Kaivoslastauskoneen hybridisointianalyysi virtuaali- ja in-loop -simuloinnin avulla

Työssä tutkitaan raskaiden työkoneiden hybridisointimitoitusta simuloimalla. Työssä esitetään simulation-in-the-loop-simulointiin perustuva järjestelmä, jolla esimerkkitapauksena oleva kaivoslastauskone työympäristöineen voidaan mallintaa mekaaniselta osaltaan monikappaledynamiikkaan perustuvalla ohjelmistolla ja hybridijärjestelmän osalta Simulinkissa. Yhdistetty simulointi mahdollistaa hybridityökoneen virtuaalimallin ohjaamisen käyttäjän toimesta reaaliajassa. Simuloinnista saadaan tuloksena mm. työsykli, jota voidaan käyttää hybridisointimitoitukseen. Hybridisointi toteutetaan kahdella erilaisella kokoonpanolla, joista analysoidaan suorituskykyä sekä polttoaineen kulutusta. Tuloksia verrataan pelkästään dieselmoottoria voimanlähteenä käyttävään lastauskoneeseen. Työssä tehty tutkimus osoittaa, että (sarja-) hybridisoinnilla voidaan saavuttaa merkittäviä etuja raskaiden työkoneiden polttoainetehokkuudessa. Dieselmoottoria voidaan ajaa sellaisessa staattisessa toimintapisteessä, jonka hyötysuhde on korkea riippumatta työkoneen kuormituksesta. Saavutettu hyöty on toteutetussa tutkimuksessa parhaimmillaan jopa 56 % vähennys polttoaineenkulutuksessa. Lisäksi tarvittava dieselin nimellisteho pienenee huomattavasti. Tutkimuksen osana esitellään myös Hardware-in-the-Loop -laitteisto, jonka avulla voidaan liittää oikea sähkömoottori ja taajuudenmuuttaja osaksi virtuaalisesti simuloitua työkonetta.

Energy Efficiency of a Diesel-Electric MobileWorking Machine

The power demand of many mobile working machines such as mine loaders, straddle carriers and harvesters varies significantly during operation, and typically, the average power demand of a working machine is considerably lower than the demand for maximum power. Consequently, for most of the time, the diesel engine of a working machine operates at a poor efficiency far from its optimum efficiency range. However, the energy efficiency of dieseldriven working machines can be improved by electric hybridization. This way, the diesel engine can be dimensioned to operate within its optimum efficiency range, and the electric drive with its energy storages responds to changes in machine loading. A hybrid working machine can be implemented in many ways either as a parallel hybrid, a series hybrid or a combination of these two. The energy efficiency of hybrid working machines can be further enhanced by energy recovery and reuse. This doctoral thesis introduces the component models required in the simulation model of a working machine. Component efficiency maps are applied to the modelling; the efficiency maps for electrical machines are determined analytically in the whole torque–rotational speed plane based on the electricalmachine parameters. Furthermore, the thesis provides simulation models for parallel, series and parallel-series hybrid working machines. With these simulation models, the energy consumption of the working machine can be analysed. In addition, the hybridization process is introduced and described. The thesis provides a case example of the hybridization and dimensioning process of a working machine, starting from the work cycle of the machine. The selection and dimensioning of the hybrid system have a significant impact on the energy consumption of a hybrid working machine. The thesis compares the energy consumption of a working machine implemented by three different hybrid systems (parallel, series and parallel-series) and with different component dimensions. The payback time of a hybrid working machine and the energy storage lifetime are also estimated in the study.

