Pulmonary and systemic vascular dysfunction in young offspring of mothers with preeclampsia.

BACKGROUND: Adverse events in utero may predispose to cardiovascular disease in adulthood. The underlying mechanisms are unknown. During preeclampsia, vasculotoxic factors are released into the maternal circulation by the diseased placenta. We speculated that these factors pass the placental barrier and leave a defect in the circulation of the offspring that predisposes to a pathological response later in life. The hypoxia associated with high-altitude exposure is expected to facilitate the detection of this problem. METHODS AND RESULTS: We assessed pulmonary artery pressure (by Doppler echocardiography) and flow-mediated dilation of the brachial artery in 48 offspring of women with preeclampsia and 90 offspring of women with normal pregnancies born and permanently living at the same high-altitude location (3600 m). Pulmonary artery pressure was roughly 30% higher (mean+/-SD, 32.1+/-5.6 versus 25.3+/-4.7 mm Hg; P<0.001) and flow-mediated dilation was 30% smaller (6.3+/-1.2% versus 8.3+/-1.4%; P<0.0001) in offspring of mothers with preeclampsia than in control subjects. A strong inverse relationship existed between flow-mediated dilation and pulmonary artery pressure (r=-0.61, P<0.001). The vascular dysfunction was related to preeclampsia itself because siblings of offspring of mothers with preeclampsia who were born after a normal pregnancy had normal vascular function. Augmented oxidative stress may represent an underlying mechanism because thiobarbituric acid-reactive substances plasma concentration was increased in offspring of mothers with preeclampsia. CONCLUSIONS: Preeclampsia leaves a persistent defect in the systemic and the pulmonary circulation of the offspring. This defect predisposes to exaggerated hypoxic pulmonary hypertension already during childhood and may contribute to premature cardiovascular disease in the systemic circulation later in life.

Development of Asphalt Dynamic Modulus Master Curve Using Falling Weight Deflectometer Measurements, TR-659

The asphalt concrete (AC) dynamic modulus (|E*|) is a key design parameter in mechanistic-based pavement design methodologies such as the American Association of State Highway and Transportation Officials (AASHTO) MEPDG/Pavement-ME Design. The objective of this feasibility study was to develop frameworks for predicting the AC |E*| master curve from falling weight deflectometer (FWD) deflection-time history data collected by the Iowa Department of Transportation (Iowa DOT). A neural networks (NN) methodology was developed based on a synthetically generated viscoelastic forward solutions database to predict AC relaxation modulus (E(t)) master curve coefficients from FWD deflection-time history data. According to the theory of viscoelasticity, if AC relaxation modulus, E(t), is known, |E*| can be calculated (and vice versa) through numerical inter-conversion procedures. Several case studies focusing on full-depth AC pavements were conducted to isolate potential backcalculation issues that are only related to the modulus master curve of the AC layer. For the proof-of-concept demonstration, a comprehensive full-depth AC analysis was carried out through 10,000 batch simulations using a viscoelastic forward analysis program. Anomalies were detected in the comprehensive raw synthetic database and were eliminated through imposition of certain constraints involving the sigmoid master curve coefficients. The surrogate forward modeling results showed that NNs are able to predict deflection-time histories from E(t) master curve coefficients and other layer properties very well. The NN inverse modeling results demonstrated the potential of NNs to backcalculate the E(t) master curve coefficients from single-drop FWD deflection-time history data, although the current prediction accuracies are not sufficient to recommend these models for practical implementation. Considering the complex nature of the problem investigated with many uncertainties involved, including the possible presence of dynamics during FWD testing (related to the presence and depth of stiff layer, inertial and wave propagation effects, etc.), the limitations of current FWD technology (integration errors, truncation issues, etc.), and the need for a rapid and simplified approach for routine implementation, future research recommendations have been provided making a strong case for an expanded research study.

Jatkuvatoimisen sellukeittimen säätöjen suorituskyvyn analysointi

Työssä tarkastellaan yleisiä menetelmiä säätöpiirien suorituskyvyn analysointiin ja sovelletaan niitä jatkuvatoimisen sellukeittimen säätöihin. Esitellyt menetelmät tarjoavat keinoja myös huonon säätötuloksen syyn selvittämiseen ja vinkkejä paremman suorituskyvyn saavuttamiseksi. Analyysissä edettiin top-down periaatteen mukaisesti lähtien liikkeelle keittimen tärkeimmästä säädöstä eli kappaluvun säädöstä. Sitten etsittiin tähän vaikuttavia tekijöitä mitatuista suureista. Seuraavaksi arvioitiin tärkeimmäksi katsotun tekijän (hakepinnankorkeus) säädön suorituskyky, jossa havaittiin parannettavaa. Lopuksi hakepinnankorkeuden säädön viritystämuutettiin ja tehtiin identifiointikoe säätörakenteen uudelleen järjestelyä varten.

