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Allen, N. H., Espinoza, N., Jordan, A., Lopez-Morales, M., Apai, D., Rackham, B. V., et al. (2022). ACCESS: Tentative Detection of H2O in the Ground-based Optical Transmission Spectrum of the Low-density Hot Saturn HATS-5b. Astron. J., 164(4), 153.
Abstract: We present a precise ground-based optical transmission spectrum of the hot Saturn HATS-5b (T (eq) = 1025 K), obtained as part of the ACCESS survey with the IMACS multi-object spectrograph mounted on the Magellan Baade Telescope. Our spectra cover the 0.5-0.9 mu m region and are the product of five individual transits observed between 2014 and 2018. We introduce the usage of additional second-order light in our analyses, which allows us to extract an “extra” transit light curve, improving the overall precision of our combined transit spectrum. We find that the favored atmospheric model for this transmission spectrum is a solar-metallicity atmosphere with subsolar C/O, whose features are dominated by H2O and with a depleted abundance of Na and K. If confirmed, this would point to a “clear” atmosphere at the pressure levels probed by transmission spectroscopy for HATS-5b. Our best-fit atmospheric model predicts a rich near-IR spectrum, which makes this exoplanet an excellent target for future follow-up observations with the James Webb Space Telescope, both to confirm this H2O detection and to superbly constrain the atmosphere's parameters.
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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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Alves, P. N., Melo, I. C., Santos, R. D., da Rocha, F. V., & Caixeta, J. V. (2022). How did COVID-19 affect green-fuel supply chain? – A performance analysis of Brazilian ethanol sector. Res. Transp. Econ., 93, 101137.
Abstract: The COVID-19 pandemic affected many supply chains worldwide, including the Brazilian green-fuel ethanol supply chain. Our analysis considered sustainability variables (social, environmental, and economic) to investigate the pandemic's effects on the ethanol industries of 15 ethanol producing Brazilian states, comparing data from 2020 to 2019 and applying a novel Data Envelopment Analysis (DEA): the Double Frontier Slack-Based Measure Malmquist Productivity Index (DF-SBM MPI). The findings show that all states suffered negative impacts from the pandemic and some incurred a risk of collapsing it. The least negatively impacted states were Sao Paulo and Mato Grosso. Sao Paulo's ethanol sector is a benchmark for income derived from trade in carbon-credits by RenovaBio certified mills, while Mato Grosso's sector is able to take advantage of the largest spread between ethanol and gasoline prices, certainly a competitive advantage for ethanol producers. We recommend the implementation of public policies to support, mainly, the most affected states by assisting their mills to become environmentally certified participants to take advantage of income opportunities available in the carbon-credit trading market. We recommend, among other actions, a temporary ethanol sales tax reduction, an extension of debt repayment schedules, and stimulating an increase in the fleet of flex-fuel vehicles.
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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., 54, 711–743.
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). Airy heat bullets. Eur. Phys. J. Plus., 137(10), 1201.
Abstract: New localized structured solutions for the three-dimensional linear heat (diffusion) equation are presented. These new solutions are written in terms of Airy functions. They are constructed as wave packet-like structures formed by a superposition of Bessel functions through the introduction of spectral functions. These diffusive solutions accelerate along their propagation direction, while in the plane orthogonal to it, they retain their confined structure. These heat (diffusion) densities retain a complete localized form in space as they propagate, and may be considered the heat analogue of Airy light bullets.
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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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Asenjo, F. A., Hojman, S. A., Moya-Cessa, H. M., & Soto-Eguibar, F. (2022). Supersymmetric relativistic quantum mechanics in time-domain. Phys. Lett. A, 450, 128371.
Abstract: A supersymmetric relativistic quantum theory in the temporal domain is developed for bi-spinor fields satisfying the Dirac equation. The simplest time-domain supersymmetric theory can be postulated for fields with time-dependent mass, showing an equivalence with the bosonic supersymmetric theory in time-domain. Solutions are presented and they are used to produce probability oscillations between mass states. As an application of this idea, we study the two-neutrino oscillation problem, showing that flavour state oscillations may emerge from the supersymmetry originated by the time-dependence of the unique mass of the neutrino.(c) 2022 Elsevier B.V. All rights reserved.