Lämmöntuotanto älykkäässä energiaverkossa

Energiaverkon edistäminen tulee olemaan tulevaisuudessa suuressa roolissa niin Suomessa kuin maailmalla. Tällöin yhtenä vaihtoehtona on älykkyyden tuominen koko energiajärjestelmään niin sähkön kuin lämmönkin osalta. Tällöin voidaan vähentää kulutushuippujen aikaista energian käyttöä ja saada koko verkoston energiankulutus profiili tasaiseksi. Tässä työssä tarkastellaan spot-sähkön hyödyntämismahdollisuuksia lämmöntuotannossa. Työssä kehitetyn mallinnustyökalun avulla tutkittiin erilaisia ajotapoja ja erilaisten järjestelmien kokonaiskustannuksia. Työssä esitellään esimerkkilaskennan avulla spot-sähkön käytöstä syntyviä kustannuksia ja keinoja vaikuttaa niihin eri järjestelmä muutoksin. Malli mahdollistaa erilaisten järjestelmien ja niiden ajotapojen tutkimista. Mallilla voidaan myös tutkia erilaisten alueiden kulutuskäyttäytymistä ja hyödyntää sitä kustannuslaskelmiin. Voidaan todeta, että työssä muodostunut malli on toimiva ja sitä voidaan hyödyntää alueellisten lämmöntuotantojärjestelmien ajotapojen optimointiin.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

The role of nuclear power in the future energy system

Currently widely accepted consensus is that greenhouse gas emissions produced by the mankind have to be reduced in order to avoid further global warming. The European Union has set a variety of CO2 reduction and renewable generation targets for its member states. The current energy system in the Nordic countries is one of the most carbon free in the world, but the aim is to achieve a fully carbon neutral energy system. The objective of this thesis is to consider the role of nuclear power in the future energy system. Nuclear power is a low carbon energy technology because it produces virtually no air pollutants during operation. In this respect, nuclear power is suitable for a carbon free energy system. In this master's thesis, the basic characteristics of nuclear power are presented and compared to fossil fuelled and renewable generation. Nordic energy systems and different scenarios in 2050 are modelled. Using models and information about the basic characteristics of nuclear power, an opinion is formed about its role in the future energy system in Nordic countries. The model shows that it is possible to form a carbon free Nordic energy system. Nordic countries benefit from large hydropower capacity which helps to offset fluctuating nature of wind power. Biomass fuelled generation and nuclear power provide stable and predictable electricity throughout the year. Nuclear power offers better energy security and security of supply than fossil fuelled generation and it is competitive with other low carbon technologies.

The functional fit between collaborative software and work systems:Qualification of work system needs to software functionality

The study develops an approach that tries to validate software functionality to work systems needs in SMEs. The formulated approach is constructed by using a SAAS based software i.e., work collaboration service (WCS), and SMEs as the elements of study. Where the WCS’s functionality is qualified to the collaboration needs that exist in operational and project work within SMEs. For this research constructivist approach and case study method is selected because the nature of the current study requires an in depth study of the work collaboration service as well as a detailed study of the work systems within different enterprises. Four different companies are selected in which fourteen interviews are conducted to gather data pertaining. The work systems method and framework are used as a central part of the approach to collect, analyze and interpret the enterprises work systems model and the underlying collaboration needs on operational and project work. On the other hand, the functional model of the WCS and its functionality is determined from functional model analysis, software testing, documentation and meetings with the service vendor. The enterprise work system model and the WCS model are compared to reveal how work progression differs between the two and make visible unaddressed stages of work progression. The WCS functionality is compared to work systems collaboration needs to ascertain if the service will suffice the needs of the project and operational work under study. The unaddressed needs provide opportunities to improve the functionality of the service for better conformity to the needs of enterprise and work. The results revealed that the functional models actually differed in how operational and project work progressed within the stages. WCS shared similar stages of work progression apart from the stages of identification and acceptance, and progress and completion stages were only partially addressed. Conclusion is that the identified unaddressed needs such as, single point of reference, SLA and OLA inclusion etc., should be implemented or improved within the WCS at appropriate stages of work to gain better compliance of the service to the needs of the enterprise an work itself. The developed approach can hence be used to carry out similar analysis for the conformance of pre-built software functionality to work system needs with SMEs.

Étude de la mise en oeuvre de la réforme du curriculum québécois du primaire, de 1997 à 2003

Les politiques éducatives se multiplient à l’échelle des pays industrialisés mais celles-ci semblent rencontrer de graves problèmes d’atteinte de résultats concrets et satisfaisants. La mise en œuvre d’une politique, notamment les stratégies gouvernementales « hybrides » sélectionnées, c’est-à-dire des stratégies d’implantation de politiques qui misent à la fois sur des stratégies « top-down » et aussi « bottom-up », semble être un élément-clé à considérer pour leur efficience (Gather-Thurler, 2000; Van Zanten, 2004; Fullan, 2005, 2007). Or, les connaissances concernant ces stratégies de mise en œuvre sont partielles, encore peu développées et les raisons qui expliquent ces choix politiques se font rares; ce qui rend la production de politiques effectives et durables difficile (Fullan, 2000; Leithwood et Earl, 2000; Van Zanten, 2004). Le Québec a entrepris, en 1997, une réforme à grande échelle de son système d’éducation; réforme qui mise explicitement sur des stratégies gouvernementales « hybrides » pour sa réalisation. Cette étude s’attarde à cette réforme, plus spécifiquement à sa réforme du curriculum au primaire, afin de : 1) retracer les grands moments-clés de la mise en œuvre de la politique associés aux différentes dimensions du concept d’« hybridité »; 2) identifier et décrire les stratégies gouvernementales « hybrides » qui leur sont associées; 3) formuler des hypothèses explicatives provisoires et les valider; 4) élaborer un modèle explicatif et 5) expliciter l’incidence du modèle proposé sur les théories existantes. Cette étude de cas est effectuée par l’entremise de deux formes de cueillette de données complémentaires : une analyse documentaire et des entrevues semi-dirigées. Une analyse documentaire est réalisée à partir des documents formels de l’autorité publique (N=14) et d’une revue de presse, de 1995 à 2003 (N=648). Les entrevues (N=23) visent, pour leur part, à recueillir les propos des : 1) concepteurs et décideurs; 2) opérationnalisateurs; 3) concepteurs-opérationnalisateurs et 4) experts. La combinaison des données recueillies permet d’établir la comparaison entre le processus et la structure (Meny et Thoenig, 1989), le prescriptif et l’effectif, afin de comprendre la vraie dynamique qui a animé le processus de mise en œuvre de la politique étudiée. Ainsi, l’examen du processus de mise en œuvre de la réforme du curriculum québécois du primaire permet de retracer le modèle d’implantation de la politique curriculaire québécoise. Ce modèle d’implantation novateur fait état du fait que des stratégies hybrides non improvisées et exigeantes furent pensées et proposées par les autorités québécoises. Ce modèle d’implantation élaboré permettait de penser que la politique curriculaire québécoise allait, possiblement, permettre d’obtenir des résultats tangibles et durables dans les milieux scolaires. L’analyse de la structure de mise en œuvre révèle, pour sa part, que les stratégies d’implantation qui se sont déployées sur le terrain rejoignaient presqu’intégralement les stratégies « hybrides » initialement prévues. Le processus d’implantation a cependant connu une évolution différente de celle attendue. La mise en œuvre concrète qui s’est vécue sur le terrain fut difficile et hasardeuse, malgré l’approche « hybride » adoptée. Les éléments qui expliquent les difficultés d’implantation vécues sont présentés et analysés dans cette étude.

