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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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Bottcher, L., Montealegre, P., Goles, E., & Gersbach, H. (2020). Competing activists-Political polarization. Physica A, 545, 13 pp.
Abstract: Recent empirical findings suggest that societies have become more polarized in various countries. That is, the median voter of today represents a smaller fraction of society compared to two decades ago and yet, the mechanisms underlying this phenomenon are not fully understood. Since interactions between influential actors ("activists'') and voters play a major role in opinion formation, e.g. through social media, we develop a macroscopic opinion model in which competing activists spread their political ideas in specific groups of society. These ideas spread further to other groups in declining strength. While unilateral spreading shifts the opinion distribution, competition of activists leads to additional phenomena: Small heterogeneities among competing activists cause them to target different groups in society, which amplifies polarization. For moderate heterogeneities, we obtain target cycles and further amplification of polarization. In such cycles, the stronger activist differentiates himself from the weaker one, while the latter aims to imitate the stronger activist. (C) 2019 Elsevier B.V. All rights reserved.
Keywords: Political polarization; Opinion formation; Activists; Markov chains; Game theory
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Cominetti, R., Quattropani, M., & Scarsini, M. (2022). The Buck-Passing Game. Math. Oper. Res., Early Access.
Abstract: We consider two classes of games in which players are the vertices of a directed graph. Initially, nature chooses one player according to some fixed distribution and gives the player a buck. This player passes the buck to one of the player's out-neighbors in the graph. The procedure is repeated indefinitely. In one class of games, each player wants to minimize the asymptotic expected frequency of times that the player receives the buck. In the other class of games, the player wants to maximize it. The PageRank game is a particular case of these maximizing games. We consider deterministic and stochastic versions of the game, depending on how players select the neighbor to which to pass the buck. In both cases, we prove the existence of pure equilibria that do not depend on the initial distribution; this is achieved by showing the existence of a generalized ordinal potential. If the graph on which the game is played admits a Hamiltonian cycle, then this is the outcome of prior-five Nash equilibrium in the minimizing game. For the minimizing game, we then use the price of anarchy and stability to measure fairness of these equilibria.
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Fierro, R., Leiva, V., & Balakrishnan, N. (2015). Statistical Inference on a Stochastic Epidemic Model. Commun. Stat.-Simul. Comput., 44(9), 2297–2314.
Abstract: In this work, we develop statistical inference for the parameters of a discrete-time stochastic SIR epidemic model. We use a Markov chain for describing the dynamic behavior of the epidemic. Specifically, we propose estimators for the contact and removal rates based on the maximum likelihood and martingale methods, and establish their asymptotic distributions. The obtained results are applied in the statistical analysis of the basic reproduction number, a quantity that is useful in establishing vaccination policies. In order to evaluate the population size for which the results are useful, a numerical study is carried out. Finally, a comparison of the maximum likelihood and martingale estimators is conducted by means of Monte Carlo simulations.
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Hernandez, R. (2022). A criterion of univalence in C-n in terms of the Schwarzian derivative. Stud. Univ. Babes-Bolyai Math., 67(2), 421–430.
Abstract: Using the Loewner Chain Theory, we obtain a new criterion of univalence in C-n in terms of the Schwarzian derivative for locally biholomorphic mappings. We as well derive explicitly the formula of this Schwarzian derivative using the numerical method of approximation of zeros due by Halley.
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Leiva, V., Ruggeri, F., Saulo, H., & Vivanco, J. F. (2017). A methodology based on the Birnbaum-Saunders distribution for reliability analysis applied to nano-materials. Reliab. Eng. Syst. Saf., 157, 192–201.
Abstract: The Birnbaum-Saunders distribution has been widely studied and applied to reliability studies. This paper proposes a novel use of this distribution to analyze the effect on hardness, a material mechanical property, when incorporating nano-particles inside a polymeric bone cement. A plain variety and two modified types of mesoporous silica nano-particles are considered. In biomaterials, one can study the effect of nano-particles on mechanical response reliability. Experimental data collected by the authors from a micro-indentation test about hardness of a commercially available polymeric bone cement are analyzed. Hardness is modeled with the Birnbaum-Saunders distribution and Bayesian inference is performed to derive a methodology, which allows us to evaluate the effect of using nano-particles at different loadings by the R software. (C) 2016 Elsevier Ltd. All rights reserved.
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Mellado, P. (2022). Intrinsic topological magnons in arrays of magnetic dipoles. Sci. Rep., 12(1), 1420.