SIMULATION OF AMB SUPPORTED ROTOR DURING DROP ON RETAINER BEARINGS

The active magnetic bearings present a new technology which has many advantages compared to traditional bearing designs. Active magnetic bearings, however, require retainer bearings order to prevent damages in the event of a component, power or a control loop failure. In the dropdown situation, when the rotor drops from the magnetic bearings to the retainer bearings, the design parameters of the retainer bearings have a significant influence on the behaviour of the rotor. In this study, the dynamics of an active magnetic bearings supported electric motor during rotor drop on retainer bearings is studied using a multibody simulation approach. Various design parameters of retainer bearings are studied using a simulation model while results are compared with those found in literature. The retainer bearings are modelled using a detailed ball bearing model, which accounts damping and stiffness properties, oil film and friction between races and rolling elements. The model of the ball bearings includes inertia description of rollingelements. The model of the magnetic bearing system contains unbalances of the rotor and stiffness and damping properties of support. In this study, a computationally efficient contact model between the rotor and the retainer bearings is proposed. In addition, this work introduces information for the design of physicalprototype and its retainer bearings.

On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Paperikoneen laatusäätöjärjestelmän uusinnan vaikutukset laatusäätöjen suorituskykyyn

Tutkimuksen kohteena olleen UPM-Kymmene Oyj Kajaanin tehtaan PK3:n laatusäätöjärjestelmä ja mittapalkki uusittiin, jolloin haluttiin selvittää uusinnan vaikutuksia laatusäätöjen suorituskykyyn ja paperin laatuun. Työn kirjallisessa osassa perehdyttiin paperinvalmistusprosessin osiin kyseisen sanomalehtipaperikoneen tapauksessa sekä keskeisimpiin paperin laatuominaisuuksiin liittyviin mittaus- ja säätölaitteisiin sekä niiden toimintaan. Seurattaviksi paperin laatusuureiksi valittiin neliömassa, kuivamassa, kosteus ja paksuus, jotka ovat sanomalehtipaperin tärkeimpiä online-mitattavia ominaisuuksia. Paperin laatusuureiden seurantaan käytetään erilaisia tunnuslukuja ja työkaluja, joita on esitelty tässä työssä. Laatusuureiden konesuuntaisen ja poikkisuuntaisen seurannan tunnusluvuksi valittiin yleisesti käytössä oleva 2σ-keskiarvohajonta. Säätöjen suorituskykyä seurattiin suorituskykykolmion ohjausmatkaindeksien (CTI) ja erosuureen integraalien (IAE) avulla. Kokeellisessa osassa kerättiin mittaustietoja sekä vanhan että uuden laatusäätöjärjestelmän aikana. Seurattavat ajotilanteet paperikoneella jaettiin stabiiliin ajoon ja muutostilanteisiin, jotka käsittävät katkot ja lajinvaihtotilanteet. Stabiilin ajon aikana selvitettiin laatusuureiden hajontojen ja säätöjen suorituskykyindeksien normaaleissa tasoissa tapahtuneet muutokset. Muutostilanteiden osalta haluttiin selvittää, nopeuttaako järjestelmäuusinta katkoista toipumista ja lajinvaihtoaikaa. Stabiilin ajon seurannasta saatujen tulosten perusteella neliömassan ja kuivamassan konesuuntaiset hajonnat kasvoivat järjestelmäuusinnan myötä, mutta kosteuden konesuuntaiset hajonnat pienenivät. Laatusuureiden poikkisuuntaisista hajonnoista neliömassan sekä kuivamassan hajonnat kasvoivat ja kosteuden sekä paksuuden hajonnat pienenivät joidenkin lajien osalta. Poikkisuuntaisten laatusuureiden, etenkin paksuuden, toipuminen katkon jälkeen nopeutui. Myös lajinvaihtoon kuluva aika lyheni poikkisuuntaisilla laatusuureilla. Muutostilanteiden konesuuntaisten hajontojen asettumisajat eivät juuri parantuneet.