Development of Safety and Quality Management System in Shrimp Farming

Shrimp Aquaculture has provided tremendous opportunity for the economic and social upliftment of rural communities in the coastal areas of our country Over a hundred thousand farmers, of whom about 90% belong to the small and marginal category, are engaged in shrimp farming. Penaeus monodon is the most predominant cultured species in India which is mainly exported to highly sophisticated, quality and safety conscious world markets. Food safety has been of concem to humankind since the dawn of history and the concern about food safety resulted in the evolution of a cost effective, food safety assurance method, the Hazard Analysis Critical Control Point (HACCP). Considering the major contribution of cultured Penaeus monodon to the total shrimp production and the economic losses encountered due to disease outbreak and also because traditional methods of quality control and end point inspection cannot guarantee the safety of our cultured seafood products, it is essential that science based preventive approaches like HACCP and Pre requisite Programmes (PRP) be implemented in our shrimp farming operations. PRP is considered as a support system which provides a solid foundation for HACCP. The safety of postlarvae (PL) supplied for brackish water shrimp farming has also become an issue of concern over the past few years. The quality and safety of hatchery produced seeds have been deteriorating and disease outbreaks have become very common in hatcheries. It is in this context that the necessity for following strict quarantine measures with standards and code of practices becomes significant. Though there were a lot of hue and cry on the need for extending the focus of seafood safety assurance from processing and exporting to the pre-harvest and hatchery rearing phases, an experimental move in this direction has been rare or nil. An integrated management system only can assure the effective control of the quality, hygiene and safety related issues. This study therefore aims at designing a safety and quality management system model for implementation in shrimp farming and hatchery operations by linking the concepts of HACCP and PRP.

Photovoltaic hybrid systems for rural electrification in the Mekong countries

In rural areas of the Mekong Countries, the problem of electricity supplying rural communities is particularly alarming. Supplying power to these areas requires facilities that are not economically viable. However, government programs are under way to provide this product that is vital to community well being. A nation priority of Mekong Countries is to provide electrical power to people in rural areas, within normal budgetary constraints. Electricity must be introduced into rural areas in such a way that maximize the technical, economic and social benefit. Another consideration is the source of electrical generation and the effects on the natural environment. The main research purpose is to implement field tests, monitoring and evaluation of the PV-Diesel Hybrid System (PVHS) at the Energy Park of School of Renewable Energy Technology (SERT) in order to test the PVSH working under the meteorological conditions of the Mekong Countries and to develop a software simulation called RES, which studies the technical and economic performance of rural electrification options. This software must be easy to use and understand for the energy planner on rural electrification projects, to evaluate the technical and economic performance of the PVHS based on the renewable energy potential for rural electrification of the Mekong Country by using RES. Finally, this project aims to give guidance for the possible use of PVHS application in this region, particularly in regard to its technical and economic sustainability. PVHS should be promoted according to the principles of proper design and adequate follow up with maintenance, so that the number of satisfied users will be achieved. PVHS is not the only possible technology for rural electrification, but for the Mekong Countries it is one of the most proper choices. Other renewable energy options such as wind, biomass and hydro power need to be studied in future.

Structural Dynamics Model of a Cartesian Robot

Methods are developed for predicting vibration response characteristics of systems which change configuration during operation. A cartesian robot, an example of such a position-dependent system, served as a test case for these methods and was studied in detail. The chosen system model was formulated using the technique of Component Mode Synthesis (CMS). The model assumes that he system is slowly varying, and connects the carriages to each other and to the robot structure at the slowly varying connection points. The modal data required for each component is obtained experimentally in order to get a realistic model. The analysis results in prediction of vibrations that are produced by the inertia forces as well as gravity and friction forces which arise when the robot carriages move with some prescribed motion. Computer simulations and experimental determinations are conducted in order to calculate the vibrations at the robot end-effector. Comparisons are shown to validate the model in two ways: for fixed configuration the mode shapes and natural frequencies are examined, and then for changing configuration the residual vibration at the end of the mode is evaluated. A preliminary study was done on a geometrically nonlinear system which also has position-dependency. The system consisted of a flexible four-bar linkage with elastic input and output shafts. The behavior of the rocker-beam is analyzed for different boundary conditions to show how some limiting cases are obtained. A dimensional analysis leads to an evaluation of the consequences of dynamic similarity on the resulting vibration.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Image classification for a large number of object categories

