Catalytic performance of sheet-like Fe/ZSM-5 zeolites for the selective oxidation of benzene with nitrous oxide

Hierarchical Fe/ZSM-5 zeolites were synthesized with a diquaternary ammonium surfactant containing a hydrophobic tail and extensively characterized by XRD, Ar porosimetry, TEM, DRUV-Vis, and UV-Raman spectroscopy. Their catalytic activities in catalytic decomposition of NO and the oxidation of benzene to phenol with NO as the oxidant were also determined. The hierarchical zeolites consist of thin sheets limited in growth in the b-direction (along the straight channels of the MFI network) and exhibit similar high hydrothermal stability as a reference Fe/ZSM-5 zeolite. Spectroscopic and catalytic investigations point to subtle differences in the extent of Fe agglomeration with the sheet-like zeolites having a higher proportion of isolated Fe centers than the reference zeolite. As a consequence, these zeolites have a somewhat lower activity in catalytic NO decomposition (catalyzed by oligomeric Fe), but display higher activity in benzene oxidation (catalyzed by monomeric Fe). The sheet-like zeolites deactivate much slower than bulk Fe/ZSM-5, which is attributed to the much lower probability of secondary reactions of phenol in the short straight channels of the sheets. The deactivation rate decreases with decreasing Fe content of the Fe/ZSM-5 nanosheets. It is found that carbonaceous materials are mainly deposited in the mesopores between the nanosheets and much less so in the micropores. This contrasts the strong decrease in the micropore volume of bulk Fe/ZSM-5 due to rapid clogging of the continuous micropore network. The formation of coke deposits is limited in the nanosheet zeolites because of the short molecular trafficking distances. It is argued that at high Si/Fe content, coke deposits mainly form on the external surface of the nanosheets. © 2012 Elsevier Inc. All rights reserved.

Green function for the diffusion limit of one-dimensional continuous time random walks

It is shown how the fractional probability density diffusion equation for the diffusion limit of one-dimensional continuous time random walks may be derived from a generalized Markovian Chapman-Kolmogorov equation. The non-Markovian behaviour is incorporated into the Markovian Chapman-Kolmogorov equation by postulating a Levy like distribution of waiting times as a kernel. The Chapman-Kolmogorov equation so generalised then takes on the form of a convolution integral. The dependence on the initial conditions typical of a non-Markovian process is treated by adding a time dependent term involving the survival probability to the convolution integral. In the diffusion limit these two assumptions about the past history of the process are sufficient to reproduce anomalous diffusion and relaxation behaviour of the Cole-Cole type. The Green function in the diffusion limit is calculated using the fact that the characteristic function is the Mittag-Leffler function. Fourier inversion of the characteristic function yields the Green function in terms of a Wright function. The moments of the distribution function are evaluated from the Mittag-Leffler function using the properties of characteristic functions and a relation between the powers of the second moment and higher order even moments is derived. (C) 2004 Elsevier B.V. All rights reserved.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.

Constructing networks of continuous-time velocity-based models

A conventional local model (LM) network consists of a set of affine local models blended together using appropriate weighting functions. Such networks have poor interpretability since the dynamics of the blended network are only weakly related to the underlying local models. In contrast, velocity-based LM networks employ strictly linear local models to provide a transparent framework for nonlinear modelling in which the global dynamics are a simple linear combination of the local model dynamics. A novel approach for constructing continuous-time velocity-based networks from plant data is presented. Key issues including continuous-time parameter estimation, correct realisation of the velocity-based local models and avoidance of the input derivative are all addressed. Application results are reported for the highly nonlinear simulated continuous stirred tank reactor process.

