Fatigue in rubber vulcanisates

A study has been made of the effect of single extensions and continuous fatigue on the structures of various natural rubber networks. The change in network structure of a conventional vulcanisate on a single extension manifests itself as permanent set. The change in network structure has been assessed by the use of the chemical probes, propan-2-thiol/piperidine, hexane-thiol/piperidine and triphenyl phosphine, which determine the polysulphide and disulphide crosslink densities and main chain modification respectively. The permanent set induced on a single extension of a conventional sulphur vulcanisate has been shown to result from the destruction and reformation of polysulphide crosslinks. The magnitude of the effect was dependent upon the degree of extension and showed a maximum at extensions corresponding to the onset of stress-induced crystallisation. The incorporation of a reinforcing filler, HAF-carbon black, magnified the effect. Vulcanisates that possessed only mono and disulphide crosslinks did not show any significant permanent set. The continuous changes in network structure during fatigue have also been determined, and the effects of carbon black and antioxidants on these changes and the fatigue life have been assessed. During fatigue the overall crosslink density increased slightly, which resulted from the destruction of polysulphide crosslinks. and their replacement by principally disulphide crosslinks. Antioxidants reduced the rate of destruction of polysulphide crosslinks and increased the fatigue life of the rubber network. The fatigue life of the network also depended upon the concentration of free chain ends. These chain ends were incorporated into the network by masticating rubber under nitrogen in the presence of bis (diisopropyl)thiophosphoryl disulphide, which improved the fatigue resistance by up to 9%.

The effect of certain hydrophilic polymers on suspension stability

The adsorption of nonionic surface active agents of polyoxyethylene glycol monoethers of n hexadecanols on polystyrene latex and nonionic cellulose polymers of hydroxyethyl cellulose, hydroxypropyl cellulose and hydroxypropyl methylcellulose on polystyrene latex and ibuprofen drug particles have been studied. The adsorbed layer thicknesses were determined by means of microelectrophoretic and viscometric methods. The conformation of the adsorbed molecules at the solid-liquid interface was deduced from the molecular areas and the adsorbed layer thicknesses. Comparison of the adsorption results obtained from polystyrene latex and ibuprofen particles was made to explain the conformation difference between these two adsorbates. Sedimentation volumes and redispersibility values were the main criteria used to evaluate suspension stability. At low concentrations of surface active agents, hard caked suspensions were found, probably due to the attraction between the uncoated areas or, the mutual adsorption of the adsorbed molecules on the bare surface of the particles in the sediment. At high concentrations of hydroxypropyl cellulose and hydroxypropyl methylcellulose, heavily caked sediments were attributed to network structure formation by the adsorbed molecules. An attempt was made to relate the characteristics of the suspensions to the potential energy of interaction curves. Generally, the agreement between theory and experiment was good, but for hydroxyethyl cellulose-ibuprofen systems discrepancies were found. Experimental studies showed that hydroxyethyl cellulose flocculated polystyrene latex over a rather wide range of concentrations; similarly, hydroxyethyl cellulose-ibuprofen suspensions were also flocculated. Therefore, it ls suggested that a term to account for flocculation energy of the polymer should be added to the total energy of interaction. A rheometric method was employed to study the flocculation energy of the polymer.

Statistical mechanics of distribution networks

This thesis presents an analysis of the stability of complex distribution networks. We present a stability analysis against cascading failures. We propose a spin [binary] model, based on concepts of statistical mechanics. We test macroscopic properties of distribution networks with respect to various topological structures and distributions of microparameters. The equilibrium properties of the systems are obtained in a statistical mechanics framework by application of the replica method. We demonstrate the validity of our approach by comparing it with Monte Carlo simulations. We analyse the network properties in terms of phase diagrams and found both qualitative and quantitative dependence of the network properties on the network structure and macroparameters. The structure of the phase diagrams points at the existence of phase transition and the presence of stable and metastable states in the system. We also present an analysis of robustness against overloading in the distribution networks. We propose a model that describes a distribution process in a network. The model incorporates the currents between any connected hubs in the network, local constraints in the form of Kirchoff's law and a global optimizational criterion. The flow of currents in the system is driven by the consumption. We study two principal types of model: infinite and finite link capacity. The key properties are the distributions of currents in the system. We again use a statistical mechanics framework to describe the currents in the system in terms of macroscopic parameters. In order to obtain observable properties we apply the replica method. We are able to assess the criticality of the level of demand with respect to the available resources and the architecture of the network. Furthermore, the parts of the system, where critical currents may emerge, can be identified. This, in turn, provides us with the characteristic description of the spread of the overloading in the systems.