L'increment de bases de dades que cada vegada contenen imatges més difícils i amb un nombre més elevat de categories, està forçant el desenvolupament de tècniques de representació d'imatges que siguin discriminatives quan es vol treballar amb múltiples classes i d'algorismes que siguin eficients en l'aprenentatge i classificació. Aquesta tesi explora el problema de classificar les imatges segons l'objecte que contenen quan es disposa d'un gran nombre de categories. Primerament s'investiga com un sistema híbrid format per un model generatiu i un model discriminatiu pot beneficiar la tasca de classificació d'imatges on el nivell d'anotació humà sigui mínim. Per aquesta tasca introduïm un nou vocabulari utilitzant una representació densa de descriptors color-SIFT, i desprès s'investiga com els diferents paràmetres afecten la classificació final. Tot seguit es proposa un mètode par tal d'incorporar informació espacial amb el sistema híbrid, mostrant que la informació de context es de gran ajuda per la classificació d'imatges. Desprès introduïm un nou descriptor de forma que representa la imatge segons la seva forma local i la seva forma espacial, tot junt amb un kernel que incorpora aquesta informació espacial en forma piramidal. La forma es representada per un vector compacte obtenint un descriptor molt adequat per ésser utilitzat amb algorismes d'aprenentatge amb kernels. Els experiments realitzats postren que aquesta informació de forma te uns resultats semblants (i a vegades millors) als descriptors basats en aparença. També s'investiga com diferents característiques es poden combinar per ésser utilitzades en la classificació d'imatges i es mostra com el descriptor de forma proposat juntament amb un descriptor d'aparença millora substancialment la classificació. Finalment es descriu un algoritme que detecta les regions d'interès automàticament durant l'entrenament i la classificació. Això proporciona un mètode per inhibir el fons de la imatge i afegeix invariança a la posició dels objectes dins les imatges. S'ensenya que la forma i l'aparença sobre aquesta regió d'interès i utilitzant els classificadors random forests millora la classificació i el temps computacional. Es comparen els postres resultats amb resultats de la literatura utilitzant les mateixes bases de dades que els autors Aixa com els mateixos protocols d'aprenentatge i classificació. Es veu com totes les innovacions introduïdes incrementen la classificació final de les imatges.

Localización de faltas en sistemas de distribución de energía eléctrica usando métodos basados en el modelo y métodos basados en el conocimiento

