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For the hierarchical inverse gamma and inverse gamma model, we calculate the Bayes posterior estimator of the rate parameter of the inverse gamma distribution under Stein's loss function which pena...

Diabetes is a very common occurring disease, diagnosed by hyperglycemia. The established mode of diagnosis is the analysis of blood glucose level with the help of a hand-held glucometer. Nowadays, it is also known for affecting multi-organ functions, particularly the microvasculature of the cardiovascular system. In this work, an alternative diagnostic system based on the heart rate variability (HRV) analysis and artificial neural network (ANN) and support vector machine (SVM) have been proposed. The experiment and data recording has been performed on male Wister rats of 10–12 week of age and 200 ± 20 gm of weight. The digital lead-I electrocardiogram (ECG) data are recorded from control (n = 5) and Streptozotocin-induced diabetic rats (n = 5). Nine time-domain linear HRV parameters are computed from 60 s of ECG data epochs and used for the training and testing of backpropagation ANN and SVM. Total 526 (334 Control and 192 diabetics) such datasets are computed for the testing of ANN for the identification of the diabetic conditions. The ANN has been optimized for architecture 9:5:1 (Input: hidden: output neurons, respectively) with the optimized learning rate parameter at 0.02. With this network, a very good classification accuracy of 96.2% is achieved. While similar accuracy of 95.2% is attained using SVM. Owing to the successful implementation of HRV parameters based automated classifiers for diabetic conditions, a non-invasive, ECG based online prognostic system can be developed for accurate and non-invasive prediction of the diabetic condition.

The activation energy of a reaction is a key rate parameter, and understanding the mechanism of a reaction requires an explanation for the activation energy. Iron substitutes for zinc in the zinc b...

Polycaprolactone/babassu compounds were prepared in a laboratory internal mixer, their melt crystallization behavior was investigated using a differential scanning calorimeter, and their kinetics was correlated using the Pseudo-Avrami model, with cooling rates ranging from 2.5 to 15 °C min−1. Babassu filler did not affect significantly crystallization parameters, with maximum differences of 1.2 K in temperature and 3% in crystallinity, within experimental uncertainty. Regarding Pseudo-Avrami parameters, data were correlated for the interval 5–95% conversion, which seems to be appropriate for processing applications; the uncertainty associated with Pseudo-Avrami individual parameters is less than 1%, whereas the “rate parameter” K was found to be well represented by a quadratic function.

To increase plant equipment efficiency and reduce plant operation costs, as well as to address the problem of industrial critical equipment health diagnosis, we suggest developing a deep learning-based LSTM model by merging data from pressure sensors while they are operational. By modifying the learning rate parameter of the model and judging the correctness of the model with different parameter values by integrating the data after pre-processing, the LSTM model with a learning rate of 0.006, a number of nodes in the hidden layer of 8, and a number of neurons of 2 was selected. The ideal model is used to predict the remaining life of the pressure sensor, demonstrating that the model has the ability to predict with high accuracy and provides effective strategies and maintenance plans for plant equipment inspection in conjunction with health degradation photos.

Combined with finite element numerical simulation analysis, the hot-spin forming technology of cylindrical AZ80 magnesium alloy parts was studied in the paper. The multipass hot-spin forming of magnesium alloy shell parts was simulated by the ABAQUS software to analyze the stress and strain distribution and change during spinning for the preliminary test process parameters in the magnesium alloy spinning test. Then, the process parameters were optimized during the hot spinning test, especially the matching relationship between temperature parameter and thinning rate parameter, and the hot spinning magnesium alloy shell parts with the expected technical specifications were finished.

It is difficult to predict student graduation status in a college. Higher education needs to predict student behavior from active students so that it can be seen the failure factor of students who do not graduate on time. Data mining classification techniques used to predict students are using artificial neural networks. Artificial neural network is one method to predict student graduation. This researcher tries to apply artificial neural network methods using genetic algorithms to predict student graduation. In this study using the learning rate parameter 0.1 with optimization using genetic algorithms then evaluating to get accuracy. The results of this study get an accuracy value for artificial neural network models of 71.48% and accuracy for artificial neural network models based on genetic algorithms by 99.33% with an accuracy difference of 27.85%.

