bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection
Authors: Liu Yanbin , Yan Yan , Chen Ling , Han Yahong , Yang Yi
Published: September 2019

In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the stateof-the-art online learning methods in the online-batch setting.
Orthogonality-Promoting Dictionary Learning via Bayesian Inference
Authors: Luo Lei , Xu Jie , Deng Cheng , Huang Heng
Published: September 2019

Dictionary Learning (DL) plays a crucial role in numerous machine learning tasks. It targets at finding the dictionary over which the training set admits a maximally sparse representation. Most existing DL algorithms are based on solving an optimization problem, where the noise variance and sparsity level should be known as the prior knowledge. However, in practice applications, it is difficult to obtain these knowledge. Thus, non-parametric Bayesian DL has recently received much attention of researchers due to its adaptability and effectiveness. Although many hierarchical priors have been used to promote the sparsity of the representation in non-parametric Bayesian DL, the problem of redundancy for the dictionary is still overlooked, which greatly decreases the performance of sparse coding. To address this problem, this paper presents a novel robust dictionary learning framework via Bayesian inference. In particular, we employ the orthogonality-promoting regularization to mitigate correlations among dictionary atoms. Such a regularization, encouraging the dictionary atoms to be close to being orthogonal, can alleviate overfitting to training data and improve the discrimination of the model. Moreover, we impose Scale mixture of the Vector variate Gaussian (SMVG) distribution on the noise to capture its structure. A Regularized Expectation Maximization Algorithm is developed to estimate the posterior distribution of the representation and dictionary with orthogonality-promoting regularization. Numerical results show that our method can learn the dictionary with an accuracy better than existing methods, especially when the number of training signals is limited.
Robust Metric Learning on Grassmann Manifolds with Generalization Guarantees
Authors: Luo Lei , Xu Jie , Deng Cheng , Huang Heng
Published: September 2019

In recent research, metric learning methods have attracted increasing interests in machine learning community and have been applied to many applications. However, the existing metric learning methods usually use a fixed L2-norm to measure the distance between pairwise data samples in the projection space, which cannot provide an effective mechanism to automatically remove the noise that exist in data samples. To address this issue, we propose a new robust formulation of metric learning. Our new model constructs a projection from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability, where the errors between sample points are considered as an MLE (maximum likelihood estimation)-like estimator. An efficient iteratively reweighted algorithm is derived to solve the proposed metric learning model. More importantly, we establish the generalization bounds for the proposed algorithm by utilizing the techniques of U-statistics. Experiments on six benchmark datasets clearly show that the proposed method achieves consistent improvements in discrimination accuracy, in comparison to state-of-the-art methods.
Complex Unitary Recurrent Neural Networks Using Scaled Cayley Transform
Authors: Maduranga Kehelwala D. G. , Helfrich Kyle E. , Ye Qiang
Published: September 2019

Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well known difficulty in using RNNs is the vanishing or exploding gradient problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN) which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the scaling matrix is fixed before training and a hyperparameter is introduced to tune the matrix for each particular task. In this paper, we develop a unitary RNN architecture based on a complex scaled Cayley transform. Unlike the real orthogonal case, the transformation uses a diagonal scaling matrix consisting of entries on the complex unit circle which can be optimized using gradient descent and no longer requires the tuning of a hyperparameter. We also provide an analysis of a potential issue of the modReLU activiation function which is used in our work and several other unitary RNNs. In the experiments conducted, the scaled Cayley unitary recurrent neural network (scuRNN) achieves comparable or better results than scoRNN and other unitary RNNs without fixing the scaling matrix.
A Distillation Approach to Data Efficient Individual Treatment Effect Estimation
Authors: Makar Maggie , Swaminathan Adith , Kıcıman Emre
Published: September 2019

The potential for using machine learning algorithms as a tool for suggesting optimal interventions has fueled significant interest in developing methods for estimating heterogeneous or individual treatment effects (ITEs) from observational data. While several methods for estimating ITEs have been recently suggested, these methods assume no constraints on the availability of data at the time of deployment or test time. This assumption is unrealistic in settings where data acquisition is a significant part of the analysis pipeline, meaning data about a test case has to be collected in order to predict the ITE. In this work, we present Data Efficient Individual Treatment Effect Estimation (DEITEE), a method which exploits the idea that adjusting for confounding, and hence collecting information about confounders, is not necessary at test time. DEITEE allows the development of rich models that exploit all variables at train time but identifies a minimal set of variables required to estimate the ITE at test time. Using 77 semi-synthetic datasets with varying data generating processes, we show that DEITEE achieves significant reductions in the number of variables required at test time with little to no loss in accuracy. Using real data, we demonstrate the utility of our approach in helping soon-to-be mothers make planning and lifestyle decisions that will impact newborn health.
Towards Better Interpretability in Deep Q-Networks
Authors: Annasamy Raghuram Mandyam , Sycara Katia
Published: September 2019