Väsymisen huomiointi laitteen ohjauksessa

Työssä selvitettiin väsymisen huomioivan ja minimoivan laitteen ohjausmenetelmiä. Väsymisilmiön huomioiva älykäs laite monitoroi itsenäisesti mm. väsymissäröjen kasvua ja muuttaa toimintaansa sen mukaisesti. Reagoinnin hyötyinä saavutetaan väsyttävästi kuormitetulle laitteelle mm. pidempi käyttöikä ja riskin hallinta, jossa laite tietää, miten sitä voidaan käyttää ennen vauriota ja sen jälkeen. Kunnossapitoon liittyen ennustetaan jäljellä olevaa käyttöikää, jolloin voidaan suunnitella huolto. Tutkimuksessa käsiteltiin mm. laitteiden ohjauksen tarvitsemia mittausmenetelmiä, mittaustiedon käsittelyä, vaurion luokittelua ja vauriota minimoivan ohjauksen rakennetta. Lisäksi käsiteltiin lyhyesti vaurion luokittelussa sekä ohjausreaktioiden ratkaisemisessa tarvittavia oppivia menetelmiä. Väsymistä minimoivan laitteen ohjauksen perusedellytys on laitteen kokemien rasitusten ja/tai suorituksen mittaaminen. Mittaustulosten perusteella määritetään vaurioitumista kuvaavat suureet. Ohjauksen vaurioon reagoivassa osassa määritetään tieto vaurioitumisen kriittisyydestä ja tämän perusteella tarvittava ohjauksen optimaalinen muutos sekä optimaalinen ohjaussignaali tai muu korjaava toimenpide. Ohjaus optimoidaan vaurioitumisnopeus minimoiden ja suorituskyky maksimoiden. Näiden välille etsitään sopiva tasapaino, jossa suorituskyvyn häviö on pieni mahdollisimman suurella vaurioitumisen pienenemisellä. Tämän jälkeen mittauksien avulla saadaan tieto korjatun ohjauksen vaikutuksesta vauriosuureisiin.

Analyzing Carbon Trade Permits in Optimization Framework

The threats caused by global warming motivate different stake holders to deal with and control them. This Master's thesis focuses on analyzing carbon trade permits in optimization framework. The studied model determines optimal emission and uncertainty levels which minimize the total cost. Research questions are formulated and answered by using different optimization tools. The model is developed and calibrated by using available consistent data in the area of carbon emission technology and control. Data and some basic modeling assumptions were extracted from reports and existing literatures. The data collected from the countries in the Kyoto treaty are used to estimate the cost functions. Theory and methods of constrained optimization are briefly presented. A two-level optimization problem (individual and between the parties) is analyzed by using several optimization methods. The combined cost optimization between the parties leads into multivariate model and calls for advanced techniques. Lagrangian, Sequential Quadratic Programming and Differential Evolution (DE) algorithm are referred to. The role of inherent measurement uncertainty in the monitoring of emissions is discussed. We briefly investigate an approach where emission uncertainty would be described in stochastic framework. MATLAB software has been used to provide visualizations including the relationship between decision variables and objective function values. Interpretations in the context of carbon trading were briefly presented. Suggestions for future work are given in stochastic modeling, emission trading and coupled analysis of energy prices and carbon permits.

Sadevälkkeen mallinnus ilmavalvontatutkajärjestelmää varten

Tutkat muodostavat Suomen rauhanajan ilmavalvonnan rungon. Ilmatilassa on lentokoneiden lisäksi paljon muitakin kohteita, jotka ilmavalvontatutka havaitsee. Naita ei toivottuja kaikuja kutsutaan välkkeeksi. Sadevälke on tilavuusvälkettä. Tämän työn tarkoituksena on löytää menetelmä tai malli, jolla voitaisiin mallintaa sadevälkkeen vaikutus ilmavalvontatutkassa. Toisaalta myös sadevälkkeen suodatus on työn keskeinen tavoite. Käytettyjä suodatusmenetelmiä olivat adaptiivinen suodatus ja doppler-suodatus. Suodinpankkiin eli doppler-suodatukseen lisättiin vielä CFAR Työn tuloksena voi todeta, että sadevälkkeen suodatus onnistui hyvin mutta itse sadevälkkeen mallintamista tulee kehittää edelleen. Työssä käytetyt menetelmät on esitetty algoritmimuodossa. Mittausaineiston keräys suoritettiin keskivalvontatutkalla ja SP-testerillä. Varsinaiset suodatuskokeet ja mallin testaus tehtiin Matlab-ohjelmistolla.

Simulation of moisture profiles in a Latossol in Dourados region, in the State of Mato Grosso do Sul, Brazil

The understanding of unsaturated soil water flow at process-level is essential to develop proper management actions for environmental protection in agricultural systems. One important tool for simulation of soil water flow that has been used worldwide is the SWAP model. The aim of this work was to test and to calibrate the SWAP model by inverse modeling to describe moisture profiles in a Brazilian very clayey Latossol in Dourados, State of Mato Grosso do Sul, Brazil. The SWAP model was tested in an experimental field of 0.09 ha cultivated with soybean and soil profiles were sampled eight times between December 2006 and October 2007. The SWAP input values (i.e. soil water retention curves and meteorological data) were based on in-situ measurements. Simulations with uncalibrated soil water retention curves resulted in moisture profiles that were too wet for almost all sampling dates, in particular between 0-10 cm depth. After calibration of soil water retention curves, there was a good improvement in the simulated moisture profiles, which were within the range of measured values for almost all depths and sampling dates.

Sensibility analysis of the pearl model for pesticide leaching in the State of Mato Grosso do Sul, Brazil

For an accurate use of pesticide leaching models it is necessary to assess the sensitivity of input parameters. The aim of this work was to carry out sensitivity analysis of the pesticide leaching model PEARL for contrasting soil types of Dourados river watershed in the state of Mato Grosso do Sul, Brazil. Sensitivity analysis was done by carrying out many simulations with different input parameters and calculating their influence on the output values. The approach used was called one-at-a-time sensitivity analysis, which consists in varying independently input parameters one at a time and keeping all others constant with the standard scenario. Sensitivity analysis was automated using SESAN tool that was linked to the PEARL model. Results have shown that only soil characteristics influenced the simulated water flux resulting in none variation of this variable for scenarios with different pesticides and same soil. All input parameters that showed the greatest sensitivity with regard to leached pesticide are related to soil and pesticide properties. Sensitivity of all input parameters was scenario dependent, confirming the need of using more than one standard scenario for sensitivity analysis of pesticide leaching models.

Post-industrial 2.0 – Social Web as the second coming of the post-industrial society

This study presents a review of theories of the so-called post-industrial society, and proposes that the concept of post-industrial society can be used to understand the recent developments of the World Wide Web, often described as Web 2.0 or social Web. The study combines theories ranging from post-war management science and cultural studies to software development, and tries to build a holistic view of the development of the post-industrial society, and especially the Internet. The discourse on the emergence of a post-industrial society after the World Wars has addressed the ways in which the growing importance of information, and innovations in digital communications technology, are changing our society. It is furthermore deeply connected with the discourse on the postmodern society, which emphasizes cultural fragmentation, intertextuality, and pluralism. The Internet age is characterized by increasing masses of information that are managed through various technologies. While 1990s Internet technologies often used the network as a traditional broadcasting channel with added interactivity, Web 2.0 technologies are specifically designed to utilize the network model by facilitating communication between various services and devices, and analyzing the relationships between users and objects in order to produce intelligent insight. The wide adoption of the Internet, and recently of Internet-enabled mobile devices, is furthermore continuously producing new ways of communicating, consuming, and producing. Applications of the social Web, such as social media or social networking services, are permanently changing our traditional social, cultural, and economic practices. The study first presents an overview of the post-industrial society, the Internet, and the concept of Web 2.0. Then the concept of social Web is described with an analysis of the term social media, the brief histories of the interactive Web and social networking services, and a description of the concept ―long tail‖, used to represent the masses of information available in the Web that do not receive mainstream attention. Finally, methods for retrieving and filtering information, modeling social and cultural relationships, and communicating with customers, are presented.

A Test Bed Applied Mobile Robot Navigation

It is presented a test bed applied to studies on dynamics, control, and navigation of mobile robots. A cargo ship scale model was chosen, which can be radio-controlled or operated autonomously through an embedded control system. A control program, which manages on board mission execution, is implemented on a microcontroller. Navigation is based on an electronic compass, which includes automatic compensation for pitch and roll motions. Heading control loop is based on this sensor, and on a rudder positioning system. A propulsion control system is also implemented. Typical manoeuvres as the turning test and "zig-zag", were implemented and tested. They are included on a manoeuvre library, and can be accessed independently or in combined modes. The embedded system is also in charge of signal acquisition and storing during the missions. It is possible to analyse experiments on identification of ship dynamics, control, and navigation, through the data transferred to a PC by serial communication. Navigation is going to be improved by including inertial sensors on board, and a DGPS. Preliminary tests are aimed to ship identification, and manoeuvrability, using free model tests. Future steps include extending this system for developing other mobile robots as, ROVs, AUVs, and aerial vehicles.

Traitement cérébral de sons émotionnels : une perspective électrophysiologique

Des sons émotionnels furent présentés comme stimuli cibles lors d'une tâche auditive de type oddball. Les effets acoustiques furent départagés des effets émotionnels à l'aide d'une tâche contrôle similaire utilisant une version brouillée des sons originaux et dépourvue de propriétés émotionnelles. Les résultats du oddball émotionnel qui ont différé du oddball contrôle ont montré des effets de valence inversés dans les composantes électrophysiologiques P2 et P300; la valence négative ayant une amplitude plus grande dans la fenêtre de 130-270ms mais moins intense autour de 290-460ms, lorsque comparée aux valences positives et neutres. Les résultats P2 peuvent être interprétés comme une mobilisation attentionnelle précoce privilégiant les stimuli potentiellement dangereux, tandis que les résultats de la P300 pourrait indiquer une évaluation moins détaillée de ces stimuli.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.