Epidemics of wheat blast, caused the Triticum pathotype of Magnaporthe oryzae, were studied in the Santa Cruz del la Sierra region of Bolivia to quantify and compare the temporal dynamics of the disease under different growing conditions. Six plots of a susceptible wheat cultivar were planted at Cuatro Cañadas (CC), Okinawa 1 (OK1), and Okinawa 2 (OK2) in 2015. Spike blast incidence (INC) and severity (SEV) and leaf blast severity (LEAF) were quantified in each plot at regular intervals on a 10 × 10 grid (n = 100 clusters of spikes), beginning at head emergence (Feekes growth stage 10.5), for a total of nine assessments at CC, six at OK1, and six at OK2. Spike blast increased over time for 20 to 30 days before approaching a mean INC of 100% and a mean SEV of 60 to 75%. The logistic model was the most appropriate for describing the temporal dynamics of spike blast. The highest absolute rates of disease increase occurred earliest at OK1 and latest at OK2, and in all cases it coincided with major rain events. Estimated y0 values (initial blast intensity) were significantly (P < 0.05) higher at OK1 than at CC or OK2, whereas rL values (the logistic rate parameter) were significantly higher at OK2 than at CC or OK1. It took about 10 fewer days for SEV to reach 10, 15, or 20% at OK1 compared with OK2 and CC. Based on survival analyses, the survivor functions for time to 10, 15 and 20% SEV (ts) were significantly different between OK1 and the other locations, with the probabilities of SEV reaching the thresholds being highest at OK1. LEAF at 21 days after Feekes 10.5 had a significant effect on ts at OK1. For every 5% increase in LEAF, the chance of SEV reaching the thresholds by day 21 increased by 30 to 55%.

Oscillatory behavior in tissue biology is ubiquitous and may be observed in the form of either chemical or mechanical signals. We present here and solve a mechanical cell model that exhibits oscillations emerging from delayed viscoelastic rheological laws. These include a time delay between the mechanical response and the rest-length changes, which evolve proportionally to the delayed cell deformation and use a remodeling rate parameter. We show that different regimes (no oscillatory response, sustained oscillations, and unstable oscillations) are obtained for different values of the delay or the remodeling rate. The results are analytically demonstrated in a one-dimensional problem with one and two cells that are represented by simple line elements. Oscillations of the cell deformations are obtained whenever different delays coexist, or when the delay is size-dependent. We also extend our results to a multicellular two-dimensional vertex model that includes the same rheological law, and which inherits the presence of critical values of the delay or remodeling rate. We numerically show that indeed the size-dependent rest-length changes induce oscillations in the cell shapes and areas.

The geometry and frictional properties of a fault system are key parameters required to understand its seismic behavior. The Main Himalayan Thrust in Nepal is the type example of a continental megathrust and forms part of a fault system which accommodates a significant fraction of India‐Eurasia convergence. Despite extensive study of this zone of shortening, the geometry of the fault system remains controversial. Here, we use interseismic, coseismic, and postseismic geodetic data in Nepal to investigate the proposed downdip geometries. We use interseismic and coseismic data from previous studies, acquired before and during the 2015 urn:x-wiley:jgrb:media:jgrb53987:jgrb53987-math-0001 7.8 Gorkha earthquake. We then supplement these by processing our own postseismic deformation data, acquired following the Gorkha earthquake. We find that kinematic modeling of geodetic data alone cannot easily distinguish between the previously proposed geometries. We therefore develop a mechanical joint coseismic‐postseismic slip inversion which simultaneously solves for the distribution of coseismic slip and rate‐strengthening friction parameters. We run this inversion using the proposed geometries and find that they are all capable of explaining the majority of geodetic data. We find values for the rate parameter, urn:x-wiley:jgrb:media:jgrb53987:jgrb53987-math-0002, from the rate‐and‐state friction law that are between 0.8 and urn:x-wiley:jgrb:media:jgrb53987:jgrb53987-math-0003, depending on the geometry used. These values are in agreement with results from laboratory studies and those inferred from other earthquakes. We suggest that the limitations of earthquake cycle geodesy partly explain the continued controversy over the geometry and role of various faults in the Nepal Himalaya.

The paper discusses the results of the tool-life tests of PCBN cutting tool with PVD coating and the mechanism which could be accounted for declining of its wear rate. The results of research showed that in condition of finishing of hardened steel (52–54 HRC), PCBN with (TiAlSiY)N/CrN PVD coating demonstrated significantly lower wear rate parameter in comparison with a standard composite. The maximum VB value of the modified tool is 3.2 times smaller after 24 min of the total cutting time. The evaluation of morphology of the worn areas of the tools had been done. No significant differences of crater form and depth on the rake face of coated and non coated tools were found. At the same time the evidences of intensive plastic deformation occurring on the clearance face of the tool with protective coating caused by wear land topography peculiarities were found. Plastic flow-zone formed at this area can significantly reduce the abrasive effect on the tool surface and thus increase the overall tool-life.

