Development of methods based on voltammetry for the characterisation of liquids

The growing interest into multi-sensor systems able to determine general attributes of a process under monitoring, has recently involved the qualitative analysis of liquids; various methodologies to develop taste sensors, often referred to as “e-tongues” have been presented in the literature. The common concept in the different approaches, lies in the combination of signals originated by poorly specific sensors for the characterization of liquids. The fundamental idea of this PhD work is to investigate how an adequate signal processing approach, applied to a mature and affordable sensor technique (voltammetry), can address the issue of extracting an aggregate chemical information, useful to characterize the liquid under measurement. In this Thesis, a general description of electronic taste sensor systems is given, followed by a description of the working principles of e-tongues based on voltammetry. Then, the methodology that represents the core of this PhD Thesis work is introduced: the sensor device, the control software and the data processing approach are described in this sequence. Finally, a few case studies are shown, selected according to their relevancy with respect to the peculiarities of the approach described in this Thesis

Mathematical Programming formulations for the efficient solution of the kk-sum approval voting problem

In this paper we address the problem of electing a committee among a set of $m$ candidates and on the basis of the preferences of a set of $n$ voters. We consider the approval voting method in which each voter can approve as many candidates as she/he likes by expressing a preference profile (boolean $m$-vector). In order to elect a committee, a voting rule must be established to `transform' the $n$ voters' profiles into a winning committee. The problem is widely studied in voting theory; for a variety of voting rules the problem was shown to be computationally difficult and approximation algorithms and heuristic techniques were proposed in the literature. In this paper we follow an Ordered Weighted Averaging approach and study the $k$-sum approval voting (optimization) problem in the general case $1 \leq k <n$. For this problem we provide different mathematical programming formulations that allow us to solve it in an exact solution framework. We provide computational results showing that our approach is efficient for medium-size test problems ($n$ up to 200, $m$ up to 60) since in all tested cases it was able to find the exact optimal solution in very short computational times

Weighted Voronoi Region Algorithms for Political Districting

Automated political districting shares with electronic voting the aim of preventing electoral manipulation and pursuing an impartial electoral mechanism. Political districting can be modelled as multiobjective partitioning of a graph into connected components, where population equality and compactness must hold if a majority voting rule is adopted. This leads to the formulation of combinatorial optimization problems that are extremely hard to solve exactly. We propose a class of heuristics, based on discrete weighted Voronoi regions, for obtaining compact and balanced districts, and discuss some formal properties of these algorithms. Their performance has been tested on randomly generated rectangular grids, as well as on real-life benchmarks; for the latter instances the resulting district maps are compared with the institutional ones adopted in the Italian political elections from 1994 to 2001

Weighted Voronoi Region Algorithms for Political Districting

Automated political districting shares with electronic voting the aim of preventing electoral manipulation and pursuing an impartial electoral mechanism. Political districting can be modelled as multiobjective partitioning of a graph into connected components, where population equality and compactness must hold if a majority voting rule is adopted. This leads to the formulation of combinatorial optimization problems that are extremely hard to solve exactly. We propose a class of heuristics, based on discrete weighted Voronoi regions, for obtaining compact and balanced districts, and discuss some formal properties of these algorithms. Their performance has been tested on randomly generated rectangular grids, as well as on real-life benchmarks; for the latter instances the resulting district maps are compared with the institutional ones adopted in the Italian political elections from 1994 to 2001

Effective monitoring of landfills: flux measurements and thermography enhance efficiency and reduce environmental impact

This work presents a methodology for estimating the behaviour of a landfill system in terms of biogas release to the atmosphere. Despite the various positions towards the impact of methane on global warming, there is a general agreement about the fact that methane from landfill represents about 23% of the total anthropogenic CH4 released to the atmosphere. Despite the importance of this topic, no internationally accepted protocol exists to quantify the leakage of biogas from the landfill cover. To achieve this goal, this paper presents a field method based on accumulation chamber flux measurements. In addition, the results obtained from a nine-year-long monitoring activity on an Italian municipal solid waste (MSW) landfill are presented. The connection between such flux measurements of biogas release and thermal anomalies detected by infrared radiometry is also discussed. The main overall benefit of the presented approach is a significant increase in the recovered energy from the landfill site by means of an optimal collection of biogas, which implies a reduction of the total anthropogenic methane originated from the disposal of waste

