From caging to rouse dynamics in polymer melts with intramolecular barriers: a critical test of the mode coupling theory

By means of computer simulations and solution of the equations of the mode coupling theory (MCT),we investigate the role of the intramolecular barriers on several dynamic aspects of nonentangled polymers. The investigated dynamic range extends from the caging regime characteristic of glass-formers to the relaxation of the chain Rouse modes. We review our recent work on this question,provide new results, and critically discuss the limitations of the theory. Solutions of the MCT for the structural relaxation reproduce qualitative trends of simulations for weak and moderate barriers. However, a progressive discrepancy is revealed as the limit of stiff chains is approached. This dis-agreement does not seem related with dynamic heterogeneities, which indeed are not enhanced by increasing barrier strength. It is not connected either with the breakdown of the convolution approximation for three-point static correlations, which retains its validity for stiff chains. These findings suggest the need of an improvement of the MCT equations for polymer melts. Concerning the relaxation of the chain degrees of freedom, MCT provides a microscopic basis for time scales from chain reorientation down to the caging regime. It rationalizes, from first principles, the observed deviations from the Rouse model on increasing the barrier strength. These include anomalous scaling of relaxation times, long-time plateaux, and nonmonotonous wavelength dependence of the mode correlators.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Matching stages of heavy-ion collision models

Heavy-ion reactions and other collective dynamical processes are frequently described by different theoretical approaches for the different stages of the process, like initial equilibration stage, intermediate locally equilibrated fluid dynamical stage, and final freeze-out stage. For the last stage, the best known is the Cooper-Frye description used to generate the phase space distribution of emitted, noninteracting particles from a fluid dynamical expansion or explosion, assuming a final ideal gas distribution, or (less frequently) an out-of-equilibrium distribution. In this work we do not want to replace the Cooper-Frye description, but rather clarify the ways of using it and how to choose the parameters of the distribution and, eventually, how to choose the form of the phase space distribution used in the Cooper-Frye formula. Moreover, the Cooper-Frye formula is used in connection with the freeze-out problem, while the discussion of transition between different stages of the collision is applicable to other transitions also. More recently, hadronization and molecular dynamics models have been matched to the end of a fluid dynamical stage to describe hadronization and freeze-out. The stages of the model description can be matched to each other on space-time hypersurfaces (just like through the frequently used freeze-out hypersurface). This work presents a generalized description of how to match the stages of the description of a reaction to each other, extending the methodology used at freeze-out, in simple covariant form which is easily applicable in its simplest version for most applications.

Fusion of the extended modified liquid drop model for nucleation and dynamical nucleation theory

We present a new phenomenological approach to nucleation, based on the combination of the extended modified liquid drop model and dynamical nucleation theory. The new model proposes a new cluster definition, which properly includes the effect of fluctuations, and it is consistent both thermodynamically and kinetically. The model is able to predict successfully the free energy of formation of the critical nucleus, using only macroscopic thermodynamic properties. It also accounts for the spinodal and provides excellent agreement with the result of recent simulations.

Heavy metal ions are potent inhibitors of protein folding.

Environmental and occupational exposure to heavy metals such as cadmium, mercury and lead results in severe health hazards including prenatal and developmental defects. The deleterious effects of heavy metal ions have hitherto been attributed to their interactions with specific, particularly susceptible native proteins. Here, we report an as yet undescribed mode of heavy metal toxicity. Cd2+, Hg2+ and Pb2+ proved to inhibit very efficiently the spontaneous refolding of chemically denatured proteins by forming high-affinity multidentate complexes with thiol and other functional groups (IC(50) in the nanomolar range). With similar efficacy, the heavy metal ions inhibited the chaperone-assisted refolding of chemically denatured and heat-denatured proteins. Thus, the toxic effects of heavy metal ions may result as well from their interaction with the more readily accessible functional groups of proteins in nascent and other non-native form. The toxic scope of heavy metals seems to be substantially larger than assumed so far.