Several methods have been proposed in open literatures for detecting changes in disease outbreak or incidence. Most of these methods are likelihood-based as well as the direct application of Shewhart, CUSUM and EWMA schemes. We use CUSUM, EWMA and EWMA-CUSUM multi-chart schemes to detect changes in disease incidence. Multi-chart is a combination of several single charts that detects changes in a process and have been shown to have elegant properties in the sense that they are fast in detecting changes in a process as well as being computationally less expensive. Simulation results show that the multi-CUSUM chart is faster than EWMA and EWMA-CUSUM multi-charts in detecting shifts in the rate parameter. A real illustration with health data is used to demonstrate the efficiency of the schemes.

In this paper, we study the equilibrium proportional reinsurance and investment strategies for an insurer in an environment with parameter uncertainties. The insurer can buy proportional reinsurance business to hedge its insurance risks. However, the insurer is ambiguous about the insurance claims and risky assets. Specifically, the insurance claim is exponentially distributed and the rate parameter is uncertain. Besides, the return of a stock is uncertain. The insurer holds ambiguous beliefs over these states. The goal of the insurer is to maximize the smooth ambiguity utility proposed in Klibanoff et al. (2005). The equilibrium control is introduced to derive the time-consistent solution. In the end, a sensitivity analysis is presented to show the economic behaviors of the insurer under the smooth ambiguity. Results reveal that the uncertain beliefs play an important role in the equilibrium reinsurance and investment strategies. When the insurer is more risk averse towards ambiguity, the insurer will invest less in the ambiguous asset and more in the non-ambiguous asset.

In this paper, we define a fractional negative binomial process FNBP by replacing the Poisson process by a fractional Poisson process FPP in the gamma subordinated form of the negative binomial process. It is shown that the one-dimensional distributions of the FPP and the FNBP are not infinitely divisible. Also, the space fractional Pólya process SFPP is defined by replacing the rate parameter λ by a gamma random variable in the definition of the space fractional Poisson process. The properties of the FNBP and the SFPP and the connections to PDEs governing the density of the FNBP and the SFPP are also investigated.

The performance of resultant topological structure of Kohonen Self Organizing Map SOM is highly dependent of the learning rate and neighborhood parameters. In literature there are plenty many different types of approaches to and proposals for those parameters. It has been investigated that in general the learning rate and neighborhood parameters are data independent and predefined before the training period. Here in this paper a novel approach has been proposed to change the learning rate parameter according to the interaction of neurons with data. During training, the worst matching neuron also tracked and used to trace the formation of topological structure of SOM. A slight modification on conventional learning rate with proposed method has a considerable influence on resultant topologies in a positive way. The effects of this approach have been tested with the real world problem and different synthetic data.

Dieback disease by the fungus Botryodiplodia theobromae in Jabon seedlings (Anthocephalus cadamba) inhibits the regeneration of Jabon plant. Synthetic fungicides utilization to several problems such as residue on the environment and pathogen resistance. Chitosan is a potential natural compound used as an alternative to control plant disease.This research aims to examine the effectiveness of chitosan to control B. theobromae as causal agent of dieback on Jabon seedlings and to examine the effect of chitosan to control the viscosity of dieback disease on Jabon seedlings. The results showed that the chitosan solution can decrease the severity of disease in Jabon seedlings. However, it was different with the disease incidence rate parameter. Chitosan solution was not significantly affecting the disease because B. theobromae caused dieback symptoms on Jabon seedlings. The most effective treatment was chitosan solution with a concentration of 0.1% before inoculation with the severity of the disease by 25%. Chitosan solution viscosity was responsible in affecting the percentage of dieback disease in Jabon seeds. The higher the viscosity of chitosan, the higher the percentage of dieback disease attacks. The best viscosity to suppress the development of dieback disease on Jabon seedlings was 8.80 with 7.90% attack.Key words: Anthocephalus cadamba , Botryodiplodia theobromae, chitosan, dieback.

The paper by Torrielli, Repetto and Solari investigates an issue of some importance: that the rate parameter relating an extreme distribution to its parent is not a constant when the parent is serially correlated. Subsequent discussion and response contained areas of agreement, areas of disagreement and some unresolved areas. This contribution to the discussion resolves outstanding issues by extending the wind simulations to very long periods, up to 5 million years, to reveal the asymptotic behaviour.