Received signal strength in large-scale wireless relay sensor network: a stochastic ray approach

The authors consider a point percolation lattice representation of a large-scale wireless relay sensor network (WRSN) deployed in a cluttered environment. Each relay sensor corresponds to a grid point in the random lattice and the signal sent by the source is modelled as an ensemble of photons that spread in the space, which may 'hit' other sensors and are 'scattered' around. At each hit, the relay node forwards the received signal to its nearest neighbour through direction-selective relaying. The authors first derive the distribution that a relay path reaches a prescribed location after undergoing certain number of hops. Subsequently, a closed-form expression of the average received signal strength (RSS) at the destination can be computed as the summation of all signal echoes' energy. Finally, the effect of the anomalous diffusion exponent ß on the mean RSS in a WRSN is studied, for which it is found that the RSS scaling exponent e is given by (3ß-1)/ß. The results would provide useful insight into the design and deployment of large-scale WRSNs in future. © 2011 The Institution of Engineering and Technology.

Exploring statistical and population aspects of network complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples.

The Conditional Phase-type distribution: Graphical Models for Continuous Non-Normal Data:The Conditional Phase-type (C-Ph) distribution:

Conditional Gaussian (CG) distributions allow the inclusion of both discrete and continuous variables in a model assuming that the continuous variable is normally distributed. However, the CG distributions have proved to be unsuitable for survival data which tends to be highly skewed. A new method of analysis is required to take into account continuous variables which are not normally distributed. The aim of this paper is to introduce the more appropriate conditional phase-type (C-Ph) distribution for representing a continuous non-normal variable while also incorporating the causal information in the form of a Bayesian network.

A dynamical neural network for hitting an approaching object

Besides making contact with an approaching ball at the proper place and time, hitting requires control of the effector velocity at contact. A dynamical neural network for the planning of hitting movements was derived in order to account for both these requirements. The model in question implements continuous required velocity control by extending the Vector Integration To Endpoint model while providing explicit control of effector velocity at interception. It was shown that the planned movement trajectories generated by the model agreed qualitatively with the kinematics of hitting movements as observed in two recent experiments. Outstanding features of this comparison concerned the timing and amplitude of the empirical backswing movements, which were largely consistent with the predictions from the model. Several theoretical implications as well as the informational basis and possible neural underpinnings of the model were discussed.

Assessing reproducibility of data obtained with instruments based on continuous measurements

Data obtained with any research tool must be reproducible, a concept referred to as reliability. Three techniques are often used to evaluate reliability of tools using continuous data in aging research: intraclass correlation coefficients (ICC), Pearson correlations, and paired t tests. These are often construed as equivalent when applied to reliability. This is not correct, and may lead researchers to select instruments based on statistics that may not reflect actual reliability. The purpose of this paper is to compare the reliability estimates produced by these three techniques and determine the preferable technique. A hypothetical dataset was produced to evaluate the reliability estimates obtained with ICC, Pearson correlations, and paired t tests in three different situations. For each situation two sets of 20 observations were created to simulate an intrarater or inter-rater paradigm, based on 20 participants with two observations per participant. Situations were designed to demonstrate good agreement, systematic bias, or substantial random measurement error. In the situation demonstrating good agreement, all three techniques supported the conclusion that the data were reliable. In the situation demonstrating systematic bias, the ICC and t test suggested the data were not reliable, whereas the Pearson correlation suggested high reliability despite the systematic discrepancy. In the situation representing substantial random measurement error where low reliability was expected, the ICC and Pearson coefficient accurately illustrated this. The t test suggested the data were reliable. The ICC is the preferred technique to measure reliability. Although there are some limitations associated with the use of this technique, they can be overcome.

Modeling continuous self-report measures of perceived emotion using generalized additive mixed models

Emotion research has long been dominated by the “standard method” of displaying posed or acted static images of facial expressions of emotion. While this method has been useful it is unable to investigate the dynamic nature of emotion expression. Although continuous self-report traces have enabled the measurement of dynamic expressions of emotion, a consensus has not been reached on the correct statistical techniques that permit inferences to be made with such measures. We propose Generalized Additive Models and Generalized Additive Mixed Models as techniques that can account for the dynamic nature of such continuous measures. These models allow us to hold constant shared components of responses that are due to perceived emotion across time, while enabling inference concerning linear differences between groups. The mixed model GAMM approach is preferred as it can account for autocorrelation in time series data and allows emotion decoding participants to be modelled as random effects. To increase confidence in linear differences we assess the methods that address interactions between categorical variables and dynamic changes over time. In addition we provide comments on the use of Generalized Additive Models to assess the effect size of shared perceived emotion and discuss sample sizes. Finally we address additional uses, the inference of feature detection, continuous variable interactions, and measurement of ambiguity.