The continuous and discrete path variance problem on trees

In this paper we consider the problem of locating path-shaped facilities on a tree minimizing the variance objective function. This kind of objective function is generally adopted in location problems which arise in the public sector applications, such as the location of evacuation routes or mass transit routes. We consider the general case in which a positive weight is assigned to each vertex of the tree and positive real lengths are associated to the edges. We study both the case in which the path is continuous, that is, the end points of the optimal path can be either vertices or points along an edge, and the case in which the path is discrete, that is, the end points of the optimal path must lie in some vertex of the tree. Given a tree with n vertices, for both these problems we provide algorithms with O(n2) time complexity and we extend our results also to the case in which the length of the path is bounded above. Even in this case we provide polynomial algorithms with the same O(n2) complexity. In particular, our algorithm for the continuous path-variance problem improves upon a log n term the previous best known algorithm for this problem provided in [T. Cáceres, M.C. López-de-los-Mozos, J.A. Mesa (2004). The path-variance problem on tree networks, Discrete Applied Mathematics, 145, 72-79]. Finally, we show that no nestedness property holds for (discrete and continuous) point-variance problem with respect to the corresponding path-variance.Ministerio de Ciencia y TecnologíaAzioni Integrate Italia-Spagna (Ministero dell'istruzione, dell'università e della ricerca

Extensive facility location problems on networks with equity measures

AbstractThis paper deals with the problem of locating path-shaped facilities of unrestricted length on networks. We consider as objective functions measures conceptually related to the variability of the distribution of the distances from the demand points to a facility. We study the following problems: locating a path which minimizes the range, that is, the difference between the maximum and the minimum distance from the vertices of the network to a facility, and locating a path which minimizes a convex combination of the maximum and the minimum distance from the vertices of the network to a facility, also known in decision theory as the Hurwicz criterion. We show that these problems are NP-hard on general networks. For the discrete versions of these problems on trees, we provide a linear time algorithm for each objective function, and we show how our analysis can be extended also to the continuous case

Unreliable point facility location problems on networks

In this paper we study facility location problems on graphs under the most common criteria, such as, median, center and centdian, but we incorporate in the objective function some reliability aspects. Assuming that facilities may become unavailable with a certain probability, the problem consists of locating facilities minimizing the overall or the maximum expected service cost in the long run, or a convex combination of the two. We show that the k-facility problem on general networks is NP-hard. Then, we provide efficient algorithms for these problems for the cases of k = 1, 2, both on general networks and on trees. We also explain how our methodology extends to handle a more general class of unreliable point facility location problems related to the ordered median objective function.Ministerio de Ciencia y TecnologíaJunta de Andalucí

A combinatorial optimization approach to scenario filtering in portfolio selection

Recent studies stressed the fact that covariance matrices computed from empirical financial time series appear to contain a high amount of noise. This makes the classical Markowitz Mean-Variance Optimization model unable to correctly evaluate the performance associated to selected portfolios. Since the Markowitz model is still one of the most used practitioner-oriented tool, several filtering methods have been proposed in the literature to fix the problem. Among them, the two most promising ones refer to the Random Matrix Theory or to the Power Mapping strategy. The basic idea of these methods is to transform the correlation matrix maintaining the Mean-Variance Optimization model. However, experimental analysis shows that these two strategies are not adequately effective when applied to real financial datasets. In this paper we propose an alternative filtering method based on Combinatorial Optimization. We advance a new Mixed Integer Quadratic Programming model to filter those observations that may influence the performance of a portfolio in the future. We discuss the properties of this new model and we test it on some real financial datasets. We compare the out-of-sample performance of our portfolios with the one of the portfolios provided by the two above mentioned alternative strategies. We show that our method outperforms them. Although our model can be solved efficiently with standard optimization solvers the computational burden increases for large datasets. To overcome this issue we also propose a heuristic procedure that empirically showed to be both efficient and effective