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

S-100B Concentrations Predict Disease-Free Survival in Stage III Melanoma Patients

Elevation of the tumor marker S-100B in melanoma patients is a highly specific indicator of recurrence. The role of S-100B in disease-free survival (DFS) was evaluated in stage III melanoma patients (staged with fluorodeoxyglucose positron emission tomography [FDG-PET] and computed tomography [CT]) with palpable lymph node metastases who underwent therapeutic lymph node dissection. S-100B and LDH were measured on the day before surgery (d = -1) and on days 1, 2, and 7 postoperatively. Multivariate logistic regression was used to study factors associated with preoperative elevation of S-100B. Univariate (log-rank test) and multivariate (Cox regression) survival analyses were performed to identify factors associated with DFS. Between 2004 and 2008, 56 patients (median age 57, range 24-93) years, 27 males (48%) and 29 females (52%) entered the study. Preoperative S-100B elevation was found in 27 patients (48%) and elevated LDH in 20 patients (36%). No association was found between these two markers at any time. Multivariate analysis showed that elevated S-100B preoperatively (hazard ratio [HR] 2.7, P = .03) was associated with DFS. S-100B elevation was associated with increased tumor size (odds ratio [OR] 3.40; P = .03). Elevated S-100B preoperatively in patients with optimally staged clinical stage III melanoma is associated with decreased disease-free survival. S100-B could be used as a prognostic marker in the stratification of new adjuvant trials to select stage III melanoma patients for adjuvant systematic treatment

Nitric Oxide Controls Constitutive Freezing Tolerance in Arabidopsis by Attenuating the Levels of Osmoprotectants, Stress-Related Hormones and Anthocyanins

[EN] Plant tolerance to freezing temperatures is governed by endogenous constitutive components and environmental inducing factors. Nitric oxide (NO) is one of the endogenous components that participate in freezing tolerance regulation. A combined metabolomic and transcriptomic characterization of NO-deficient nia1,2noa1¿2 mutant plants suggests that NO acts attenuating the production and accumulation of osmoprotective and regulatory metabolites, such as sugars and polyamines, stress-related hormones, such as ABA and jasmonates, and antioxidants, such as anthocyanins and flavonoids. Accordingly, NO-deficient plants are constitutively more freezing tolerant than wild type plants.This work was supported by grants BIO2011-27526 and BIO2014-56067-P from the Spanish Ministry of Economy and Competitiveness and FEDER funds. 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Integrated production scheduling and distribution planning with time windows

Ensuring timely product deliveries in supply chains depends on the decisions made at various stages of the supply chain, including the production stage where commodities are made available, and the distribution stage where shipments are made to customers within requested time windows. Delivery times depend on the distribution plans, which are inherently linked to the production schedules, as a commodity must first be produced or procured before being sent onwards in the supply chain. One way to ensure that the delivery times are respected is to perform direct shipments, but this is often costly. In contrast, products can be consolidated whereby several customers are visited on a given vehicle route, but this may result in either early or late deliveries. The challenge is then to devise lean production and distribution schedules that eliminate any redundancy in delivery times. In this chapter, we present an integrated production and outbound distribution scheduling problem with time windows arising in a supply chain. The problem involves jointly deciding on production and distribution operations where a manufacturer is committed first to processing a given set of orders and then to distributing them to the respective customers in different locations. The orders first undergo single processing through a set of identical parallel machines. Once completed, they are delivered by a fleet of vehicles in such a way so as to meet the customer time windows. The objective is to improve the timeliness of the deliveries, which is achieved by minimizing the earliness or tardiness of each order in reaching the customer. The chapter formally introduces the problem, describes integer linear programming formulations for two variants of the problem, and presents computational results on solving randomly generated instances with the proposed formulations.</p

Mice, microbes and models of infection.

We urgently need animal models to study infectious disease. Mice are susceptible to a similar range of microbial infections as humans. Marked differences between inbred strains of mice in their response to pathogen infection can be exploited to analyse the genetic basis of infections. In addition, the genetic tools that are available in the laboratory mouse, and new techniques to monitor the expression of bacterial genes in vivo, make it the principal experimental animal model for studying mechanisms of infection and immunity