Intermittent chemotherapy plus either intermittent or continuous cetuximab for first-line treatment of patients with KRAS wild-type advanced colorectal cancer (COIN-B): a randomised phase 2 trial

Background: Advanced colorectal cancer is treated with a combination of cytotoxic drugs and targeted treatments. However, how best to minimise the time spent taking cytotoxic drugs and whether molecular selection can refine this further is unknown. The primary aim of this study was to establish how cetuximab might be safely and effectively added to intermittent chemotherapy.

Methods: COIN-B was an open-label, multicentre, randomised, exploratory phase 2 trial done at 30 hospitals in the UK and one in Cyprus. We enrolled patients with advanced colorectal cancer who had received no previous chemotherapy for metastases. Randomisation was done centrally (by telephone) by the Medical Research Council Clinical Trials Unit using minimisation with a random element. Treatment allocation was not masked. Patients were assigned (1:1) to intermittent chemotherapy plus intermittent cetuximab or to intermittent chemotherapy plus continuous cetuximab. Chemotherapy was FOLFOX (folinic acid and oxaliplatin followed by bolus and infused fluorouracil). Patients in both groups received FOLFOX and weekly cetuximab for 12 weeks, then either had a planned interruption (those taking intermittent cetuximab) or planned maintenance by continuing on weekly cetuximab (continuous cetuximab). On RECIST progression, FOLFOX plus cetuximab or FOLFOX was recommenced for 12 weeks followed by further interruption or maintenance cetuximab, respectively. The primary outcome was failure-free survival at 10 months. The primary analysis population consisted of patients who completed 12 weeks of treatment without progression, death, or leaving the trial. We tested BRAF and NRAS status retrospectively. The trial was registered, ISRCTN38375681.

Findings: We registered 401 patients, 226 of whom were enrolled. Results for 169 with KRAS wild-type are reported here, 78 (46%) assigned to intermittent cetuximab and 91 (54%) to continuous cetuximab. 64 patients assigned to intermittent cetuximab and 66 of those assigned to continuous cetuximab were included in the primary analysis. 10-month failure-free survival was 50% (lower bound of 95% CI 39) in the intermittent group versus 52% (lower bound of 95% CI 41) in the continuous group; median failure-free survival was 12·2 months (95% CI 8·8–15·6) and 14·3 months (10·7–20·4), respectively. The most common grade 3–4 adverse events were skin rash (21 [27%] of 77 patients vs 20 [22%] of 92 patients), neutropenia (22 [29%] vs 30 [33%]), diarrhoea (14 [18%] vs 23 [25%]), and lethargy (20 [26%] vs 19 [21%]).

Interpretation: Cetuximab was safely incorporated in two first-line intermittent chemotherapy strategies. Maintenance of biological monotherapy, with less cytotoxic chemotherapy within the first 6 months, in molecularly selected patients is promising and should be validated in phase 3 trials.

Nonlocality of two- and three-mode continuous variable systems

We address the nonlocality of fully inseparable three-mode Gaussian states generated either by bilinear three-mode Hamiltonians or by a sequence of bilinear two-mode Hamiltonians. Two different tests revealing nonlocality are considered, in which the dichotomic Bell operator is represented by the displaced parity and by the pseudospin operator respectively. Three-mode states are also considered as a conditional source of two-mode non-Gaussian states, whose nonlocality properties are analysed. We found that the non-Gaussian character of the conditional states allows violation of Bell's inequalities (by parity and pseudospin tests) stronger than with a conventional twin-beam state. However, the non-Gaussian character is not sufficient to reveal nonlocality through a dichotomized quadrature measurement strategy.