Bootstrap approaches for estimation and confidence intervals of long term memory processes

In this work, we investigate an alternative bootstrap approach based on a result of Ramsey [F.L. Ramsey, Characterization of the partial autocorrelation function, Ann. Statist. 2 (1974), pp. 1296-1301] and on the Durbin-Levinson algorithm to obtain a surrogate series from linear Gaussian processes with long range dependence. We compare this bootstrap method with other existing procedures in a wide Monte Carlo experiment by estimating, parametrically and semi-parametrically, the memory parameter d. We consider Gaussian and non-Gaussian processes to prove the robustness of the method to deviations from normality. The approach is also useful to estimate confidence intervals for the memory parameter d by improving the coverage level of the interval.

Framework for the long-term operation of a mobile robot via the Internet

This paper describes an autonomous docking system and web interface that allows long-term unaided use of a sophisticated robot by untrained web users. These systems have been applied to the biologically inspired RatSLAM system as a foundation for testing both its long-term stability and its practicality. While docking and web interface systems already exist, this system allows for a significantly larger margin of error in docking accuracy due to the mechanical design, thereby increasing robustness against navigational errors. Also a standard vision sensor is used for both long-range and short-range docking, compared to the many systems that require both omni-directional cameras and high resolution Laser range finders for navigation. The web interface has been designed to accommodate the significant delays experienced on the Internet, and to facilitate the non- Cartesian operation of the RatSLAM system.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

The effect of Fe doping on adsorption of CO2/N2within carbon nanotubes : a density functional theory study with dispersion corrections

An ab initio density functional theory (DFT) study with correction for dispersive interactions was performed to study the adsorption of N2 and CO2 inside an (8, 8) single-walled carbon nanotube. We find that the approach of combining DFT and van der Waals correction is very effective for describing the long-range interaction between N2/CO2 and the carbon nanotube (CNT). Surprisingly, exohedral doping of an Fe atom onto the CNT surface will only affect the adsorption energy of the quadrupolar CO2 molecule inside the CNT (20–30%), and not that of molecular N2. Our results suggest the feasibility of enhancement of CO2/N2 separation in CNT-based membranes by using exohedral doping of metal atoms.

Graphyne and graphdiyne : versatile catalysts for dehydrogenation of light metal complex hydrides

The interaction between new two-dimensional carbon allotropes, i.e. graphyne (GP) and graphdiyne (GD), and light metal complex hydrides LiAlH4, LiBH4, and NaAlH4 was studied using density functional theory (DFT) incorporating long range van der Waals dispersion correction. The light metal complex hydrides show much stronger interaction with GP and GP than that with fullerene due to the well defined pore structure. Such strong interactions greatly affect the degree of charge donation from the alkali metal atom to AlH4 or BH4, consequently destabilizing the Al-H or B-H bonds. Compared to the isolated light metal complex hydride, the presence of GP or GD can lead to a significant reduction of the hydrogen removal energy. Most interestingly, the hydrogen removal energies for LiBHx on GP and with GD are found to be lowered at all the stages (x from 4 to 1) whereas the H-removal energy in the third stage is increased for LiBH4 on fullerene. In addition, the presence of uniformly distributed pores on GP and GD is expected to facilitate the dehydrogenation of light metal complex hydrides. The present results highlight new interesting materials to catalyze light metal complex hydrides for potential application as media for hydrogen storage. Since GD has been successfully synthesized in a recent experiment, we hope the present work will stimulate further experimental investigations in this direction.

Ultrasonic attenuation in strongly disordered electronic systems

The attenuation of long-wavelength phonons due to their interaction with electronic excitations in disordered systems is investigated here. Lattice strain couples to electronic stress, and thus ultrasonic attenuation measures electronic viscosity. The enhancement and critical divergence of electronic viscosity due to localization effects is calculated for the first time. Experimental consequences for the anomalous increase of ultrasonic attenuation in disordered metals close to the metal-insulator transition are discussed. In the localized regime, the appropriate model is one of electronic two-level systems (TLS’s) coupled to phonons. The TLS consists of a pair of states with one localized state occupied and the other unoccupied. The density of such low-excitation-energy TLS’s is nonzero due to long-range Coulomb interactions. The question of whether these could be significant low-energy excitations in glasses is touched upon.

