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Seismic phase detection, identification and first-onset picking are basic but essential routines to analyse earthquake data. As both the number of seismic stations, globally and regionally, and the number of experiments greatly increase due to ever greater availability of instrumentation, automated data processing becomes more and more essential. For example for modern seismic experiments involving 100s to even 1000s instruments, conventional human analyst-based identification and picking of seismic phases is becoming unfeasible, and the introduction of automatic algorithms mandatory. In this paper, we introduce DeepPhasePick, an automatic two-stage method that detects and picks P and S seismic phases from local earthquakes. The method is entirely based on highly optimized deep neural networks, consisting of a first stage that detects the phases using a convolutional neural network, and a second stage that uses two recurrent neural networks to pick both phases. Detection is performed on three-component seismograms. P - and S -picking is then conducted on the vertical and the two-horizontal components, respectively. Systematic hyperparameter optimization was applied to select the best model architectures and to define both the filter applied to pre-process the seismic data as well as the characteristics of the window sample used to feed the models. We trained DeepPhasePick using seismic records extracted from two sets of manually picked event waveforms originating from northern Chile (∼39 000 records for detection and ∼36 000 records for picking). In different tectonic regimes, DeepPhasePick demonstrated the ability to both detect P and S phases from local earthquakes with high accuracy, as well as predict P - and S -phase time onsets with an analyst level of precision. DeepPhasePick additionally computes onset uncertainties based on the Monte Carlo Dropout technique as an approximation of Bayesian inference. This information can then further feed an associator algorithm in an earthquake location procedure. [ABSTRACT FROM AUTHOR]

This paper proposes a mathematical model for intermodal chains with seaborne transport, in which the optimization of a multi-objective model enables conflicting objectives to be handled simultaneously. Through the assessment of 'door-to-door' transport in terms of costs, time, and environmental impact, the most suitable maritime route and the optimized fleet are jointly proposed to maximize the opportunities for success of intermodal chains versus trucking. The NSGA-II algorithm is applied to resolve the model. The Pareto fronts obtained not only permit decision-making in the short-term but also enable long-term strategies to be defined according to the behaviour of these frontiers when sensitivity analysis is undertaken. A real-life case in Chile is studied to test the usefulness of the model. Aside from identifying the most suitable Motorway of the Sea with its optimized fleet for Chile, the application case has provided several significant findings to promote the intermodal option regardless of its location. [ABSTRACT FROM AUTHOR]

This paper investigates the causal effect of COVID-19 quarantine measures on the electricity consumption of different types of regulated customers. In particular, the average energy consumption of a group of customers in Chile is examined, exploiting the weekly variations in the application of the stages of the Step by Step Plan as a natural experiment. Step by Step Plan was the Chile COVID containment strategy, which involved implementing partial lockdowns based on the COVID incidence in the municipalities, and adjusted on a weekly basis. An econometric model is specified, and estimated using the technique Difference-in-Difference-in-Difference (DDD) to quantify the causal impact of these measures. After applying the model, it is found that the quarantine measures have had a negative effect on the trend weekly consumption of customers located in municipalities where some of the phases of the Plan have been applied. This effect is significantly different depending on each stage of the plan analysis, and statistically significant. It is also observed that, on average, the savings for a representative customer in the sample range from $ 8 to $ 6. 15 for some municipalities, and the sample costs range from $ 0. 02 to $ 0. 26 for others, depending on the municipality analyzed. The results can be expressed in a total of 2.95 tons less CO 2 emitted on a monthly basis. This shows a great impact in terms of sustainability and planning of energy demand and supply. • Quarantines by Covid 19 affected energy consumption, like a "Natural Experiment". • Using Panel Data of 400 thousand observations with geographic, temporal and Daylight Saving Time controls. • Difference-in-Difference-in-Difference method to quantify causal impact of quarantines on electric consumption. • Applying quarantines generated Energetic Savings from 8 cents to 6.15 dollars depending on client type. • An Event Study was designed to check the estimation results. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a lot-sizing methodology for an inventory system that faces time-dependent random demands and that seeks to minimize total cost as a function of order, purchase, holding and shortage costs. A two-stage stochastic programming framework is derived to optimize lot-sizing decisions over a time horizon. To this end, we simulate a demand time-series by using a generalized autoregressive moving average structure. The modeling includes covariates of the demand, which are used as predictors of this. We describe an algorithm that summarizes the methodology and we discuss its computational framework. A case study with unpublished real-world data is presented to illustrate the potential of this methodology. We report that the accuracy of the demand variance estimator improves when a temporal structure is considered, instead of assuming time-independent demand. The methodology is useful in decisions related to inventory logistics management when the demand shows patterns of temporal dependence. [ABSTRACT FROM AUTHOR]

The public health system has restricted economic resources. Because of that, it is necessary to know how the resources are being used and if they are properly distributed. Several works have applied classical approaches based in Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) for this purpose. However, if we have hospitals with different casemix, this is not the best approach. In order to avoid biases in the comparisons, other works have recommended the use of hospital production data corrected by the weights from Diagnosis Related Groups (DRGs), to adjust the casemix of hospitals. However, not all countries have this tool fully implemented, which limits the efficiency evaluation. This paper proposes a new approach for evaluating the efficiency of hospitals. It uses a graph-based clustering algorithm to find groups of hospitals that have similar production profiles. Then, DEA is used to evaluate the technical efficiency of each group. The proposed approach is tested using the production data from 2014 of 193 Chilean public hospitals. The results allowed to identify different performance profiles of each group, that differs from other studies that employs data from partially implemented DRGs. Our results are able to deliver a better description of the resource management of the different groups of hospitals. We have created a website with the results (). Data can be requested to the authors. [ABSTRACT FROM AUTHOR]

