On Synchronous Robotic Networks—Part II: Time Complexity of Rendezvous and Deployment Algorithms

This paper analyzes a number of basic coordination algorithms running on synchronous robotic networks. We provide upper and lower bounds on the time complexity of the move-toward-average and circumcenter laws, both achieving rendezvous, and of the centroid law, achieving deployment over a region of interest. The results are derived via novel analysis methods, including a set of results on the convergence rates of linear dynamical systems defined by tridiagonal Toeplitz and circulant matrices.     

Drying of persimmons (Diospyros kaki L.) and the following changes in the studied bioactive compounds and the total radical scavenging activities

Fresh persimmons were subjected to two different processes: sun-drying during 1 month and dehydration at 60 °C during 12 h. To assess the effect of this process on nutritional and health-related properties of persimmons dietary fibers, minerals, trace elements, polyphenols and the total radical scavenging activities (TRSAs) were determined before and after processing. It was found that the contents of dietary fibers, minerals and trace elements in fresh and dried persimmons fruits were comparable. Total polyphenols in fresh persimmons was higher than in dried fruits (1.3 vs. 0.9 and 0.8 mg/100 g FW, respectively) and percentage of inhibition was higher than in dried fruits (70% vs. 59% and 55% and 58% vs. 53% and 46% for 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid) [ABTS] radicals, respectively (P>0.05 in all cases). In conclusion: (1) the differences in the contents of dietary fibers, minerals and trace elements in fresh and dried persimmons are not significant; (2) the contents of polyphenols and the level of the TRSA are higher in fresh persimmons than in dried fruits; however, both variables are also high in dried persimmons; (3) when fresh fruits are not available, proper dried persimmons could be used as a valuable substitute.     

STATISTICAL ANALYSIS ON COKE YIELDS IN VACUUM RESIDS AS A FUNCTION OF COMPOUND CLASSES AND ELEMENTAL ANALYSIS

This paper describes the statistical analysis of data on coke formation (measured by microcarbon residue) in five resids, as a function of compound classes, in order to identify variables wiih a significant effect on the former. In this sense, the SAS VARCLUS and Principal Component Analysis were used. The analysis carried out leads to a lineal combination of the H/C ratio, vanadium and nitrogen content as a model to explain coke yield, under non-catalytic conditions, as determined by MCR. Of all these factors, the H/C ratio has the most important contribution.     

Kaolinite-mullite reaction series: a TEM study

The kaolinite-mullite reaction series in single crystal kaolinite has been characterized by transmission electron microscopy. The exotherm observed at 980° C is attributed to the formation of a spinel phase. Mullite crystallites have also been observed with the spinel phase and both phases have a composition near that of 32 mullite. Subsequent heat treatment leads to the growth of mullite crystallites on the original kaolinite plates with the c-axis perpendicular to the plate.     

Interactive quadrangulation with Reeb atlases and connectivity textures

Creating high-quality quad meshes from triangulated surfaces is a highly nontrivial task that necessitates consideration of various application specific metrics of quality. In our work, we follow the premise that automatic reconstruction techniques may not generate outputs meeting all the subjective quality expectations of the user. Instead, we put the user at the center of the process by providing a flexible, interactive approach to quadrangulation design. By combining scalar field topology and combinatorial connectivity techniques, we present a new framework, following a coarse to fine design philosophy, which allows for explicit control of the subjective quality criteria on the output quad mesh, at interactive rates. Our quadrangulation framework uses the new notion of Reeb atlas editing, to define with a small amount of interactions a coarse quadrangulation of the model, capturing the main features of the shape, with user prescribed extraordinary vertices and alignment. Fine grain tuning is easily achieved with the notion of connectivity texturing, which allows for additional extraordinary vertices specification and explicit feature alignment, to capture the high-frequency geometries. Experiments demonstrate the interactivity and flexibility of our approach, as well as its ability to generate quad meshes of arbitrary resolution with high-quality statistics, while meeting the user’s own subjective requirements.     

Conductive PE-carbon composites by elongation flow injection moulding

In Part 1 and Part 2 of this paper the preparation of linear polyethylene (PE)-carbon black processed composites with conducting electrical properties was examined by means of elongation flow injection moulding. Mould geometry was optimized in the form of oriented double-armed bars so as to give enhanced mechanical properties combined with a high degree of electrical homogeneity. The present paper deals with composites using a high molecular weight PE matrix. It is shown that the injection-moulded composite material exhibits not only a lower percolation threshold, _c, than the conventionally pressure-moulded isotropic sample, but also conductivities two to three orders of magnitude larger than the latter. The radial and axial conductivity profiles, for concentrations well above _c, are discussed in the light of the molecular orientation variations across the bars as determined by birefringence. A segregation of primary filler particles, during flow-induced orientation, into axial channels has been shown to explain the enhancement of conductivity detected in the injected mouldings. In addition, for filler concentrations near 7%, -profile analysis indicates the development of a uniform conductive-stiff inner cylinder, several millimetres wide, homogeneously extending along the full length of the injected material.     

Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks

The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based on minimizing the error between measured and simulated values of pressure and flow. This minimization is based on a search of parameter values to be calibrated, including pipe roughness, nodal demand, and leakage flow. The resulting hydraulic problem contains several variables. In addition, a limited set of known monitored pressure and flow values creates an indeterminate problem with more variables than equations. Seeking to address the lack of monitored data for the calibration of Water Distribution Networks (WDNs), this paper uses a meta-model based on an Artificial Neural Network (ANN) to estimate pressure on all nodes of a network. The calibration of pipe roughness applies a metaheuristic search method called Particle Swarm Optimization (PSO) to minimize the objective function represented by the difference between simulated and forecasted pressure values. The proposed method is evaluated at steady state and over an extended period for a real District Metering Area (DMA), named Campos do Conde II, and the hypothetical network named C-town, which is used as a benchmark for calibration studies.     

Bayesian bivariate generalized Lindley model for survival data with a cure fraction

The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie–Gumbel–Morgenstern (FGM), Clayton and Gumbel–Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.