Computer simulation of polytypes

Polytypes have been simulated, treating them as analogues of a one-dimensional spin-half Ising chain with competing short-range and infinite-range interactions. Short-range interactions are treated as random variables to approximate conditions of growth from melt as well as from vapour. Besides ordered polytypes up to 12R, short stretches of long-period polytypes (up to 33R) have been observed. Such long-period sequences could be of significance in the context of Frank's theory of polytypism. The form of short-range interactions employed in the study has been justified by carrying out model potential calculations.

Observation of reentrant spin glass behavior in LaCo0.5Ni0.5O3

The magnetic and transport properties of LaCo0.5Ni0.5O3 have been studied. The dc magnetization and the ac susceptibility studies suggest the presence of a magnetic-phase transition from a ferromagnetic (FM) to a spin glass phase at a low temperature. This type of reentrant spin-glass (RSG) behavior attached to a long-range ordered ferromagnet is observed in this system. A magnetoresistance of ~10% is observed at 5 K which is unsaturated up to 11 Tesla suggests the presence of antiferromagnetic (AFM) interactions. It is likely that the competition between such AFM interactions with FM interactions yield an RSG phase.

Valence states and magnetic properties of LaNi1-xMnxO3 (for 0≤x≤0.2 and x=0.5)

Single-phase LaNi1-xMnxO3 samples in the compositional range 0long-range magnetic coupling occurs via solvent electrons in a narrow conduction band. The conductivity changes from that of a narrow-band metal for x<0.01 to that more characteristic of diffusive motion for x>0.05, but any motional enthalpy appears to remain small ( Delta Hm approximately=0). The x=0.1 sample exhibits ferrimagnetic spin glass behaviour below 40K, and the ferromagnetic interactions increase with manganese concentration. The oxide with x=0.50 is ferromagnetic with a well defined Curie temperature.

Aspects and Applications of Pulse Sequence Design for Solution-State Nuclear Magnetic Resonance Spectroscopy

NMR spectroscopy enables the study of biomolecules from peptides and carbohydrates to proteins at atomic resolution. The technique uniquely allows for structure determination of molecules in solution-state. It also gives insights into dynamics and intermolecular interactions important for determining biological function. Detailed molecular information is entangled in the nuclear spin states. The information can be extracted by pulse sequences designed to measure the desired molecular parameters. Advancement of pulse sequence methodology therefore plays a key role in the development of biomolecular NMR spectroscopy. A range of novel pulse sequences for solution-state NMR spectroscopy are presented in this thesis. The pulse sequences are described in relation to the molecular information they provide. The pulse sequence experiments represent several advances in NMR spectroscopy with particular emphasis on applications for proteins. Some of the novel methods are focusing on methyl-containing amino acids which are pivotal for structure determination. Methyl-specific assignment schemes are introduced for increasing the size range of 13C,15N labeled proteins amenable to structure determination without resolving to more elaborate labeling schemes. Furthermore, cost-effective means are presented for monitoring amide and methyl correlations simultaneously. Residual dipolar couplings can be applied for structure refinement as well as for studying dynamics. Accurate methods for measuring residual dipolar couplings in small proteins are devised along with special techniques applicable when proteins require high pH or high temperature solvent conditions. Finally, a new technique is demonstrated to diminish strong-coupling induced artifacts in HMBC, a routine experiment for establishing long-range correlations in unlabeled molecules. The presented experiments facilitate structural studies of biomolecules by NMR spectroscopy.

Dilute Solution Properties of Methyl Methacrylate/Acrylonitrile Random Copolymers, 2a)

The Stockmayer-Fixman relation was used to evaluate the short range and long range interaction parameters for methyl methacrylate/acrylonitrile copolymers of 0,566 and 0,657 mole fraction of monomeric units of acrylonitrile in the solvents acetonitrile, 2-butanone, dimethyl formamide, and y-butyrolactone, at different temperatures (30, 45, and 60 “C). The values of KO were found to be lower than those of the parent homopolymers, and their values depend on both solvent and temperature. Even negative Ko-values were obtained, in cases in which the Mark Houwink exponent a is nearly unity. The values of the polymer-solvent interaction parameter, x, , are high and close to 0,5, indicating that these solvents are not good. The values of the excess interaction parameter, xAB, are negative and are not affected by temperature. The large extension of these copolymer chains, as exhibited by a and a;-values, can be understood in terms of unusual short range interactions only. Similar results were obtained for some cellulose derivatives.

