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Alvarenga, T. C., De Lima, R. R., Simao, S. D., Junior, L. C. B., Bueno, J. S. D., Alvarenga, R. R., et al. (2022). Ensemble of hybrid Bayesian networks for predicting the AMEn of broiler feedstuffs. Comput. Electron. Agric., 198, 107067.
Abstract: To adequately meet the nutritional needs of broilers, it is necessary to know the values of apparent metabolizable energy corrected by the nitrogen balance (AMEn) of the feedstuffs. To determine AMEn values, biological assays, feedstuff composition tables, or prediction equations are used as a function of the chemical composition of feedstuffs, the latter using statistical methodologies such as multiple linear regression, neural networks, and Bayesian networks (BN). BN is a statistical and computational methodology that consists of graphical (graph) and probabilistic models of quantitative and/or qualitative variables. Ensembles of BN in the area of broiler nutrition are expected, as there is no research showing their AMEn prediction performance. The purpose of this article is to propose and use ensembles of hybrid Bayesian networks (EHBNs) and obtain prediction equations for the AMEn of feedstuffs used in broiler nutrition from their chemical compositions. We trained 100, 1,000, and 10,000 EHBN, and in this way, empirical distributions were found for the coefficients of the covariates (crude protein, ether extract, mineral matter, and crude fiber). Thus, the mean, median, and mode of these distributions were calculated to build prediction equations for AMEn. It is observed that the method for obtaining the coefficients of the covariates discussed in this article is an unprecedented proposal in the field of broiler nutrition. The data used to obtain the equations were obtained by meta-analysis, and the data for the validation of the equations were obtained from metabolic tests. The proposed equations were evaluated by precision measures such as the mean square error (MSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The best equations for predicting AMEn were derived from the mean or mode coefficients for the 10,000 EHBN results. In conclusion, the methodology used is a good tool to obtain prediction equations for AMEn as a function of the chemical composition of their feedstuffs. The coefficients were found to differ from those found by other methodologies, such as the usual neural network or multiple linear regressions. The field of broiler nutrition contributed with new equations and with a never-applied methodology and differentiated in obtaining its coefficients by empirical distributions.
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Alvarez-Miranda, E., & Pereira, J. (2022). A Districting Application with a Quality of Service Objective. Mathematics, 10(1), 13.
Abstract: E-commerce sales have led to a considerable increase in the demand for last-mile delivery companies, revealing several problems in their logistics processes. Among these problems, are not meeting delivery deadlines. For example, in Chile, the national consumer service (SERNAC) indicated that in 2018, late deliveries represented 23% of complaints in retail online sales and were the second most common reason for complaints. Some of the causes are incorrectly designed delivery zones because in many cases, these delivery zones do not account for the demographic growth of cities. The result is an imbalanced workload between different zones, which leads to some resources being idle while others fail to meet their workload in satisfactory conditions. The present work proposes a hybrid method for designing delivery zones with an objective based on improving the quality of express delivery services. The proposed method combines a preprocess based on the grouping of demand in areas according to the structure of the territory, a heuristic that generates multiple candidates for the distribution zones, and a mathematical model that combines the different distribution zones generated to obtain a final territorial design. To verify the applicability of the proposed method, a case study is considered based on the real situation of a Chilean courier company with low service fulfillment in its express deliveries. The results obtained from the computational experiments show the applicability of the method, highlighting the validity of the aggregation procedure and improvements in the results obtained using the hybrid method compared to the initial heuristic. The final solution improves the ability to meet the conditions associated with express deliveries, compared with the current situation, by 12 percentage points. The results also allow an indicative sample of the critical service factors of a company to be obtained, identifying the effects of possible changes in demand or service conditions.
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Alvarez-Miranda, E., Pereira, J., Vargas, C., & Vila, M. (2022). Variable-depth local search heuristic for assembly line balancing problems. Int. J. Prod. Res., Early Access.
Abstract: Assembly lines are production flow systems wherein activities are organised around a line consisting of various workstations through which the product flows. At each station, the product is assembled through a subset of operations. The assembly line balancing problem (ALBP) consists of allocating operations between stations to maximise the system efficiency. In this study, a variable-depth local search algorithm is proposed for solving simple assembly line balancing problems (SALBPs), which are the most widely studied versions of the ALBP. Although the state-of-the-art techniques for solving the SALBP consist of exact enumeration-based methods or heuristics, this paper proposes a local search-based heuristic using variable-length sequences that allow the solution space to be efficiently explored. The proposed algorithm improves the best solution known for multiple instances reported in the literature, indicating that its efficiency is comparable to those of the state-of-the-art method for solving the SALBP. Moreover, the characteristics of the instances for which the proposed procedure provides a better solution than previously reported construction procedures are investigated.