Context. The implementation of fringe tracking for optical interferometers is inevitable when optimal exploitation of the instrumental capacities is desired. Fringe tracking allows continuous fringe observation, considerably increasing the sensitivity of the interferometric system. In addition to the correction of atmospheric path-length differences, a decent control algorithm should correct for disturbances introduced by instrumental vibrations, and deal with other errors propagating in the optical trains. Aims. In an effort to improve upon existing fringe-tracking control, especially with respect to vibrations, we attempt to construct control schemes based on Kalman filters. Kalman filtering is an optimal data processing algorithm for tracking and correcting a system on which observations are performed. As a direct application, control schemes are designed for GRAVITY, a future four-telescope near-infrared beam combiner for the Very Large Telescope Interferometer (VLTI). Methods. We base our study on recent work in adaptive-optics control. The technique is to describe perturbations of fringe phases in terms of an a priori model. The model allows us to optimize the tracking of fringes, in that it is adapted to the prevailing perturbations. Since the model is of a parametric nature, a parameter identification needs to be included. Different possibilities exist to generalize to the four-telescope fringe tracking that is useful for GRAVITY. Results. On the basis of a two-telescope Kalman-filtering control algorithm, a set of two properly working control algorithms for four-telescope fringe tracking is constructed. The control schemes are designed to take into account flux problems and low-signal baselines. First simulations of the fringe-tracking process indicate that the defined schemes meet the requirements for GRAVITY and allow us to distinguish in performance. In a future paper, we will compare the performances of classical fringe tracking to our Kalman-filter control. Conclusions. Kalman-filter based control schemes will likely become the next standard for fringe tracking, providing the possibility to maximize the tracking performance. The results of the present study are currently being incorporated into the final design of GRAVITY. [ABSTRACT FROM AUTHOR]

This paper determines the tractor stock and aggregate adoption growth of agricultural tractors in Chile. A logistic model was applied to determine the adoption growth and the adoption speed of agricultural tractors. Model parameters were determined by a non-linear algorithm. The tractor stock balance and the tractorization curve were used to estimate variation rate between t and t-1. This variation was associated with the introduction of tractors and those units removed from the stock. The logistic model showed a significant adjustment level and allowed estimating the rate of adoption and the adoption saturation level. It is concluded that the methodology can be applied to study technological adoption in other regions and other countries. [ABSTRACT FROM AUTHOR]

ACO is a metaheuristic inspired in the behavior of natural ant colonies to solve combinatorial optimization problems, based on simple agents that work cooperatively communicating by artificial pheromone trails. In this paper a model to solve the municipal waste collection problem by containers is presented, which applies a concept of partial collection sequences that must be joined to minimize the total collection distance. The problem to join the partial collection sequences is represented as a TSP, which is solved by an ACO algorithm. Based on the literature, algorithm parameters are experimentally calibrated and range of variations that represents good average solutions are recommended. The model is applied to a waste collection sector of the San Pedro de la Paz commune in Chile, obtaining recollection routes with less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach. [ABSTRACT FROM AUTHOR]

A solution is provided to automatically identify river reaches based on their planform typologies via a holistic synthesis of multi-dimensional, quali-quantitative information. Inspired by fuzzy logics, an algorithm is proposed which appraises the degree of fit of a range of archetypes based on formal criteria, applied over a discretized version of the river. The algorithm is fed by the values of a set of indicators relevant to the formal criteria. Indicators are determined from interpretation of remotely sensed data for each discrete "slice". To ensure a spatially holistic view, the local indicators are first integrated over a variable significant length, thereby guaranteeing contextualization at the appropriate scale. For each indicator a specific Value Function (VF) translates its objective value into a partial fitness judgment; these judgments are then integrated within a multiattribute VF. The locally highest scoring VF determines the corresponding planform choice in each slice. As a by-product, the stretch (collection of consecutive slices) corresponding to that planform archetype is identified. Implementation of the algorithm for the Baker River in Chile shows promising results. The approach ensures self-consistency and repeatability of outputs. Beyond an initial subjective assessment of input criteria based on expert judgment, the method can be fully automated. As such it can support systematic, large scale river analyses and monitoring applications. • Planform typology of a river reach is a key element of geomorphic characterization • A high degree of subjectivity and ambiguity is involved when identifying it • This paper proposes a new method that uses explicit criteria to discern amongst planform archetypes • The algorithm adopts a fuzzy-like battery of multiattribute Value Functions and it is completely automatable • It ensures self-consistency thus opening the way to systematic monitoring and analysis of large river networks [ABSTRACT FROM AUTHOR]

In the present paper, we review the use of two-state, Generalized Auto Regressive Conditionally Heteroskedastic Markovian stochastic processes (MS-GARCH). These show the quantitative model of an active stock trading algorithm in the three main Latin-American stock markets (Brazil, Chile, and Mexico). By backtesting the performance of a U.S. dollar based investor, we found that the use of the Gaussian MS-GARCH leads, in the Brazilian market, to a better performance against a buy and hold strategy (BH). In addition, we found that the use of t-Student MS-ARCH models is preferable in the Chilean market. Lastly, in the Mexican case, we found that is better to use Gaussian time-fixed variance MS models. Their use leads to the best overall performance than the BH portfolio. Our results are of use for practitioners by the fact that MS-GARCH models could be part of quantitative and computer algorithms for active trading in these three stock markets. [ABSTRACT FROM AUTHOR]