Molecular Dynamics Investigation of Structure and Transport in the K2O-2SiO2 System Using a Partial Charge Based Model Potential

Potassium disilicate glass and melt have been investigated by using a new partial charge based potential model in which nonbridging oxygens are differentiated from bridging oxygens by their charges. The model reproduces the structural data pertaining to the coordination polyhedra around potassium and the various bond angle distributions excellently. The dynamics of the glass has been studied by using space and time correlation functions. It is found that K ions migrate by a diffusive mechanism in the melt and by hops below the glass transition temperature. They are also found to migrate largely through nonbridging oxygenrich sites in the silicate matrix, thus providing support to the predictions of the modified random network model.

Molecular dynamics Investigation of structure and transport in the K2O-2SiO2 system using a partial charge based model potential

Potassium disilicate glass and melt have been investigated by using anew partial charge based potential model in which nonbridging oxygens are differentiated from bridging oxygens by their charges. The model reproduces the structural data pertaining to the coordination polyhedra around potassium and the various bond angle distributions excellently. The dynamics of the glass has been studied by using space and time correlation functions. It is found that K ions migrate by a diffusive mechanism in the melt and by hops below the glass transition temperature. They are also found to migrate largely through nonbridging oxygen-rich sites in the silicate matrix, thus providing support to the predictions of the modified random network model.

Multiscale model reduction methods for deterministic and stochastic partial differential equations

Partial differential equations (PDEs) with multiscale coefficients are very difficult to solve due to the wide range of scales in the solutions. In the thesis, we propose some efficient numerical methods for both deterministic and stochastic PDEs based on the model reduction technique.

For the deterministic PDEs, the main purpose of our method is to derive an effective equation for the multiscale problem. An essential ingredient is to decompose the harmonic coordinate into a smooth part and a highly oscillatory part of which the magnitude is small. Such a decomposition plays a key role in our construction of the effective equation. We show that the solution to the effective equation is smooth, and could be resolved on a regular coarse mesh grid. Furthermore, we provide error analysis and show that the solution to the effective equation plus a correction term is close to the original multiscale solution.

For the stochastic PDEs, we propose the model reduction based data-driven stochastic method and multilevel Monte Carlo method. In the multiquery, setting and on the assumption that the ratio of the smallest scale and largest scale is not too small, we propose the multiscale data-driven stochastic method. We construct a data-driven stochastic basis and solve the coupled deterministic PDEs to obtain the solutions. For the tougher problems, we propose the multiscale multilevel Monte Carlo method. We apply the multilevel scheme to the effective equations and assemble the stiffness matrices efficiently on each coarse mesh grid. In both methods, the $\KL$ expansion plays an important role in extracting the main parts of some stochastic quantities.

For both the deterministic and stochastic PDEs, numerical results are presented to demonstrate the accuracy and robustness of the methods. We also show the computational time cost reduction in the numerical examples.

Modelling and control of nonlinear systems using Gaussian processes with partial model information

Gaussian processes are gaining increasing popularity among the control community, in particular for the modelling of discrete time state space systems. However, it has not been clear how to incorporate model information, in the form of known state relationships, when using a Gaussian process as a predictive model. An obvious example of known prior information is position and velocity related states. Incorporation of such information would be beneficial both computationally and for faster dynamics learning. This paper introduces a method of achieving this, yielding faster dynamics learning and a reduction in computational effort from O(Dn2) to O((D - F)n2) in the prediction stage for a system with D states, F known state relationships and n observations. The effectiveness of the method is demonstrated through its inclusion in the PILCO learning algorithm with application to the swing-up and balance of a torque-limited pendulum and the balancing of a robotic unicycle in simulation. © 2012 IEEE.