The experiments of Rahinov et al. ( Combust. Flame , 145 (2006) 105–116) measuring NH 2 concentration profiles in methane–air laminar flat flames doped with ammonia were re-evaluated. The flames were simulated with the FlameMaster code using a modified NOx combustion mechanism of the CRECK Modeling Group. Based on local sensitivity analysis results, Arrhenius parameters A , n , E of reaction steps NH 2 + H = NH + H 2 and NH 3 + OH = NH 2 + H 2 O were selected for optimization, which took into account not only the experimental data of Rahinov et al., but also related direct measurements and theoretical determinations as optimization targets. The optimized mechanism described the measured concentration profiles better than the original one, while the new rate parameter values were within the prior uncertainty limits obtained from the evaluation of literature data. The optimization process also provided new posterior uncertainty limits, which are within the prior uncertainty limits.

Error Back Propagation algorithm is one of the most popular method for training artificial neural networks. Unfortunately, this is also one of the slowest due to constant and small learning rate parameter used to update weights of neurons. There are therefore many methods for dynamic change this parameter in the adaptive manner. In this paper has been presented new method based on estimation of learning rate with respect of approximation for the value of output error.

This paper presents a new strategy of handwritten digit recognition based on the improved VGG16 model that aims at the lack of texture features, the large differences between handwritings, and the difficulty of extracting effective information. In order to maximize the efficiency of the improved VGG16 model, an automatic drop-based learning rate scheduling is proposed to improve the SGD algorithm in the learning procedure. And automatic adaptation technique of the SGD optimizer's learning rate parameter according to the changes of accuracy rate in the previous training iterations is utilized for the training of the model, which not only apparently improves the learning results, but also speeds up the training convergence. The improved VGG16 model was evaluated on the extended MNIST dataset, achieving a high recognition accuracy rate of 99.97%. Experimental results demonstrate that the improved VGG16 model has obviously higher recognition accuracy than traditional classifiers, has stronger feature extraction ability and can meet the requirements of handwritten digits classification and recognition.

BackgroundCardiac fibrillation is thought to be maintained by rotational activity, with pivoting regions called phase singularities (PSs). Despite a century of research, no clear quantitative framework exists to model the fundamental processes responsible for the continuous formation and destruction of rotors in fibrillation.ObjectiveWe conducted a multi-modality, multi-species study of AF/VF under the hypothesis that PS formation/destruction in fibrillation can be modelled as self-regenerating renewal processes, producing exponential distributions of inter-event times governed by constant rate-parameters defined by the prevailing properties of the system.MethodsPS formation/destruction was studied and cross-validated in 5 models, using basket recordings and optical mapping from: i) human persistent AF (n = 20), ii) tachypaced sheep AF (n = 5), iii) rat AF (n = 4), iv) rat VF (n = 11) and v) computer simulated AF (SIM). Hilbert phase maps were constructed. PS lifetime data were fitted by exponential probability distribution functions (PDFs) computed using maximum entropy theory, and the rate parameter (λ) determined. A systematic review was conducted to cross-validate with source data from literature.ResultsPS destruction/formation distributions showed good fits to an exponential in all systems (R2≥ 0.90). In humans,λ= 4.6%/ms (95%CI,4.3,4.9)), sheep 4.4%/ms (95%CI,4.1,4.7)), rat AF 38%/ms (95%CI,22,55), rat VF 46%/ms (95%CI,31.2,60.2) and SIM 5.4%/ms (95%CI,4.1,6.7). All PS distributions identified through systematic review were exponential with λ comparable to experimental data.ConclusionThese results provide a universal quantitative framework to explain rotor formation and destruction in AF/VF, and a platform for therapeutic advances in cardiac fibrillation.

Recurrence plots and recurrence plots quantification analysis were applied to the planetary gears system to identify faults in the system. In this chapter the recurrence rate parameter adopted in the gear fault detection is presented. It is indicated in terms of recurrence statistics that the response of the gears system with faults is more periodic. This is caused by selected harmonics which are more pronounced in system with faults. Usefulness of other recurrence parameters is also discussed.

