INTEGRATED SOLID WASTE MANAGEMENT: A MULTICRITERIA APPROACH

The paper presents the first results of a long term research aimed at producing a decision support system to deal with the integrated solid waste management planning at regional level. In the last years urban waste management has received a strong attention from the public authority in Italy culminating in a new national law, which has priorities such as waste prevention (waste avoidance and reduction) reuse and recycling. Italian Legislation requires to consider not only a series of waste management options aimed at source reduction but also to integrate the environmental soundness with economical viability and social equity. To support this integrated solid waste management it is necessary to ascertain the environmental, economic and social impacts associated with various waste management options so that decision makers can trade them off to achieve a better waste management strategy. To deal with the problem a three level process is suggested: zoning of the territory, implementation of the waste plan, Environmental Impact Assessment (EIA) on the new facilities. The paper focuses, in a non technical way, on a dynamic mixed integer linear programming model to be used in the second phase of the previous process. A multicriteria approach has been adopted to manage waste as an integrated system of recollection, transportation, recovery and disposal activities. At the moment four objective functions have been defined: total cumulative distance, total discounted net cost, total cumulative impact on traffic due to waste transportation, total cumulative landfilling. The model includes different types of collection, as well as different technologies. The model gives the possibility to locate in the same site more facilities. In this way it is possible to construct waste integrated platforms which permit to reduce costs and impacts. The model chooses the sites to be developed, the types of technology that will be installed on such sites, and the schedule of activity. In accordance with the input concentration for each technology it is possible to specify the appropriate output coefficients. The model computes the yields of the intermediate technologies directly from the model parameters, such parameters are exogenously determined, case by case, on the basis of the technical information; all the yields are automatically recomputed by the model when they vary. In this way high flexibility is introduced into the model. According to the preference of the decision maker specific constraints can be introduced in order to limit the admitted technologies; such restrictions have yearly validity. In this way a good representation of a dynamic situation can be reached. The main aspects that can be studied in space and time are: waste recollection at municipality, destination of each type of waste, technologies operating at facilities, landfilling, material and energy recovering, cost, traffic impact due to waste transportation. The results of a first application referred to the Province of Ravenna, in Emilia Romagna Region, Northern Italy are presented in the final section.Environmental Economics and Policy,

Integrated participatory modelling of irrigated agriculture: the case study of the reorganisation of a water management system in Italy

The paper presents an application of the new version of the 'Decision Support for Irrigated agriculture' DSIrr designed to integrate water and agricultural policy analysis and to support participatory decision process. The tool is a scenario manager for bio-economic farm models considering climatic, agronomic, hydraulic, socio-economic and environmental aspects. The paper offers some insight on the decomposition approach adopted to integrate economic analysis at different scales by illustrating a case study conducted in Italy to support an ex ante evaluation of a water management system reorganisation. Reduce water consumption is a strategic objective which pricing policy cannot address given technical constraints. The replacement of the existing low-efficiency irrigation system could be the solution, but the recover of cost creates an affordability problem. Results suggest that a dual network, integrating agricultural and rural urban sectors, represents a real challenge for the Irrigation Board since this option meets the environmental goal and pass the economic sustainability test.Water, Agriculture, Economic analysis, Modelling and tools, Participatory process, Agricultural and Food Policy, Land Economics/Use, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy,

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to the multi-class, multi-view settings and the multi-class Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is obtained by solving a single quadratic optimization problem, as in standard SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results obtained on the task of object recognition, using several challenging datasets, demonstrate the competitiveness of our algorithms compared with other state-of-the-art methods.Comment: 72 page

Equivalence between spectral properties of graphs with and without loops

In this paper we introduce a spectra preserving relation between graphs with loops and graphs without loops. This relation is achieved in two steps. First, by generalizing spectra results got on (m, k)-stars to a wider class of graphs, the (m, k, s)-stars with or without loops. Second, by defining a covering space of graphs with loops that allows to remove the presence of loops by increasing the graph dimension. The equivalence of the two class of graphs allows to study graph with loops as simple graph without loosing information