Deep reinforcement learning techniques have demonstrated superior performance in a wide variety of environments. As improvements in training algorithms continue at a brisk pace, theoretical or empirical studies on understanding what these networks seem to learn, are far behind. In this paper we propose an interpretable neural network architecture for Q-learning which provides a global explanation of the model’s behavior using key-value memories, attention and reconstructible embeddings. With a directed exploration strategy, our model can reach training rewards comparable to the state-of-the-art deep Q-learning models. However, results suggest that the features extracted by the neural network are extremely shallow and subsequent testing using out-of-sample examples shows that the agent can easily overfit to trajectories seen during training.
Biologically Motivated Algorithms for Propagating Local Target Representations
Authors: Ororbia Alexander G. , Mali Ankur
Published: September 2019

Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E. We compare our procedures to several other biologicallymotivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation. In two benchmarks, we find that both of our proposed algorithms yield stable performance and strong generalization compared to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
Non-Parametric Transformation Networks for Learning General Invariances from Data
Authors: Pal Dipan K. , Savvides Marios
Published: September 2019

ConvNets, through their architecture, only enforce invariance to translation. In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn general invariances and symmetries directly from data. NPTNs are a natural generalization of ConvNets and can be optimized directly using gradient descent. Unlike almost all previous works in deep architectures, they make no assumption regarding the structure of the invariances present in the data and in that aspect are flexible and powerful. We also model ConvNets and NPTNs under a unified framework called Transformation Networks (TN), which yields a better understanding of the connection between the two. We demonstrate the efficacy of NPTNs on data such as MNIST with extreme transformations and CIFAR10 where they outperform baselines, and further outperform several recent algorithms on ETH-80. They do so while having the same number of parameters. We also show that they are more effective than ConvNets in modelling symmetries and invariances from data, without the explicit knowledge of the added arbitrary nuisance transformations. Finally, we replace ConvNets with NPTNs within Capsule Networks and show that this enables Capsule Nets to perform even better.
Policy Optimization with Model-Based Explorations
Authors: Pan Feiyang , Cai Qingpeng , Zeng An-Xiang , Pan Chun-Xiang , Da Qing , He Hualin , He Qing , Tang Pingzhong
Published: September 2019

Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games. However, these methods suffer from high variances and high sample complexity. On the other hand, model-based reinforcement learning methods that learn the transition dynamics are more sample efficient, but they often suffer from the bias of the transition estimation. How to make use of both model-based and model-free learning is a central problem in reinforcement learning.In this paper, we present a new technique to address the tradeoff between exploration and exploitation, which regards the difference between model-free and model-based estimations as a measure of exploration value. We apply this new technique to the PPO algorithm and arrive at a new policy optimization method, named Policy Optimization with Modelbased Explorations (POME). POME uses two components to predict the actions’ target values: a model-free one estimated by Monte-Carlo sampling and a model-based one which learns a transition model and predicts the value of the next state. POME adds the error of these two target estimations as the additional exploration value for each state-action pair, i.e, encourages the algorithm to explore the states with larger target errors which are hard to estimate. We compare POME with PPO on Atari 2600 games, and it shows that POME outperforms PPO on 33 games out of 49 games.
On Reinforcement Learning for Full-Length Game of StarCraft
Authors: Pang Zhen-Jia , Liu Ruo-Ze , Meng Zhou-Yu , Zhang Yi , Yu Yang , Lu Tong
Published: September 2019

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties include huge state space, varying action space, long horizon, etc. In this paper, we investigate a set of techniques of reinforcement learning for the full-length game of StarCraft II. We investigate a hierarchical approach, where the hierarchy involves two levels of abstraction. One is the macro-actions extracted from expert’s demonstration trajectories, which can reduce the action space in an order of magnitude yet remain effective. The other is a two-layer hierarchical architecture, which is modular and easy to scale. We also investigate a curriculum transfer learning approach that trains the agent from the simplest opponent to harder ones. On a 64×64 map and using restrictive units, we train the agent on a single machine with 4 GPUs and 48 CPU threads. We achieve a winning rate of more than 99% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93% winning rate against the most difficult noncheating built-in AI (level-7) within days. We hope this study could shed some light on the future research of large-scale reinforcement learning.
Adversarial Dropout for Recurrent Neural Networks
Authors: Park Sungrae , Song Kyungwoo , Ji Mingi , Lee Wonsung , Moon Il-Chul
Published: September 2019

Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques for RNNs were introduced to respond to these demands, but we conjecture that the dropout on RNNs could have been improved by adopting the adversarial concept. This paper investigates ways to improve the dropout for RNNs by utilizing intentionally generated dropout masks. Specifically, the guided dropout used in this research is called as adversarial dropout, which adversarially disconnects neurons that are dominantly used to predict correct targets over time. Our analysis showed that our regularizer, which consists of a gap between the original and the reconfigured RNNs, was the upper bound of the gap between the training and the inference phases of the random dropout. We demonstrated that minimizing our regularizer improved the effectiveness of the dropout for RNNs on sequential MNIST tasks, semi-supervised text classification tasks, and language modeling tasks.
Analysis of a supercritical organic Rankine cycle for low-grade waste heat recovery
Authors: Xiao Song , Chen Xiaoyu , Qi Lin , Liu Yanna
Published: September 2019