Three-dimensional nitrogen-doped graphene supported molybdenum disulfide nanoparticles as an advanced catalyst for hydrogen evolution reaction

An efficient three-dimensional (3D) hybrid material of nitrogen-doped graphene sheets (N-RGO) supporting molybdenum disulfide (MoS2) nanoparticles with high-performance electrocatalytic activity for hydrogen evolution reaction (HER) is fabricated by using a facile hydrothermal route. Comprehensive microscopic and spectroscopic characterizations confirm the resulting hybrid material possesses a 3D crumpled few-layered graphene network structure decorated with MoS2 nanoparticles. Electrochemical characterization analysis reveals that the resulting hybrid material exhibits efficient electrocatalytic activity toward HER under acidic conditions with a low onset potential of 112 mV and a small Tafel slope of 44 mV per decade. The enhanced mechanism of electrocatalytic activity has been investigated in detail by controlling the elemental composition, electrical conductance and surface morphology of the 3D hybrid as well as Density Functional Theory (DFT) calculations. This demonstrates that the abundance of exposed active sulfur edge sites in the MoS2 and nitrogen active functional moieties in N-RGO are synergistically responsible for the catalytic activity, whilst the distinguished and coherent interface in MoS 2 /N-RGO facilitates the electron transfer during electrocatalysis. Our study gives insights into the physical/chemical mechanism of enhanced HER performance in MoS2/N-RGO hybrids and illustrates how to design and construct a 3D hybrid to maximize the catalytic efficiency.

Large-scale molecular dynamics simulations of HLA-A*0201 complexed with a tumor-specific antigenic peptide:can the α3 and β2m domains be neglected?

Large-scale massively parallel molecular dynamics (MD) simulations of the human class I major histo-compatibility complex (MHC) protein HLA-A*0201 bound to a decameric tumor-specific antigenic peptide GVY-DGREHTV were performed using a scalable MD code on high-performance computing platforms. Such computational capabilities put us in reach of simulations of various scales and complexities. The supercomputing resources available Large-scale massively parallel molecular dynamics (MD) simulations of the human class I major histocompatibility complex (MHC) protein HLA-A*0201 bound to a decameric tumor-specific antigenic peptide GVYDGREHTV were performed using a scalable MD code on high-performance computing platforms. Such computational capabilities put us in reach of simulations of various scales and complexities. The supercomputing resources available for this study allow us to compare directly differences in the behavior of very large molecular models; in this case, the entire extracellular portion of the peptide–MHC complex vs. the isolated peptide binding domain. Comparison of the results from the partial and the whole system simulations indicates that the peptide is less tightly bound in the partial system than in the whole system. From a detailed study of conformations, solvent-accessible surface area, the nature of the water network structure, and the binding energies, we conclude that, when considering the conformation of the α1–α2 domain, the α3 and β2m domains cannot be neglected. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1803–1813, 2004

The Analysis and Optimization of Survivability of MPLS Networks

The problem of MPLS networks survivability analysis is considered in this paper. The survivability indexes are defined which take into account the specificity of MPLS networks and the algorithm of its estimation is elaborated. The problem of MPLS network structure optimization under the constraints on the survivability indexes is considered and the algorithm of its solution is suggested. The experimental investigations were carried out and their results are presented.

Growing Neural Networks Using Nonconventional Activation Functions

In the paper, an ontogenic artificial neural network (ANNs) is proposed. The network uses orthogonal activation functions that allow significant reducing of computational complexity. Another advantage is numerical stability, because the system of activation functions is linearly independent by definition. A learning procedure for proposed ANN with guaranteed convergence to the global minimum of error function in the parameter space is developed. An algorithm for structure network structure adaptation is proposed. The algorithm allows adding or deleting a node in real-time without retraining of the network. Simulation results confirm the efficiency of the proposed approach.

Networks, space and organisational performance:a study of the determinants of industrial research income generation by universities

This paper examines the extent to which both network structure and spatial factors impact on the organizational performance of universities as measured by the generation of industrial research income. Drawing on data concerning the interactions of universities in the UK with large research and development (R&D)-intensive firms, the paper employs both social network analysis and regression analysis. It is found that the structural position of a university within networks with large R&D-intensive firms is significantly associated with the level of research income gained from industry. Spatial factors, on the other hand, are not found to be clearly associated with performance, suggesting that universities operate on a level playing field across regional environments once other factors are controlled for.

Sensor Location Problem for a Multigraph

MSC 2010: 05C50, 15A03, 15A06, 65K05, 90C08, 90C35

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

Late adolescents in transition: Social networks, parent relations, and well -being

A two-year longitudinal study was conducted to investigate late adolescents in transition. An initial investigation with senior high school students assessed students prior to leaving home for college and after college entrance. Of the original 131 participants recontacted two years after their graduation, 78 returned surveys. The study (a) explored changes in social network structure and function, (b) determined whether late adolescent-parent-peer relations change over time, and (c) identified prospectively the impact of social support, adolescent-parent-peer relations, and attachment security on well-being and feelings about the transition after high school. Students attending college locally reported an increase in total network support at Time 2. Regardless of location, more support from friends was received after the transition from high school, whereas family support did not vary across time. Parent relations were closer after the transition and were predictive of various well-being measures and feelings about the transition from high school. ^

Phylogenetic analysis of within -host serially-sampled viral data

The primary goal of this dissertation is the study of patterns of viral evolution inferred from serially-sampled sequence data, i.e., sequence data obtained from strains isolated at consecutive time points from a single patient or host. RNA viral populations have an extremely high genetic variability, largely due to their astronomical population sizes within host systems, high replication rate, and short generation time. It is this aspect of their evolution that demands special attention and a different approach when studying the evolutionary relationships of serially-sampled sequence data. New methods that analyze serially-sampled data were developed shortly after a groundbreaking HIV-1 study of several patients from which viruses were isolated at recurring intervals over a period of 10 or more years. These methods assume a tree-like evolutionary model, while many RNA viruses have the capacity to exchange genetic material with one another using a process called recombination. ^ A genealogy involving recombination is best described by a network structure. A more general approach was implemented in a new computational tool, Sliding MinPD, one that is mindful of the sampling times of the input sequences and that reconstructs the viral evolutionary relationships in the form of a network structure with implicit representations of recombination events. The underlying network organization reveals unique patterns of viral evolution and could help explain the emergence of disease-associated mutants and drug-resistant strains, with implications for patient prognosis and treatment strategies. In order to comprehensively test the developed methods and to carry out comparison studies with other methods, synthetic data sets are critical. Therefore, appropriate sequence generators were also developed to simulate the evolution of serially-sampled recombinant viruses, new and more through evaluation criteria for recombination detection methods were established, and three major comparison studies were performed. The newly developed tools were also applied to "real" HIV-1 sequence data and it was shown that the results represented within an evolutionary network structure can be interpreted in biologically meaningful ways. ^

Functional application of lactic acid bacteria exopolysaccharide in complex food systems

