Articles publicats (Matemàtica)

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    Open Access
    Note on the product of the largest and the smallest eigenvalue of a graph
    (De Gruyter, 2024-06) Abiad, Aida; Dalfó, Cristina; Fiol Mora, Miguel Ángel
    In this note, we use eigenvalue interlacing to derive an inequality between a graph’s maximum degree and its maximum and minimum adjacency eigenvalues. The equality case is fully characterized.
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    Open Access
    On large regular (1, 1, k)-mixed graphs
    (Elsevier, 2024-06) Dalfó, Cristina; Erskine, G; Exoo, G; Fiol Mora, Miguel Ángel; López Lorenzo, Ignacio; Massegué Buisan, Arnau; Tuite, J
    An (r,z,k)-mixed graph G has every vertex with undirected degree r, directed in- and out-degree z, and diameter k. In this paper, we study the case r = z = 1, proposing some new constructions of (1,1,k)-mixed graphs with a large number of vertices N. Our study is based on computer techniques for small values of k and the use of graphs on alphabets for general k. In the former case, the constructions are either Cayley or lift graphs. In the latter case, some infinite families of (1,1,k)-mixed graphs are proposed with diameter of the order of 2log2 N.
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    Open Access
    Existence and nonexistence of Puiseux inverse integrating factors in analytic monodromic singularities
    (Wiley, 2024) García, I. A. (Isaac A.); Giné, Jaume; Rodero, Ana Livia
    In this work, we present some criteria about the existence and nonexistence of both Puiseux inverse integrating factors 𝑉 and Puiseux first integrals 𝐻 for planar analytic vector fields having a monodromic singularity. These functions are a wide generalization of their formal ℝ[[𝑥, 𝑦]] or algebraic counterpart in Cartesian coordinates(𝑥, 𝑦). We prove that none of the functions 𝐻 and𝑉 can be used to characterize degenerate centers although the existence of 𝐻 is a sufficient center condition.
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    Open Access
    Collecting and delivering fattened pigs to the abattoir
    (MDPI, 2024) Pla Aragonés, Lluís Miquel; Bao, Yun; Llagostera Blasco, Pol; Juan, Ángel; Panadero, Javier
    In the context of pig farming, this paper addresses the optimization problem of collecting fattened pigs from farms to deliver them to the abattoir. Assuming that the pig sector is organized as a competitive supply chain with narrow profit margins, our aim is to apply analytics to cope with the uncertainty in production costs and revenues. Motivated by a real-life case, the paper analyzes a rich Team Orienteering Problem (TOP) with a homogeneous fleet, stochastic demands, and maximum workload. After describing the problem and reviewing the related literature, we introduce the PJS heuristic. Our approach is first compared with exact methods, which are revealed as computationally unfeasible. Later, a scenario analysis based on a real instance was performed to gain insight into the practical aspects. Our findings demonstrate a positive correlation between the number of alternative routes explored, the number of trips, the transportation cost, and the maximum reward. Regarding the variability in the number of pigs to collect, when a truck can visit more than one farm, better solutions can be found with higher variability since the load can be combined more efficiently.
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    Open Access
    Is Deep Learning useful for decision making in pig production?
    (Elsevier, 2024) Bao, Yun; Llagostera Blasco, Pol; Pla Aragonés, Lluís Miquel
    Numerous recent papers based on deep learning (DL) have been published covering a wide range of applications to pig production. These applications provide information susceptible of being used to make better decisions. However, the potential use as tools for supporting pig production decisions or the integration in existing or new decision models have not been explored yet. The goal of this systematic literature review (SLR) is to provide an overview of recent developments in cutting-edge DL methodologies proposed in pig production and how they can serve to improve decision making processes. The revised papers are analyzed under different dimensions: (1) authors and research institutions that have made the biggest contributions to DL for image processing, computer vision and other innovative applications in pig farms; (2) coverage of the echelons in the pig supply chain (3) technical aspects like data collection techniques, DL models, DL backbones, graphics processing units (GPUs), and evaluation metrics and (4) value of information. The review is briefly extended to DL applications in other livestock species not yet present in pig production to enrich the discussion. The revised applications suggest that DL is mostly applied to automatize data gathering and processing and to monitor animals or on farm activities. The current challenges and future research agenda are also identified envisioning the integration of DL and operational research(OR) methods as a way to produce more efficient decision-making support tools for the pig industry.