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

Examining factors associated with excess mortality in older people (age ≥ 70 years) with diabetes - a 10-year cohort study of older people with and without diabetes

Aims: To compare all-cause mortality in older people with or without diabetes and consider the associated risk of comorbidity and polypharmacy. Methods: A 10-year cohort study using data from the Health Innovation Network database (2003-2013) comparing mortality in people aged ≥ 70 years with diabetes (DM cohort) (n = 35 717) and without diabetes (No DM cohort) (n = 307 918). Results: The mean age of the DM cohort was 78.1 ± 5.8 years vs. 79.0 ± 6.3 years in the No DM cohort. Mean diabetes duration was 8.2 ± 8.1 years, and 30% had diabetes for > 10 years. The DM cohort had a greater comorbidity load and people in this cohort were prescribed more therapies than the No DM cohort. The 5- and 10-year survival rates were lower in the DM cohort at 64% and 39%, respectively, compared with 72% and 50% in the No DM cohort. The excess mortality in the DM cohort was greatest in those aged <75 years with longer duration diabetes, the relative hazard for mortality was higher in females. Although comorbidity and polypharmacy were associated with increased mortality risk in the DM cohort, this risk was lower compared with the No DM cohort. The hazard ratios (95% confidence interval) for comorbidities > 4 and medicines ≥ 7 were 1.29 (1.19 to 1.41) and 1.34 (1.25 to 1.43) in the DM cohort and 1.63 (1.57 to 1.70) and 1.48 (1.40 to 1.56) in the No DM cohort, respectively. Conclusions: There is significant excess mortality in older people with diabetes, which is unexplained by comorbidity or polypharmacy. This excess is greatest in the younger old with longer disease duration, suggesting that it may be related to the effect of diabetes exposure.

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. ^

(Appendix A) Pigment content, microbial biomass and activity, and grain size characteristics in caged and control sites of the Arctic long-term observatory HAUSGARTEN

Present theories of deep-sea community organization recognize the importance of small-scale biological disturbances, originated partly from the activities of epibenthic megafaunal organisms, in maintaining high benthic biodiversity in the deep sea. However, due to technical difficulties, in situ experimental studies to test hypotheses in the deep sea are lacking. The objective of the present study was to evaluate the potential of cages as tools for studying the importance of epibenthic megafauna for deep-sea benthic communities. Using the deep-diving Remotely Operated Vehicle (ROV) "VICTOR 6000", six experimental cages were deployed at the sea floor at 2500 m water depth and sampled after 2 years (2y) and 4 years (4y) for a variety of sediment parameters in order to test for caging artefacts. Photo and video footage from both experiments showed that the cages were efficient at excluding the targeted fauna. The cage also proved to be appropriate to deep-sea studies considering the fact that there was no fouling on the cages and no evidence of any organism establishing residence on or adjacent to it. Environmental changes inside the cages were dependent on the experimental period analysed. In the 4y experiment, chlorophyll a concentrations were higher in the uppermost centimeter of sediment inside cages whereas in the 2y experiment, it did not differ between inside and outside. Although the cages caused some changes to the sedimentary regime, they are relatively minor compared to similar studies in shallow water. The only parameter that was significantly higher under cages at both experiments was the concentration of phaeopigments. Since the epibenthic megafauna at our study site can potentially affect phytodetritus distribution and availability at the seafloor (e.g. via consumption, disaggregation and burial), we suggest that their exclusion was, at least in part, responsible for the increases in pigment concentrations. Cages might be suitable tools to study the long-term effects of disturbances caused by megafaunal organisms on the diversity and community structure of smaller-sized organisms in the deep sea, although further work employing partial cage controls, greater replication, and evaluating faunal components will be essential to unequivocally establish their utility.

Size and variability of crop productivity both impacted by CO2 enrichment and warming-A case study of 4 year field experiment in a Chinese paddy

Acknowledgement Construction and maintenance of the experiment system was funded by the state Special Fund for Agro-scientific Research in the Public Interest “Climate Change Impacts on Crop Production and Mitigation” under a grant number 200903003. This work was financially supported by Ministry of Science and Technology of China under a grant number 2012BAC19B01 and Department of Science and Technology of Jiangsu province under a grant number BK20150684. The international cooperation was funded by “111 project” (B12009) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The contribution of Pete Smith was funded by the Chinese Ministry of Agriculture and the United Kingdom Department for Environment, Food and Rural Affairs (DEFRA) under UK-China Sustainable Agriculture Innovation Network (SAIN). The contribution of Timothy Filley was also funded by the state foreign expert agency under a project of Foreign High-end expert program. The authors thank Jiangsu Tianniang Agro-Technology Company Ltd. for the assistance in maintaining the experiment system.