In this modern era, restaurants are becoming very popular, especially in big cities. However, this can lead to density or queues of visitors at a restaurant, which should be avoided during the current Covid-19 pandemic. So that accurate information that can predict the density of restaurant will be very useful. In predicting the density of restaurants, data processing on the number of visitors obtained from one of the restaurants is carried out using artificial intelligence in the form of LSTM (Long Short Term Memory) RNN (Recurrent Neural Network). The results of the research on Recurrent Neural Network based on LSTM (Long Short Term Memory) at the best learning rate parameter of 0.001 and a maximum epoch of 2000 resulted in an MSE value of 0.00000278 on the training data and 0.0069 on the test data

316L austenitic stainless steel foam (ASSF) with 55–81% porosities was made by P/M route using urea as space holder. The cold compacted at 500 MPa pressure and sintered at 1200 °C for 60 min. Microstructure, cell size, cell walls thickness and micro-hardness were evaluated. Twins boundaries are visible in cell wall regions. XRD, FESEM, and EDX analysis proved that no residue of space holder particles remained in the sintered foam samples. The compressive deformation behaviour of sintered foam samples are studied under three different strain rate (0.001, 0.01, and 0.1 s−1). The yield stress (σy), average plateaus stress (σpl), elastic modulus (Ef) and energy absorption (Eab) increases with increase in relative density (ρrd), and also increased with increasing strain rate. Densification strain decreased with increasing strain rate and follows linear relationship with ρrd. The yield stress, elastic modulus, and densification strain were compared with Gibson-Ashby's model and are found to be in closed agreement. The stain rate parameter (Σ) marginally decreases with increase in ρrd and follows linear relationship with ρrd.

This paper surveys the results of estimating learning rate (LR) equations for the photovoltaic (PV) industry at the world level, and reports new results, placing emphasis on estimation issues, and other shortcomings surveyed recently. The results are reported in detail, one relevant finding being that the learning rate parameter might reach values substantially higher than those usually reported (18–20%). This result, however, does not necessarily translate to other energies. The relevance of selecting the estimation sample, dynamic specification, and omitted variables in simple standard specifications for the estimated learning rate is highlighted. A solution for the LR in dynamic non stationary models is presented. The modeling of silicon prices is also discussed, and the concept of the total learning rate (TLR) is introduced. Probability confidence intervals for the main estimated learning rate parameters are analyzed, and the time decomposition of PV module prices is discussed, highlighting the role of fossil energy prices. It is found that the total LR might reach values above 27% with a 95% probability.

A strong case has been made for the role and value of mechanistic explanation in neuroscience and molecular biology. A similar demonstration in other domains of scientific investigation, however, remains an important challenge of scope for the new mechanists. This article helps answer that challenge by demonstrating one valuable role mechanisms play in phylogenetics. Using the transition/transversion (ti/tv) rate parameter as a case example, this article argues that models embedded with mechanisms produce stronger phylogenetic tree hypotheses, as measured by maximum likelihood logL values. Two important implications for the new mechanistic account of explanation are considered.

Diversification of assets by an investor offers reduced exposure to risk compared to investing in a single asset. A multi-asset option gives an investor this advantage as its payout depends on the overall performance of several underlying assets. This study uses an information-based model to derive an approximate price for European call multi-asset options. The single asset price is derived using the risk-neutral pricing approach, and the multi-asset case uses the notion of comonotonicity. A numerical illustration is looked at to validate the theoretical results and to show the accuracy of the information-based model. The results show that prices from the information-based model provide a close fit to the empirical prices using a suitable information flow rate parameter. Hence, by making use of the information available in the market, an investor can price multi-asset European call options.

Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems. Making accurate imputation is critical to many applications in intelligent transportation systems. In this paper, we formulate the missing data imputation problem in spatiotemporal traffic data in a low-rank tensor completion (LRTC) framework and define a novel truncated nuclear norm (TNN) on traffic tensors of location × day × time of day. In particular, we introduce an universal rate parameter to control the degree of truncation on all tensor modes in the proposed LRTC-TNN model, and this allows us to better characterize the hidden patterns in spatiotemporal traffic data. Based on the framework of the Alternating Direction Method of Multipliers (ADMM), we present an efficient algorithm to obtain the optimal solution for each variable. We conduct numerical experiments on four spatiotemporal traffic data sets, and our results show that the proposed LRTC-TNN model outperforms many state-of-the-art imputation models with missing rates/patterns. Moreover, the proposed model also outperforms other baseline models in extreme missing scenarios.

