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O algoritmo AF Suppression™, da St. Jude Medical, já comprovado clinicamente, é um parâmetro de estimulação projetado especificamente para suprimir a fibrilação atrial (FA). Elimina a estimulação rápida desnecessária produzida pelo marcapasso(MP), associada à estimulação de overdrive fixa, quando o paciente está em repouso. Realiza ainda o overdrive quando a freqüência atrial intrínseca do paciente aumenta em resposta à atividade física. É um recurso valioso para administrar a fibrilação atrial paroxística e persistente em pacientes selecionados, que necessitam de estimulação segundo indicações padrão, produzindo redução benéfica dos sintomas e dos custos associados ao tratamento da FA.[...]

O algoritmo AF Suppression™, da St. Jude Medical, já comprovado clinicamente, é um parâmetro de estimulação projetado especificamente para suprimir a fibrilação atrial (FA). Elimina a estimulação rápida desnecessária produzida pelo marcapasso(MP), associada à estimulação de overdrive fixa, quando o paciente está em repouso. Realiza ainda o overdrive quando a freqüência atrial intrínseca do paciente aumenta em resposta à atividade física. É um recurso valioso para administrar a fibrilação atrial paroxística e persistente em pacientes selecionados, que necessitam de estimulação segundo indicações padrão, produzindo redução benéfica dos sintomas e dos custos associados ao tratamento da FA.[...]

O algoritmo AF Suppression™, da St. Jude Medical, já comprovado clinicamente, é um parâmetro de estimulação projetado especificamente para suprimir a fibrilação atrial (FA). Elimina a estimulação rápida desnecessária produzida pelo marcapasso(MP), associada à estimulação de overdrive fixa, quando o paciente está em repouso. Realiza ainda o overdrive quando a freqüência atrial intrínseca do paciente aumenta em resposta à atividade física. É um recurso valioso para administrar a fibrilação atrial paroxística e persistente em pacientes selecionados, que necessitam de estimulação segundo indicações padrão, produzindo redução benéfica dos sintomas e dos custos associados ao tratamento da FA.[...]

Un Flow Shop es un sistema de producción en el cual una serie de trabajos debe seguir un flujo unidireccional para ser procesada en varias estaciones de trabajo. En este artículo se introduce el Permutation Flow Shop Scheduling Problem with Shifts Constraints (PFSSPSC), una variante del Permutation Flow Shop Scheduling Problem (PFSSP) que busca minimizar el tiempo de finalización de todos los trabajos y se imponen restricciones sobre los turnos de procesamiento de los trabajos. El artículo propone un algoritmo híbrido, compuesto por un algoritmo genético y un algoritmo VNS (Variable Neighborhood Search), para resolver el PFSSPSC. Los resultados muestran que este algoritmo obtiene mejores soluciones en cuanto a calidad y tiempo de ejecución comparado con otros tres algoritmos heurísticos.  

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport-like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities. La identificación fotográfica es una herramienta importante para la estimación de la abundancia y el monitoreo de las tendencias poblacionales en el tiempo. Sin embargo, corresponder las fotografías con los individuos conocidos requiere de mucho tiempo. Motivados por los avances recientes en el reconocimiento de imágenes, decidimos acoger un reto de datos científicos en la plataforma de colaboración masiva Kaggle para automatizar la identificación de ballenas francas del Atlántico norte (Eubalaena glacialis), especie que se encuentra en peligro de extinción. La solución ganadora identificó automáticamente a las ballenas individuales con una certeza del 87% y con una serie de redes neurales convolucionales para identificar la región de interés en una imagen, rotar, recortar, y crear fotografías estandarizadas de tamaño y orientación uniforme y después identificar al individuo correcto a partir de estas fotografías tamaño pasaporte. Los avances recientes en el aprendizaje profundo acoplados a este flujo de trabajo completamente automatizado han producido resultados impresionantes y tienen el potencial para revolucionar los métodos tradicionales de recolección de datos de abundancia y distribución de las poblaciones silvestres. La presentación de estos resultados ante un público amplio debería reducir aún más el vacío que existe entre los datos científicos y las comunidades científicas para la conservación. 照片识别是评估种群丰度和监测种群动态的重要工具。然而，人工地将照片与已知个体进行比对需耗费 大量时间。受到最近图像识别领域发展的启发我们在众包平台Kaggle网站举办了ー项数据科学挑战，来自动 识到濒危的北太平洋露脊鲸(Eubalaena glad alis)。获胜的方案能以87%的准确率自动识别鲸鱼个体，它利用 一系列卷积神经网络来找到图像上关注的区域，加以旋转、裁剪，并包！建统ー尺寸和方向的标准化照片，然后从 这些类似护照的照片中正确识别出鲸鱼个体。目前深度学习领域的进展加上这种完全自动化的工作流程，已取 得了显著成果，并有可能给野生动物种群丰度和分布的传统数据收集方法带来变革。我们将这些结果呈现给广 大受众以期进ー步缩小数据科学和保护科学群体之间的距离。

En Trujillo, una ciudad al norte de Perú, el número de hidrantes es actualmente 497; solo el 10% está operativo en el centro de la ciudad. Los bomberos no pueden atender emergencias en tiempo óptimo, lo que aumenta los daños materiales y las víctimas debido a la falta de suministro de agua de hidrantes inoperativos y su distribución no óptima. En esta investigación, se diseñó una red de hidrantes mediante un algoritmo genético (AG). Se evaluaron muchas soluciones y se seleccionó la más eficiente con una función de calidad basada en las distancias entre los hidrantes y un punto común. La mejor solución resultante reduce el tiempo de respuesta a emergencias y propone una redistribución estratégica de los hidrantes.  

