Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

ORGANIZATION: COMPARISON MAMDANI FUZZY LOGIC vs SUGENO

Developing world-wide economy in an extremely fast rate of economic crisis followed by analysis required orientation at both the macro and micro economic indicators are interdependent in three-dimensional space by viewing reports and unified and combinations of input data to each other. These analyzes currently use concept (concept) of fuzzy logic to describe how close to reality phenomena or processes that are highly unstable. So many factors input is influenced by feedback organizations

Alternative approaches to evaluation in empirical microeconomics

This paper reviews a range of the most popular policy evaluation methods in empirical microeconomics: social experiments, natural experiments, matching methods, instrumental variables, discontinuity design and control functions. It discusses the identification of both the traditionally used average parameters and more complex distributional parameters. In each case, the necessary assumptions and the data requirements are considered. The adequacy of each approach is discussed drawing on the empirical evidence from the education and labor market policy evaluation literature. We also develop an education evaluation model which we use to carry through the discussion of each alternative approach. A full set of STATA datasets are provided free online which contain Monte-Carlo replications of the various specifications of the education evaluation model. There are also a full set of STATA .do files for each of the estimation approaches described in the paper. The .do-files can be used together with the datasets to reproduce all the results in the paper.

Alternative Approaches to Evaluation in Empirical Microeconomics

This paper reviews some of the most popular policy evaluation methods in empirical microeconomics: social experiments, natural experiments, matching, instrumental variables, discontinuity design, and control functions. It discusses identification of traditionally used average parameters and more complex distributional parameters. The adequacy, assumptions, and data requirements of each approach are discussed drawing on empirical evidence from the education and employment policy evaluation literature. A workhorse simulation model of education returns is used throughout the paper to discuss and illustrate each approach. The full set of STATA datasets and do-files are available free online and can be used to reproduce all estimation and simulation results.evaluation methods

Decision Support System for Managing Reverse Supply Chain

Reverse logistics are becoming more and more important in the overall Industry area because of the environment and business factors. Planning and implementing a suitable reverse logistics network could bring more profit, customer satisfaction, and an excellent social picture for companies. But, most of the logistics networks are not equipped to handle the return products in reverse channels. Reverse logistics processes and plans rely heavily on reversing the supply chain so that companies can correctly identify and categorize returned products for disposition, an area that offers many opportunities for additional revenue. The science of reverse logistics includes return policy administration, product recall protocols, repairs processing, product repackaging, parts management, recycling, product disposition management, maximizing liquidation values and much more. The focus of this project is to develop a reverse logistics management system/ tools (RLMS). The proposed tools are demonstrated in the following order. First, we identify the risks involved in the reverse supply chain. Survey tool is used to collect data and information required for analysis. The methodologies that are used to identify key risks are the six sigma tools, namely Define, Measure, Analyse, Improve and Control (DMAIC), SWOT analysis, cause and effect, and Risk Mapping. An improved decision-making method using fuzzy set theory for converting linguistic data into numeric risk ratings has been attempted. In this study, the concept of ‘Left and Right dominance approach’(Chen and Liu, 2001) and Method of ‘In center of centroids’ (Thoran et al., 2012a,b) for generalized trapezoidal fuzzy numbers has been used to quantify the ‘degree of risk’ in terms of crisp ratings. After the analysis, the key risks are identified are categorized, and an action requirement plan suggested for providing guidelines for the managers to manage the risk successfully in the context of reverse logistics. Next, from risk assessment findings, information technology risk presents the highest risk impact on the performance of the reverse logistics, especially lack of use of a decision support system (DSS). We propose a novel multi-attribute decision (MADM) support tool that can categorizes return products and make the best alternative selection of recovery and disposal option using carefully considered criteria using MADM decision making methodologies such as fuzzy MOORA and VIKOR. The project can be applied to all types of industries. Once the returned products are collected and categorized at the retailers/ Points of return (PoR), an optimized network is required to determine the number of reprocessing centres to be opened and the optimized optimum material flow between retailers, reprocessing, recycling and disposal centers at minimum costs. The research develops a mixed integer linear programming model for two scenarios, namely considering direct shipping from retailer/ PoR to the respective reprocessing centers and considering the use of centralized return centers (CRC). The models are solved using LINGO 15 software and excel solver tools respectively. The advantage of the implementation of our solution is that it will help improve performance and reduce time. This benefits the company by having a reduction in their cost due to uncertainties and also contributes to better customer satisfaction. Implementation of these tools at ABZ computer distributing company demonstrates how the reverse logistics management tools can used in order to be beneficial to the organization. The tool is designed to be easily implemented at minimal cost and serves as a valuable tool for personnel faced with significant and costly decisions regarding risk assessment, decision making and network optimization in the reverse supply chain practices

Alternative Approaches to Evaluation in Empirical Microeconomics

This paper reviews a range of the most popular policy evaluation methods in empirical microeconomics: social experiments, natural experiments, matching methods, instrumental variables, discontinuity design and control functions. It discusses the identification of both the traditionally used average parameters and more complex distributional parameters. In each case, the necessary assumptions and the data requirements are considered. The adequacy of each approach is discussed drawing on the empirical evidence from the education and labor market policy evaluation literature. We also develop an education evaluation model which we use to carry through the discussion of each alternative approach. A full set of STATA datasets are provided free online which contain Monte-Carlo replications of the various specifications of the education evaluation model. There are also a full set of STATA .do files for each of the estimation approaches described in the paper. The .do-files can be used together with the datasets to reproduce all the results in the paper.Evaluation methods, policy evaluation, matching methods, instrumental variables, social experiments, natural experiments, difference-in-differences, discontinuity design, control function.

Satellite image segmentation using RVM and Fuzzy clustering

Image segmentation is common but still very challenging problem in the area of image processing but it has its application in many industries and medical field for example target tracking, object recognition and medical image processing. The task of image segmentation is to divide image into number of meaningful pieces on the basis of features of image such as color, texture. In this thesis some recently developed fuzzy clustering algorithms as well as supervised learning classifier Relevance Vector Machine has been used to get improved solution. First of all various fuzzy clustering algorithms such as FCM, DeFCM are used to produce different clustering solutions and then we improve each solution by again classifying remaining pixels of satellite image using Relevance Vector Machine (RVM classifier. Result of different supervised learning classifier such as Support Vector Machine (SVM), Relevance Vector Machine (RVM), K-nearest neighbors (KNN) has been compared on basis of error rate and time. One of the major drawback of any clustering algorithm is their input argument that is number of clusters in unlabelled data. In this thesis an attempt has been made to evaluate optimal number of clusters present in satellite image using DAVIES-BOULDIN Index