Significance Understanding the structure and function of the human brain at a cellular level is a fundamental aim of neuroscience. Tremendous progress has been made in recent years based on different in vivo and ex vivo approaches, including major advances in brain MRI. However, uncertainties remain in determining how brain MRI measurements relate to the brain’s underlying cellular composition. In this paper we use a recently developed MRI technique, quantitative gradient recalled echo (qGRE), and information on gene profiles in the human brain available from the Allen Human Brain Atlas. We demonstrate that qGRE and related MRI techniques can be used to probe the underlying cellular composition of the human brain in vivo., fMRI revolutionized neuroscience by allowing in vivo real-time detection of human brain activity. While the nature of the fMRI signal is understood as resulting from variations in the MRI signal due to brain-activity-induced changes in the blood oxygenation level (BOLD effect), these variations constitute a very minor part of a baseline MRI signal. Hence, the fundamental (and not addressed) questions are how underlying brain cellular composition defines this baseline MRI signal and how a baseline MRI signal relates to fMRI. Herein we investigate these questions by using a multimodality approach that includes quantitative gradient recalled echo (qGRE), volumetric and functional connectivity MRI, and gene expression data from the Allen Human Brain Atlas. We demonstrate that in vivo measurement of the major baseline component of a GRE signal decay rate parameter (R2t*) provides a unique genetic perspective into the cellular constituents of the human cortex and serves as a previously unidentified link between cortical tissue composition and fMRI signal. Data show that areas of the brain cortex characterized by higher R2t* have high neuronal density and have stronger functional connections to other brain areas. Interestingly, these areas have a relatively smaller concentration of synapses and glial cells, suggesting that myelinated cortical axons are likely key cortical structures that contribute to functional connectivity. Given these associations, R2t* is expected to be a useful signal in assessing microstructural changes in the human brain during development and aging in health and disease.

Chapters 3, 4 and 5 have presented the role of population games [1, 2, 3, 4, 5, 6] in control applications involving only one coupled constraint. This chapter addresses the issue of dealing with multiple coupled constraints using a population-games approach that allows the population mass to vary along the time, i.e., considering birth and death. To this end, density games are studied. First, it is proposed to extend the mean dynamics with strategy-interaction constraints to the case considering a reproduction rate parameter, i.e., the density-dependent mean dynamics with non-complete population-interaction structures.

We present a new, efficient algorithm for inferring, from time-series data or high-throughput data (e.g., flow cytometry), stochastic rate parameters for chemical reaction network models. Our algorithm combines the Gillespie stochastic simulation algorithm (including approximate variants such as tau-leaping) with the cross-entropy method. Also, it can work with incomplete datasets missing some model species, and with multiple datasets originating from experiment repetitions. We evaluate our algorithm on a number of challenging case studies, including bistable systems (Schlogl’s and toggle switch) and experimental data.

The wake behind a sphere, rotating about an axis aligned with the streamwise direction, has been experimentally investigated in a low-velocity water tunnel using LIF visualizations and PIV measurements. The measurements focused on the evolution of the flow regimes that appear depending on two control parameters, namely the Reynolds number $Re$ and the dimensionless rotation or swirl rate $\Omega$, which is the ratio of the maximum azimuthal velocity of the body to the free stream velocity. In the present investigation, we cover the range of $Re$ smaller than 400 and $\Omega$ from 0 and 4. Different wakes regimes such as an axisymmetric flow, a low helical state and a high helical mode are represented in the ($Re$, $\Omega$) parameter plane.

We conducted a series of experiments to explore the rate and state frictional properties of gabbro at conditions thought to be representative of slow slip events (SSEs) in subduction zones. The experiments were conducted using a heated direct shear apparatus. We tested both solid and simulated gouge samples at low effective normal stress (5-30 MPa) over a broad range of temperatures (20–600°C) under dry and hydrated conditions. In tests performed on dry solid samples we observed stable sliding at low temperatures (20–150°C), stick-slip at high temperatures (350–600°C) and a transitional “episodic slow slip” behavior at intermediate temperatures (200–300°C). In tests performed on dry gouge samples we observed stable sliding at all temperatures. Under hydrated conditions, the gouge samples exhibited episodic slow slip and stick-slip behavior at temperatures between 300–500°C. Our results show a decrease in the rate parameter (a– b) with temperature for both solid and gouge samples; friction transitions from velocity-strengthening to velocity-weakening at temperature of about 150°C for both solid and gouge samples. We do not observe transition to velocity-strengthening friction at the high end of the tested temperature range. Our results suggest that the occurrence of slow slip events and the downdip limit of the seismogenic zone on subduction megathrusts cannot be solely explained by the temperature dependence of frictional properties of gabbro. Further experimental studies are needed to evaluate the effects of water fugacity and compositional heterogeneity (e.g., the presence of phyllosilicates) on frictional stability of subduction megathrusts. This article is protected by copyright. All rights reserved.

