Recent Developments in Complex and Spatially Correlated Functional Data

As high-dimensional and high-frequency data are being collected on a large scale, the development of new statistical models is being pushed forward. Functional data analysis provides the required statistical methods to deal with large-scale and complex data by assuming that data are continuous functions, e.g., a realization of a continuous process (curves) or continuous random fields (surfaces), and that each curve or surface is considered as a single observation. Here, we provide an overview of functional data analysis when data are complex and spatially correlated. We provide definitions and estimators of the first and second moments of the corresponding functional random variable. We present two main approaches: The first assumes that data are realizations of a functional random field, i.e., each observation is a curve with a spatial component. We call them 'spatial functional data'. The second approach assumes that data are continuous deterministic fields observed over time. In this case, one observation is a surface or manifold, and we call them 'surface time series'. For the two approaches, we describe software available for the statistical analysis. We also present a data illustration, using a high-resolution wind speed simulated dataset, as an example of the two approaches. The functional data approach offers a new paradigm of data analysis, where the continuous processes or random fields are considered as a single entity. We consider this approach to be very valuable in the context of big data.Comment: Some typos fixed and new references adde

Nonparametric Estimation of Functional Dynamic Factor Model

For many phenomena, data are collected on a large scale, resulting in high-dimensional and high-frequency data. In this context, functional data analysis (FDA) is attracting interest. FDA deals with data that are defined on an intrinsically infinite-dimensional space. These data are called functional data. However, the infinite-dimensional data might be driven by a small number of latent variables. Hence, factor models are relevant for functional data. In this paper, we study functional factor models for time-dependent functional data. We propose nonparametric estimators under stationary and nonstationary processes. We obtain estimators that consider the time-dependence property. Specifically, we use the information contained on the covariances at different lags. We show that the proposed estimators are consistent. Through Monte Carlo simulations, we find that our methodology outperforms the common estimators based on functional principal components. We also apply our methodology to monthly yield curves. In general, the suitable integration of time-dependent information improves the estimation of the latent factors.Comment: 28 pages, 6 figure

Nonparametric trend estimation in functional time series with application to annual mortality rates

Here, we address the problem of trend estimation for functional time series. Existing contributions either deal with detecting a functional trend or assuming a simple model. They consider neither the estimation of a general functional trend nor the analysis of functional time series with a functional trend component. Similarly to univariate time series, we propose an alternative methodology to analyze functional time series, taking into account a functional trend component. We propose to estimate the functional trend by using a tensor product surface that is easy to implement, to interpret, and allows to control the smoothness properties of the estimator. Through a Monte Carlo study, we simulate different scenarios of functional processes to show that our estimator accurately identifies the functional trend component. We also show that the dependency structure of the estimated stationary time series component is not significantly affected by the error approximation of the functional trend component. We apply our methodology to annual mortality rates in France

Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios

[EN] One of the three main use cases of the fifth generation of mobile networks (5G) is massive machine-type communications (mMTC). The latter refers to the highly synchronized accesses to the cellular base stations from a great number of wireless devices, as a product of the automated exchange of small amounts of data. Clearly, an efficient mMTC is required to support the Internet-of-Things (IoT). Nevertheless, the method to change from idle to connected mode, known as the random access procedure (RAP), of 4G has been directly inherited by 5G, at least, until the first phase of standardization. Research has demonstrated the RAP is inefficient to support mMTC, hence, access control schemes are needed to obtain an adequate performance. In this paper, we compare the benefits of using different filtering methods to configure an access control scheme included in the 5G standards: the access class barring (ACB), according to the intensity of access requests. These filtering methods are a key component of our proposed ACB configuration scheme, which can lead to more than a three-fold increase in the probability of successfully completing the random access procedure under the most typical network configuration and mMTC scenario.This research has been supported in part by the Ministry of Economy and Competitiveness of Spain under Grant TIN2013-47272-C2-1-R and Grant TEC2015-71932-REDT. The research of I. Leyva-Mayorga was partially funded by grant 383936 CONACYT-GEM 2014.Leyva-Mayorga, I.; Rodríguez-Hernández, MA.; Pla, V.; Martínez Bauset, J. (2019). Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios. Electronics. 8(1):1-18. https://doi.org/10.3390/electronics8010027S11881Laya, A., Alonso, L., & Alonso-Zarate, J. (2014). Is the Random Access Channel of LTE and LTE-A Suitable for M2M Communications? A Survey of Alternatives. IEEE Communications Surveys & Tutorials, 16(1), 4-16. doi:10.1109/surv.2013.111313.00244Biral, A., Centenaro, M., Zanella, A., Vangelista, L., & Zorzi, M. (2015). The challenges of M2M massive access in wireless cellular networks. Digital Communications and Networks, 1(1), 1-19. doi:10.1016/j.dcan.2015.02.001Tello-Oquendo, L., Leyva-Mayorga, I., Pla, V., Martinez-Bauset, J., Vidal, J.-R., Casares-Giner, V., & Guijarro, L. (2018). Performance Analysis and Optimal Access Class Barring Parameter Configuration in LTE-A Networks With Massive M2M Traffic. IEEE Transactions on Vehicular Technology, 67(4), 3505-3520. doi:10.1109/tvt.2017.2776868Tavana, M., Rahmati, A., & Shah-Mansouri, V. (2018). Congestion control with adaptive access class barring for LTE M2M overload using Kalman filters. Computer Networks, 141, 222-233. doi:10.1016/j.comnet.2018.01.044Lin, T.-M., Lee, C.-H., Cheng, J.-P., & Chen, W.-T. (2014). PRADA: Prioritized Random Access With Dynamic Access Barring for MTC in 3GPP LTE-A Networks. IEEE Transactions on Vehicular Technology, 63(5), 2467-2472. doi:10.1109/tvt.2013.2290128De Andrade, T. P. C., Astudillo, C. A., Sekijima, L. R., & Da Fonseca, N. L. S. (2017). The Random Access Procedure in Long Term Evolution Networks for the Internet of Things. IEEE Communications Magazine, 55(3), 124-131. doi:10.1109/mcom.2017.1600555cmWang, Z., & Wong, V. W. S. (2015). Optimal Access Class Barring for Stationary Machine Type Communication Devices With Timing Advance Information. IEEE Transactions on Wireless Communications, 14(10), 5374-5387. doi:10.1109/twc.2015.2437872Tello-Oquendo, L., Pacheco-Paramo, D., Pla, V., & Martinez-Bauset, J. (2018). Reinforcement Learning-Based ACB in LTE-A Networks for Handling Massive M2M and H2H Communications. 2018 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2018.8422167Leyva-Mayorga, I., Rodriguez-Hernandez, M. A., Pla, V., Martinez-Bauset, J., & Tello-Oquendo, L. (2019). Adaptive access class barring for efficient mMTC. Computer Networks, 149, 252-264. doi:10.1016/j.comnet.2018.12.003Kalalas, C., & Alonso-Zarate, J. (2017). Reliability analysis of the random access channel of LTE with access class barring for smart grid monitoring traffic. 2017 IEEE International Conference on Communications Workshops (ICC Workshops). doi:10.1109/iccw.2017.7962744Leyva-Mayorga, I., Tello-Oquendo, L., Pla, V., Martinez-Bauset, J., & Casares-Giner, V. (2016). Performance analysis of access class barring for handling massive M2M traffic in LTE-A networks. 2016 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2016.7510814Arouk, O., & Ksentini, A. (2016). General Model for RACH Procedure Performance Analysis. IEEE Communications Letters, 20(2), 372-375. doi:10.1109/lcomm.2015.2505280Zhang, Z., Chao, H., Wang, W., & Li, X. (2014). Performance Analysis and UE-Side Improvement of Extended Access Barring for Machine Type Communications in LTE. 2014 IEEE 79th Vehicular Technology Conference (VTC Spring). doi:10.1109/vtcspring.2014.7023042Cheng, R.-G., Chen, J., Chen, D.-W., & Wei, C.-H. (2015). Modeling and Analysis of an Extended Access Barring Algorithm for Machine-Type Communications in LTE-A Networks. IEEE Transactions on Wireless Communications, 14(6), 2956-2968. doi:10.1109/twc.2015.2398858Widrow, B., Glover, J. R., McCool, J. M., Kaunitz, J., Williams, C. S., Hearn, R. H., … Goodlin, R. C. (1975). Adaptive noise cancelling: Principles and applications. Proceedings of the IEEE, 63(12), 1692-1716. doi:10.1109/proc.1975.1003

