Accelerated and real-time creep and creep-rupture results for aramid fibers

This article presents results from conventional creep tests (CCT) and two accelerated test methods (the stepped isothermal method (SIM) and the stepped isostress method (SSM)) to determine the creep and creep-rupture behavior of two different aramid fibers, Kevlar 49 and Technora. CCT are regarded as the true behavior of the yarn, but they are impractical for long-term use where failures are expected only after many years. All the tests were carried out on the same batches of yarns, and using the same clamping arrangements, so the tests should be directly comparable. For both materials, SIM testing gives good agreement with CCT and gave stress-rupture lifetimes that followed the same trend. However, there was significant variation for SSM testing, especially when testing Technora fibers. The results indicate that Kevlar has a creep strain capacity that is almost independent of stress, whereas Technora shows a creep strain capacity that depends on stress. Its creep strain capacity is approximately two to three times that of Kevlar 49. The accelerated test methods give indirect estimates for the activation energy and the activation volume of the fibers. The activation energy for Technora is about 20% higher than that for Kevlar, meaning that it is less sensitive to the effects of increasing temperature. The activation volume for both materials was similar, and in both cases, stress dependent. Copyright © 2012 Wiley Periodicals, Inc.

First-passage time distribution and non-Markovian diffusion dynamics of protein folding

We study the kinetics of protein folding via statistical energy landscape theory. We concentrate on the local-connectivity case, where the configurational changes can only occur among neighboring states, with the folding progress described in terms of an order parameter given by the fraction of native conformations. The non-Markovian diffusion dynamics is analyzed in detail and an expression for the mean first-passage time (MFPT) from non-native unfolded states to native folded state is obtained. It was found that the MFPT has a V-shaped dependence on the temperature. We also find that the MFPT is shortened as one increases the gap between the energy of the native and average non-native folded states relative to the fluctuations of the energy landscape. The second- and higher-order moments are studied to infer the first-passage time distribution. At high temperature, the distribution becomes close to a Poisson distribution, while at low temperatures the distribution becomes a Levy-type distribution with power-law tails, indicating a nonself-averaging intermittent behavior of folding dynamics. We note the likely relevance of this result to single-molecule dynamics experiments, where a power law (Levy) distribution of the relaxation time of the underlined protein energy landscape is observed.

Benthic foraminiferal distribution in surface sediments along continental slope of the southern Okinawa Trough: dependance on water masses and food supply

Benthic foraminiferal analysis of 29 samples in surface sediments from the southern Okinawa Trough is carried out. The results indicate that benthic foraminiferal abundance decreases rapidly with increasing water depth. Percentage frequencies of agglutinated foraminifera further confirm the modem shallow carbonate lysocline in the southern Okinawa Trough. From continental shelf edge to the bottom of Okinawa Trough, benthic foraminiferal fauna in the surface sediments can be divided into 5 assemblages: (1) Continental shelf break assemblage, dominated by Cibicides pseudoungerianus, corresponds to subsurface water mass of the Kuroshio Current; (2) upper continental slope assemblage, dominated by Cassidulina carinata, Globocassidulina subglobosa, corresponds to intermediate water mass of the Kuroshio Current; (3) intermediate continental slope assemblage, dominated by Uvigerina hispida, corresponds to the Okinawa Trough deep water mass above the carbonate lysocline; (4), lower continental slope - trough bottom assemblage, dominated by Pullenia bulloides, Epistominella exigua and Cibicidoides hyalinus, corresponds to deep water mass of the Okinawa Trough; and (5) trough bottom agglutinated assemblage, dominated by Rhabdammina spp., Bathysiphon flavidus, corresponds to I strongly dissolved environment of the trough bottom. The benthic foraminiferal fauna in the southern Okinawa Trough are controlled jointly by water masses and food supply. Water temperature, oxygen concentration and carbonate dissolution of the water masses are important controlling factors especially for the continental shelf break and trough bottom assemblages. The food supply also plays an important role in these benthic foraminiferal assemblages along the western slope. of the Okinawa Trough. Both the abundance and the 5 assemblages of benthic foraminifera correspond well to the organic matter supply along the continental slope and a lateral transport of TSM (total suspended matter) and POC (particulate organic carbon) from the shelf break to the deep water is also an important food supply for benthic fauna in this region.

Land Use and Military Supply in the Highland Zone of Roman Britain

Davies, Jeffrey. 'Land Use and Military Supply in the Highland Zone of Roman Britain', In: Artefacts and Archaeology. Aspects of the Celtic and Roman World (University of Wales Press, 2002), pp.44-61 RAE2008

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Convergence of numerical time-averaging and stationary measures via Poisson equations

Numerical approximation of the long time behavior of a stochastic di.erential equation (SDE) is considered. Error estimates for time-averaging estimators are obtained and then used to show that the stationary behavior of the numerical method converges to that of the SDE. The error analysis is based on using an associated Poisson equation for the underlying SDE. The main advantages of this approach are its simplicity and universality. It works equally well for a range of explicit and implicit schemes, including those with simple simulation of random variables, and for hypoelliptic SDEs. To simplify the exposition, we consider only the case where the state space of the SDE is a torus, and we study only smooth test functions. However, we anticipate that the approach can be applied more widely. An analogy between our approach and Stein's method is indicated. Some practical implications of the results are discussed. Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.

Optimal kinetics for quantification of antigen-induced cytokines in human peripheral blood mononuclear cells by real-time PCR and by ELISA.

Real-time polymerase chain reaction (PCR) has recently been described as a new tool to measure and accurately quantify mRNA levels. In this study, we have applied this technique to evaluate cytokine mRNA synthesis induced by antigenic stimulation with purified protein derivative (PPD) or heparin-binding haemagglutinin (HBHA) in human peripheral blood mononuclear cells (PBMC) from Mycobacterium tuberculosis-infected individuals. Whereas PPD and HBHA optimally induced IL-2 mRNA after respectively 8 and 16 to 24 h of in vitro stimulation, longer in vitro stimulation times were necessary for optimal induction of interferon-gamma (IFN-gamma) mRNA, respectively 16 to 24 h for PPD and 24 to 96 h for HBHA. IL-13 mRNA was optimally induced by in vitro stimulation after 16-48 h for PPD and after 48 to 96 h for HBHA. Comparison of antigen-induced Th1 and Th2 cytokines appears, therefore, valuable only if both cytokine types are analysed at their optimal time point of production, which, for a given cytokine, may differ for each antigen tested. Results obtained by real-time PCR for IFN-gamma and IL-13 mRNA correlated well with those obtained by measuring the cytokine concentrations in cell culture supernatants, provided they were high enough to be detected. We conclude that real-time PCR can be successfully applied to the quantification of antigen-induced cytokine mRNA and to the evaluation of the Th1/Th2 balance, only if the kinetics of cytokine mRNA appearance are taken into account and evaluated for each cytokine measured and each antigen analysed.

A novel neural/symbolic hybrid approach to heuristically optimized fuel allocation and automated revision of heuristics in nuclear engineering

FUELCON is an expert system for optimized refueling design in nuclear engineering. This task is crucial for keeping down operating costs at a plant without compromising safety. FUELCON proposes sets of alternative configurations of allocation of fuel assemblies that are each positioned in the planar grid of a horizontal section of a reactor core. Results are simulated, and an expert user can also use FUELCON to revise rulesets and improve on his or her heuristics. The successful completion of FUELCON led this research team into undertaking a panoply of sequel projects, of which we provide a meta-architectural comparative formal discussion. In this paper, we demonstrate a novel adaptive technique that learns the optimal allocation heuristic for the various cores. The algorithm is a hybrid of a fine-grained neural network and symbolic computation components. This hybrid architecture is sensitive enough to learn the particular characteristics of the ‘in-core fuel management problem’ at hand, and is powerful enough to use this information fully to automatically revise heuristics, thus improving upon those provided by a human expert.

Coupled 3-D finite difference time domain and finite volume methods for solving microwave heating in porous media

Computational results for the microwave heating of a porous material are presented in this paper. Combined finite difference time domain and finite volume methods were used to solve equations that describe the electromagnetic field and heat and mass transfer in porous media. The coupling between the two schemes is through a change in dielectric properties which were assumed to be dependent both on temperature and moisture content. The model was able to reflect the evolution of temperature and moisture fields as the moisture in the porous medium evaporates. Moisture movement results from internal pressure gradients produced by the internal heating and phase change.

An ontological characterization of time-series and state-sequences for data mining

Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining.