Ecological criteria for evaluating candidate sites for marine reserves

Several schemes have been developed to help select the locations of marine reserves. All of them combine social, economic, and biological criteria, and few offer any guidance as to how to prioritize among the criteria identified. This can imply that the relative weights given to different criteria are unimportant. Where two sites are of equal value ecologically; then socioeconomic criteria should dominate the choice of which should be protected. However, in many cases, socioeconomic criteria are given equal or greater weight than ecological considerations in the choice of sites. This can lead to selection of reserves with little biological value that fail to meet many of the desired objectives. To avoid such a possibility, we develop a series of criteria that allow preliminary evaluation of candidate sites according to their relative biological values in advance of the application of socioeconomic criteria. We include criteria that,. while not strictly biological, have a strong influence on the species present or ecological processes. Out scheme enables sites to be assessed according to their biodiversity, the processes which underpin that diversity, and the processes that support fisheries and provide a spectrum of other services important to people. Criteria that capture biodiversity values include biogeographic representation, habitat representation and heterogeneity, and presence of species or populations of special interest (e.g., threatened species). Criteria that capture sustainability of biodiversity and fishery values include the size of reserves necessary to protect viable habitats, presence of exploitable species, vulnerable life stages, connectivity among reserves, links among ecosystems, and provision of ecosystem services to people. Criteria measuring human and natural threats enable candidate sites to be eliminated from consideration if risks are too great, but also help prioritize among sites where threats can be mitigated by protection. While our criteria can be applied to the design of reserve networks, they also enable choice of single reserves to be made in the context of the attributes of existing protected areas. The overall goal of our scheme is to promote the development of reserve networks that will maintain biodiversity and ecosystem functioning at large scales. The values of eco-system goods and services for people ultimately depend on meeting this objective.

Outcome variables for osteoarthritis clinical trials: the OMERACT-OARSI set of responder criteria

Improvement in analysis and reporting results of osteoarthritis (OA) clinical trials has been recently obtained because of harmonization and standardization of the selection of outcome variables (OMERACT 3 and OARSI). Moreover, OARSI has recently proposed the OARSI responder criteria. This composite index permits presentation of results of symptom modifying clinical trials in OA based on individual patient responses (responder yes/no). The 2 organizations (OMERACT and OARSI) established. a task force aimed at evaluating: (1) the variability of observed placebo and active treatment effects using the OARSI responder criteria; and (2) the possibility of proposing a simplified set of criteria. The conclusions of the task force were presented and discussed during the OMERACT 6 conference, where a simplified set of responder criteria (OMERACT-OARSI set of criteria) was proposed.

Integrating environmental and economic life cycle analysis in product development: a material selection case study

Purpose Achieving sustainability by rethinking products, services and strategies is an enormous challenge currently laid upon the economic sector, in which materials selection plays a critical role. In this context, the present work describes an environmental and economic life cycle analysis of a structural product, comparing two possible material alternatives. The product chosen is a storage tank, presently manufactured in stainless steel (SST) or in a glass fibre reinforced polymer composite (CST). The overall goal of the study is to identify environmental and economic strong and weak points related to the life cycle of the two material alternatives. The consequential win-win or trade-off situations will be identified via a Life Cycle Assessment/Life Cycle Costing (LCA/LCC) integrated model. Methods The LCA/LCC integrated model used consists in applying the LCA methodology to the product system, incorporating, in parallel, its results into the LCC study, namely those of the Life Cycle Inventory (LCI) and the Life Cycle Impact Assessment (LCIA). Results In both the SST and CST systems the most significant life cycle phase is the raw materials production, in which the most significant environmental burdens correspond to the Fossil fuels and Respiratory inorganics categories. The LCA/LCC integrated analysis shows that the CST has globally a preferable environmental and economic profile, as its impacts are lower than those of the SST in all life cycle stages. Both the internal and external costs are lower, the former resulting mainly from the composite material being significantly less expensive than stainless steel. This therefore represents a full win-win situation. As a consequence, the study clearly indicates that using a thermoset composite material to manufacture storage tanks is environmentally and economically desirable. However, it was also evident that the environmental performance of the CST could be improved by altering its End-of-Life stage. Conclusions The results of the present work provide enlightening insights into the synergies between the environmental and the economic performance of a structural product made with alternative materials. Further, they provide conclusive evidence to support the integration of environmental and economic life cycle analysis in the product development processes of a manufacturing company, or in some cases even in its procurement practices.

Utilização do método Multicritério AHP, na aquisição de um sistema ERP

The selection of an Enterprise Resource Planning (ERP) system is one of the most sensitive and highest impact processes in the area of information systems and technologies, because it supports and integrates the whole business of an organization. Hence the importance of deciding the best solution in order to contribute to the organization's competitiveness in a global and increasingly demanding market. Therefore, it is essential to provide tools to support decision making, turning complex and often intangible decisions into simple and quantifiable scenarios. This study addressed the adoption of the Analytical Hierarchy Process (AHP) multicriteria decision method to support the selection of an ERP system. The literature review was the source used to obtain the set of the most relevant criteria to be considered in this decision, which were subsequently validated through systematic application of various surveys of experts and people related to the field of ERP systems. To support the application of AHP, according to the model obtained in the study, it was developed a web application that will be available to the general public. The responsible for the acquisition of ERP systems can use it to easily apply the AHP method based on validated decision model. On the other hand, the web application can be used as a validation tool, allowing collecting data for future developments of the decision model.

Increasing availability through maintainability growth using partial Multi Criteria Decision Making (pMCDM)

In the last decades considerations about equipments' availability became an important issue, as well as its dependence on components characteristics such as reliability and maintainability. This is particularly of outstanding importance if one is dealing with high risk industrial equipments, where these factors play an important and fundamental role in risk management when safety or huge economic values are in discussion. As availability is a function of reliability, maintainability, and maintenance support activities, the main goal is to improve one or more of these factors. This paper intends to show how maintainability can influence availability and present a methodology to select the most important attributes for maintainability using a partial Multi Criteria Decision Making (pMCDM). Improvements in maintainability can be analyzed assuming it as a probability related with a restore probability density function [g(t)].

Conceptual model for decomposing the value for the customer

Value has been defined in different theoretical contexts as need, desire, interest, standard /criteria, beliefs, attitudes, and preferences. The creation of value is key to any business, and any business activity is about exchanging some tangible and/or intangible good or service and having its value accepted and rewarded by customers or clients, either inside the enterprise or collaborative network or outside. “Perhaps surprising then is that firms often do not know how to define value, or how to measure it” (Anderson and Narus, 1998 cited by [1]). Woodruff echoed that we need “richer customer value theory” for providing an “important tool for locking onto the critical things that managers need to know”. In addition, he emphasized, “we need customer value theory that delves deeply into customer’s world of product use in their situations” [2]. In this sense, we proposed and validated a novel “Conceptual Model for Decomposing the Value for the Customer”. To this end, we were aware that time has a direct impact on customer perceived value, and the suppliers’ and customers’ perceptions change from the pre-purchase to the post-purchase phases, causing some uncertainty and doubts.We wanted to break down value into all its components, as well as every built and used assets (both endogenous and/or exogenous perspectives). This component analysis was then transposed into a mathematical formulation using the Fuzzy Analytic Hierarchy Process (AHP), so that the uncertainty and vagueness of value perceptions could be embedded in this model that relates used and built assets in the tangible and intangible deliverable exchange among the involved parties, with their actual value perceptions.

Membrane selectivity versus sensor response in hydrogenated amorphous silicon CHEMFETs using a semi-empirical model

Toxic amides, such as acrylamide, are potentially harmful to Human health, so there is great interest in the fabrication of compact and economical devices to measure their concentration in food products and effluents. The CHEmically Modified Field Effect Transistor (CHEMFET) based onamorphous silicon technology is a candidate for this type of application due to its low fabrication cost. In this article we have used a semi-empirical modelof the device to predict its performance in a solution of interfering ions. The actual semiconductor unit of the sensor was fabricated by the PECVD technique in the top gate configuration. The CHEMFET simulation was performed based on the experimental current voltage curves of the semiconductor unit and on an empirical model of the polymeric membrane. Results presented here are useful for selection and design of CHEMFET membranes and provide an idea of the limitations of the amorphous CHEMFET device. In addition to the economical advantage, the small size of this prototype means it is appropriate for in situ operation and integration in a sensor array.

Sustainable Tourism and Agriculture Multifunctionality by PAR: A Variable Selection Approach

Copyright © 2013 Springer Netherlands.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

A feedback-based decentralised coordination model for distributed open real-time systems

Moving towards autonomous operation and management of increasingly complex open distributed real-time systems poses very significant challenges. This is particularly true when reaction to events must be done in a timely and predictable manner while guaranteeing Quality of Service (QoS) constraints imposed by users, the environment, or applications. In these scenarios, the system should be able to maintain a global feasible QoS level while allowing individual nodes to autonomously adapt under different constraints of resource availability and input quality. This paper shows how decentralised coordination of a group of autonomous interdependent nodes can emerge with little communication, based on the robust self-organising principles of feedback. Positive feedback is used to reinforce the selection of the new desired global service solution, while negative feedback discourages nodes to act in a greedy fashion as this adversely impacts on the provided service levels at neighbouring nodes. The proposed protocol is general enough to be used in a wide range of scenarios characterised by a high degree of openness and dynamism where coordination tasks need to be time dependent. As the reported results demonstrate, it requires less messages to be exchanged and it is faster to achieve a globally acceptable near-optimal solution than other available approaches.

Analysis of the domain of applicability of an algorithm for a resources system selection problem for Distributed/Agile/Virtual Enterprises integration

The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Location model for CCA-treated - wood waste remediation units

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Road drivers performance appraisal using a MCDA method: a case study in the road transport company

Performance evaluation increasingly assumes a more important role in any organizational environment. In the transport area, the drivers are the company’s image and for this reason it is important to develop and increase their performance and commitment to the company goals. This evaluation can be used to motivate driver to improve their performance and to discover training needs. This work aims to create a performance appraisal evaluation model of the drivers based on the multi-criteria decision aid methodology. The MMASSI (Multicriteria Methodology to Support Selection of Information Systems) methodology was adapted by using a template supporting the evaluation according to the freight transportation company in study. The evaluation process involved all drivers (collaborators being evaluated), their supervisors and the company management. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used.