Monte Carlo simulation of Ising chains showing several phase transitions

A Monte Carlo simulation of Ising chains with competing short-range and infiniterange interactions has been carried out. Results show that whenever the system does not enter a metastable state, variation of temperature brings about phase transitions in the Ising chain. These phase transitions, except for two sets of interaction strengths, are generally of higher order and involve changes in the long-range order while the short-range order remains unaffected.

Molecular dynamics studies of nanoparticle impacts

This thesis concerns the dynamics of nanoparticle impacts on solid surfaces. These impacts occur, for instance, in space, where micro- and nanometeoroids hit surfaces of planets, moons, and spacecraft. On Earth, materials are bombarded with nanoparticles in cluster ion beam devices, in order to clean or smooth their surfaces, or to analyse their elemental composition. In both cases, the result depends on the combined effects of countless single impacts. However, the dynamics of single impacts must be understood before the overall effects of nanoparticle radiation can be modelled. In addition to applications, nanoparticle impacts are also important to basic research in the nanoscience field, because the impacts provide an excellent case to test the applicability of atomic-level interaction models to very dynamic conditions. In this thesis, the stopping of nanoparticles in matter is explored using classical molecular dynamics computer simulations. The materials investigated are gold, silicon, and silica. Impacts on silicon through a native oxide layer and formation of complex craters are also simulated. Nanoparticles up to a diameter of 20 nm (315000 atoms) were used as projectiles. The molecular dynamics method and interatomic potentials for silicon and gold are examined in this thesis. It is shown that the displacement cascade expansionmechanism and crater crown formation are very sensitive to the choice of atomic interaction model. However, the best of the current interatomic models can be utilized in nanoparticle impact simulation, if caution is exercised. The stopping of monatomic ions in matter is understood very well nowadays. However, interactions become very complex when several atoms impact on a surface simultaneously and within a short distance, as happens in a nanoparticle impact. A high energy density is deposited in a relatively small volume, which induces ejection of material and formation of a crater. Very high yields of excavated material are observed experimentally. In addition, the yields scale nonlinearly with the cluster size and impact energy at small cluster sizes, whereas in macroscopic hypervelocity impacts, the scaling 2 is linear. The aim of this thesis is to explore the atomistic mechanisms behind the nonlinear scaling at small cluster sizes. It is shown here that the nonlinear scaling of ejected material yield disappears at large impactor sizes because the stopping mechanism of nanoparticles gradually changes to the same mechanism as in macroscopic hypervelocity impacts. The high yields at small impactor size are due to the early escape of energetic atoms from the hot region. In addition, the sputtering yield is shown to depend very much on the spatial initial energy and momentum distributions that the nanoparticle induces in the material in the first phase of the impact. At the later phases, the ejection of material occurs by several mechanisms. The most important mechanism at high energies or at large cluster sizes is atomic cluster ejection from the transient liquid crown that surrounds the crater. The cluster impact dynamics detected in the simulations are in agreement with several recent experimental results. In addition, it is shown that relatively weak impacts can induce modifications on the surface of an amorphous target over a larger area than was previously expected. This is a probable explanation for the formation of the complex crater shapes observed on these surfaces with atomic force microscopy. Clusters that consist of hundreds of thousands of atoms induce long-range modifications in crystalline gold.

Graphene Analogues of BN: Novel Synthesis and Properties

Enthused by the fascinating properties of graphene, we have prepared graphene analogues of BN by a chemical method with a control on the number of layers. The method involves the reaction of boric acid with urea, wherein the relative proportions of the two have been varied over a wide range. Synthesis with a high proportion of urea yields a product with a majority of 1-4 layers. The surface area of BN increases progressively with the decreasing number of layers, and the high surface area BN exhibits high CO, adsorption, but negligible H, adsorption. Few-layer BN has been solubilized by interaction with Lewis bases. We have used first-principles simulations to determine structure, phonon dispersion, and elastic properties of BN with planar honeycomb lattice-based n-layer forms. We find that the mechanical stability of BN with respect to out-of-plane deformation is quite different from that of graphene, as evident in the dispersion of their flexural modes. BN is softer than graphene and exhibits signatures of long-range ionic interactions in its optical phonons. Finally, structures with different stacking sequences of BN have comparable energies, suggesting relative abundance of slip faults, stacking faults, and structural inhomogeneities in multilayer BN.