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Aranis, A., de la Cruz, R., Montenegro, C., Ramirez, M., Caballero, L., Gomez, A., et al. (2022). Meta-Estimation of Araucanian Herring, Strangomera bentincki (Norman, 1936), Biological Indicators in the Central-South Zone of Chile (32 degrees-47 degrees LS). Front. Mar. Sci., 9, 886321.
Abstract: Araucanian herring, Strangomera bentincki, is ecologically and economically important. Its complexity, like that of other pelagic fish, arises from seasonal population changes related to distribution with different spatial dynamics and demographic fractions, subject to strong environmental and fishing exploitation variations. This implies the necessity for a thorough understanding of biological processes, which are interpreted with the help of various activities, and directly or indirectly allow to infer and deliver adequate indicators. These activities facilitate a correct technical analysis and consistent conclusions for resource management and administration. In this context, the present study identified and addressed the need to integrate information on Araucanian herring lengths made available in historical series from commercial fleet fishing and sources such as special monitoring, hydroacoustic cruises, and monitoring during closed seasons. The study focused on methodologies widely used in biostatistics that allow analyzing the feasibility of integrating data from different origins, focused on evaluating the correct management of size structures that vary by origin, sample size, and volumes extracted. We call this tool meta-estimation. It estimates the integration of biological-fishery size indicators that originated mainly from commercial fishing and research fisheries for central-south pelagic fishery with data of catch between January and July 2018.
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Arbelaez, H., Hernandez, R., & Sierra, W. (2022). Lower and upper order of harmonic mappings. J. Math. Anal. Appl., 507(2), 125837.
Abstract: In this paper, we define both the upper and lower order of a sense-preserving harmonic mapping in D. We generalize to the harmonic case some known results about holomorphic functions with positive lower order and we show some consequences of a function having finite upper order. In addition, we improve a related result in the case when there is equality in a known distortion theorem for harmonic mappings with finite upper order. Some examples are provided to illustrate the developed theory. (C) 2021 Elsevier Inc. All rights reserved.
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Armstrong, M., Valencia, J., Lagos, G., & Emery, X. (2022). Constructing Branching Trees of Geostatistical Simulations. Math. Geosci., Early Access.
Abstract: This paper proposes the use of multi-stage stochastic programming with recourse for optimised strategic open-pit mine planning. The key innovations are, firstly, that a branching tree of geostatistical simulations is developed to take account of uncertainty in ore grades, and secondly, scenario reduction techniques are applied to keep the trees to a manageable size. Our example shows that different mine plans would be optimal for the downside case when the deposit turns out to be of lower grade than expected compared to when it is of higher grade than expected. Our approach further provides th
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Asenjo, F. A., & Hojman, S. A. (2022). Light-like propagation of self-interacting Klein-Gordon fields in cosmology. Eur. Phys. J. Plus., 137(1), 20.
Abstract: It is showed that complex scalar fields with a self-interaction potential may propagate along null geodesics on isotropic flat Friedmann-Lemaitre-Robertson-Walker universes with different time-dependent scale factors. This effect appears for certain kinds of self-interactions only, for different forms of potentials, and even for the massive case.
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Ayala, A., Claeys, X., Escapil-Inchauspé, P., & Jerez-Hanckes, C. (2022). Local Multiple Traces Formulation for electromagnetics: Stability and preconditioning for smooth geometries. J. Comput. Appl. Math., Early Access.
Abstract: We consider the time-harmonic electromagnetic transmission problem for the unit sphere. Appealing to a vector spherical harmonics analysis, we prove the first stability result of the local multiple traces formulation (MTF) for electromagnetics, originally introduced by Hiptmair and Jerez-Hanckes (2012) for the acoustic case, paving the way towards an extension to general piecewise homogeneous scatterers. Moreover, we investigate preconditioning techniques for the local MTF scheme and study the accumulation points of induced operators. In particular, we propose a novel second-order inverse approximation of the operator. Numerical experiments validate our claims and confirm the relevance of the preconditioning strategies.
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Ayala, F., Saez, E., & Magna-Verdugo, C. (2022). Computational modelling of dynamic soil-structure interaction in shear wall buildings with basements in medium stiffness sandy soils using a subdomain spectral element approach calibrated by micro-vibrations. Eng. Struct., 252, 113668.
Abstract: This paper presents a strategy for modelling dynamic soil-structure interaction (DSSI) using the spectral element method (SEM) with a Discontinuous Galerkin approach, calibrated by micro-vibrations. The proposed methodology allows not only to adjust the vibration frequencies of the structure but also the observed vibration modes. First, models of two structural shear wall buildings with basements in medium dense sandy soils are developed to estimate empirical modal characteristics and calibrate the structural subdomain and low-strain site properties. Convenient 3D arrays of multiple seismic sensors are used to obtain the environmental vibrations measurements. Afterwards, an optimization process is conducted to calibrate volumetric models of structures. This optimization is performed by preserving the most relevant modal frequencies and shapes to achieve an equivalent dynamic response. Finally, structural models are placed into a neighbouring soil model (soil subdomain), approximating nonlinear soil behaviour by an equivalent linear strategy. Using this complete soil-structure interaction model, relevant engineering performance parameters are assessed via simulations of buildings subjected to a plane wave excitation. The results show the significant effect DSSI have in shear-wall buildings with basements and the importance of considering the flexibility of the foundation in the interpretation of the results. In general, results indicate that DSSI effects are strongly dependent on the input frequency content, which might cause a reduction of the inter-story drifts. Furthermore, a significant period lengthening of the studied structures up to 47% is found, as well as a considerable decrease in story shear up to 220% and a maximum lateral roof displacement reduction of 34% when compared against fixed base referential responses.
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Azar, M., Carrasco, R. A., & Mondschein, S. (2022). Dealing with Uncertain Surgery Times in Operating Room Scheduling. Eur. J. Oper. Res., 299(1), 377–394.
Abstract: The operating theater is one of the most expensive units in the hospital, representing up to 40% of the total expenses. Because of its importance, the operating room scheduling problem has been addressed from many different perspectives since the early 1960s. One of the main difficulties that
has reduced the applicability of the current results is the high variability in surgery duration, making schedule recommendations hard to implement.
In this work, we propose a time-indexed scheduling formulation to solve the operational problem. Our main contribution is that we propose the use of chance constraints related to the surgery duration's probability distribution for each surgeon to improve the scheduling performance. We show how to implement these chance constraints as linear ones in our time-indexed formulation, enhancing the performance of the resulting schedules significantly.
Through data analysis of real historical instances, we develop specific constraints that improve the schedule, reducing the need for overtime without affecting the utilization significantly. Furthermore, these constraints give the operating room manager the possibility of balancing overtime and utilization through a tunning parameter in our formulation. Finally, through simulations and the use of real instances, we report the performance for four different metrics, showing the importance of using historical data to get the right balance between the utilization and overtime.
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Balbontin, C., Hensher, D. A., & Beck, M. J. (2022). Advanced modelling of commuter choice model and work from home during COVID-19 restrictions in Australia. Transp. Res. E-Logist. Transp. Rev., 162, 102718.
Abstract: The decision to work from home (WFH) or to commute during COVID-19 is having a major structural impact on individuals' travel, work and lifestyle. There are many possible factors influencing this non-marginal change, some of which are captured by objective variables while others are best represented by a number of underlying latent traits captured by attitudes towards WFH and the use of specific modes of transport for the commute that have a bio-security risk such as public transport (PT). We develop and implement a hybrid choice model to investigate the sources of influence, accounting for the endogenous nature of latent soft variables for workers in metropolitan areas in New South Wales and Queensland. The data was collected between September-October 2020, during a period of no lockdown and relatively minor restrictions on workplaces and public gatherings. The results show that one of the most important attributes defining the WFH loving attitude is the workplace policy towards WFH, with workers that can decide where to work having a higher probability of WFH, followed by those that are being directed to, relative to other workplace policies. The bio-security concern with using shared modes such as public transport is a key driver of WFH and choosing to commute via the safer environment of the private car.
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Barrera, J., Moreno, E., & Munoz, G. (2022). Convex envelopes for ray-concave functions. Optim. Let., to appear.