The effects of polycyclic aromatic hydrocarbons and heavy metals on terrestrial annelids in urban soils

The effect of soil contamination by polycyclic aromatic hydrocarbons (PAH) and heavy metals on earthworms and enchytraeids was studied in urban parks, in Brno, Czech Republic. In spring and autumn 2007, annelids were collected and soil samples taken in lawns along transects, at three different distances (1, 5 and 30 m) from streets with heavy traffic. In both seasons, two parks with two transects each were sampled. Earthworms were collected using the electrical octet method. Enchytraeids were extracted by the wet funnel method from soil cores. All collected annelids were counted and identified. Basic chemical parameters and concentrations of 16 PAH, Cd, Cu, Pb, and Zn were analysed from soil from each sampling point. PAH concentrations were rather low, decreasing with the distance from the street in spring but not in autumn. Heavy metal concentrations did not decrease significantly with increasing distance. Annelid densities did not significantly differ between distances, although there was a trend of increase in the number of earthworms with increasing distance. There were no significant correlations between soil content of PAH or heavy metals and earthworm or enchytraeid densities. Earthworm density and biomass were negatively correlated with soil pH; and enchytraeid density was positively correlated with soil phosphorus.

Multidelayed random walks: Theory and application to the neolithic transition in Europe

We present a model in which particles (or individuals of a biological population) disperse with a rest time between consecutive motions (or migrations) which may take several possible values from a discrete set. Particles (or individuals) may also react (or reproduce). We derive a new equation for the effective rest time T˜ of the random walk. Application to the neolithic transition in Europe makes it possible to derive more realistic theoretical values for its wavefront speed than those following from the single-delayed framework presented previously [J. Fort and V. Méndez, Phys. Rev. Lett. 82, 867 (1999)]. The new results are consistent with the archaeological observations of this important historical process

Effects of 2 different prior endurance exercises on whole-body fat oxidation kinetics: light vs. heavy exercise.

This study aimed to compare the effects of 2 different prior endurance exercises on subsequent whole-body fat oxidation kinetics. Fifteen men performed 2 identical submaximal incremental tests (Incr2) on a cycle ergometer after (i) a ∼40-min submaximal incremental test (Incr1) followed by a 90-min continuous exercise performed at 50% of maximal aerobic power-output and a 1-h rest period (Heavy); and (ii) Incr1 followed by a 2.5-h rest period (Light). Fat oxidation was measured using indirect calorimetry and plotted as a function of exercise intensity during Incr1 and Incr2. A sinusoidal equation, including 3 independent variables (dilatation, symmetry and translation), was used to characterize the fat oxidation kinetics and to determine the intensity (Fat(max)) that elicited the maximal fat oxidation (MFO) during Incr. After the Heavy and Light trials, Fat(max), MFO, and fat oxidation rates were significantly greater during Incr2 than Incr1 (p < 0.001). However, Δ (i.e., Incr2-Incr1) Fat(max), MFO, and fat oxidation rates were greater in the Heavy compared with the Light trial (p < 0.05). The fat oxidation kinetics during Incr2(Heavy) showed a greater dilatation and rightward asymmetry than Incr1(Heavy), whereas only a greater dilatation was observed in Incr2(Light) (p < 0.05). This study showed that although to a lesser extent in the Light trial, both prior exercise sessions led to an increase in Fat(max), MFO, and absolute fat oxidation rates during Incr2, inducing significant changes in the shape of the fat oxidation kinetics.

Exact results for Wilson loops in arbitrary representations

We compute the exact vacuum expectation value of 1/2 BPS circular Wilson loops of TeX = 4 U(N) super Yang-Mills in arbitrary irreducible representations. By localization arguments, the computation reduces to evaluating certain integrals in a Gaussian matrix model, which we do using the method of orthogonal polynomials. Our results are particularly simple for Wilson loops in antisymmetric representations; in this case, we observe that the final answers admit an expansion where the coefficients are positive integers, and can be written in terms of sums over skew Young diagrams. As an application of our results, we use them to discuss the exact Bremsstrahlung functions associated to the corresponding heavy probes.

Developmental changes in the heavy subunit of neurofilaments in the corpus callosum of the cat.