Lactic acid bacteria expolysaccharides (LAB-EPS), in particular those formed from sucrose have the potential to improve food and beverage rheology and enhance their sensory properties potentially replacing or reducing expensive hydrocolloids currently used as improvers in food and beverage industries. Addition of sucrose not only enables EPS formation but also affects organic acid formation, thus influencing the sensory properties of the resulting food/beverage products. The first part of the study the organoleptic modulation of barley malt derived wort fermented using in situ produced bacterial polysaccharides has been investigated. Weisella cibaria MG1 was capable to produce exopolysaccharides during sucrosesupplemented barley malt derived wort fermentation. Even though the strain dominated the (sucrose-supplemented) wort fermentation, it was found to produce EPS (14.4 g l-1) with lower efficiency than in SucMRS (34.6 g l-1). Higher maltose concentration in wort led to the increased formation of oligosaccharide (OS) at the expense of EPS. Additionally, small amounts of organic acids were formed and ethanol remained below 0.5% (v/v). W. cibaria MG1 fermented worts supplemented with 5 or 10% sucrose displayed a shear-thinning behaviour indicating the formation of polymers. This report showed how novel and nutritious LAB fermented wort-base beverage with prospects for further advancements can be formulated using tailored microbial cultures. In the next step, the impact of exopolysaccharide-producing Weissella cibaria MG1 on the ability to improve rheological properties of fermented plant-based milk substitute plant based soy and quinoa grain was evaluated. W. cibaria MG1 grew well in soy milk, exceeding a cell count of log 8 cfu/g within 6 h of fermentation. The presence of W. cibaria MG1 led to a decrease in gelation and fermentation time. EPS isolated from soy yoghurts supplemented with sucrose were higher in molecular weight (1.1 x 108 g/mol vs 6.6 x 107 g/mol), and resulted in reduced gel stiffness (190 ± 2.89 Pa vs 244 ± 15.9 Pa). Soy yoghurts showed typical biopolymer gels structure and the network structure changed to larger pores and less cross-linking in the presence of sucrose and increasing molecular weight of the EPS. In situ investigation of Weissella cibaria MG1 producing EPS on quinoa-based milk was performed. The production of quinoa milk, starting from wholemeal quinoa flour, was optimised to maximise EPS production. On doing that, enzymatic destructuration of protein and carbohydrate components of quinoa milk was successfully achieved applying alpha-amylase and proteases treatments. Fermented wholemeal quinoa milk using Weissella cibaria MG1 showed high viable cell counts (>109 cfu/mL), a pH of 5.16, and significantly higher water holding capacity (WHC, 100 %), viscosity (> 0. 5 Pa s) and exopolysaccharide (EPS) amount (40 mg/L) than the chemically acidified control. High EPS (dextran) concentration in quinoa milk caused earlier aggregation because more EPS occupy more space, and the chenopodin were forced to interact with each other. Direct observation of microstructure in fermented quinoa milk indicated that the network structures of EPS-protein could improve the texture of fermented quinoa milk. Overall, Weissella cibaria MG1 showed favorable technology properties and great potential for further possible application in the development of high viscosity fermented quinoa milk. The last part of the study investigate the ex-situ LAB-EPS (dextran) application compared to other hydrocolloids as a novel food ingredient to compensate for low protein in biscuit and wholemeal wheat flour. Three hydrocolloids, xanthan gum, dextran and hydroxypropyl methylcellulose, were incorporated into bread recipes based on high-protein flours, low-protein flours and coarse wholemeal flour. Hydrocolloid levels of 0–5 % (flour basis) were used in bread recipes to test the water absorption. The quality parameters of dough (farinograph, extensograph, rheofermentometre) and bread (specific volume, crumb structure and staling profile) were determined. Results showed that xanthan had negative impact on the dough and bread quality characteristics. HPMC and dextran generally improved dough and bread quality and showed dosage dependence. Volume of low-protein flour breads were significantly improved by incorporation of 0.5 % of the latter two hydrocolloids. However, dextran outperformed HPMC regarding initial bread hardness and staling shelf life regardless the flour applied in the formulation.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Cross-section and panel estimates of peer effects in early adolescent cannabis use: With a little help from my ‘friends once removed’

Peer effects in adolescent cannabis are difficult to estimate, due in part to the lack of appropriate data on behaviour and social ties. This paper exploits survey data that have many desirable properties and have not previously been used for this purpose. The data set, collected from teenagers in three annual waves from 2002-2004 contains longitudinal information about friendship networks within schools (N = 5,020). We exploit these data on network structure to estimate peer effects on adolescents from their nominated friends within school using two alternative approaches to identification. First, we present a cross-sectional instrumental variable (IV) estimate of peer effects that exploits network structure at the second degree, i.e. using information on friends of friends who are not themselves ego’s friends to instrument for the cannabis use of friends. Second, we present an individual fixed effects estimate of peer effects using the full longitudinal structure of the data. Both innovations allow a greater degree of control for correlated effects than is commonly the case in the substance-use peer effects literature, improving our chances of obtaining estimates of peer effects than can be plausibly interpreted as causal. Both estimates suggest positive peer effects of non-trivial magnitude, although the IV estimate is imprecise. Furthermore, when we specify identical models with behaviour and characteristics of randomly selected school peers in place of friends’, we find effectively zero effect from these ‘placebo’ peers, lending credence to our main estimates. We conclude that cross-sectional data can be used to estimate plausible positive peer effects on cannabis use where network structure information is available and appropriately exploited.