The organic Rankine cycle (ORC) is an important method of producing electricity from industrial waste heat and renewable energy resources. This paper presents a thermo-economic analysis of a supercritical ORC for low-grade waste heat recovery using four working fluids. Based on the first law of thermodynamics, R152a was found to be the best performing fluid, producing the highest values for net power production and thermal efficiency. When the mass flow of waste heat is about 1 t/h and at an evaporation temperature of 136°C, the petroleum saved is 12·3 m3/year and carbon dioxide emissions are reduced by 41·2 t/year.
Application of green solvent for biodiesel production from sesame oil
Authors: Kadapure Santosh Ashok , Kadapure Poonam , Manoj Pujari , Raghvi Dodwad , Chanaveer Sajjan , Shubham Shinde
Published: September 2019

The use of abundant biosources for biodiesel production has attracted the attention of various academicians, as well as the industrial communities. This work provides an alternative biodiesel-manufacturing process that uses green chemistry principles in the form of deep eutectic solvents (DESs) to eliminate use of hazardous chemicals. Three different types of DES mixtures, glycerol-based (DES 1), urea-based (DES 2) and oxalic-acid-based (DES 3), were used with sesame oil. The laboratory-scale results showed that DES 1 and DES 2 were more effective for improving the yield of fatty acid methyl esters. Furthermore, a molar ratio of 6:1 and a contact time of 60 min proved to be optimum conditions. It was possible to get around 12% higher yield compared with the non-DES process under optimum conditions, suggesting that DESs can be used effectively as a co-solvent. The properties of purified biodiesel met the ASTM D 6751-19 standards.
Scaling effects on the centrifuge modelling of energy piles in saturated sand
Authors: Ng C. W. W. , Zhang C. , Farivar A. , Gomaa S. M. M. H.
Published: September 2019

Although various researchers have reported that scaling effects should be considered carefully when modelling the mechanical behaviour of pile in sand by a geotechnical centrifuge, scaling effects on the coupled thermo-mechanical behaviour of the energy pile have not been reported. In this study, the scaling effects on the centrifuge modelling of floating energy piles are assessed by the so-called ‘modelling of models’ technique. Two centrifuge models of energy piles were tested at the 28 and 56 g-levels to simulate the same prototype in saturated medium dense sand. The ratio of pile diameter to mean sand particle size (D/d50) ranged from 92 to 184. Identical working load was applied to the two piles with a factor of safety of 2·5 and each pile was subjected to seven heating–cooling cycles. After the tests, measured thermally induced settlements of both energy piles were similar (1·3%D). Furthermore, a good agreement was obtained between the two piles in the distribution of the axial load along each energy pile. It is evident that the scaling effects due to particle size on the centrifuge modelling of floating energy piles with a ratio of D/d50 larger than 92 can be ignored.
The coupled effect of suction and net stress on the air permeability of compacted soils
Authors: Roy S. , Rajesh S.
Published: September 2019

The coupled effect of net mean stress and suction on the air permeability of compacted soils has been investigated in this study. The air permeability of compacted soils was conducted at various pre-equilibrated gravimetric water contents and at different net mean stresses using the falling head method. The pre-equilibrated gravimetric water contents at different net mean stresses were related to soil suction using stress-dependent water retention curves (WRCs) measured experimentally by combining the results of the axis translation technique and the dew point potentiometer. The existing statistical model for predicting the air permeability function (APF) was modified to account for net stress and higher suction. An alternative approach to estimate the stress-dependent WRC from air permeability measurements was proposed. The prediction of the WRC and air permeability under different net stress conditions from the modified APF model was found to be in close agreement with the experimental results.
Élites empresariales y proceso de democratización en Paraguay
Authors: Ortiz-Sandoval Luis , Rojas Guillermo
Published: September 2019

Este estudio expone la primera época del proceso de democratización en Paraguay que inició en 1987 y culminó en 1992. El mismo se basó en un enfoque sociohistórico, movilizando información cualitativa desde testimonios sobre el proceso en cuestión, así como información estadística descriptiva que permitió entender el contexto de los cambios en el período referido. En este marco, se expone de qué manera las élites empresariales paraguayas, en tanto sector más dinámico de la clase dominante, no solo impulsaron el derrocamiento del régimen autoritario del ex dictador Alfredo Stroessner, sino que también orientaron los rumbos de la gestión económica y el rediseño de la arquitectura institucional de la república en función a sus intereses y de los privilegios obtenidos durante la dictadura. Entre los hallazgos, se expone que dichas élites ensayaron, durante la “primera transición”, mecanismos de acomodación y neutralización de los avances en derechos sociales, en la medida en que estos implicaron una democratización sustantiva del sistema político. Si bien la apertura democrática fue apuntalada como un cambio necesario, conllevó riesgosas implicaciones para la realización de los intereses específicos de las élites. En conclusión, la democracia quedó marcada por la captura del Estado por los sectores económicos que salieron fortalecidos después del régimen autoritario, dejando debilitadas a las clases trabajadoras en su posibilidad de oponer resistencia y acceder a derechos.
La transparencia como control democrático en los consejos ciudadanos: el caso del municipio de León, Guanajuato, 2009-2012
Authors: Godínez-Terrones José de Jesús
Published: September 2019

En este trabajo se analiza la transparencia como forma de control democrático en los consejos ciudadanos del municipio de León, Guanajuato (México), durante la administración municipal 2009-2012. Desde la información del portal de transparencia municipal se propone analizar si la participación ciudadana en estos consejos es transparente en cuanto a su reglamentación y desempeño. Desde 1989 hasta 2012, el Partido Acción Nacional gobernó el Municipio; en estos 23 años se generó y consolidó la reglamentación y funcionamiento de los consejos ciudadanos como la expresión de participación ciudadana del partido político que gobernó el municipio. La investigación que se realiza es de tipo cuantitativo descriptivo, las muestras usadas son los reglamentos y actas que aparecen en el portal de transparencia municipal de León, Guanajuato. Se encuentra que los consejos ciudadanos son de dos tipos: directivos y consultivos. De un total de 29 consejos, únicamente 12 publican sus actas y no hay sanción por esta conducta. La participación ciudadana por el diseño institucional gubernamental se presenta más como una simulación que como condición para una rendición de cuentas y control democrático de las acciones gubernamentales.
Innovación institucional para la rendición de cuentas: el Sistema Nacional Anticorrupción en México
Authors: Monsivais-Carrillo Alejandro
Published: September 2019

El combate a la corrupción requiere adoptar reformas institucionales integrales que establezcan las bases para fortalecer al Estado de derecho y la rendición de cuentas. El presente artículo argumenta, sin embargo, que se requiere del respaldo vigoroso y sostenido de actores decisivos para que esas reformas funcionen conforme a lo esperado. De lo contrario, por más innovadoras que sean, pueden verse limitadas o anuladas en la práctica. Este argumento se desarrolla con base en un análisis del Sistema Nacional Anticorrupción (SNA) de México. El SNA representa el caso de una innovación institucional para la rendición de cuentas, establecida en un país caracterizado por una corrupción política persistente y arraigada. El análisis muestra que el SNA fue producto de un proceso legislativo que contó con la eficaz intervención de redes de expertos y de organizaciones altamente especializadas en el tema, en una coyuntura favorecida por los escándalos de corrupción que afectaron al gobierno en turno. Esto permitió que la iniciativa original del gobierno fuera reemplazada por un esquema institucional mucho más ambicioso y progresivo. El análisis muestra, asimismo, que el SNA ha enfrentado resistencias de distinta índole, lo mismo en el plano nacional que subnacional. En particular, las expresiones ambivalentes o débiles de compromiso hacia ese sistema, mostradas por actores clave en momentos decisivos, han limitado su plena implementación.
On Decomposition of a Binary Context Without Losing Formal Concepts
Authors: Mongush Choduraa M. , Bykova Valentina V.
Published: September 2019

The #P-complete problem of finding all the formal concepts of a given context and the decomposition method for its solving are investigated. As parts of the decomposition is proposed to use fragments of the initial context, called boxes. Such decomposition allows to decompose the given context without losing formal concepts and thereby to reduce the execution time of the algorithms for solving considered task. The number of boxes, obtained at each iteration of the decomposition, is determined based on studies of the boxes structure and the rules for stopping of the decomposition process are established
VIOLÊNCIA SEXUAL CONTRA CRIANÇAS E ADOLESCENTES NA RESERVA INDÍGENA DE DOURADOS, ALDEIAS JAGUAPIRU E BORORÓ, ESTADO DE MATO GROSSO DO SUL, BRASIL
Authors: De Oliveira Victoria Georgia Cheuiche , De Oliveira Jorge Eremites
Published: September 2019

Neste trabalho é analisada a situação histórica marcada por violência sexual contra crianças e adolescentes que residem nas aldeias Jaguapiru e Bororó, localizadas no interior da Reserva Indígena de Dourados, município de Dourados, estado de Mato Grosso do Sul, Brasil. O objetivo é discutir a problemática enfrentada por duas comunidades multiétnicas, constituídas majoritariamente por famílias Guarani, Kaiowá e Terena, em uma unidade da Federação que oficialmente possui a segunda maior população indígena do país, bem como analisar as causas e consequências de crimes desta natureza no tempo presente. Por se tratar de uma população que vive às margens do abandono por parte do Estado Brasileiro e da sociedade nacional, verifica-se grande dificuldade enfrentada localmente na prevenção, apuração e punição de cada delito notificado às autoridades competentes. O estudo ainda expõe como tem funcionado a punição interna desses crimes nas comunidades e, também, sobre as situações de destituição do poder familiar indígena, especificamente quando o indígena menor de idade, vítima de agressão sexual, é retirado de sua comunidade e colocado em abrigo mantido pelo poder público. O assunto é analisado a partir de uma abordagem que busca concatenar uma perspectiva jurídica e antropológica a respeito de um tema ainda pouco discutido no âmbito do Direito e de outros campos afins do conhecimento científico.
RAYLEIGH — RITZ METHOD AND METHOD OF INITIAL PARAMETERS IN THE PROBLEM OF CALCULATION OF DYNAMIC CHARACTERISTICS OF MULTIBEAM ELASTIC STRUCTURES
Authors: Avramenko A. A. , Malykhina O. I.
Published: September 2019