In the corpus callosum of the cat, the heavy subunit of neurofilaments (NFH) can be demonstrated with the monoclonal antibody NE14, as early as P11, not at P3, and only in a few axons. At P18-19 and more markedly at P29, many more callosal axons have become positive to NE14 and this is similar to what is found in the adult. In contrast, callosal axons become positive to the neurofilament antibody SMI-32 only between P29 and P39 and remain positive in the adult. Treatment with alkaline phosphatase prevents axonal staining with NE14, but results in SMI-32 staining of a few callosal axons as early as P11, but not at P3. Between P11 and P19 the number of axons stained with SMI-32 after alkaline phosphatase treatment increases, in parallel with that of axons stained with NE14. Thus NE14 appears to recognize a phosphorylated form of NFH, while SMI-32 appears to recognize an epitope of NFH which is either masked by phosphate or inaccessible until between P29 and P39, unless the tissue is treated with alkaline phosphatase. These two forms of NFH appear towards the end of the period of massive developmental elimination of callosal axons. They are also synchronous with changes in the spacing of neurofilaments quantified in a separate ultrastructural study. These cytoskeletal changes may terminate the juvenile-labile state of callosal axons and allow further axial growth of the axon.

Mesoscale numerical analysis of the historical November 1982 heavy precipitation event over Andorra (Eastern Pyrenees)

From 6 to 8 November 1982 one of the most catastrophic flash-flood events was recorded in the Eastern Pyrenees affecting Andorra and also France and Spain with rainfall accumulations exceeding 400 mm in 24 h, 44 fatalities and widespread damage. This paper aims to exhaustively document this heavy precipitation event and examines mesoscale simulations performed by the French Meso-NH non-hydrostatic atmospheric model. Large-scale simulations show the slow-evolving synoptic environment favourable for the development of a deep Atlantic cyclone which induced a strong southerly flow over the Eastern Pyrenees. From the evolution of the synoptic pattern four distinct phases have been identified during the event. The mesoscale analysis presents the second and the third phase as the most intense in terms of rainfall accumulations and highlights the interaction of the moist and conditionally unstable flows with the mountains. The presence of a SW low level jet (30 m s-1) around 1500 m also had a crucial role on focusing the precipitation over the exposed south slopes of the Eastern Pyrenees. Backward trajectories based on Eulerian on-line passive tracers indicate that the orographic uplift was the main forcing mechanism which triggered and maintained the precipitating systems more than 30 h over the Pyrenees. The moisture of the feeding flow mainly came from the Atlantic Ocean (7-9 g kg-1) and the role of the Mediterranean as a local moisture source was very limited (2-3 g kg-1) due to the high initial water vapour content of the parcels and the rapid passage over the basin along the Spanish Mediterranean coast (less than 12 h).

Debating the centre : the theory of principal qualificand (mukhyavisesya) in classical Indian philosophy

The theory of language has occupied a special place in the history of Indian thought. Indian philosophers give particular attention to the analysis of the cognition obtained from language, known under the generic name of śābdabodha. This term is used to denote, among other things, the cognition episode of the hearer, the content of which is described in the form of a paraphrase of a sentence represented as a hierarchical structure. Philosophers submit the meaning of the component items of a sentence and their relationship to a thorough examination, and represent the content of the resulting cognition as a paraphrase centred on a meaning element, that is taken as principal qualificand (mukhyaviśesya) which is qualified by the other meaning elements. This analysis is the object of continuous debate over a period of more than a thousand years between the philosophers of the schools of Mimāmsā, Nyāya (mainly in its Navya form) and Vyākarana. While these philosophers are in complete agreement on the idea that the cognition of sentence meaning has a hierarchical structure and share the concept of a single principal qualificand (qualified by other meaning elements), they strongly disagree on the question which meaning element has this role and by which morphological item it is expressed. This disagreement is the central point of their debate and gives rise to competing versions of this theory. The Mïmāmsakas argue that the principal qualificand is what they call bhāvanā ̒bringing into being̒, ̒efficient force̒ or ̒productive operation̒, expressed by the verbal affix, and distinct from the specific procedures signified by the verbal root; the Naiyāyikas generally take it to be the meaning of the word with the first case ending, while the Vaiyākaranas take it to be the operation expressed by the verbal root. All the participants rely on the Pāninian grammar, insofar as the Mimāmsakas and Naiyāyikas do not compose a new grammar of Sanskrit, but use different interpretive strategies in order to justify their views, that are often in overt contradiction with the interpretation of the Pāninian rules accepted by the Vaiyākaranas. In each of the three positions, weakness in one area is compensated by strength in another, and the cumulative force of the total argumentation shows that no position can be declared as correct or overall superior to the others. This book is an attempt to understand this debate, and to show that, to make full sense of the irreconcilable positions of the three schools, one must go beyond linguistic factors and consider the very beginnings of each school's concern with the issue under scrutiny. The texts, and particularly the late texts of each school present very complex versions of the theory, yet the key to understanding why these positions remain irreconcilable seems to lie elsewhere, this in spite of extensive argumentation involving a great deal of linguistic and logical technicalities. Historically, this theory arises in Mimāmsā (with Sabara and Kumārila), then in Nyāya (with Udayana), in a doctrinal and theological context, as a byproduct of the debate over Vedic authority. The Navya-Vaiyākaranas enter this debate last (with Bhattoji Dïksita and Kaunda Bhatta), with the declared aim of refuting the arguments of the Mïmāmsakas and Naiyāyikas by bringing to light the shortcomings in their understanding of Pāninian grammar. The central argument has focused on the capacity of the initial contexts, with the network of issues to which the principal qualificand theory is connected, to render intelligible the presuppositions and aims behind the complex linguistic justification of the classical and late stages of this debate. Reading the debate in this light not only reveals the rationality and internal coherence of each position beyond the linguistic arguments, but makes it possible to understand why the thinkers of the three schools have continued to hold on to three mutually exclusive positions. They are defending not only their version of the principal qualificand theory, but (though not openly acknowledged) the entire network of arguments, linguistic and/or extra-linguistic, to which this theory is connected, as well as the presuppositions and aims underlying these arguments.

