search for anomalous production of the Higgs boson using the razor kinematic variables. We search for anomalous Higgs boson production using proton-proton collisions at center of mass energy √s=8 TeV collected by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to an integrated luminosity of 19.8 fb^-1.

In the second part we present a novel method for using a quantum annealer to train a classifier to recognize events containing a Higgs boson decaying to two photons. We train that classifier using simulated proton-proton collisions at √s=8 TeV producing either a Standard Model Higgs boson decaying to two photons or a non-resonant Standard Model process that produces a two photon final state.

The production mechanisms of the Higgs boson are precisely predicted by the Standard Model based on its association with the mechanism of electroweak symmetry breaking. We measure the yield of Higgs bosons decaying to two photons in kinematic regions predicted to have very little contribution from a Standard Model Higgs boson and search for an excess of events, which would be evidence of either non-standard production or non-standard properties of the Higgs boson. We divide the events into disjoint categories based on kinematic properties and the presence of additional b-quarks produced in the collisions. In each of these disjoint categories, we use the razor kinematic variables to characterize events with topological configurations incompatible with typical configurations found from standard model production of the Higgs boson.

We observe an excess of events with di-photon invariant mass compatible with the Higgs boson mass and localized in a small region of the razor plane. We observe 5 events with a predicted background of 0.54 ± 0.28, which observation has a p-value of 10^-3 and a local significance of 3.35σ. This background prediction comes from 0.48 predicted non-resonant background events and 0.07 predicted SM higgs boson events. We proceed to investigate the properties of this excess, finding that it provides a very compelling peak in the di-photon invariant mass distribution and is physically separated in the razor plane from predicted background. Using another method of measuring the background and significance of the excess, we find a 2.5σ deviation from the Standard Model hypothesis over a broader range of the razor plane.

In the second part of the thesis we transform the problem of training a classifier to distinguish events with a Higgs boson decaying to two photons from events with other sources of photon pairs into the Hamiltonian of a spin system, the ground state of which is the best classifier. We then use a quantum annealer to find the ground state of this Hamiltonian and train the classifier. We find that we are able to do this successfully in less than 400 annealing runs for a problem of median difficulty at the largest problem size considered. The networks trained in this manner exhibit good classification performance, competitive with the more complicated machine learning techniques, and are highly resistant to overtraining. We also find that the nature of the training gives access to additional solutions that can be used to improve the classification performance by up to 1.2% in some regions.

A possibility for non-invasive diagnosis of superficial scald in 'Rocha' pear based on chlorophyll a fluorescence, colorimetry, and the relation between alpha-farnesene and conjugated trienols

This study aimed to identify physiological markers in superficially scalded 'Rocha' pear (Pyrus communis L 'Rocha') that would relate to chlorophyll a fluorescence (CF), allowing a non-invasive diagnosis of the disorder. Conditions chosen before shelf life provided two fruit groups with different developing patterns and severity of superficial scald: T fruit fully developed the disorder in storage, while C fruit developed it progressively throughout shelf life. Principal component analysis (PCA) of all the measured variables, and simple linear correlations among several major parameters and scald index (SI)/shelf life showed that scald and ripening/aging were concurring processes, and that it was not possible to isolate a particular variable that could deliver a direct non-invasive diagnosis of the disorder. For both fruit groups the SI resulted from the balance between the reducing power (OD200) and the content of conjugated trienols (CTos) and alpha-farnesene (alpha-Farn) in the fruit peel. At OD200 > 150 there was a linear relationship between CTos and OD200, suggesting that the level of antioxidants was self-adjusted in order to compensate the CTos level. However, at OD200 < 150 this relationship disappeared. A consistent linear relationship between dos and alpha-Farn existed throughout shelf life in both fruit groups, contrarily to the early storage stage, when those compounds do not relate linearly. The CF variables F-0, F-v/F-m, and the colorimetric variables, L* and h degrees were used in multi-linear regressions with other physiological variables. The regressions were made on one of the fruit groups and validated through the other. Reliable regressions to alpha-Farn and CTos were obtained (R approximate to 0.6; rmsec approximate to rmsep). Our results suggest that a model based on CF and colorimetric parameters could be used to diagnose non-invasively both the contents and the relationship between alpha-Farn and CTos and hence the stage of scald development. (C) 2011 Elsevier By. All rights reserved.