This paper studies the stability and Hopf bifurcation in a class of high-dimension neural network involving the discrete and distributed delays under a new framework. By introducing some virtual neurons to the original system, the impact of distributed delay can be described in a simplified way via an equivalent new model. This paper extends the existing works on neural networks to high-dimension cases, which is much closer to complex and real neural networks. Here, we first analyze the Hopf bifurcation in this special class of high dimensional model with weak delay kernel from two aspects: one is induced by the time delay, the other is induced by a rate parameter, to reveal the roles of discrete and distributed delays on stability and bifurcation. Sufficient conditions for keeping the original system to be stable, and undergoing the Hopf bifurcation are obtained. Besides, this new framework can also apply to deal with the case of the strong delay kernel and corresponding analysis for different dynamical behaviors is provided. Finally, the simulation results are presented to justify the validity of our theoretical analysis.

Fault slip and healing history may crucially affect the fault seismogenic potential in the earthquake nucleation regime. Here we report direct shear friction tests on simulated gouges derived from a carbonate fault breccia, and from a clay/carbonate fault-core gouge, retrieved from a surface exposure of the Longmenshan Fault Zone (LFZ) which hosted the 2008 Wenchuan earthquake. The experiments were conducted under dry and hydrothermal conditions, at temperatures up to 140°C, at an effective normal stress of 50 MPa, and involved sequential velocity-stepping (VS), slide-hold-slide (SHS), and velocity-stepping stages. Dry tests performed on breccia-derived samples showed no dependence of (quasi) steady state friction (μss) on SHS or VS history, and a log linear relation between transient peak healing (Δμpk) and hold time, or classical “Dieterich-type” healing behavior. By contrast, all experiments conducted under hydrothermal conditions were characterized by “non-Dieterich” healing behavior. This included (1) an increase in μss upon resliding after a hold period and (2) an increase in friction rate parameter (a − b), after SHS testing. Comparison with previous results suggests that the healing behavior seen in our wet tests may be attributed to solution transfer processes occurring during hold periods. Our findings imply that the shallow portions of faults with carbonate/clay-rich cores (e.g., the LFZ) can heal much faster than previously recognized, while the upper limit of the seismogenic zone may migrate to deeper levels during interseismic periods. These effects have important implications for understanding the seismic cycle in tectonically active carbonate terrains.

As one of the most important extreme weather event types, extreme precipitation events have significant impacts on human and natural environment. This study assesses the projected long term trends in frequency of occurrence of extreme precipitation events represented by heavy precipitation days, very heavy precipitation days, very wet days and extreme wet days over Ontario, based on results of 21 CMIP3 GCM runs. To achieve this goal, first, all model data are linearly interpolated onto 682 grid points (0.45° × 0.45°) in Ontario; Next, biases in model daily precipitation amount are corrected with a local intensity scaling method to make the total wet days and total wet day precipitation from each of the GCMs are consistent with that from the climate forecast system reanalysis data, and then the four indices are estimated for each of the 21 GCM runs for 1968–2000, 2046–2065 and 2081–2100. After that, with the assumption that the rate parameter of the Poisson process for the occurrence of extreme precipitation events may vary with time as climate changes, the Poisson regression model which expresses the log rate as a linear function of time is used to detect the trend in frequency of extreme events in the GCMs simulations; Finally, the trends and their uncertainty are estimated. The result shows that in the twenty-first century annual heavy precipitation days, very heavy precipitation days and very wet days and extreme wet days are likely to significantly increase over major parts of Ontario and particularly heavy precipitation days, very wet days are very likely to significantly increase in some sub-regions in eastern Ontario. However, trends of seasonal indices are not significant.

A model describing an early blight epidemic and the growth dynamics of tomato interacting together is developed. The model is formulated as a set of differential equations for the rate of change in the amount of healthy H, diseased Y and defoliated D area in the disease situation, and healthy H DF and defoliated D DF in the disease-free situation. Model parameters were estimated through fitting the model to experimental data obtained from glasshouse experiments which comprised tomato plants inoculated with A. solani at three inoculation times; early (t INOC = 23 days after transplanting (DAT)), intermediate (33 DAT) and late (43 DAT) in experiment 1 and t INOC = 22, 30 and 38 DAT in experiment 2. Defoliation rates were 2.5 times higher for late inoculated (older) plants compared with the early inoculated (younger) plants. Values of the logistic rate parameter for disease increase were about three-fold higher in the late inoculations (0.380, 0.305 day−1) when compared with the early inoculations (0.151, 0.095 day−1). Based on the good fit of the model outputs to observed data with R 2 > 0.995, the model can be considered as offering a good description of the dynamic interaction between the early blight epidemic and growth of tomato.

International audience; CO2 geosequestration or enhanced oil recovery (EOR) by CO2 injection in hydrocarbon reservoirs is suggested as a short-term solution for limiting CO2 atmospheric accumulation. In the case of oxy-combustion CO2 capture, the main annex gas associated with CO2 is O-2 in important proportion (=7%). Even if hydrocarbon oxidation processes by O-2 are well-known in high-temperaturelow-pressure (HT-LP) conditions, scarce data are available under reservoir conditions (high-pressurelow-temperature, HP-LT). To predict the hydrocarbon evolution in the presence of O-2 in an oil-depleted reservoir, it is necessary to investigate their reactivity. As a matter of fact, a double approach combining experimentation and modeling was performed in this study. Experiments were carried out on a model compound (n-octane), by injecting O-2/N-2 gas mixtures in a HP-LT titanium reactor. In parallel, a detailed kinetic model for n-octane, generated by the software EXGAS, was applied. Several reactions were added, and some rate parameters have been adjusted to adapt the model to reservoir conditions. The modified model was validated by experiments performed at different reaction temperatures and O-2 concentrations. The consistency between experimentations and modified oxidation model is promising for the development of a tool allowing the prediction of hydrocarbon reservoir stability.

The mean load of a cyclic loading has a large effect on fatigue crack growth rates in metallic materials and bonded joints. In metallic structures, this effect has been attributed to plasticity-induced crack closure, but little is known about the mechanism responsible for this mean load effect on fatigue crack growth in adhesively bonded joints. This paper presents a computational investigation of the plasticity-induced crack closure mechanism affecting disbond growth in adhesively bonded joints under fatigue loading. The results show that the ratios of crack-opening and crack-closure are approximately independent of the level of plastic constraint, indicated by the ratio between the plastic zone size and the adhesive thickness. An effective strain-energy release rate parameter, which accounts for the crack closure behaviour, has been developed as a new correlating parameter for disbond growth. Comparisons with the experimental results pertinent to four different adhesive bonded joints reveal that this new correlating parameter is capable of unifying the fatigue growth rates by eliminating the effect of mean loads.

Studies of wealth inequality often assume that an observed wealth distribution reflects a system in equilibrium. This constraint is rarely tested empirically. We introduce a simple model that allows equilibrium but does not assume it. To geometric Brownian motion (GBM) we add reallocation: all individuals contribute in proportion to their wealth and receive equal shares of the amount collected. We fit the reallocation rate parameter required for the model to reproduce observed wealth inequality in the United States from 1917 to 2012. We find that this rate was positive until the 1980s, after which it became negative and of increasing magnitude. With negative reallocation, the system cannot equilibrate. Even with the positive reallocation rates observed, equilibration is too slow to be practically relevant. Therefore, studies which assume equilibrium must be treated skeptically. By design they are unable to detect the dramatic conditions found here when data are analysed without this constraint.

Hyperbranched polymers (HBPs) formed by a self-condensing vinyl copolymerization, SCVCP, of monofunctional monomers M gradually fed into a reactor containing inimers AB*, which had been charged initially, were investigated using a kinetic model. Under the assumption that no cyclization occurred during polymerization, changes in the average degree of polymerization (DP), the degree-of-polymerization dispersity, the degree of branching (DB), and the number of structural units in the hyperbranched copolymers at different feed rates of monomers M were calculated by a generating function method. The distribution of the degree of polymerization could be narrowed with the addition of monomers M, and the degree-of-polymerization dispersity was increased slightly at a slower feed rate parameter of M, ϕ. A higher DB of the HBPs could be obtained at a slower feed rate and a specific stopping ratio of M, θ. For instance, a final DB of approximately 0.62 was attained at ϕ = 0.2 and θ = 1.2, which was higher than the maximum DB of 0.5 attained in a batch system with monomers M at θ = 0.6.

Formation of gold nanoparticles (GNPs) is a physicochemical process with two paralleling aspects, i.e., the redox process and the crystallization. Herein the classical JMAK model which was originally developed for phase transformation process was applied for formation kinetics of GNPs prepared through both chemical and biogenic approaches; the relationship between JMAK model parameters, the crystallization behaviors and the dimensionality of the GNPs were clarified. It reveals that the rate parameter k mainly depends on the nucleation rate, and the Avrami exponent n could reflect the nucleation mechanism and characterize the geometric dimension of GNPs. Also, the crystallization is the rate-determining process that controls the dynamic regime of GNPs formation. Mechanistic differences between the chemical and bio synthetic routes, as well as those between spherical and flaky GNPs are also clarified via interpretation of the JMAK model parameters.