Joint topology optimization, power control and spectrum allocation for intra-vehicular multi-hop sensor networks using dandelion-encoded heuristics

In the last years the interest in multi-hop communications has gained momentum within the research community due to the challenging characteristics of the intra-vehicular radio environment and the stringent robustness imposed on critical sensors within the vehicle. As opposed to point-to-point network topologies, multi-hop networking allows for an enhanced communication reliability at the cost of an additional processing overhead. In this context this manuscript poses a novel bi-objective optimization problem aimed at jointly minimizing (1) the average Bit Error Rate (BER) of sensing nodes under a majority fusion rule at the central data collection unit; and (2) the mean delay experienced by packets forwarded by such nodes due to multi-hop networking, frequency channel switching time multiplexing at intermediate nodes. The formulated paradigm is shown to be computationally tractable via a combination of evolutionary meta-heuristic algorithms and Dandelion codes, the latter capable of representing tree-like structures like those modeling the multi-hop routing approach. Simulations are carried out for realistic values of intra-vehicular radio channels and co-channel interference due to nearby IEEE 802.11 signals. The obtained results are promising and pave the way towards assessing the practical performance of the proposed scheme in real setups

Multiple roots of systems of equations by repulsion merit functions

In this paper we address the problem of computing multiple roots of a system of nonlinear equations through the global optimization of an appropriate merit function. The search procedure for a global min- imizer of the merit function is carried out by a metaheuristic, known as harmony search, which does not require any derivative information. The multiple roots of the system are sequentially determined along several ite- rations of a single run, where the merit function is accordingly modified by penalty terms that aim to create repulsion areas around previously computed minimizers. A repulsion algorithm based on a multiplicative kind penalty function is proposed. Preliminary numerical experiments with a benchmark set of problems show the effectiveness of the proposed method.Fundação para a Ciência e a Tecnologia (FCT

Transiting exoplanets from the CoRoT space mission. XV. CoRoT-15b: a brown dwarf transiting companion

We report the discovery by the CoRoT space mission of a transiting brown dwarf orbiting a F7V star with an orbital period of 3.06 days. CoRoT-15b has a radius of 1.12 +0.30 -0.15 Rjup, a mass of 63.3 +- 4.1 Mjup, and is thus the second transiting companion lying in the theoretical mass domain of brown dwarfs. CoRoT-15b is either very young or inflated compared to standard evolution models, a situation similar to that of M-dwarfs stars orbiting close to solar-type stars. Spectroscopic constraints and an analysis of the lightcurve favors a spin period between 2.9 and 3.1 days for the central star, compatible with a double-synchronisation of the system.Comment: 7 pages, 6 figures, accepted in A&

Transiting exoplanets from the CoRoT space mission IX. CoRoT-6b: a transiting `hot Jupiter' planet in an 8.9d orbit around a low-metallicity star

The CoRoT satellite exoplanetary team announces its sixth transiting planet in this paper. We describe and discuss the satellite observations as well as the complementary ground-based observations - photometric and spectroscopic - carried out to assess the planetary nature of the object and determine its specific physical parameters. The discovery reported here is a `hot Jupiter' planet in an 8.9d orbit, 18 stellar radii, or 0.08 AU, away from its primary star, which is a solar-type star (F9V) with an estimated age of 3.0 Gyr. The planet mass is close to 3 times that of Jupiter. The star has a metallicity of 0.2 dex lower than the Sun, and a relatively high $^7$Li abundance. While thelightcurveindicatesamuchhigherlevelof activity than, e.g., the Sun, there is no sign of activity spectroscopically in e.g., the [Ca ] H&K lines

CD4 T lymphocyte autophagy is upregulated in the salivary glands of primary Sjögren’s syndrome patients and correlates with focus score and disease activity

Background: Primary Sjögren’s syndrome (pSS) is a common chronic autoimmune disease characterized by lymphocytic infiltration of exocrine glands and peripheral lymphocyte perturbation. In the current study, we aimed to investigate the possible pathogenic implication of autophagy in T lymphocytes in patients with pSS. Methods: Thirty consecutive pSS patients were recruited together with 20 patients affected by sicca syndrome a nd/or chronic sialoadenitis and 30 healthy controls. Disease activity and damage were evaluated according to SS disease activity index, EULAR SS disease activity index, and SS disease damage index. T lymphocytes were analyzed for the expression of autophagy-specific markers by biochemical, molecular, and histological assays in peripheral blood and labial gland biopsies. Serum interleukin (IL)-23 and IL-21 levels were quantified by enzyme-linked immunosorbent assay. Results: Our study provides evidence for the first time that autophagy is upregulated in CD4+ T lymphocyte salivary glands from pSS patients. Furthermore, a statistically significant correlation was detected between lymphocyte autophagy levels, disease activity, and damage indexes. We also found a positive correlation between autophagy enhancement and the increased salivary gland expression of IL-21 and IL-23, providing a further link between innate and adaptive immune responses in pSS. Conclusions: These findings suggest that CD4+ T lymphocyte autophagy could play a key role in pSS pathogenesis. Additionally, our data highlight the potential exploitation of T cell autophagy as a biomarker of disease activity and provide new ground to verify the therapeutic implications of autophagy as an innovative drug target in pSS

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems

Penalty-free feasibility boundary convergent multi-objective evolutionary algorithm for the optimization of water distribution systems

This paper presents a new penalty-free multi-objective evolutionary approach (PFMOEA) for the optimization of water distribution systems (WDSs). The proposed approach utilizes pressure dependent analysis (PDA) to develop a multi-objective evolutionary search. PDA is able to simulate both normal and pressure deficient networks and provides the means to accurately and rapidly identify the feasible region of the solution space, effectively locating global or near global optimal solutions along its active constraint boundary. The significant advantage of this method over previous methods is that it eliminates the need for ad-hoc penalty functions, additional “boundary search” parameters, or special constraint handling procedures. Conceptually, the approach is downright straightforward and probably the simplest hitherto. The PFMOEA has been applied to several WDS benchmarks and its performance examined. It is demonstrated that the approach is highly robust and efficient in locating optimal solutions. Superior results in terms of the initial network construction cost and number of hydraulic simulations required were obtained. The improvements are demonstrated through comparisons with previously published solutions from the literature

A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy

A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics