994 resultados para Artificial heart


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Kanadalainen kirjailija L. M. (Ludy Maud) Montgomery (1874-1942) tunnetaan parhaiten lasten ja nuorten kirjallisuudestaan, erityisesti Anne of Green Gables -romaanistaan (suom. Annan nuoruusvuodet). Graduni tutkii kuitenkin Montgomeryn vähemmän tunnettua omaelämäkerrallista tuotantoa, hänen päiväkirjojaan, jotka on julkaistu viidessä osassa (1985-2004). Keskityn romanssin kuvaukseen Montgomeryn päiväkirjojen ensimmäisessä osassa ja tutkin, millaisia tekstuaalisia persoonia (personas) kertoja luo autobiografisessa prosessissa. Tähän narratiiviseen prosessiin vaikuttavat erityisesti päiväkirjan yleisö tai lukijat (audience), samoin kuin fiktiiviset esikuvat ja fiktiivisesti kirjoittaminen (fictionalisation) sekä kysymys päiväkirjan kertovasta ja kerrotusta minästä (narrated and narrating I). Romanssi on pääosassa Montgomeryn päiväkirjojen ensimmäisessä julkaistussa osassa, joka kuvaa hänen teini- ja varhaisaikuisuusvuosiaan. Väitän, että vaikka päiväkirjateksti saattaa vaikuttaa rehelliseltä ja todenmukaiselta kuvaukselta elämästä, varsinkin Montgomeryn tapauksessa kyseessä on silti läpikotaisin editoitu ja muokattu teksti, jota päiväkirjan tekijä hallitsee rautaisella otteella ja joka käyttää hyväkseen fiktiivisiä keinoja. Aineistonani käytän sekä Montgomeryn julkaistuja että julkaisemattomia päiväkirjoja, joita säilytetään University of Guelphin arkistoissa, Kanadan Ontariossa. Päiväkirjat ovat moneen kertaan sekä Montgomeryn että julkaistujen päiväkirjojen editoijien muokkaamia, joten niiden tutkiminen lähilukemalla (close-reading) ja eri versioita vertaillen on erityisen tärkeää. Teorian osalta keskityn lähinnä Pohjois-Amerikassa kirjoitettuun autobiografia- ja (naisten) päiväkirjatutkimukseen. Väitän gradussani, että Montgomeryn kuvaus heteroseksuaalisesta romanssista välttää tarjoamasta lukijalle romanssijuonen tyypillistä katharsista. Montgomeryn romanttiset persoonat ovat yllättävän epäromanttisia ja ristiriidassa keskenään. Romantiikan traditiolle tyypillistä kieltä ja personifikaatiota käytetään joko korostetun liioitellusti tai humoristisesti ja parodioiden.

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The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

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The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

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In this study, we investigated measures of nonlinear dynamics and chaos theory in regards to heart rate variability in 27 normal control subjects in supine and standing postures, and 14 subjects in spontaneous and controlled breathing conditions. We examined minimum embedding dimension (MED), largest Lyapunov exponent (LLE) and measures of nonlinearity (NL) of heart rate time series. MED quantifies the system's complexity, LLE predictability and NL, a measure of deviation from linear processes. There was a significant decrease in complexity (P<0.00001), a decrease in predictability (P<0.00001) and an increase in nonlinearity (P=0.00001) during the change from supine to standing posture. Decrease in MED, and increases in NL score and LLE in standing posture appear to be partly due to an increase in sympathetic activity of the autonomous nervous system in standing posture. An improvement in predictability during controlled breathing appears to be due to the introduction of a periodic component. (C) 2000 published by Elsevier Science B.V.

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For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

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Hypertension is a major risk factor for stroke, ischaemic heart disease, and the development of heart failure. Hypertension-induced heart failure is usually preceded by the development of left ventricular hypertrophy (LVH), which represents an adaptive and compensatory response to the increased cardiac workload. Biomechanical stress and neurohumoral activation are the most important triggers of pathologic hypertrophy and the transition of cardiac hypertrophy to heart failure. Non-clinical and clinical studies have also revealed derangements of energy metabolism in hypertensive heart failure. The goal of this study was to investigate in experimental models the molecular mechanisms and signalling pathways involved in hypertension-induced heart failure with special emphasis on local renin-angiotensin-aldosterone system (RAAS), cardiac metabolism, and calcium sensitizers, a novel class of inotropic agents used currently in the treatment of acute decompensated heart failure. Two different animal models of hypertensive heart failure were used in the present study, i.e. hypertensive and salt-sensitive Dahl/Rapp rats on a high salt diet (a salt-sensitive model of hypertensive heart failure) and double transgenic rats (dTGR) harboring human renin and human angiotensinogen genes (a transgenic model of hypertensive heart failure with increased local RAAS activity). The influence of angiotensin II (Ang II) on cardiac substrate utilization and cardiac metabolomic profile was investigated by using gas chromatography coupled to time-of-flight mass spectrometry to detect 247 intermediary metabolites. It was found that Ang II could alter cardiac metabolomics both in normotensive and hypertensive rats in an Ang II receptor type 1 (AT1)-dependent manner. A distinct substrate use from fatty acid oxidation towards glycolysis was found in dTGR. Altered cardiac substrate utilization in dTGR was associated with mitochondrial dysfunction. Cardiac expression of the redox-sensitive metabolic sensor sirtuin1 (SIRT1) was increased in dTGR. Resveratrol supplementation prevented cardiovascular mortality and ameliorated Ang II-induced cardiac remodeling in dTGR via blood pressure-dependent pathways and mechanisms linked to increased mitochondrial biogenesis. Resveratrol dose-dependently increased SIRT1 activity in vitro. Oral levosimendan treatment was also found to improve survival and systolic function in dTGR via blood pressure-independent mechanisms, and ameliorate Ang II-induced coronary and cardiomyocyte damage. Finally, using Dahl/Rapp rats it was demonstrated that oral levosimendan as well as the AT1 receptor antagonist valsartan improved survival and prevented cardiac remodeling. The beneficial effects of levosimendan were associated with improved diastolic function without significantly improved systolic changes. These positive effects were potentiated when the drug combination was administered. In conclusion, the present study points to an important role for local RAAS in the pathophysiology of hypertension-induced heart failure as well as its involvement as a regulator of cardiac substrate utilization and mitochondrial function. Our findings suggest a therapeutic role for natural polyphenol resveratrol and calcium sensitizer, levosimendan, and the novel drug combination of valsartan and levosimendan, in prevention of hypertension-induced heart failure. The present study also provides a better understanding of the pathophysiology of hypertension-induced heart failure, and may help identify potential targets for novel therapeutic interventions.

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In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations-uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations. (C) 2009 Elsevier B.V. All rights reserved.

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The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.

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Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.

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This paper describes a technique for artificial generation of learning and test sample sets suitable for character recognition research. Sample sets of English (Latin), Malayalam, Kannada and Tamil characters are generated easily through their prototype specifications by the endpoint co-ordinates, nature of segments and connectivity.

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In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

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This paper deals with the application of artificial commutation for a normally rated inverter connecting a weak AC system in a multiterminal HVDC (MTDC) system. Artificial commutation is achieved using series capacitors. A modular digital simulation technique is developed to study the dynamic performance of the system. It is shown that by a proper selection of the value of the capacitor it is possible to limit the valve stresses and the DC harmonics to acceptable levels and achieve an improved performance during severe transient conditions. The determination of the value of the series capacitor is based on a parametric study.

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Heart failure is a common, severe, and progressive condition associated with high mortality and morbidity. Because of population-aging in the coming decades, heart failure is estimated to reach epidemic proportions. Current medical and surgical treatments have reduced mortality, but the prognosis for patients has remained poor. Transplantation of skeletal myoblasts has raised hope of regenerating the failing heart and compensating for lost cardiac contractile tissue. In the present work, we studied epicardial transplantation of tissue-engineered myoblast sheets for treatment of heart failure. We employed a rat model of myocardial infarction-induced acute and chronic heart failure by left anterior descending coronary artery ligation. We then transplanted myoblast sheets genetically modified to resist cell death after transplantation by expressing antiapoptotic gene bcl2. In addition, we evaluated the regenerative capacity of myoblast sheets expressing the cardioprotective cytokine hepatocyte growth factor in a rat chronic heart failure model. Furthermore, we utilized in vitro cardiomyocyte and endothelial cell culture models as well as microarray gene expression analysis to elucidate molecular mechanisms mediating the therapeutic effects of myoblast sheet transplantation. Our results demonstrate that Bcl2-expression prolonged myoblast sheet survival in rat hearts after transplantation and induced secretion of cardioprotective, proangiogenic cytokines. After acute myocardial infarction, these sheets attenuated left ventricular dysfunction and myocardial damage, and they induced therapeutic angiogenesis. In the chronic heart failure model, inhibition of graft apoptosis by Bcl-2 improved cardiac function, supported survival of cardiomyocytes in the infarcted area, and induced angiogenesis in a vascular endothelial growth factor receptor 1- and 2-dependent mechanism. Hepatocyte growth factor-secreting myoblast sheets further enhanced the angiogenic efficacy of myoblast sheet therapy. Moreover, myoblast-secreted paracrine factors protected cardiomyocytes against oxidative stress in an epidermal growth factor receptor- and c-Met dependent manner. This protection was associated with induction of antioxidative genes and activation of the unfolded protein response. Our results provide evidence that inhibiting myoblast sheet apoptosis can enhance the sheets efficacy for treating heart failure after acute and chronic myocardial infarction. Furthermore, we show that myoblast sheets can serve as vehicles for delivery of growth factors, and induce therapeutic angiogenesis in the chronically ischemic heart. Finally, myoblasts induce, in a paracine manner, a cardiomyocyte-protective response against oxidative stress. Our study elucidates novel mechanisms of myoblast transplantation therapy, and suggests effective means to improve this therapy for the benefit of the heart failure patient.

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