Abstract
Considering the new paradigm in the industry, where clients request small amounts of a growing range of products, the times of crisis and the fierce competition for businesses survival, it is crucial for Organizations to be able to reduce the waste of raw materials. This waste could be minimized with the introduction of decision support systems in the process. In this paper we propose an information system that enables the optimization of purchases and consumption of materials, promoting waste reduction and boosting profitability.

Abstract
Energy system pathways which are projected to deliver minimum possible deployment cost, combined with low Greenhouse Gas (GHG) emissions, are usually considered as 'no-regrets' options. However, the question remains whether such energy pathways present 'no-regrets' when also considering the wider environmental resource impacts, in particular those on land and water resources. This paper aims to determine whether the energy pathways of the UK's Carbon Plan are environmental "no-regrets" options, defined in this study as simultaneously exhibiting low impact on land and water services resulting from resource appropriation for energy provision. This is accomplished by estimating the land area and water abstraction required by 2050 under the four pathways of the Carbon Plan with different scenarios for energy crop composition, yield, and power station locations. The outcomes are compared with defined limits for sustainable land appropriation and water abstraction.The results show that of the four Carbon Plan pathways, only the "Higher Renewables, more energy efficiency" pathway is an environmental "no-regrets" option, and that is only if deployment of power stations inland is limited. The study shows that policies for future low-carbon energy systems should be developed with awareness of wider environmental impacts. Failing to do this could lead to a setback in achieving GHG emission reductions goals, because of unforeseen additional competition between the energy sector and demand for land and water services in other sectors.

Abstract
Type 2 diabetes involves defective insulin secretion with islet inflammation governed in part by IL-1ß. Prolonged exposure of islets to high concentrations of IL-1ß (>24 h, 20 ng/ml) impairs beta cell function and survival. Conversely, exposure to lower concentrations of IL-1ß for >24 h improves these same parameters. The impact on insulin secretion of shorter exposure times to IL-1ßand the underlying molecular mechanisms are poorly understood and were the focus of this study. Treatment of rat primary beta cells, as well as rat or human whole islets, with 0.1 ng/ml IL-1ß for 2 h increased glucose-stimulated (but not basal) insulin secretion, whereas 20 ng/ml was without effect. Similar differential effects of IL-1ß depending on concentration were observed after 15 min of KCl stimulation but were prevented by diazoxide. Studies on sorted rat beta cells indicated that the enhancement of stimulated secretion by 0.1 ng/ml IL-1ß was mediated by the NF-ßB pathway and c-JUN/JNK pathway acting in parallel to elicit focal adhesion remodeling and the phosphorylation of paxillin independently of upstream regulation by focal adhesion kinase. Because the beneficial effect of IL-1ß was dependent in part upon transcription, gene expression was analyzed by RNAseq. There were 18 genes regulated uniquely by 0.1 but not 20 ng/ml IL-1ß, which are mostly involved in transcription and apoptosis. These results indicate that 2h of exposure of beta cells to a low but not a high concentration of IL-1ß enhances glucose-stimulated insulin secretion through focal adhesion and actin remodeling, as well as modulation of gene expression. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Abstract
The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier) implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

Abstract

Abstract
Simulated Annealing (SA) is a powerful tool for optimization problems that have several local optima. This tool has the ability to escape from a local optima accepting relatively bad solutions for a period and searching for good solutions in your neighborhood. This paper describes the use of SA based on Gaussian Probability Density Function as a decision support criteria in resolution of Transmission Expansion Planning (TEP) problem. This method consists in starting from an initial solution with all possible circuits added and over the iterations removing, replacing or adding new circuits. The method proved to be a reasonable computational effort and proved able to find optimal values known in the literature.