Abstract: Convexification based on convex envelopes is ubiquitous in the non-linear optimization literature. Thanks to considerable efforts of the optimization community for decades, we are able to compute the convex envelopes of a considerable number of functions that appear in practice, and thus obtain tight and tractable approximations to challenging problems. We contribute to this line of work by considering a family of functions that, to the best of our knowledge, has not been considered before in the literature. We call this family ray-concave functions. We show sufficient conditions that allow us to easily compute closed-form expressions for the convex envelope of ray-concave functions over arbitrary polytopes. With these tools, we are able to provide new perspectives to previously known convex envelopes and derive a previously unknown convex envelope for a function that arises in probability contexts.
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Barrera, J., Moreno, E., Munoz, G., & Romero, P. (2022). Exact reliability optimization for series-parallel graphs using convex envelopes. Networks, to appear.
Abstract: Given its wide spectrum of applications, the classical problem of all-terminal network reliability evaluation remains a highly relevant problem in network design. The associated optimization problem-to find a network with the best possible reliability under multiple constraints-presents an even more complex challenge, which has been addressed in the scientific literature but usually under strong assumptions over failures probabilities and/or the network topology. In this work, we propose a novel reliability optimization framework for network design with failures probabilities that are independent but not necessarily identical. We leverage the linear-time evaluation procedure for network reliability in the series-parallel graphs of Satyanarayana and Wood (1985) to formulate the reliability optimization problem as a mixed-integer nonlinear optimization problem. To solve this nonconvex problem, we use classical convex envelopes of bilinear functions, introduce custom cutting planes, and propose a new family of convex envelopes for expressions that appear in the evaluation of network reliability. Furthermore, we exploit the refinements produced by spatial branch-and-bound to locally strengthen our convex relaxations. Our experiments show that, using our framework, one can efficiently obtain optimal solutions in challenging instances of this problem.
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Beck, A. T., Ribeiro, L. D., Valdebenito, M., & Jensen, H. (2022). Risk-Based Design of Regular Plane Frames Subject to Damage by Abnormal Events: A Conceptual Study.148(1), 04021229.
Abstract: Constructed facilities should be robust with respect to the loss of load-bearing elements due to abnormal events. Yet, strengthening structures to withstand such damage has a significant impact on construction costs. Strengthening costs should be justified by the threat and should result in smaller expected costs of progressive collapse. In regular frame structures, beams and columns compete for the strengthening budget. In this paper, we present a risk-based formulation to address the optimal design of regular plane frames under element loss conditions. We address the threat probabilities for which strengthening has better cost-benefit than usual design, for different frame configurations, and study the impacts of strengthening extent and cost. The risk-based optimization reveals optimum points of compromise between competing failure modes: local bending of beams, local crushing of columns, and global pancake collapse, for frames of different aspect ratios. The conceptual study is based on a simple analytical model for progressive collapse, but it provides relevant insight for the design and strengthening of real structures.
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Bernales, A., Reus, L., & Valdenegro, V. (2022). Speculative bubbles under supply constraints, background risk and investment fraud in the art market. J. Corp. Financ., Early Access.
Abstract: We examine the unexplored effects on art markets of artist death (asset supply constraints), collectors' wealth (background risk) and forgery risk (risk of investment fraud), under short-sale constraints and risk aversion. Speculative bubbles emerge and have the form of an option strangle (a put option and a call option), in which strike prices are affected by art supply constraints and the association of the artworks' emotional value with both collectors' wealth and forgery, while the options' underlying asset is the stochastic heterogeneous beliefs of agents. We show that speculative bubbles increase with four elements: art supply constraints; a more negative correlation between collectors' wealth and the artworks' emotional value; a more positive relationship between forgery and the artworks' emotional value; and more heterogeneous beliefs. These four sources of speculation increase the expected turnover rate; however, they also augment the variance of speculative bubbles, which generates price discounts (i.e. risk premiums) for holding artworks. Consequently, the net impact of speculation is not necessarily increased art prices. This study not only contributes to the art market literature, but also to studies about speculative bubbles in other financial markets under heterogeneous beliefs, short-sale constraints and risk-averse investors, since we additionally consider the simultaneous effect of asset supply constraints, investors' background risk and the risk of investment fraud.
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Bertossi, L. (2022). Declarative Approaches to Counterfactual Explanations for Classification. Theory Pract. Log. Program., Early Access.
Abstract: We propose answer-set programs that specify and compute counterfactual interventions on entities that are input on a classification model. In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely responsibility scores. The approach and the programs can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus of this study is on the specification and computation of best counterfactual entities, that is, those that lead to maximum responsibility scores. From them one can read off the explanations as maximum responsibility feature values in the original entity. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints. Several examples of programs written in the syntax of the DLV ASP-solver, and run with it, are shown.