Partial oxidation reforming of biomass fuel gas over nickel-based monolithic catalyst with naphthalene as model compound

With naphthalene as biomass tar model compound, partial oxidation reforming (with addition of O-2) and dry reforming of biomass fuel gas were investigated over nickel-based monoliths at the same conditions. The results showed that both processes had excellent performance in upgrading biomass raw fuel gas. Above 99% of naphthalene was converted into synthesis gases (H-2+CO). About 2.8 wt% of coke deposition was detected on the catalyst surface for dry reforming process at 750 degrees C during 108 h lifetime test. However, no Coke deposition was detected for partial oxidation reforming process, which indicated that addition of O-2 can effectively prohibit the coke formation. O-2 Can also increase the CH4 conversion and H-2/CO ratio of the producer gas. The average conversion of CH4 in dry and partial oxidation reforming process was 92% and 95%, respectively. The average H-2/CO ratio increased from 0.95 to 1.1 with the addition of O-2, which was suitable to be used as synthesis gas for dimethyl ether (DME) synthesis.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.

Political Violence and Child Adjustment in Northern Ireland: Testing Pathways in a Social-Ecological Model Including Single- and Two-Parent Families

Moving beyond simply documenting that political violence negatively impacts children, we tested a social ecological hypothesis for relations between political violence and child outcomes. Participants were 700 mother child (M = 12.1 years, SD = 1.8) dyads from 18 working-class, socially deprived areas in Belfast, Northern Ireland, including single- and two-parent families. Sectarian community violence was associated with elevated family conflict and children's reduced security about multiple aspects of their social environment (i.e., family, parent child relations, and community), with links to child adjustment problems and reductions in prosocial behavior. By comparison, and consistent with expectations, links with negative family processes, child regulatory problems, and child outcomes were less consistent for nonsectarian community violence. Support was found for a social ecological model for relations between political violence and child outcomes among both single- and two-parent families, with evidence that emotional security and adjustment problems were more negatively affected in single-parent families. The implications for understanding social ecologies of political violence and children's functioning are discussed.

Testing a social ecological model for relations between political violence and child adjustment in Northern Ireland

Relations between political violence and child adjustment are matters of international concern. Past research demonstrates the significance of community, family, and child psychological processes in child adjustment, supporting study of interrelations between multiple social ecological factors and child adjustment in contexts of political violence. Testing a social ecological model, 300 mothers and their children (M = 12.28 years, SD = 1.77) from Catholic and Protestant working class neighborhoods in Belfast, Northern Ireland, completed measures or community discord, family relations, and children's regulatory processes (i.e., emotional security) and outcomes. Historical political violence in neighborhoods based on objective records (i.e., politically motivated deaths) were related to family members' reports of current sectarian antisocial behavior and nonsectarian antisocial behavior. Interparental conflict and parental monitoring and children's emotional security about both the community and family contributed to explanatory pathways for relations between sectarian antisocial behavior in communities and children's adjustment problems. The discussion evaluates support for social ecological models for relations between political violence and child adjustment and its implications for understanding relations in other parts of the world.

Experimentally validated partial least squares model for dynamic line rating

This study presents a model based on partial least squares (PLS) regression for dynamic line rating (DLR). The model has been verified using data from field measurements, lab tests and outdoor experiments. Outdoor experimentation has been conducted both to verify the model predicted DLR and also to provide training data not available from field measurements, mainly heavily loaded conditions. The proposed model, unlike the direct measurement based DLR techniques, enables prediction of line rating for periods ahead of time whenever a reliable weather forecast is available. The PLS approach yields a very simple statistical model that accurately captures the physical performance of the conductor within a given environment without requiring a predetermination of parameters as required by many physical modelling techniques. Accuracy of the PLS model has been tested by predicting the conductor temperature for measurement sets other than those used for training. Being a linear model, it is straightforward to estimate the conductor ampacity for a set of predicted weather parameters. The PLS estimated ampacity has proven its accuracy through an outdoor experiment on a piece of the line conductor in real weather conditions.

Partial least squares model for dynamic rating of overhead line in wind intensive areas based on field measurements

This paper presents a statistical model for the thermal behaviour of the line model based on lab tests and field measurements. This model is based on Partial Least Squares (PLS) multi regression and is used for the Dynamic Line Rating (DLR) in a wind intensive area. DLR provides extra capacity to the line, over the traditional seasonal static rating, which makes it possible to defer the need for reinforcement the existing network or building new lines. The proposed PLS model has a number of appealing features; the model is linear, so it is straightforward to use for predicting the line rating for future periods using the available weather forecast. Unlike the available physical models, the proposed model does not require any physical parameters of the line, which avoids the inaccuracies resulting from the errors and/or variations in these parameters. The developed model is compared with physical model, the Cigre model, and has shown very good accuracy in predicting the conductor temperature as well as in determining the line rating for future time periods. 

Creatine deficiency syndomes : a new model of partial GAMT deficiency affecting brain cell development

Les syndromes de déficiences cérébrales en créatine (CCDS) sont dus à des mutations dans les gènes GATM et G AMT (codant pour les enzymes AGAT et G AMT de la voie de synthèse de créatine) ainsi que SLC6A8 (transporteur de créatine), et génèrent une absence ou une très forte baisse de créatine (Cr) dans le cerveau, mesurée par spectroscopic de résonance magnétique. Les patients CCDS développent des handicaps neurologiques sévères. Les patients AGAT et GAMT peuvent être traités avec des doses importantes de Cr, mais gardent dans la plupart des cas des séquelles neurologiques irréversibles. Aucun traitement efficace n'existe à ce jour pour la déficience en SLC6A8. Bien que de nombreux modèles aient été développés pour comprendre la Cr cérébrale en conditions physiologiques, les pathomécanismes des CCDS ne sont pas encore compris. Des souris transgéniques pour les gènes Gatm, Gamt et Slc6a8 ont été générées, mais elles ne miment que partiellement la pathologie humaine. Parmi les CCDS, la déficience en GAMT est la plus sévère, en raison de l'accumulation cérébrale de l'intermédiaire guanidinoacétate (GAA). Alors que la toxicité cérébrale du GAA a été étudiée par exposition directe au GAA d'animaux adultes sains, les mécanismes de la toxicité du GAA en condition de déficience en GAMT dans le cerveau en développement sont encore inconnus. Le but de ce projet était donc de développer un modèle de déficience en GAMT dans des cultures 3D primaires de cellules nerveuses de rat en agrégats par knock-down du gène GAMT, en utilisant un virus adéno-associé (AAV) induisant le mécanisme d'interférence à l'ARN (RNAi). Le virus scAAV2, à la multiplicité d'infection de 1000, s'est révélé le plus efficace pour transduire tous les types de cellules nerveuses des cultures (neurones, astrocytes, oligodendrocytes), et générer un knock-down maximal de la protéine GAMT de 85% (jour in vitro 18). Cette déficience partielle en GAMT s'est révélée insuffisante pour générer une déficience en Cr, mais a causé l'accumulation attendue de GAA, à des doses comparables aux niveaux observés dans le LCR des patients GAMT. Le GAA a induit une croissance axonale anarchique accompagnée d'une baisse de l'apoptose naturelle, suivis par une induction tardive de mort cellulaire non-apoptotique. Le co-traitement par la Cr a prévenu tous les effets toxiques du GAA. Ce travail montre que l'accumulation de GAA en absence de déficience en Cr est suffisante pour affecter le développement du tissu nerveux, et suggère que des formes de déficiences en GAMT supplémentaires, ne présentant pas de déficiences en Cr, pourraient être découvertes par mesure du GAA, en particulier à travers les programmes récemment proposés de dépistage néonatal de la déficience en GAMT. -- Cerebral creatine deficiency syndromes (CCDS) are caused by mutations in the genes GATM and GAMT (respectively coding for the two enzymes of the creatine synthetic pathway, AGAT and GAMT) as well as SLC6A8 (creatine transporter), and lead to the absence or very strong decrease of creatine (Cr) in the brain when measured by magnetic resonance spectroscopy. Affected patients show severe neurological impairments. While AGAT and GAMT deficient patients can be treated with high dosages of Cr, most remain with irreversible brain sequelae. No treatment has been successful so far for SLC6A8 deficiency. While many models have helped understanding the cerebral Cr pathways in physiological conditions, the pathomechanisms underlying CCDS are yet to be elucidated. Transgenic mice carrying mutations in the Gatm, Gamt and Slc6a8 genes have been developed, but only partially mimic the human pathology. Among CCDS, GAMT deficiency is the most severe, due to the CNS accumulation of the guanidinoacetate (GAA) intermediate. While brain toxicity of GAA has been explored through direct GAA exposure of adult healthy animals, the mechanisms underlying GAA toxicity in GAMT deficiency conditions on the developing CNS are yet unknown. The aim of this project was thus to develop and characterize a GAMT deficiency model in developing brain cells by gene knockdown, by adeno-associated virus (AAV)-driven RNA interference (RNAi) in rat 3D organotypic primary brain cell cultures in aggregates. scAAV2 with a multiplicity of infection of 1000 was shown as the most efficient serotype, was able to transduce all brain cell types (neurons, astrocytes, oligodendrocytes) and to induce a maximal GAMT protein knockdown of 85% (day in vitro 18). Metabolite analysis showed that partial GAMT knockdown was insufficient to induce Cr deficiency but generated the awaited GAA accumulation at concentrations comparable to the levels observed in cerebrospinal fluid of GAMT-deficient patients. Accumulated GAA induced axonal hypersprouting paralleled with inhibition of natural apoptosis, followed by a later induction in non-apoptotic cell death. Cr supplementation led to the prevention of all GAA-induced toxic effects. This work shows that GAA accumulation without Cr deficiency is sufficient to affect CNS development, and suggests that additional partial GAMT deficiencies, which may not show the classical brain Cr deficiency, may be discovered through GAA measurement including by recently proposed neonatal screening programs for GAMT deficiency.

The impact of resolution on the adjustment and decadal variability of the Atlantic Meridional Overturning Circulation in a coupled climate model

Variations in the Atlantic Meridional Overturning Circulation (MOC) exert an important influence on climate, particularly on decadal time scales. Simulation of the MOC in coupled climate models is compromised, to a degree that is unknown, by their lack of fidelity in resolving some of the key processes involved. There is an overarching need to increase the resolution and fidelity of climate models, but also to assess how increases in resolution influence the simulation of key phenomena such as the MOC. In this study we investigate the impact of significantly increasing the (ocean and atmosphere) resolution of a coupled climate model on the simulation of MOC variability by comparing high and low resolution versions of the same model. In both versions, decadal variability of the MOC is closely linked to density anomalies that propagate from the Labrador Sea southward along the deep western boundary. We demonstrate that the MOC adjustment proceeds more rapidly in the higher resolution model due the increased speed of western boundary waves. However, the response of the Atlantic Sea Surface Temperatures (SSTs) to MOC variations is relatively robust - in pattern if not in magnitude - across the two resolutions. The MOC also excites a coupled ocean-atmosphere response in the tropical Atlantic in both model versions. In the higher resolution model, but not the lower resolution model, there is evidence of a significant response in the extratropical atmosphere over the North Atlantic 6 years after a maximum in the MOC. In both models there is evidence of a weak negative feedback on deep density anomalies in the Labrador Sea, and hence on the MOC (with a time scale of approximately ten years). Our results highlight the need for further work to understand the decadal variability of the MOC and its simulation in climate models.

Measuring the impacts of distortions in the European Union cotton sector: a partial equilibrium analysis using the ATPSM model framework

The “cotton issue” has been a topic of several academic discussions for trade policy analysts. However the design of trade and agricultural policy in the EU and the USA has become a politically sensitive matter throughout the last five years. This study utilizing the Agricultural Trade Policy Simulation Model (ATPSM) aims to gain insights into the global cotton market, to explain why domestic support for cotton has become an issue, to quantify the impact of the new EU agricultural policy on the cotton sector, and to measure the effect of eliminating support policies on production and trade. Results indicate that full trade liberalization would lead the four West African countries to better terms of trade with the EU. If tariff reduction follows the so-called Swiss formula, world prices would increase by 3.5%.