The solution of the problem of dynamic synthesis based on the application of the Rayleigh Ritz method is considered. A method is proposed for determining the dynamic characteristics of a composite beam structure, taking into account the calculation of the shapes of oscillations of partial substructures using the method of initial parameters. Two variants of the formation of coordinate functions, using static and dynamic condensation, are considered. When carrying out static condensation, in order to increase the accuracy of the result, the internal physical degrees of freedom of the element were added to the boundary degrees of freedom to the reduced model degrees of freedom. When conducting dynamic condensation, modal degrees of freedom were added to the physical degrees of freedom of the boundary nodes of the reduced model, which are, in fact, coefficients in the accepted decomposition of the field of partial forms calculated with fixed boundary degrees of freedom. In the framework of the proposed approach, test calculations were carried out for a beam with variable mass-stiffness characteristics along the length, which showed good convergence of the target parameters to exact values. The proposed approach can be used to carry out calculations of composite elastic structures based on the method of initial parameters in cases where the application of the finite element method is irrational or difficult. In addition, if necessary, on the basis of the approach considered in this paper, the combined use of these two methods can be organized.
Producción de biodiésel a partir de las grasas extraídas de la borra de café: esterifcación con H2SO4 y transesterifcación con KOH
Authors: Aiello Mazzarri Cateryna , Salazar Yenmilet , Urribarrí Aidin , Arenas Dávila Elsy , Sánchez Fuentes John , Ysambertt Fredy
Published: September 2019

La búsqueda de materias primas no alimenticias es uno de los principales retos en la producción de biocombustibles. Para biodiésel, las investigaciones se orientan hacia el uso de cultivosno convencionales, así como hacia residuos o desechos con alto contenido de grasas y aceites. Laborra de café contiene una buena proporción de grasas y es un residuo comercial y doméstico. Eneste trabajo, se evaluó la producción de biodiésel utilizando las grasas extraídas de la borra del cafémediante un proceso en dos etapas. Las grasas extraídas de la borra de café con ebullición a reﬂujocon hexano como solvente presentaron una elevada acidez, 32,07 ± 0,01% (70,24 ± 0,03 mg KOH/ggrasa), lo que indica un alto contenido de ácidos grasos libres (AGL). Primero, las grasas se esterifcaron a 60ºC y 100 rpm, variando las condiciones de reacción. A las mejores condiciones, concentraciónde H2SO4 al 0,7%, RMG:MeOH de 1:6 durante 120 min, la acidez disminuyó por debajo del 1%, conel 94,92% de conversión AGL a ésteres metílicos. Se sometieron a un proceso de transesterifcacióncon KOH (1,5% m/v) en presencia de metanol (RMG:MeOH de 1:15) a 60ºC y 100 rpm durante 30 min.El biodiésel se separó por decantación y se purifcó mediante lavados sucesivos con agua acidulada,que resultó ser una mezcla de ésteres metílicos de los ácidos linoleico (48,40%), palmítico (36,21%),esteárico (8,69%) y oleico (6,69%), cuyas propiedades se ajustan a los requerimientos de las normasASTM D 6751 y EN 14214 .
Análisis del servicio de video streaming basado en el algoritmo FDASH sobre LTE
Authors: Campo Wilmar Yesid , Escobar Zapata Andrés Felipe , Imbachi Paz Juan Carlos
Published: September 2019

El video streaming es el servicio que mayor porcentaje de tráfco genera con respecto altotal de datos en las redes móviles de evolución de largo plazo LTE (por sus siglas en inglés). Por otraparte, el protocolo de Streaming Adaptativo Dinámico sobre HTPP (DASH, por sus siglas en inglés)ha sido seleccionado para la transmisión del video en las redes LTE. Así, en este artículo, se presentael análisis del comportamiento del servicio de video mediante una implementación de DASH bajológica difusa denominada FDASH (fuzzy DASH) en una red LTE, considerando diferente número deusuarios y traspasos (handover). Se analizan parámetros de calidad de servicio, como retardo, pérdida de paquetes, variación de pérdida de paquetes y rendimiento. Mediante este trabajo, se presentaFDASH como opción para el consumo del servicio de video, ya que permite adaptarse a las diferentescondiciones y exigencias de la tecnología inalámbrica móvil LTE.
Correlación del CBR con la resistencia a la compresión inconfnada
Authors: Sandoval Vallejo Eimar Andres , Rivera Mena William Albeiro
Published: September 2019

Los espesores y propiedades de las capas de una estructura de pavimento son altamentedeterminados por la rigidez de la subrasante. En algunos casos, no es posible la ejecución de pruebas directas para determinar dicha rigidez, al menos en la frecuencia requerida, y es necesario usarcorrelaciones con otras propiedades del suelo. Este artículo presenta resultados de un programaexperimental realizado para obtener correlaciones entre el CBR (por sus siglas en inglés) inalteradode suelos fnos con su resistencia a la compresión inconfnada o algunas propiedades índice. Se realizaron ensayos de CBR de laboratorio, resistencia a la compresión inconfnada, límites de Atterberg,granulometría y humedad natural. El número de muestras (38) fue seleccionado para garantizar seguridad y poder estadístico del 95 % y un coefciente de correlación de Pearson (r) mínimo de 0,60.Aunque no fue posible correlacionar el CBR con las propiedades índice evaluadas, se obtuvieroncorrelaciones entre el CBR natural y saturado, y la resistencia a la compresión inconfnada. Las correlaciones obtenidas, que tuvieron valores r > 0,80, fueron comparadas con algunas correlaciones en laliteratura entre el CBR y otros ensayos de resistencia no drenada. Para la misma resistencia, los CBRen este estudio son considerablemente menores que los de dichas correlaciones.
Social organization forms and institutional dynamics in the Tapajós-Arapiuns Reserve, state of Pará, Brazil
Authors: Andrade Marcelo Moraes de , Silva Danielle Wagner
Published: September 2019

In this text, we will discuss the interdependence in processes that influence, directly or indirectly, the conditions of natural resources’ appropriation and list social interactions that will draw corresponding institutional frameworks to which dwellers of the Tapajós-Arapiuns Extractive Reserve will affiliate. The research that supports this paper has a multidisciplinary character, relying on the usage of various tools for obtaining information. The analytical notion of institutions (formal and informal rules) has been used to reference the developed interpretations. The considered object of analysis reflects over practices in thedwellers’ way of life, taking into account the alternatives of correlation between social organization and production practices. It is perceived that dwellers’ social frameworks of organization were constructed, not only by traditional institutional apparatus, but also by using coordination instruments and landuse planning, to manage common resources. These principles create an institutional order based on a presumed rationality, with distinct unfoldings and social impacts.
The factors affecting the illness perception in patients staying in a state hospital
Authors: Kiyici Nida , Koc Zeliha , Saglam Zeynep , Masat Sevil
Published: September 2019

This study was conducted as descriptive with an aim to determine the factors affecting the illness perception in patients. The study was conducted with the participation of 130 patients receiving treatment in a state hospital between March 25 and May 11, 2018. The data were collected using a questionnaire form consisting of 20 questions and the Illness Perception Questionnaire. Kruskal–Wallis test, Spearman Correlation test in addition to descriptive statistics were used for the evaluation of the data. The item that the patients received the highest score from the Illness Perception Questionnaire was ‘My treatment would be effective in healing my disease’ (3.83 ± 1.21) and the lowest score was from the item ‘Nothing can improve my condition’ (1.93 ± 1.12). It is recommended to organise training in order to strengthen the patients to deal with the side effects of their diseases.

Keywords: Illness perception, behaviour, opinion.
Determination of self-efficacy levels of diabetic individuals
Authors: Keskin- Kızıltepe Selin , Koc Zeliha , Kavalali Erdogan Tugba , Saglam Zeynep
Published: September 2019

This study was conducted as descriptive with an aim to determine the self-efficacy levels of diabetic individuals. The study was conducted with the participation of 248 patients receiving inpatient treatment in a university hospital between July 20, 2017, and February 15, 2018. Data were collected using a 28-item questionnaire and Type 2 Diabetes Self-Efficacy Scale. In the evaluation of the data, descriptive statistics and one-way analysis of variance, T-test, Tukey HSD test, Levine test, Kruskal–Wallis test and Mann–Whitney U test were used. It was found that 77% of the patients had a chronic disease, 49.2% used oral anti-diabetes, 33.5% insulin and 15.7% used both oral anti-diabetes and insulin. The median score of the Type 2 Diabetes Self-Efficacy was determined as 70.00 (21–100). In this study, it is recommended to conduct training to increase the self-efficacy levels of diabetic patients.

Keywords: Diabetes, self-efficiency, behaviour, knowledge.
Algoritmo para calcular o aproximar la Raíz Cuadrada de un número real positivo
Authors: R Jesús R Guillén
Published: September 2019

En este artículo se desarrolla una variante del algoritmo tradicional para el cálculo de la raíz cuadrada, que al igual que el algoritmo tradicional se basa en la descomposición decimal del número, pero en su ejecución se usan los elementos de la demostración formal de dicho algoritmo, dando una alternativa que mejora el proceso de comprensión del mismo. En esta investigación se presenta un estudio detallado a través de varios resultados de naturaleza algorítmica que permiten ahondar en la comprensión del proceso de calcular o aproximar la raíz cuadrada de un número real positivo, lo cual hace que el artículo tenga un publico meta universitario.

Abstract

In this article a variant of the traditional algorithm for the calculation of the square root is developed, that like the traditional algorithm is based on the decimal decomposition of the number, but in its execution the elements of the formal demonstration of this algorithm are used, giving an alternative that improves the process of understanding it. In this research a detailed study is presented through several algorithmic-based results that allow us to delve into the compression of the process of calculating or approximate the square root of a positive real number, which makes the article have a university target audience.
Perfil de Sensibilidade aos Antibióticos na Infeção Urinária em Ambulatório no Distrito de Coimbra: Um Estudo Transversal
Authors: Curto Carolina , Rosendo Inês , Santiago Luiz
Published: September 2019

Introdução: As infeções do trato urinário são muito frequentes na comunidade. É essencial conhecer o padrão de sensibilidade aos antibióticos para uma terapêutica empírica racional. O objetivo deste estudo é determinar a prevalência e o perfil de suscetibilidade aos antibióticos dos microrganismos implicados em infeções urinárias a nível dos cuidados primários de saúde, no distrito de Coimbra.Material e Métodos: Neste estudo observacional e transversal, analisámos 7134 uroculturas positivas de laboratórios de ambulatório do distrito de Coimbra, no período de um ano. Na maioria dos casos, a urocultura positiva foi definida por um limiar de 105 unidadesformadoras de colónias por mililitro e executada através do sistema automatizado VITEK® 2, da bioMérieux. Usámos estatística descritiva e inferencial para determinar a prevalência e a sensibilidade aos antibióticos dos microrganismos e averiguar a relação destas com o sexo e com a idade.Resultados: Do total de uroculturas positivas, 83,4% pertencia ao sexo feminino e 41,0% pertencia à população acima dos 75 anos. A Escherichia coli foi o microrganismo mais frequente (63,9%), mas a sua prevalência foi inferior (p < 0,001) no sexo masculino e acima dos 75 anos. A sua sensibilidade à fosfomicina e à nitrofurantoína foi superior a 95% e à ciprofloxacina e ao cotrimoxazol foi inferior a 80%. As resistências foram mais frequentes (p < 0,001) no sexo masculino e nos mais idosos.Discussão: A Escherichia coli foi o microrganismo mais frequente, verificando-se menores percentagens de resistência desta à fosfomicina e à nitrofurantoína. A sensibilidade aos antibióticos foi menor em idades mais avançadas e também no sexo masculino (p < 0,001), populações onde a antibioterapia empírica pode ter maior risco de insucesso.Conclusão: Recomendamos a monitorização periódica do perfil de sensibilidade aos antibióticos, para eficaz informação aos médicos que têm de proceder a terapêutica empírica deste tipo de infeções, bem como a uniformização das técnicas de identificação das estirpes bacterianas e dos conjuntos de antibióticos a serem testados nos antibiogramas.
Análise do estresse ocupacional em funcionários de um hospital universitário
Authors: Pereira-Ferreira Jesuina Maria , Azevedo Alexandre Rodrigues Inácio , Rocha Michelle De Souza
Published: September 2019

Analisou-se como se encontra o estresse ocupacional de modo geral e, em específico, nas categorias sexo, faixa etária de idade, tempo de trabalho e vínculo de trabalho, em funcionários de um hospital universitário de Belo Horizonte-MG. Trata-se de um estudo de caso de abordagem quantitativa e a utilização da análise bivariada dos dados. Já na avaliação comparativa entre níveis de estresse ocupacional e categorias demonstrou que o sexo feminino possui maior propensão ao estresse.
Teaching Pragmatist Epistemology to Undergraduate Education Students
Authors: Saavedra-Caballero Elizabeth , De la Torre-Cabañas Carla Aranzazu , Suñiga-Muñoz Nicole , Huerta Rivero Maria Elena , Ballinas-Lázaro Zaira Celeste , Nasta-Salazar Laura , Zurita-Luévano Paola Mared , Reyes-Cárdenas Paniel , Marques-Serdán Simone
Published: September 2019

This book is an edited collection of essays made by undergraduate and postgraduate students and lecturers of education, particularly reflecting the experiences and thoughts that developed sparked by a series of lectures and readings on Pragmatist Epistemology given by Paniel Reyes-Cárdenas. The essays explore different routes of application and action that are released after considering the thoughts of the classical pragmatists: Charles Sanders Peirce, William James, John Dewey, Josiah Royce, Jane Addams, and George Herbert Mead.
Between Life and Death: Reducing Phototoxicity in Super-Resolution Microscopy
Authors: Tosheva Kalina L. , Yuan Yue , Pereira Pedro M. , Culley Siân , Henriques Ricardo
Published: September 2019

Super-Resolution Microscopy enables non-invasive, molecule-specific imaging of the internal structure and dynamics of cells with sub-diffraction limit spatial resolution. One of its major limitations is the requirement for high-intensity illumination, generating considerable cellular phototoxicity. This factor considerably limits the capacity for live-cell observations, particularly for extended periods of time. Here, we overview new developments in hardware, software and probe chemistry aiming to reduce phototoxicity. Additionally, we discuss how the choice of biological model and sample environment impacts the capacity for live-cell observations.
Perspectives on the Use of Hyaluronidase from the Red King Crab Hepatopancreas in Treating Filler Complications: In Vitro Investigations
Authors: Ponomareva Tatyana , Sliadovskii Dmitrii , Timchenko Maria , Timchenko Alexander , Sogorin Evgeny
Published: September 2019

The kinetics of the hydrolysis of hyaluronic acid (HA) of cosmetic fillers using thehomogenate of the red king crab hepatopancreas was studied for the first time. Turbidimetricanalysis of the reaction mixture revealed a bell–shaped time dependence of aggregation formation. The HA fillers were examined by atomic force microscopy (AFM) and it was found that they wererepresented by spherical–like structures. These structures were disrupted under the action of thehomogenate of the red king crab hepatopancreas. It was shown that the prepared homogenate hasthe activity which is similar to that observed in the commercially available hyaluronidase products.The preparation with hyaluronidase activity obtained from the red king crab hepatopancreas couldbe used as potentially safe product for treating filler complications.
Harvest Stage Recognition and Potential Fruit Damage Indicator for Berries Based on Hidden Markov Models and the Viterbi Algorithm
Authors: Orchard Marcos E. , Muñoz-Poblete Carlos , Huircan Juan-Ignacio , Galeas Patricio , Rozas Heraldo
Published: September 2019

This article proposes a monitoring system that allows to track transitions between different stages in the berry harvesting process (berry picking, waiting for transport, transport, and arrival to the packing) solely using information from temperature and vibration sensors located in the basket. The monitoring system assumes a characterization of the process based on Hidden Markov Models and uses the Viterbi algorithm to perform inference and estimate the most likely state trajectory. The obtained state trajectory estimate is then used to compute a potential damage indicator in real-time. The proposed methodology does not require information about the weight of the basket to identify each of the different stages, which makes it effective and more efficient than other alternatives available in the industry.
MicroRNA-mRNA Co-sequencing Identifies Transcriptional and Post-transcriptional Regulatory Networks Underlying Muscle Wasting in Cancer Cachexia
Authors: Javier Fernandez Geysson , Henrique Ferreira Juarez , José Vechetti-Júnior Ivan , Nazario de Moraes Leonardo , Santiloni Cury Sarah , Paccielli Freire Paula , Gutiérrez Jayson , Ferretti Renato , Dal-Pai-Silva Maeli , Regina Rogatto Silvia , Francisco Carvalho Robson
Published: September 2019

Cachexia is a complex metabolic syndrome characterized by loss of skeletal muscle, leading to a significant weight loss that impacts patient morbidity and mortality. Given the complexity of gene regulatory networks that control gene expression, our objective was to perform an integrative mRNA and miRNA profiling to identify genetic programs that capture essential mechanistic details that promote muscle atrophy in cancer cachexia. Here, we used RNA sequencing to analyze miRNAs and mRNAs expression profiles in tibialis anterior (TA) muscles of the Lewis lung carcinoma model of cancer cachexia. In addition, we compared these findings with RNA-Seq data from C2C12 myotubes treated in vitro with the cachectic factors tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ). Extracellular matrix (ECM) alterations were validated by picrosirius staining, western blot, and fractal dimension analyses. We found 1,008 mRNAs and 18 miRNAs differentially expressed in cachectic mice. This set of genes was associated with the ECM, proteolysis, and inflammatory response. Enrichment analysis of transcriptional factor binding sites revealed activation of the atrophy-related transcriptional factors: NF-κB, Stat3, AP-1, and FoxO. Furthermore, the integration of mRNA and miRNA expression profiles identified post-transcriptional regulation by miRNAs of genes involved in ECM organization, cell migration, transcription factors binding, ion transport, and FoxO signaling pathway. C2C12 myotubes treated with TNF-α and IFN-γ similarly down-regulate subsets of ECM genes, including collagens. Our integrative analysis of miRNA-mRNA co-profiles comprehensive characterized regulatory relationships of molecular pathways and revealed miRNAs targeting ECM-associated genes in cancer cachexia. We also confirmed in C2C12 myotubes that changes in ECM-associated genes are dependent on inflammatory signaling of the cytokines TNF-α and IFN-γ.
Well-Posedness and Long-Time Behavior of Solutions for Two-Dimensional Navier-Stokes Equations with Infinite Delay and General Hereditary Memory
Authors: Zheng Yasi , Liu Wenjun , Liu Yadong
Published: September 2019

We address the dynamics of two-dimensional Navier-Stokes models with infinite delay and hereditary memory, whose kernels are a much larger class of functions than the one considered in the literature, on a bounded domain. We prove the existence and uniqueness of weak solutions by means of Faedo-Galerkin method. Moreover, we establish the existence of global attractor for the system with the existence of a bounded absorbing set and asymptotic compact property.