Routine in linguaggio R per il controllo ottimo delle specie invasive

La proprietà di conservazione della positività dei metodi numerici applicati ai sistemi differenziali di tipo ODE e PDE a valori iniziali e/o ai bordi, è un argomento di ricerca di notevole interesse. La positività del flusso numerico è un aspetto fondamentale in numerose applicazioni che vanno dalla biologia computazionale, alla dinamica molecolare, alla modellistica in ambito ecologico, dovunque risulti fondamentale che le grandezze in gioco (popolazioni, densità, concentrazioni) non assumano valori negativi. Tale condizione, fatta eccezione per lo schema di Eulero Implicito, non è verificata dai metodi standard (Runge-Kutta o multistep), a meno di imporre restrizioni sul passo di integrazione talvolta molto significative. Tuttavia, le restrizioni sul passo di integrazione diminuiscono sensibilmente l'efficienza dei metodi numerici a tal punto da renderli di fatto inutilizzabili nelle applicazioni reali. La letteratura più recente si è quindi focalizzata sulla costruzione di integratori numerici che garantiscono la positività del flusso numerico per costruzione. Tra i lavori su questo argomento, citiamo [10, 4] in cui vengono proposte tecniche di splitting and composition per la soluzione di modelli differenziali.

Relazione attività di collaborazione occasionale "Supporto alla gestione progettuale e alla diffusione dei risultati nell'ambito del programma POR-FESR LAZIO 2014-2020, con particolare riferimento ai progetti CLINAIR di Life 2020 e COURIER di Aerospazio e Sicurezza"; elenco attività svolte e analisi risultati della collaborazione. Periodo attività 13/03/2018 - 12/06/2018.

Rapporto tecnico-scientifico del convegno "Mathematics for BioMedicine" dell'Istituto per le Applicazioni del Calcolo M. Picone del CNR, svolto a Roma in data 8-11 ottobre 2018, co-organizzato da Istituto per le Applicazioni del Calcolo M. Picone e Accademia Nazionale dei Lincei e finanziato da INdAM (Istituto Nazionale di Alta Matematica). Topics: immunology, cardiovascular disease, Neurology and aging desease, oncology, epidemiology, endocrinology, stem cells and tissue regeneration. Il report raccoglie obiettivi, abstract, agenda, dati sui partecipanti.

Topic Modelling (TM) is a widely adopted generative model used to infer the thematic organization of text corpora. When document-level covariate information is available, so-called Structural Topic Modelling (STM) is the state-of-the-art approach to embed this information in the topic mining algorithm. Usually, TM algorithms rely on unigrams as the basic text generation unit, whereas the quality and intelligibility of the identified topics would significantly benefit from the detection and usage of topical phrasemes. Following on from previous research, in this paper we propose the first iterative algorithm to extend STM with n-grams, and we test our solution on textual data collected from four well-known ToR drug marketplaces. Significantly, we employ a STM-guided n-gram selection process, so that topic-specific phrasemes can be identified regardless of their global relevance in the corpus. Our experiments show that enriching the dictionary with selected n-grams improves the usability of STM, allowing the discovery of key information hidden in an apparently "mono-thematic" dataset.

Space exploration is revealing the abundance of other solar systems, but at the same time is showing the uniqueness of our Planet. Using sophisticated Earth Observation technologies such as the European "Sentinels", belonging to the greatest Earth Observation programme ever realised, Copernicus, we are now getting plenty of information at unprecedented high spatial and temporal resolution. Novel approaches for blending most advanced technologies with field work and conservation issues aimed at understanding and modelling status and changes of ecosystems are at the heart of ECOPOTENTIAL, a large European H2020 project with 47 partners, running from 2015 to 2019. ECOPOTENTIAL works on 25 protected areas (PAs) in Europe and beyond, spanning all biogeographical regions of Europe and focusing on mountain, arid and semiarid, coastal and marine environments, adopting the view of ecosystems as "one physical system with their environment, characterized by strong interactions between geosphere and biosphere across multiple scales. ECOPOTENTIAL has strong links with other international research programmes, such as GEO ECO, eLTER, GEO BON and LifeWatch. In particular, all data, models and knowledge will be available on common and open platforms through a virtual laboratory contributing to the GEOSS, the Common Infrastructure of the Group on Earth Observation, an international organisation linking more than 100 countries and 100 institutions, aimed to share and make openly available Earth Observation data, and including also a wide programme for building a community of practice through seminars, training, citizen science actions and outreach.

We present a modified version of an existing lake ecosystem model, describing a trophic chain generated by nutrients, phytoplankton and zooplankton (NPZ model). The NPZ model takes into account the vertical dynamics of the biomasses of the main species. We tailor the model to specific ecosystems by including seasonality in the dynamics of the various compartments. Moreover, different species exhibit a different behaviour with respect to diffusion and to the rate of vertical movement. With this model, we simulate the ecosystem dynamics of Alpine lakes located in study sites of the H2020 ECOPOTENTIAL project.

Given an undirected weighted graph, in which each vertex is assigned to a color and one of them is identified as source, in the all-colors shortest path problem we look for a minimum cost shortest path that starts from the source and spans all different colors. The problem is known to be NP-Hard and hard to approximate. In this work we propose a variant of the problem in which the source is unspecified and show the two problems to be computationally equivalent. Furthermore, we propose a mathematical formulation, a compact representation for feasible solutions and a VNS metaheuristic that is based on it. Computational results show the effectiveness of the proposed approach for the two problems.

We consider a scenario in which it is necessary to monitor a geographical region of interest through a network of sensing devices. The region is divided into subregions of regular sizes (zones), such that if a sensor can even partially monitor the zone, the detected information can be considered representative of the entire subregion. The aim is to schedule the sensor active and idle states in order to maximize the lifetime of the network. We take into account two main types of scenarios. In the first one, the whole region is partitioned into zones. In the second one, a predefined number of possibly overlapping zones are randomly placed and oriented inside the region. We discuss how to transform any problem instance into a target coverage one, and solve the problem through a highly competitive column generation-based method.

Presentazione orale al side event - GEO Week 2018 - Sede: Kyoto (JP) - La GEO WEEK è la conferenza scientifica internazionale di GEO che precede il summit annuale dei 200 membri di GEO. Si tiene alternativamente in America, Asia, Europa, Africa e Oceania.

Terrestrial and marine ecosystems provide essential goods and services to human societies. In the last decades, however, anthropogenic pressure has produced a loss of ecosystem services that can seriously affect human wellbeing and climate processes at local and regional scale. In order to improve ecosystem benefits, knowledge-based conservation, management and restoration policies are urgently needed. Fundamental to all these is effective monitoring of the state and trends in ecosystem conditions and services. New monitoring methodologies are now available, combining approaches in geo- and bioscience, earth observation data, and in situ data. This digital poster synthesizes the objectives and methods of the ECOPOTENTIAL project, a European Horizon 2020 project started in June 2015, which has been designed to reach significant progress beyond the state of the art on ecosystem services. The project focuses its activities and pilot actions on a targeted set of internationally recognized protected areas (PA) in Europe, European Territories and beyond, over a broad range of habitats, ecosystems and landscapes, including wetlands, arid and mountain ecosystems. Gli ecosistemi terrestri e marini forniscono beni e servizi essenziali alle società umane. Negli ultimi decenni, tuttavia, la pressione antropica ha prodotto una perdita di servizi ecosistemici che può seriamente influenzare il benessere umano e i processi climatici su scala locale e regionale. Al fine di migliorare i benefici degli ecosistemi, sono urgentemente necessarie politiche di conservazione, gestione e ripristino basate sulla conoscenza. Fondamentale per tutto questo è il monitoraggio efficace dello stato e delle tendenze delle condizioni e dei servizi ecosistemici. Sono ora disponibili nuove metodologie di monitoraggio che combinano approcci in geo- e bioscienze, dati di osservazione della terra e dati in situ. Questo poster digitale sintetizza gli obiettivi e i metodi del progetto ECOPOTENTIAL, un progetto europeo Horizon 2020 iniziato nel giugno 2015, che è stato progettato per raggiungere progressi significativi oltre lo stato dell'arte sui servizi ecosistemici. Il progetto concentra le sue attività e azioni pilota su un insieme mirato di aree protette (PA) riconosciute a livello internazionale in Europa, nei territori europei e oltre, su una vasta gamma di habitat, ecosistemi e paesaggi, comprese le zone umide, gli ecosistemi aridi e montani.

A new technique for nonparametric regression of multichannel signals is presented. The technique is based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. In particular, two different frames are obtained by two RADWT with different Q-factors that give sparse representations of functions with low and high resonance. It is assumed that the signals are measured simultaneously on several independent channels and that they share the low resonance component and the spectral characteristics of the high resonance component. Then, a regression analysis is performed by means of the grouped lasso penalty. Furthermore, a result of asymptotic optimality of the estimator is presented using reasonable assumptions and exploiting recent results on group-lasso like procedures. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case example for the analysis and joint detection of sleep spindles and K-complex events for multiple electroencephalogram (EEG) signals.

We propose a discrete in continuous mathematical model describing the in vitro growth process of biophsy-derived mammalian cardiac progenitor cells growing as clusters in the form of spheres (Cardiospheres). The approach is hybrid: discrete at cellular scale and continuous at molecular level. In the present model cells are subject to the self-organizing collective dynamics mechanism and, additionally, they can proliferate and differentiate, also depending on stochastic processes. The two latter processes are triggered and regulated by chemical signals present in the environment. Numerical simulations show the structure and the development of the clustered progenitors and are in a good agreement with the results obtained from in vitro experiments.

We consider some discrete approximation polynomials, namely discrete de la Vallée Poussin means, which have been recently deduced from certain delayed arithmetic means of the Fourier-Jacobi partial sums, in order to get a near-best approximation in suitable spaces of continuous functions equipped with the weighted uniform norm. By the present paper we aim to analyze the behavior of such discrete de la Vallée means in weighted L1 spaces, where we provide error bounds for several classes of functions, included functions of bounded variation. In all the cases, under simple conditions on the involved Jacobi weights, we get the best approximation order. During our investigations, a weighted L1 Marcinkiewicz type inequality has been also stated.

Longitudinal defects of the internal coated surface of a metal pipe can be evaluated in a fast, precise and cheap way from thermal measurements on the external surface. In this paper, we study two classes of real situations in which the thickness of the coating is much smaller than the thickness of the metal tube: the transportation of potable water and crude oil. A very precise and stable reconstruction of damages is obtained by means of perturbation methods. To do this, first we translate a composite (coating-plus-tube) boundary value problem in a virtual one on the metallic part only. The information about possible damages is now included in the deviations delta h of the effective heat transfer coefficient from a known background value. Finally, we determine delta h by means of Thin Plate Approximation. (C) 2017 Elsevier Ltd. All rights reserved.

The literature on k-splittable flows, see Baier et al. (2002) Baier et al. (2005), provides evidence on how controlling the number of used paths enables practical applications of flows optimization in many real-world contexts. Such a modeling feature has never been integrated so far in Quickest Flows, a class of optimization problems suitable to cope with situations such as emergency evacuations, transportation planning and telecommunication systems, where one aims to minimize the makespan, i.e. the overall time needed to complete all the operations, see Pascoal et al. (2006) Pascoal et al. (2006). In order to bridge this gap, a novel optimization problem, the Quickest Multicommodity k-Splittable Flow Problem (QMCkSFP) is introduced in this paper. The problem seeks to minimize the makespan of transshipment operations for given demands of multiple commodities, while imposing restrictions on the maximum number of paths for each single commodity. The computational complexity of this problem is analyzed, showing its NP-hardness in the strong sense, and an original Mixed-Integer Programming formulation is detailed. We propose a matheuristic algorithm based on a hybridized Very Large-Scale Neighborhood Search that, utilizing the presented mathematical formulation, explores multiple search spaces to solve efficiently large instances of the QMCkSFP. High quality computational results obtained on benchmark test sets are presented and discussed, showing how the proposed matheuristic largely outperforms a state-of-the-art heuristic scheme frequently adopted in path-restricted flow problems.

The petrol station replenishment problem consists in delivering fuel oils from a set of storage depots to a set of petrol stations during a few days planning horizon. This problem is addressed by an oil company which, for example, has to decide simultaneously the weekly fuel oil replenishment plan for each station, and, for each day of the week, the tank truck (vehicle) routes from depots to stations, in order to deliver the planned fuel oil replenishment amounts to petrol stations. Assuming a fleet of homogeneous tank trucks, the aim is to minimize the total distance travelled by tank trucks during the week, while loading tank trucks possibly near to their capacity in order to maximize the resource utilization. We model the problem as a generalization of the Multi-Depot Periodic Vehicle Routing Problem (MDPVRP) and provide a mathematical formulation. Due to the large size of the real instances which the company has to deal with, we solve the problem heuristically. We propose a hybrid genetic algorithm that successfully address the problem. The algorithm is derived from a known hybrid genetic algorithm for the MDPVRP, and adopts additional techniques and features tailored for the particular fuel oil distribution problem. It is specifically designed to deal with real instances derived from the fuel oil distribution in the European context that are profoundly different from the MDPVRP instances available from the literature. The proposed algorithm is evaluated on a set of real case studies and on a set of randomly generated instances that hold the same characteristics of the former.

Several researches in the scientific, industrial and commercial fields are supporting the reduction of traditional combustion cars' use. The main purpose is to increase the quality of life into the metropolitan cities through the reduction of CO2 emissions and global warming. Accordingly, one of the most successful models is the carpooling system. Currently, people are investigating the sustainability and durability of carpooling business model from both economic and organizational point of view. The present research aims to develop a Multicriteria Decision Support System (MDSS) in order to offer a carpooling system's platform based on different criteria. The MDSS is developed from driver's point of view and settled on two levels of optimization. Firstly, a genetic algorithm is proposed to solve an orienteering problem that optimizes the total revenue of driver based on the car's capability and the time schedule. Secondly, the best optimization solutions are compared with multicriteria analysis respect to other criteria not included in the first optimization. The outcome of MDSS is a schedule for drivers, which gives maximum satisfaction in terms of profitability, punctuality and comfort of the travel.

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark - based on the methods already in use - are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances.

A comprehensive approach to Sobolev-type embeddings, involving arbitrary rearrangement-invariant norms on the entire Euclidean space R^n, is offered. In particular, the optimal target space in any such embedding is exhibited. Crucial in our analysis is a new reduction principle for the relevant embeddings, showing their equivalence to a couple of considerably simpler one-dimensional inequalities. Applications to the classes of the Orlicz-Sobolev and the Lorentz-Sobolev spaces are also presented. These contributions fill in a gap in the existing literature, where sharp results in such a general setting are only available for domains of finite measure.