Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

The E-SCREEN assay as a tool to identify estrogens: an update on estrogenic environmental pollutants.

Estrogens are defined by their ability to induce the proliferation of cells of the female genital tract. The wide chemical diversity of estrogenic compounds precludes an accurate prediction of estrogenic activity on the basis of chemical structure. Rodent bioassays are not suited for the large-scale screening of chemicals before their release into the environment because of their cost, complexity, and ethical concerns. The E-SCREEN assay was developed to assess the estrogenicity of environmental chemicals using the proliferative effect of estrogens on their target cells as an end point. This quantitative assay compares the cell number achieved by similar inocula of MCF-7 cells in the absence of estrogens (negative control) and in the presence of 17 beta-estradiol (positive control) and a range of concentrations of chemicals suspected to be estrogenic. Among the compounds tested, several "new" estrogens were found; alkylphenols, phthalates, some PCB congeners and hydroxylated PCBs, and the insecticides dieldrin, endosulfan, and toxaphene were estrogenic by the E-SCREEN assay. In addition, these compounds competed with estradiol for binding to the estrogen receptor and increased the levels of progesterone receptor and pS2 in MCF-7 cells, as expected from estrogen mimics. Recombinant human growth factors (bFGF, EGF, IGF-1) and insulin did not increase in cell yields. The aims of the work summarized in this paper were a) to validate the E-SCREEN assay; b) to screen a variety of chemicals present in the environment to identify those that may be causing reproductive effects in wildlife and humans; c) to assess whether environmental estrogens may act cumulatively; and finally d) to discuss the reliability of this and other assays to screen chemicals for their estrogenicity before they are released into the environment

The Luminosity Function Evolution of Soft X--ray selected AGN in the RIXOS survey

A sample of 198 soft X--ray selected active galactic nuclei (AGN) from the ROSAT International X--ray Optical Survey (RIXOS), is used to investigate the X--ray luminosity function and its evolution. RIXOS, with a flux limit of 3E-14 erg s-1 cm-2 (0.5 to 2.0 keV), samples a broad range in redshift over 20 deg^2 of sky, and is almost completely identified; it is used in combination with the Einstein Extended Medium Sensitivity Survey (EMSS), to give a total sample of over 600 AGN. We find the evolution of AGN with redshift to be consistent with pure luminosity evolution (PLE) models in which the rate of evolution slows markedly or stops at high redshifts z>1.8. We find that this result is not affected by the inclusion, or exclusion, of narrow emission line galaxies at low redshift in the RIXOS and EMSS samples, and is insensitive to uncertainties in the conversion between flux values measured with ROSAT and Einstein. We confirm, using a model independent Ve/Va test, that our survey is consistent with no evolution at high redshifts.Comment: 10 pages, LaTeX file, PS figures and mn.sty. Accepted in MNRA

Spatiotemporal monthly rainfall reconstruction via artificial neural network ? case study: south of Brazil

International audienceClimatological records users, frequently, request time series for geographical locations where there is no observed meteorological attributes. Climatological conditions of the areas or points of interest have to be calculated interpolating observations in the time of neighboring stations and climate proxy. The aim of the present work is the application of reliable and robust procedures for monthly reconstruction of precipitation time series. Time series is a special case of symbolic regression and we can use Artificial Neural Network (ANN) to explore the spatiotemporal dependence of meteorological attributes. The ANN seems to be an important tool for the propagation of the related weather information to provide practical solution of uncertainties associated with interpolation, capturing the spatiotemporal structure of the data. In practice, one determines the embedding dimension of the time series attractor (delay time that determine how data are processed) and uses these numbers to define the network's architecture. Meteorological attributes can be accurately predicted by the ANN model architecture: designing, training, validation and testing; the best generalization of new data is obtained when the mapping represents the systematic aspects of the data, rather capturing the specific details of the particular training set. As illustration one takes monthly total rainfall series recorded in the period 1961?2005 in the Rio Grande do Sul ? Brazil. This reliable and robust reconstruction method has good performance and in particular, they were able to capture the intrinsic dynamic of atmospheric activities. The regional rainfall has been related to high-frequency atmospheric phenomena, such as El Niño and La Niña events, and low frequency phenomena, such as the Pacific Decadal Oscillation