AN INTEGRATED TERRITORIAL SIMULATION MODEL TO EVALUATE CAP REFORM ON MEDITERRANEAN AGRICULTURE. METHODOLOGICAL PROPOSAL AND FIRST APPLICATIONS IN APULIA REGION (SOUTHERN ITALY)

The implementation of most recent CAP and water policy reforms calls for simulation analytical tools able to quantify socio-economic and environmental impacts that can be different in terms of regions and farm type. This work proposes a territorial mathematical programming model that integrates hundreds of farm models clustered in a single meta-model at regional level that can be easily standardized having the FADN as the main data source. The tool has been experimentally applied to Apulia region and several simulations have been conducted in scenarios differing in terms of agricultural policies (total decoupling, increase of the modulation rate and introduction of a flat rate system for the Single Farm Payment), price of the water resource, market conditions (price of products and cost of inputs). For each simulation, farmers’ choices - cropping patterns and techniques-, the economic assessment of the effects of such choices -revenue, costs and incomes- and environment impacts -use of factors and resulting pressures on natural resources- have been analysed. The results of the analysis show that agricultural policies measures do not affect land use pattern or the agricultural pressure on water resources. But can have major income redistributive effects. On the contrary, water policy and market conditions impact on farmers’ choices, economic performance and environmental pressure.Agricultural policies, Water policy reforms, territorial mathematical programming model., Agricultural and Food Policy, Political Economy, Research Methods/ Statistical Methods, Q18, Q25, Q51.,

A stochastic model of randomly accelerated walkers for human mobility

The recent availability of large databases allows to study macroscopic properties of many complex systems. However, inferring a model from a fit of empirical data without any knowledge of the dynamics might lead to erroneous interpretations [6]. We illustrate this in the case of human mobility [1-3] and foraging human patterns [4] where empirical long-tailed distributions of jump sizes have been associated to scale-free super-diffusive random walks called L\'evy flights [5]. Here, we introduce a new class of accelerated random walks where the velocity changes due to acceleration kicks at random times, which combined with a peaked distribution of travel times [7], displays a jump length distribution that could easily be misinterpreted as a truncated power law, but that is not governed by large fluctuations. This stochastic model allows us to explain empirical observations about the movements of 780,000 private vehicles in Italy, and more generally, to get a deeper quantitative understanding of human mobility.Comment: 10 pages, 6 figures + Supplementary informatio

Self-taught Object Localization with Deep Networks

This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i.e., without using any ground-truth bounding boxes for training. The key idea is to analyze the change in the recognition scores when artificially masking out different regions of the image. The masking out of a region that includes the object typically causes a significant drop in recognition score. This idea is embedded into an agglomerative clustering technique that generates self-taught localization hypotheses. Our object localization scheme outperforms existing proposal methods in both precision and recall for small number of subwindow proposals (e.g., on ILSVRC-2012 it produces a relative gain of 23.4% over the state-of-the-art for top-1 hypothesis). Furthermore, our experiments show that the annotations automatically-generated by our method can be used to train object detectors yielding recognition results remarkably close to those obtained by training on manually-annotated bounding boxes.Comment: WACV 201

On the multiplicity of Laplacian eigenvalues and Fiedler partitions

In this paper we study two classes of graphs, the (m,k)-stars and l-dependent graphs, investigating the relation between spectrum characteristics and graph structure: conditions on the topology and edge weights are given in order to get values and multiplicities of Laplacian matrix eigenvalues. We prove that a vertex set reduction on graphs with (m,k)-star subgraphs is feasible, keeping the same eigenvalues with reduced multiplicity. Moreover, some useful eigenvectors properties are derived up to a product with a suitable matrix. Finally, we relate these results with Fiedler spectral partitioning of the graph. The physical relevance of the results is shortly discussed