Abstract: We study a simple magnetic system composed of periodically modulated magnetic dipoles with an easy axis. Upon adjusting the geometric modulation amplitude alone, chains and two-dimensional stacked chains exhibit a rich magnon spectrum where frequency gaps and magnon speeds are easily manipulable. The blend of anisotropy due to dipolar interactions between magnets and geometrical modulation induces a magnetic phase with fractional Zak number in infinite chains and end states in open one-dimensional systems. In two dimensions it gives rise to topological modes at the edges of stripes. Tuning the amplitude in two-dimensional lattices causes a band touching, which triggers the exchange of the Chern numbers of the volume bands and switches the sign of the thermal conductivity.
Keywords: SPIN-WAVES; PHASES; CHAIN; MODES
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Pereira, J., & Vila, M. (2016). A new model for supply chain network design with integrated assembly line balancing decisions. Int. J. Prod. Res., 54(9), 2653–2669.
Abstract: Supply chain network design aims at the integration of the different actors of a supply chain within a single framework in order to optimise the total profit of the system. In this paper, we consider the integration of line balancing issues within the tactical decisions of the supply chain, and we offer a novel model and a solution approach for the problem. The new approach decomposes the problem into multiple line balancing problems and a mixed integer linear model, which is easier to solve than the previously available non-linear mixed integer formulation. The results show that the new method is able to solve previously studied models within a fraction of the reported running times, and also allows us to solve larger instances than those reported in earlier works. Finally, we also provide some analysis on the influence of the cost structure, the demand and the structure of the assembly process on the final configuration of the assemblies and the distribution network.
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Pietrobelli, C., Marin, A., & Olivari, J. (2018). Innovation in mining value chains: New evidence from Latin America. Resour. Policy, 58, 1–10.
Abstract: The paper investigates new opportunities for innovation and linkages associated to mining activities in Brazil, Chile and Peru. Three types of opportunities were researched: demand side, supply side and local specificities. The last source of opportunities is key for natural resource related activities. The evidence shows that an increasing demand is introducing important incentives for innovation and local suppliers. Nevertheless, a hierarchical value chain, dominated by few large firms, and poor linkages is blocking the diffusion of innovations and hindering suppliers' development. The emergence of a group of highly innovative suppliers, which were identified in the three countries, is explained mostly by new technological and knowledge opportunities, which are not exploited by large incumbents and open spaces for new entrants. Local specificities are also key in the explanation of local suppliers. It remains a challenge however, how these, most of which were created to satisfy local needs, will move from local to global.
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Rozas Andaur, J. M., Ruz, G. A., & Goycoolea, M. (2021). Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company. Electronics, 10(22), 2787.
Abstract: For decades, Out-of-Stock (OOS) events have been a problem for retailers and manufacturers. In grocery retailing, an OOS event is used to characterize the condition in which customers do not find a certain commodity while attempting to buy it. This paper focuses on addressing this problem from a manufacturer’s perspective, conducting a case study in a retail packaged foods manufacturing company located in Latin America. We developed two machine learning based systems to detect OOS events automatically. The first is based on a single Random Forest classifier with balanced data, and the second is an ensemble of six different classification algorithms. We used transactional data from the manufacturer information system and physical audits. The novelty of this work is our use of new predictor variables of OOS events. The system was successfully implemented and tested in a retail packaged foods manufacturer company. By incorporating the new predictive variables in our Random Forest and Ensemble classifier, we were able to improve their system’s predictive power. In particular, the Random Forest classifier presented the best performance in a real-world setting, achieving a detection precision of 72% and identifying 68% of the total OOS events. Finally, the incorporation of our new predictor variables allowed us to improve the performance of the Random Forest by 0.24 points in the F-measure.
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Sandoval, G., Alvarez-Miranda, E., Pereira, J., Rios-Mercado, R. Z., & Diaz, J. A. (2022). A novel districting design approach for on-time last-mile delivery: An application on an express postal company. Omega-Int. J. Manage. Sci., 113, 102687.
Abstract: Last-mile logistics corresponds to the last leg of the supply chain, i.e., the delivery of goods to final cus-tomers, and they comprise the core activities of postal and courier companies. Because of their role in the supply chain, last-mile operations are critical for the perception of customers regarding the perfor-mance of the whole logistic process. In this sense, the sustained growth of e-commerce, which has been abruptly catalyzed by the irruption of the COVID-19 pandemic, has hanged the habits of customers and overtaxed the operational side of delivery companies, hindering their viability and forcing their adap-tation to the novel conditions. Many of these habits will remain after we overcome the sanitary crisis, which will permanently reshape the structure and emphasis of postal supply chains, demanding compa-nies to implement organizational and operational changes to adapt to these new challenges. In this work we address a last-mile logistic design problem faced by a courier and delivery company in Chile, although the same problem is likely to arise in the last-mile delivery operation of other postal companies, in particular in the operation of express delivery services. The operational structure of the company is based on the division of an urban area into smaller territories (districts) and the outsourcing of the delivery operation of each territory to a last-mile contractor. Because of the increasing volume of postal traffic and a decreasing performance of the service, in particular for the case of express deliveries, the company is forced to redesign its current territorial arrangement. Such redesign results in a novel optimization problem that resembles a classical districting problem with the additional quality of service requirements. This novel problem is first formulated as a mathematical programming model and then a specially tailored heuristic is designed for solving it. The proposed approach is tested on instances from the real-life case study, and the obtained results show significant improvements in terms of the percent-age of on-time deliveries achieved by the proposed solution when compared to the current districting design of the company. By performing a sensitivity analysis considering different levels of demand, we show that the proposed approach is effective in providing districting designs capable of enduring signifi-cant increases in the demand for express postal services.
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Schmidt-Rivera, X. S., Rodgers, B., Odanye, T., Jalil-Vega, F., & Farmer, J. (2023). The role of aeroponic container farms in sustainable food systems – The environmental credentials. Sci. Total Environ., 860, 160420.
Abstract: Sustainable food production and consumption are key to face the current climate and environmental crisis, hence innovation to produce food with lower impacts are taking more attention. Controlled environment agriculture, also known as vertical farming, is seen as one innovative approach to reduce impacts of producing food while also improv-ing food security. Aeroponic is one of such innovations, which environmental impacts have not been well understood yet. Therefore, this study assesses the environmental impacts of aeroponic farm container system in the UK, including a full set of 19 indicators. The results show that energy requirements drive all the impacts, with climate change estimated at 1.52 kg CO2eq. per 1 kg of microgreens (pea shoots) using 2021 UK grid. Renewable powered systems improve almost all the impacts, with climate change reduced by up to 80 %, making this system competitive with con-ventional agricultural systems. This study proves that aeroponic farm container could offer lower impact food than equivalent imported to the UK, and that also could improve food security in terms of availability, stability, and access to food. Affordability issues need to be assessed in future work.
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Wickham, D., Hawkes, A., & Jalil-Vega, F. (2021). Hydrogen supply chain optimisation for the transport sector-Focus on hydrogen purity and purification requirements. Appl. Energy, 305, 117740.
Abstract: This study presents a spatially-resolved optimisation model to assess cost optimal configurations of hydrogen supply chains for the transport sector up to 2050. The model includes hydrogen grades and separation/purification technologies, offering the possibility to assess the effects that hydrogen grades play in the development of cost-effective hydrogen supply chains, including the decisions to repurpose gas distribution networks or blending hydrogen into them. The model is implemented in a case study of Great Britain, for a set of decarbonisation and learning rate scenarios. A base case with a medium carbon price scenario shows that the total discounted cost of the hydrogen supply chain is significantly higher than shown in previous studies, largely due to the additional costs from purification/separation needed to meet hydrogen purity standards for transport applications. Furthermore, it was shown that producing hydrogen from steam methane reforming with carbon capture and storage; installing new transmission pipelines; repurposing the gas distribution network to supply 100% hydrogen; and purifying hydrogen with a pressure swing adsorption system locally at the refuelling station; is a cost optimal configuration for the given technoeconomic assumptions, providing hydrogen at 6.18 pound per kg at the pump. Purification technologies were found to contribute to 14% and 30% of total discounted investment and operation costs respectively, highlighting the importance of explicitly including them into hydrogen supply chain models for the transport sector.
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Yuan, X. K., Liu, S. L., Valdebenito, M. A., Faes, M. G. R., Jerez, D. J., Jensen, H. A., et al. (2021). Decoupled reliability-based optimization using Markov chain Monte Carlo in augmented space. Adv. Eng. Softw., 157, 103020.
Abstract: An efficient framework is proposed for reliability-based design optimization (RBDO) of structural systems. The RBDO problem is expressed in terms of the minimization of the failure probability with respect to design variables which correspond to distribution parameters of random variables, e.g. mean or standard deviation. Generally, this problem is quite demanding from a computational viewpoint, as repeated reliability analyses are involved. Hence, in this contribution, an efficient framework for solving a class of RBDO problems without even a single reliability analysis is proposed. It makes full use of an established functional relationship between the probability of failure and the distribution design parameters, which is termed as the failure probability function (FPF). By introducing an instrumental variability associated with the distribution design parameters, the target FPF is found to be proportional to a posterior distribution of the design parameters conditional on the occurrence of failure in an augmented space. This posterior distribution is derived and expressed as an integral, which can be estimated through simulation. An advanced Markov chain algorithm is adopted to efficiently generate samples that follow the aforementioned posterior distribution. Also, an algorithm that re-uses information is proposed in combination with sequential approximate optimization to improve the efficiency. Numeric examples illustrate the performance of the proposed framework.
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