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Ashfaq, M., Talreja, N., Chauhan, D., Rodriguez, C. A., Mera, A. C., & Mangalaraja, R. V. (2022). Synthesis of reduced graphene oxide incorporated bimetallic (Cu/Bi) nanorods based photocatalyst materials for the degradation of gallic acid and bacteria. J. Ind. Eng. Chem., 110, 447–455.
Abstract: Gallic acid (GA) is a polyphenols compound commonly present in wastewater that immensely affects aquatic and human life. GA is also responsible for the inhibitory effects on the microbial activity in the soil, thereby decreasing the fertility of the soil. Therefore, the removal of GA from the wastewater is necessary to combat such issues. The present study focused on the synthesis of reduced graphene oxide (rGO) incorporated bimetallic (Cu/Bi) based nanorods (r-GO-Cu/Bi-NRs) and their photocatalytic applications. Incorporating GO within the CuBi2O4-NRs might decrease the bandgap value, thereby increasing the interfacial charge transfer. Moreover, GO increased the reactive sites and oxygen defects onto the r-GO-Cu/Bi-NRs that led to the separation rate of the photo-induced charge carriers and migration, thereby enhancing the photodegradation ability of the synthesized r-GO-Cu/Bi-NRs. The synthesis process of the r-GO-Cu/Bi-NRs is facile, novel, and economically viable for the photocatalytic degradation of organic pollutants.
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Atkinson, J., & Escudero, A. (2022). Evolutionary natural-language coreference resolution for sentiment analysis. Int. J. Inf. Manage. Data Insights, 2(2), 100115.
Abstract: Communicating messages on social media usually conveys much implicit linguistic knowledge, which makes it difficult to process texts for further analysis. One of the major problems, the linguistic coreference resolution task involves detecting coreference chains of entities and pronouns that coreference them. It has mostly been addressed for formal and full-sized text in which a relatively clear discourse structure can be discovered, using Natural-Language Processing techniques. However, texts in social media are short, informal and lack a lot of underlying linguistic information to make decisions so traditional methods can not be applied. Furthermore, this may significantly impact the performance of several tasks on social media applications such as opinion mining, network analysis, sentiment analysis, text categorization. In order to deal with these issues, this research address the task of linguistic co-referencing using an evolutionary computation approach. It combines discourse coreference analysis techniques, domain-based heuristics (i.e., syntactic, semantic and world knowledge), graph representation methods, and evolutionary computation algorithms to resolving implicit co-referencing within informal opinion texts. Experiments were conducted to assess the ability of the model to find implicit referents on informal messages, showing the promise of our approach when compared to related methods.
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Atkinson-Abutridy, J. (2022). Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis (Chapman and Hall/CRC, Ed.) (). Taylor & Francis.
Abstract: Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources.
Features:
- Easy-to-follow step-by-step concepts and methods
- Every chapter is introduced in a very gentle and intuitive way so students can understand the WHYs, WHAT-IFs, WHAT-IS-THIS-FORs, HOWs, etc. by themselves
- Practical programming exercises in Python for each chapter
- Includes theory and practice for every chapter, summaries, practical coding exercises for target problems, QA, and sample code and data available for download at https://www.routledge.com/Atkinson-Abutridy/p/book/9781032249797
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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., 413, 114356.
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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Bachoc, F., Porcu, E., Bevilacqua, M., Furrer, R., & Faouzi, T. (2022). Asymptotically equivalent prediction in multivariate geostatistics. Bernoulli, 28(4), 2518–2545.
Abstract: Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geo-statistics. While best linear prediction has been well understood in univariate spatial statistics, the literature for the multivariate case has been elusive so far. The new challenges provided by modern spatial datasets, being typ-ically multivariate, call for a deeper study of cokriging. In particular, we deal with the problem of misspecified cokriging prediction within the framework of fixed domain asymptotics. Specifically, we provide conditions for equivalence of measures associated with multivariate Gaussian random fields, with index set in a compact set of a d-dimensional Euclidean space. Such conditions have been elusive for over about 50 years of spatial statistics. We then focus on the multivariate Matern and Generalized Wendland classes of matrix valued covariance functions, that have been very popular for having parameters that are crucial to spatial interpolation, and that control the mean square differentiability of the associated Gaussian process. We provide sufficient conditions, for equivalence of Gaussian measures, relying on the covariance parameters of these two classes. This enables to identify the parameters that are crucial to asymptotically equivalent interpolation in multivariate geostatistics. Our findings are then illustrated through simulation studies.
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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., 16, 2221–2240.
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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