X-ray Spectra of the RIXOS source sample

We present results of an extensive study of the X-ray spectral properties of sources detected in the RIXOS survey, that is nearly complete down to a flux limit of 3e-14 cgs (0.5-2 keV). We show that for X-ray surveys containing sources with low count rate spectral slopes estimated using simple hardness ratios in the ROSAT band can be biased. Instead we analyse three-colour X-ray data using statistical techniques appropriate to the Poisson regime which removes the effects of this bias. We have then applied this technique to the RIXOS survey to study the spectral properties of the sample. For the AGN we find an average energy index of 1.05+-0.05 with no evidence for spectral evolution with redshift. Individual AGN are shown to have a range of properties including soft X-ray excesses and intrinsic absorption. Narrow Emission Line Galaxies also seem to fit to a power-law spectrum, which may indicate a non-thermal origin for their X-ray emission. We infer that most of the clusters in the sample have a bremsstrahlung temperature >3 keV, although some show evidence for a cooling flow. The stars deviate strongly from a power-law model but fit to a thermal model. Finally, we have analysed the whole RIXOS sample containing 1762 sources. We find that the mean spectral slope of the sources hardens at lower fluxes in agreement with results from other samples. However, a study of the individual sources demonstrates that the hardening of the mean is caused by the appearance of a population of very hard sources at the lowest fluxes. This has implications for the nature of the soft X-ray background.Comment: 31,LaTeX file, 2 PS files with Table 2 and 22 PS figures. MNRAS in pres

Multiconfigurational second-order perturbation study of the decomposition of the radical anion of nitromethane

The doublet potential energy surfaces involved in the decomposition of the nitromethane radical anion (CH3NO2−) have been studied by using the multistate extension of the multiconfigurational second-order perturbation method (MS-CASPT2) in conjunction with large atomic natural orbital-type basis sets. A very low energy barrier is found for the decomposition reaction: CH3NO2−→[CH3NO2]−→CH3+NO2−. No evidence has been obtained on the existence of an isomerization channel leading to the initial formation of the methylnitrite anion (CH3ONO−) which, in a subsequent reaction, would yield nitric oxide (NO). In contrast, it is suggested that NO is formed through the bimolecular reaction: CH3+NO2−→[CH3O-N-O]−→CH3O−+NO. In particular, the CASSCF/MS-CASPT2 results indicate that the methylnitrite radical anion CH3ONO− does not represent a minimum energy structure, as concluded by using density functional theory (DFT) methodologies. The inverse symmetry breaking effect present in DFT is demonstrated to be responsible for such erroneous [email protected]

Simulating non-Markovian stochastic processes

We present a simple and general framework to simulate statistically correct realizations of a system of non-Markovian discrete stochastic processes. We give the exact analytical solution and a practical an efficient algorithm alike the Gillespie algorithm for Markovian processes, with the difference that now the occurrence rates of the events depend on the time elapsed since the event last took place. We use our non-Markovian generalized Gillespie stochastic simulation methodology to investigate the effects of non-exponential inter-event time distributions in the susceptible-infected-susceptible model of epidemic spreading. Strikingly, our results unveil the drastic effects that very subtle differences in the modeling of non-Markovian processes have on the global behavior of complex systems, with important implications for their understanding and prediction. We also assess our generalized Gillespie algorithm on a system of biochemical reactions with time delays. As compared to other existing methods, we find that the generalized Gillespie algorithm is the most general as it can be implemented very easily in cases, like for delays coupled to the evolution of the system, where other algorithms do not work or need adapted versions, less efficient in computational terms.Comment: Improvement of the algorithm, new results, and a major reorganization of the paper thanks to our coauthors L. Lafuerza and R. Tora

Numerical properties of isotrivial fibrations

In this paper we investigate the numerical properties of relatively minimal isotrivial fibrations \varphi \colon X \lr C, where $X$ is a smooth, projective surface and $C$ is a curve. In particular we prove that, if $g(C) \geq 1$ and $X$ is neither ruled nor isomorphic to a quasi-bundle, then K_X^2 \leq 8 \chi(\mO_X)-2; this inequality is sharp and if equality holds then $X$ is a minimal surface of general type whose canonical model has precisely two ordinary double points as singularities. Under the further assumption that $K_X$ is ample, we obtain K_X^2 \leq 8 \chi(\mO_X)-5 and the inequality is also sharp. This improves previous results of Serrano and Tan.Comment: 30 pages. Final version, to appear in Geometriae Dedicat