Reaction-diffusion lattice gas: Theory and computer results

We report on the study of nonequilibrium ordering in the reaction-diffusion lattice gas. It is a kinetic model that relaxes towards steady states under the simultaneous competition of a thermally activated creation-annihilation $(reaction$) process at temperature T, and a diffusion process driven by a heat bath at temperature T?T. The phase diagram as one varies T and T, the system dimension d, the relative priori probabilities for the two processes, and their dynamical rates is investigated. We compare mean-field theory, new Monte Carlo data, and known exact results for some limiting cases. In particular, no evidence of Landau critical behavior is found numerically when d=2 for Metropolis rates but Onsager critical points and a variety of first-order phase transitions.

Modeling and mitigation of supply-demand mismatches

In this thesis, I develop analytical models to price the value of supply chain investments under demand uncer¬tainty. This thesis includes three self-contained papers. In the first paper, we investigate the value of lead-time reduction under the risk of sudden and abnormal changes in demand forecasts. We first consider the risk of a complete and permanent loss of demand. We then provide a more general jump-diffusion model, where we add a compound Poisson process to a constant-volatility demand process to explore the impact of sudden changes in demand forecasts on the value of lead-time reduction. We use an Edgeworth series expansion to divide the lead-time cost into that arising from constant instantaneous volatility, and that arising from the risk of jumps. We show that the value of lead-time reduction increases substantially in the intensity and/or the magnitude of jumps. In the second paper, we analyze the value of quantity flexibility in the presence of supply-chain dis- intermediation problems. We use the multiplicative martingale model and the "contracts as reference points" theory to capture both positive and negative effects of quantity flexibility for the downstream level in a supply chain. We show that lead-time reduction reduces both supply-chain disintermediation problems and supply- demand mismatches. We furthermore analyze the impact of the supplier's cost structure on the profitability of quantity-flexibility contracts. When the supplier's initial investment cost is relatively low, supply-chain disin¬termediation risk becomes less important, and hence the contract becomes more profitable for the retailer. We also find that the supply-chain efficiency increases substantially with the supplier's ability to disintermediate the chain when the initial investment cost is relatively high. In the third paper, we investigate the value of dual sourcing for the products with heavy-tailed demand distributions. We apply extreme-value theory and analyze the effects of tail heaviness of demand distribution on the optimal dual-sourcing strategy. We find that the effects of tail heaviness depend on the characteristics of demand and profit parameters. When both the profit margin of the product and the cost differential between the suppliers are relatively high, it is optimal to buffer the mismatch risk by increasing both the inventory level and the responsive capacity as demand uncertainty increases. In that case, however, both the optimal inventory level and the optimal responsive capacity decrease as the tail of demand becomes heavier. When the profit margin of the product is relatively high, and the cost differential between the suppliers is relatively low, it is optimal to buffer the mismatch risk by increasing the responsive capacity and reducing the inventory level as the demand uncertainty increases. In that case, how¬ever, it is optimal to buffer with more inventory and less capacity as the tail of demand becomes heavier. We also show that the optimal responsive capacity is higher for the products with heavier tails when the fill rate is extremely high.