A possibility for non-invasive diagnosis of superficial scald in 'Rocha' pear based on chlorophyll a fluorescence, colorimetry, and the relation between alpha-farnesene and conjugated trienols

This study aimed to identify physiological markers in superficially scalded 'Rocha' pear (Pyrus communis L 'Rocha') that would relate to chlorophyll a fluorescence (CF), allowing a non-invasive diagnosis of the disorder. Conditions chosen before shelf life provided two fruit groups with different developing patterns and severity of superficial scald: T fruit fully developed the disorder in storage, while C fruit developed it progressively throughout shelf life. Principal component analysis (PCA) of all the measured variables, and simple linear correlations among several major parameters and scald index (SI)/shelf life showed that scald and ripening/aging were concurring processes, and that it was not possible to isolate a particular variable that could deliver a direct non-invasive diagnosis of the disorder. For both fruit groups the SI resulted from the balance between the reducing power (OD200) and the content of conjugated trienols (CTos) and alpha-farnesene (alpha-Farn) in the fruit peel. At OD200 > 150 there was a linear relationship between CTos and OD200, suggesting that the level of antioxidants was self-adjusted in order to compensate the CTos level. However, at OD200 < 150 this relationship disappeared. A consistent linear relationship between dos and alpha-Farn existed throughout shelf life in both fruit groups, contrarily to the early storage stage, when those compounds do not relate linearly. The CF variables F-0, F-v/F-m, and the colorimetric variables, L* and h degrees were used in multi-linear regressions with other physiological variables. The regressions were made on one of the fruit groups and validated through the other. Reliable regressions to alpha-Farn and CTos were obtained (R approximate to 0.6; rmsec approximate to rmsep). Our results suggest that a model based on CF and colorimetric parameters could be used to diagnose non-invasively both the contents and the relationship between alpha-Farn and CTos and hence the stage of scald development. (C) 2011 Elsevier By. All rights reserved.

SCALABLE APPROXIMATION OF KERNEL FUZZY C-MEANS

Virtually every sector of business and industry that uses computing, including financial analysis, search engines, and electronic commerce, incorporate Big Data analysis into their business model. Sophisticated clustering algorithms are popular for deducing the nature of data by assigning labels to unlabeled data. We address two main challenges in Big Data. First, by definition, the volume of Big Data is too large to be loaded into a computer’s memory (this volume changes based on the computer used or available, but there is always a data set that is too large for any computer). Second, in real-time applications, the velocity of new incoming data prevents historical data from being stored and future data from being accessed. Therefore, we propose our Streaming Kernel Fuzzy c-Means (stKFCM) algorithm, which reduces both computational complexity and space complexity significantly. The proposed stKFCM only requires O(n2) memory where n is the (predetermined) size of a data subset (or data chunk) at each time step, which makes this algorithm truly scalable (as n can be chosen based on the available memory). Furthermore, only 2n2 elements of the full N × N (where N >> n) kernel matrix need to be calculated at each time-step, thus reducing both the computation time in producing the kernel elements and also the complexity of the FCM algorithm. Empirical results show that stKFCM, even with relatively very small n, can provide clustering performance as accurately as kernel fuzzy c-means run on the entire data set while achieving a significant speedup.

Examining the use of Simarouba glauca Seed Oil as a Feedstock for the Production of Biodiesel using a Small Scale Model Developed in India

Simarouba glauca, a non-edible oilseed crop native to South Florida, is gaining popularity as a feedstock for the production of biodiesel. The University of Agriculture Sciences in Bangalore, India has developed a biodiesel production model based on the principles of decentralization, small scales, and multiple fuel sources. Success of such a program depends on conversion efficiencies at multiple stages. The conversion efficiency of the field-level, decentralized production model was compared with the in-laboratory conversion efficiency benchmark. The study indicated that the field-level model conversion efficiency was less than that of the lab-scale set up. The fuel qualities and characteristics of the Simarouba glauca biodiesel were tested and found to be the standards required for fuel designation. However, this research suggests that for Simarouba glauca to be widely accepted as a biodiesel feedstock further investigation is still required.