La calidad de energía eléctrica incluye la calidad del suministro y la calidad de la atención al cliente. La calidad del suministro a su vez se considera que la conforman dos partes, la forma de onda y la continuidad. En esta tesis se aborda la continuidad del suministro a través de la localización de faltas. Este problema se encuentra relativamente resuelto en los sistemas de transmisión, donde por las características homogéneas de la línea, la medición en ambos terminales y la disponibilidad de diversos equipos, se puede localizar el sitio de falta con una precisión relativamente alta. En sistemas de distribución, sin embargo, la localización de faltas es un problema complejo y aún no resuelto. La complejidad es debida principalmente a la presencia de conductores no homogéneos, cargas intermedias, derivaciones laterales y desbalances en el sistema y la carga. Además, normalmente, en estos sistemas sólo se cuenta con medidas en la subestación, y un modelo simplificado del circuito. Los principales esfuerzos en la localización han estado orientados al desarrollo de métodos que utilicen el fundamental de la tensión y de la corriente en la subestación, para estimar la reactancia hasta la falta. Como la obtención de la reactancia permite cuantificar la distancia al sitio de falta a partir del uso del modelo, el Método se considera Basado en el Modelo (MBM). Sin embargo, algunas de sus desventajas están asociadas a la necesidad de un buen modelo del sistema y a la posibilidad de localizar varios sitios donde puede haber ocurrido la falta, esto es, se puede presentar múltiple estimación del sitio de falta. Como aporte, en esta tesis se presenta un análisis y prueba comparativa entre varios de los MBM frecuentemente referenciados. Adicionalmente se complementa la solución con métodos que utilizan otro tipo de información, como la obtenida de las bases históricas de faltas con registros de tensión y corriente medidos en la subestación (no se limita solamente al fundamental). Como herramienta de extracción de información de estos registros, se utilizan y prueban dos técnicas de clasificación (LAMDA y SVM). Éstas relacionan las características obtenidas de la señal, con la zona bajo falta y se denominan en este documento como Métodos de Clasificación Basados en el Conocimiento (MCBC). La información que usan los MCBC se obtiene de los registros de tensión y de corriente medidos en la subestación de distribución, antes, durante y después de la falta. Los registros se procesan para obtener los siguientes descriptores: a) la magnitud de la variación de tensión ( dV ), b) la variación de la magnitud de corriente ( dI ), c) la variación de la potencia ( dS ), d) la reactancia de falta ( Xf ), e) la frecuencia del transitorio ( f ), y f) el valor propio máximo de la matriz de correlación de corrientes (Sv), cada uno de los cuales ha sido seleccionado por facilitar la localización de la falta. A partir de estos descriptores, se proponen diferentes conjuntos de entrenamiento y validación de los MCBC, y mediante una metodología que muestra la posibilidad de hallar relaciones entre estos conjuntos y las zonas en las cuales se presenta la falta, se seleccionan los de mejor comportamiento. Los resultados de aplicación, demuestran que con la combinación de los MCBC con los MBM, se puede reducir el problema de la múltiple estimación del sitio de falta. El MCBC determina la zona de falta, mientras que el MBM encuentra la distancia desde el punto de medida hasta la falta, la integración en un esquema híbrido toma las mejores características de cada método. En este documento, lo que se conoce como híbrido es la combinación de los MBM y los MCBC, de una forma complementaria. Finalmente y para comprobar los aportes de esta tesis, se propone y prueba un esquema de integración híbrida para localización de faltas en dos sistemas de distribución diferentes. Tanto los métodos que usan los parámetros del sistema y se fundamentan en la estimación de la impedancia (MBM), como aquellos que usan como información los descriptores y se fundamentan en técnicas de clasificación (MCBC), muestran su validez para resolver el problema de localización de faltas. Ambas metodologías propuestas tienen ventajas y desventajas, pero según la teoría de integración de métodos presentada, se alcanza una alta complementariedad, que permite la formulación de híbridos que mejoran los resultados, reduciendo o evitando el problema de la múltiple estimación de la falta.

Analysis of protein-protein interactions in the feline calicivirus replication complex

Caliciviruses are a major cause of gastroenteritis in humans and cause a wide variety of other diseases in animals. Here, the characterization of protein-protein interactions between the individual proteins of Feline calicivirus (FCV), a model system for other members of the family Caliciviridae, is reported. Using the yeast two-hybrid system combined with a number of other approaches, it is demonstrated that the p32 protein (the picornavirus 2B analogue) of FCV interacts with p39 (2C), p30 (3A) and p76 (3CD). The FCV protease/RNA polymerase (ProPol) p76 was found to form homo-oligomers, as well as to interact with VPg and ORF2, the region encoding the major capsid protein VP1. A weak interaction was also observed between p76 and the minor capsid protein encoded by ORF3 (VP2). ORF2 protein was found to interact with VPg, p76 and VP2. The potential roles of the interactions in calicivirus replication are discussed.

Adjoint methods in data assimilation for estimating model error

Data assimilation aims to incorporate measured observations into a dynamical system model in order to produce accurate estimates of all the current (and future) state variables of the system. The optimal estimates minimize a variational principle and can be found using adjoint methods. The model equations are treated as strong constraints on the problem. In reality, the model does not represent the system behaviour exactly and errors arise due to lack of resolution and inaccuracies in physical parameters, boundary conditions and forcing terms. A technique for estimating systematic and time-correlated errors as part of the variational assimilation procedure is described here. The modified method determines a correction term that compensates for model error and leads to improved predictions of the system states. The technique is illustrated in two test cases. Applications to the 1-D nonlinear shallow water equations demonstrate the effectiveness of the new procedure.