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Bevilacqua, M., Caamano-Carrillo, C., Arellano-Valle, R. B., & Gomez, C. (2022). A class of random fields with two-piece marginal distributions for modeling point-referenced data with spatial outliers. Test, Early Access.
Abstract: In this paper, we propose a new class of non-Gaussian random fields named two-piece random fields. The proposed class allows to generate random fields that have flexible marginal distributions, possibly skewed and/or heavy-tailed and, as a consequence, has a wide range of applications. We study the second-order properties of this class and provide analytical expressions for the bivariate distribution and the associated correlation functions. We exemplify our general construction by studying two examples: two-piece Gaussian and two-piece Tukey-h random fields. An interesting feature of the proposed class is that it offers a specific type of dependence that can be useful when modeling data displaying spatial outliers, a property that has been somewhat ignored from modeling viewpoint in the literature for spatial point referenced data. Since the likelihood function involves analytically intractable integrals, we adopt the weighted pairwise likelihood as a method of estimation. The effectiveness of our methodology is illustrated with simulation experiments as well as with the analysis of a georeferenced dataset of mean temperatures in Middle East.
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Bevilacqua, M., Camano-Carrillo, C., & Porcu, E. (2022). Unifying compactly supported and Matern covariance functions in spatial statistics. J. Multivar. Anal., 189, 104949.
Abstract: The Matern family of covariance functions has played a central role in spatial statistics for decades, being a flexible parametric class with one parameter determining the smoothness of the paths of the underlying spatial field. This paper proposes a family of spatial covariance functions, which stems from a reparameterization of the generalized Wendland family. As for the Matern case, the proposed family allows for a continuous parameterization of the smoothness of the underlying Gaussian random field, being additionally compactly supported.
More importantly, we show that the proposed covariance family generalizes the Matern model which is attained as a special limit case. This implies that the (reparametrized) Generalized Wendland model is more flexible than the Matern model with an extra-parameter that allows for switching from compactly to globally supported covariance functions.
Our numerical experiments elucidate the speed of convergence of the proposed model to the Matern model. We also inspect the asymptotic distribution of the maximum likelihood method when estimating the parameters of the proposed covariance models under both increasing and fixed domain asymptotics. The effectiveness of our proposal is illustrated by analyzing a georeferenced dataset of mean temperatures over a region of French, and performing a re-analysis of a large spatial point referenced dataset of yearly total precipitation anomalies.
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Bitar, N., Goles, E., & Montealegre, P. (2022). COMPUTATIONAL COMPLEXITY OF BIASED DIFFUSION-LIMITED AGGREGATION. SIAM Discret. Math., 36(1), 823–866.
Abstract: Diffusion-Limited Aggregation (DLA) is a cluster-growth model that consists in a set of particles that are sequentially aggregated over a two-dimensional grid. In this paper, we introduce a biased version of the DLA model, in which particles are limited to move in a subset of possible directions. We denote by k-DLA the model where the particles move only in k possible directions. We study the biased DLA model from the perspective of Computational Complexity, defining two decision problems The first problem is Prediction, whose input is a site of the grid c and a sequence S of walks, representing the trajectories of a set of particles. The question is whether a particle stops at site c when sequence S is realized. The second problem is Realization, where the input is a set of positions of the grid, P. The question is whether there exists a sequence S that realizes P, i.e. all particles of S exactly occupy the positions in P. Our aim is to classify the Prediciton and Realization problems for the different versions of DLA. We first show that Prediction is P-Complete for 2-DLA (thus for 3-DLA). Later, we show that Prediction can be solved much more efficiently for 1-DLA. In fact, we show that in that case the problem is NL-Complete. With respect to Realization, we show that restricted to 2-DLA the problem is in P, while in the 1-DLA case, the problem is in L.
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Bravo, V., Hernandez, R., Ponnusamy, S., & Venegas, O. (2022). Pre-Schwarzian and Schwarzian derivatives of logharmonic mappings. Monatsh. fur Math., Early Access.
Abstract: We introduce definitions of pre-Schwarzian and Schwarzian derivatives for logharmonic mappings, and basic properties such as the chain rule, multiplicative invariance and affine invariance are proved for these operators. It is shown that the pre-Schwarzian is stable only with respect to rotations of the identity. A characterization is given for the case when the pre-Schwarzian derivative is holomorphic.
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