Diseño y simulación de un sistema de caldera basados en estándares y normas internacionales

Se muestran los resultados de la simulación de una caldera con la finalidad de producir vapor a 2 MPa y 500 grados centígrados para tener aplicación en sistemas de cogeneración usando bagazo de caña de azúcar para ser implementados en ingenios azucareros, además se muestra la simulación de los principales sistemas de control utilizados en el generador de vapor basado en un controlador lógico programabl

Automatic learning framework for pharmaceutical record matching

Pharmaceutical manufacturers need to analyse a vast number of products in their daily activities. Many times, the same product can be registered several times by different systems using different attributes, and these companies require accurate and quality information regarding their products since these products are drugs. The central hypothesis of this research work is that machine learning can be applied to this domain to efficiently merge different data sources and match the records related to the same product. No human is able to do this in a reasonable way because the number of records to be matched is extremely high. This article presents a framework for pharmaceutical record matching based on machine learning techniques in a big data environment. The proposed framework aims to explode the well-known rules for the matching of records from different databases for training machine learning models. Then the trained models are evaluated by predicting matches with records that do not follow these known rules. Finally, the production environment is simulated by generating a huge amount of combinations of records and predicting the matches. The obtained results show that, despite the good results obtained with the training datasets, in the production environment, the average accuracy of the best model is around 85%. That shows that matches which do not follow the known rules can be predicted and, considering that there is not a human way to process this amount of data, the results are promising.This work was supported by the Research Program of the Ministry of Economy and competitiveness, Government of Spain, through the DeepEMR Project, under Grant TIN2017-87548-C2-1-

Rigor periodístico de las informaciones del facebook Los no vacunados en sus seguidores. Lima. 2021

La investigación sobre el rigor periodístico de las informaciones del Facebook Los no vacunados en sus seguidores. Lima. 2021, tuvo como objetivo general: Determinar el rigor periodístico de las informaciones del Facebook Los no vacunados en sus seguidores. Lima. 2021 El nivel de la investigación es básica porque parte de un marco teórico y permanece en él debido a que esta investigación tiene como finalidad formular nuevas ideas o modificar las existentes respecto al tema del rigor periodístico. Los resultados que se obtuvo es que para los seguidores del grupo de Facebook Los no vacunados, sí existe un rigor periodístico, pero se comprobó también por la encuesta que la mayoría de los del grupo no cuestionan las informaciones que se dan a conocer sino que creen ciegamente todo lo que se publica y eso es muy peligroso porque no desarrollan un sentido crítico y los puede llevar a realizar locuras como no vacunarse, exponiendo su salud y la de sus familiares, a pesar de que hay suficiente evidencia científica que avala las vacunas y no vacunarse puede ser fatal y por lo tanto se llega a la conclusión de que esto debe cambiar porque de lo contrario las personas caerán en la desinformación

Thermal Conduction in Systems out of Hydrostatic Equilibrium

We analyse the effects of thermal conduction in a relativistic fluid, just after its departure from hydrostatic equilibrium, on a time scale of the order of thermal relaxation time. It is obtained that the resulting evolution will critically depend on a parameter defined in terms of thermodynamic variables, which is constrained by causality requirements.Comment: 16 pages, emTex (LaTex 2.09). To appear in Classical and Quantum Gravit