Drawing on examples from human mobility research, I argue in this article that the advent of big data has significantly increased the role of algorithms in mediating the geographic knowledge production process. This increased centrality of algorithmic mediation introduces much more uncertainty to the geographic knowledge generated when compared to traditional modes of geographic inquiry. This article reflects on important changes in the geographic knowledge production process associated with the shift from using traditional \"small data\" to using big data and explores how computerized algorithms could considerably influence research results. I call into question the much touted notion of data-driven geography, which ignores the potentially significant influence of algorithms on research results, and the fact that knowledge about the world generated with big data might be more an artifact of the algorithms used than the data itself. As the production of geographic knowledge is now far more dependent on computerized algorithms than before, this article asserts that it is more appropriate to refer to this new kind of geographic inquiry as algorithm-driven geographies (or algorithmic geographies) rather than data-driven geography. The notion of algorithmic geographies also foregrounds the need to pay attention to the effects of algorithms on the content, reliability, and social implications of the geographic knowledge these algorithms help generate. The article highlights the need for geographers to remain attentive to the omissions, exclusions, and marginalizing power of big data. It stresses the importance of practicing critical reflexivity with respect to both the knowledge production process and the data and algorithms used in the process.

This paper addresses the Optimized Billing Sequencing (OBS) problem to maximize billing of the order portfolio of a typical Distribution Center (DC). This is a new problem in the literature, and the search for the best billing mix has generated demands for better optimization methods for DCs. Therefore, the objective of this paper is to provide an effective algorithm that presents quick and optimized solutions for higher-complexity OBS levels. This algorithm is called Iterative Greedy Algorithm (IGA-OBS), and its performance is compared to the genetic algorithm (GA-OBS) by Pinto and Nagano. Performance evaluations were carried out after intense computational experiments for problems with different complexity levels. The results demonstrate that the GA-OBS is limited to medium-size instances, whereas the IGA-OBS is better adapted to reality, providing OBS with solutions with satisfactory time and quality. The IGA-OBS enables managers to make decisions in a more agile and consistent way in terms of the trade-off between the level of customer service and the maximization of the financial result of DCs. This paper fills a gap in the literature, makes innovative contributions, and provides suggestions for further research aimed at developing more suitable optimization methods for OBS.

This work corresponds to the optimization of the operating variables of the enzymatic hydrolysis of corn starch to obtain glucose syrups using the genetic algorithm of Matlab (2020a). For this reason, the hydrolytic process is mathematically modeled by response surface methodology. Pareto chart indicated that saccharification variables exert the highest influence on starch conversion. This mathematical model is beneficial for a better understanding and operational control of hydrolysis at the industrial level. The optimization problem solution shows that a maximum dextrose equivalent of 98.13% can be reached if the hydrolysis is performed under optimal operating conditions, which were also confirmed experimentally. The results show that to achieve the highest yield, liquefaction should be carried out at a temperature of 92oC, pH of 6.3, α-amylase dose of 1.5 mg enzyme/g starch and hydrolysis time of 1 hour; while saccharification should be conducted at a temperature of 57oC, pH of 4.9, glucoamylase dose of 1.15 mg enzyme/g starch and hydrolysis time of 34 hours. The reversion phenomenon is detected when the hydrolysis time exceeds 35 hours, with a negative incidence on the dextrose equivalent. Este trabajo corresponde a la optimización de las variables de operación de la hidrólisis enzimática de almidón de maíz para la obtención de jarabes de glucosa utilizando el algoritmo genético de Matlab (2020a). Para ello, el proceso de hidrólisis se modeló matemáticamente mediante la metodología de superficie de respuesta. El diagrama de Pareto indicó que las variables de sacarificación ejercen la mayor influencia en la conversión del almidón. Este modelo matemático es de gran utilidad para una mejor comprensión y control operacional de la hidrólisis a nivel industrial. La solución del problema de optimización muestra que puede alcanzarse un equivalente máximo de dextrosa del 98,13% si la hidrólisis se realiza en las condiciones operacionales óptimas, las cuales se comprobaron experimentalmente. Los resultados muestran que, para alcanzar el mayor rendimiento, la licuefacción debe llevarse a cabo a una temperatura de 92oC, pH de 6,3, dosis de α-amilasa de 1,5 mg de enzima/g de almidón y tiempo de hidrólisis de 1 hora; mientras que la sacarificación debe realizarse a una temperatura de 57oC, pH de 4,9, dosis de glucoamilasa de 1,15 mg de enzima/g de almidón y tiempo de hidrólisis de 34 horas. El fenómeno de reversión se detectó cuando el tiempo de hidrólisis superó las 35 horas, con una incidencia negativa sobre el equivalente en dextrosa.

We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban-rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes.