Decision-making model for designing telecom products/services based on customer preferences and non-preferences

The design of the packages of products/services to be offered by a telecom company to its clients is a complex decision-making process that must consider different criteria to achieve both customer satisfaction and optimization of the company’s resources. In this process, Intuitionistic Fuzzy Sets (IFSs) can be used to manage uncertainty and better represent both preferences and non-preferences expressed by people who value each proposed alternative. We present a novel approach to design/develop new products/services that combines the Lean Six Sigma methodology with IFSs. Its main contribution comes from considering both preferences and nonpreferences expressed by real clients, whereas existing proposals only consider their preferences. By also considering their non-preferences, it provides an additional capacity to manage the high uncertainty in the selection of the commercial plan that best suits each client’s needs. Thus, client satisfaction is increased while improving the company’s corporate image, which will lead to customer loyalty and increased revenue. To validate the presented proposal, it has been applied to a real case study of the telecom sector, in which 2135 users have participated. The results obtained have been analysed and compared with those obtained with a model that does not consider the non-preferences expressed by users.Spanish Ministry of Science and Innovation (State Research Agency)Junta de Andalucia PID2019-103880RB-I00 PID2019-109644RB-I00 PY20_0067

Modelling flexible thrust performance for trajectory prediction applications in ATM

Reduced thrust operations are of widespread use nowadays due to their inherit benefits for engine conservation. Therefore, in order to enable realistic simulation of air traffic management (ATM) scenarios for purposes such as noise and emissions assessment, a model for reduced thrust is required. This paper proposes a methodology for modelling flexible thrust by combining an assumed temperature (AT) polynomial model identified from manufacturer take-off performance data and public thrust models taken from typical ATM performance databases. The advantage of the proposed AT model is that it only depends on the take-off conditions —runway length, airport altitude, temperature, wind, etc. The results derived from this methodology were compared to simulation data obtained from manufacturer’s take-off performance tools and databases. This comparison revealed that the polynomial model provides AT estimations with sufficient accuracy for their use in ATM simulation. The Base of Aircraft Data (BADA) and the Aircraft Noise and Performance (ANP) database were chosen as representative of aircraft performance models commonly used in ATM simulation. It was observed that there is no significant degradation of the overall accuracy of their thrust models when using AT, while there is a correct capture of the corresponding thrust reduction.Peer ReviewedPostprint (published version

Functional activation and connectivity of the left inferior frontal gyrus during lexical and phonological retrieval

Being language a paradigm of structural and functional asymmetry in cognitive processing, the left Inferior Frontal Gyrus has been consistently related to speech production. In fact, it has been considered a key node in cortical networks responsible for different components of naming. However, isolating these components (e.g., lexical, syntactic, and phonological retrieval) in neuroimaging studies is difficult due to the use of different baselines and tasks. In the present study, functional activation and connectivity of the left inferior frontal gyrus was explored using functional magnetic resonance imaging. Participants performed a covert naming task (pressing a button based on a phonological characteristic). Two conditions were compared: drawings of objects and single letters (baseline condition). Differences in activation and functional connectivity were obtained for objects and letters in different areas of the left Inferior Frontal Gyrus. The pars triangularis was involved in the retrieval of lexical-phonological information, showing a pattern of connectivity with temporal areas in the search for the name of objects and with perisylvanian areas for letters. Selection of phonological information seems to involve the pars opercularis both to letters and objects but recruiting supramarginal and superior temporal areas to letters, probably related to orthographicphonological conversion. The results support the notion of the left Inferior Frontal Gyrus as a buffer forwarding neural information across cortical networks responsible for different components of speech productionThis research was funded by the Spanish Ministerio de Economía y Competitividad (Grant number: PSI2013 43594-R), Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia (Grant number: ED431C-2021/04, from the EDRF/FEDER) and by a post-doctoral fellowship from Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia (Grant number: ED481B2016/078-0). The funding sources were not involved in any aspect of the research or the submission of this studyS

A hybrid model for decision-making in the information and Communications Technology sector

The majority of businesses in the Information and Communications Technology (ICT) sector face decision-making problems on a daily basis. Most of these problems are based on contexts of uncertainty, where decisions are founded on qualitative information which may be imprecise or perception-based. In these cases, the information which is expressed by experts and users of evaluated services can be treated using processes of computing with words (CW). In this paper, we present a hybrid decision-making model especially designed for the ICT sector whereby the experts have the support of an intelligent system which provides information about the opinions of users related to those problems which are to be analysed. These opinions are obtained by using different mechanisms and techniques when users conduct business with the service provider. In addition, we employ a procedure for obtaining consensus between experts which enriches and strengthens the decision-making process

Misio kritikorako MCPTT zerbitzuaren integrazioa 5G-NFV ertzeko konexio-sare batean

5G teknologiaren helburu handienetako bat da egungo komunikazio-ekosistema hobetzea. Helburu hori lortzeko, etorkizun handiko irtenbidea da ertzeko konexio-sareen erabilpena, batez ere misio kritikoko zerbitzuak erabiltzen direnean, ertzeko konexio-sareek konputazio-mailan eta komunikazio-trukeari dagokion abiaduran ekartzen dituzten garapenak baliatu ahal izatean. Testu hau MCPTT zerbitzuan ardaztuko da; hau da, gaur egungo larrialdi-talde koordinatuei ahots-komunikazio klasikoa eskaintzen dion horretan. MCPTT zerbitzua 5G ekosisteman integratzearen erronka nagusiak zerikusia du horren funtzionamenduaren orkestrazioarekin, hain zuzen ere MCPTT zerbitzuak sareko azpiegitura beste hainbat zerbitzu eta sareko operadorerekin partekatu behar baitu. Artikulu honetan, software bidez definitutako sare-funtzioak eta sare-funtzioak birtualizatzeko estandar teknologikoak konbinatzen dituen arkitektura bat proposatzen da, 5G estandarrarekin bat datorrena. Ertzeko cluster batean, slicing sareko arkitekturaren birtualizazio-mekanismoa erabiliz, MCPTT zerbitzua nola hedatu den ere azaltzen da. Zehazki, MCPTT sareko funtzio birtuala osatzen duten unitateak deskribatzen dira. Bestalde, sistema osoaren orkestrazioa alertak arintzeko modulu batekin batera lan egiten duen monitorizazio-sistema batean oinarritzen da. Monitorizazio-sistemak zerbitzuaren eragiketarekin, baliabideen erabilerarekin edota irrati bidezko sarbidearen operazioarekin zerikusia duten hainbat parametro jaso eta biltzen ditu denbora errealean, eta, irakurritako balioen arabera, bi alerta mota eman ditzake: batetik, baliabide birtualei dagozkienak bereizten dira, eta, bestetik, irrati-baliabideei eragiten dietenak. Azkenik, MCPTT zerbitzua hedatu ondoren lortutako emaitzak ere aurkezten dira. Horretarako, lehenik eta behin, zerbitzuaren funtzionamendua dinamikoki doitzeko erabili diren monitorizazio-metrikak deskribatzen dira, eta, jarraian, sistemaren probak gauzatzeko zehaztutako agertokiak aurkezten dira. Emaitzen arabera, sistemak agertokietako eskaerei espero bezala erantzuten diela eta bere funtzionamendua dinamikoki ondo egokitzen duela frogatu da.; One of the major objectives of 5G technology is to improve the current communication ecosystem. The use of edge networking is a promising solution to this goal, especially when critical mission services are used. This text will focus on MCPTT, a service that provides a coordinated emergency team with classic voice communication. The main challenge in integrating the MCPTT service into the 5G ecosystem relates to the orchestration of its operation, as the MCPTT service has to share the network infrastructure with many other services and network operators. This paper presents an architecture that is in accordance with the 5G standard and at the same time combines software-defined networking and network functions virtualization. It also shows how MCPTT service has been deployed in a data center located at the edge, by using the slicing network architecture virtualization mechanism. Specifically, the units that constitute the MCPTT virtual network function are described. The orchestration of the entire system is based on a monitor system that works in conjunction with an alert module. The monitoring system receives in real time and gathers a number of parameters related to service operation, use of resources, and operation of radio access, and may provide two types of alert according to the values read: on the one hand, those related to virtual resources, and, on the other, those alerts affecting radio resources. Results obtained after deploying the MCPTT service are also presented. For this purpose, first, the monitoring metrics used to dynamically adjust the operation of the service are described, and then the defined testing scenarios are presented. Results show that the system responds as expected to the different demands of the scenarios, and also that it dynamically adjusts its functioning properly

IAA : Información y actualidad astronómica (27)

Sumario : Sagitario A*: el agujero negro en el corazón de la Vía Láctea.-- ESPECIAL: Año Internacional de la Astronomía.-- HISTORIAS DE ASTRONOMÍA. Chandrasekhar y los agujeros negros.-- DECONSTRUCCIÓN Y otros ENSAYOS. Binarias de rayos X.-- EL “MOBY DICK” DE...Pedro Amado.-- ACTUALIDAD.-- ENTRE BASTIDORES.-- CIENCIA: PILARES E INCERTIDUMBRES. El tiempo.-- ACTIVIDADES IAA.N

Understanding enhanced charge storage of phosphorus-functionalized graphene in aqueous acidic electrolytes

The mechanisms behind enhanced charge storage of P-functionalized carbons are unraveled for the first time using non-porous graphene oxide treated with phosphoric acid and annealed at either 400 or 800 degrees C. The electrochemical study in 1 M H2SO4 reveals that phosphorus groups boost charge storage and electrochemical stability, with more effect for the higher annealing temperature. Annealing at 800 degrees C also leads to the material withstanding 60,000 charge-discharge cycles with no capacitance loss at 1.5 V. The improvement in the electrochemical performance is shown to be mainly governed by the change in surface chemistry comprehensively studied with NMR, FTIR and XPS characterization techniques. The collective analysis of electrochemical response and surface chemistry demonstrates that enhanced charge storage by phosphorus-functionalized graphene materials is made possible due to the following synergistic mechanisms: i) non-Faradaic charging; ii) nascent hydrogen storage in the interlayer; iii) benzoquinoneto-hydroquinone redox processes; iv) phosphate-to-phosphonate like transformation. From the practical perspective, the stored charge can be boosted due to the higher capacitance upon prior electrochemical activation in the vicinity of oxygen evolution potential and the wider usable electrochemical window enabled by phosphorus-related groups. (C) 2020 The Author(s). Published by Elsevier Ltd.The authors thank the European Union (Graphene Flagship, Core 2, Grant number 785219) and the Spanish Ministry of Science and Innovation (MICINN/FEDER) (RTI2018-096199-B-I00) for the financial support of this work. J. L. G. U. is very thankful to the Spanish Ministry of Education, Science and Universities (MICINN) for the FPU grant (16/03498). We also want to acknowledge the company GRAPHENEA for supplying the graphene oxide used in this work and Yan Zhang from CIC Energigune for collecting FTIR spectra

The hidden microbial ecosystem in the perennial ice from a Pyrenean ice cave.

Over the last years, perennial ice deposits located within caves have awakened interest as places to study microbial communities since they represent unique cryospheric archives of climate change. Since the beginning of the twentieth century, the temperature has gradually increased, and it is estimated that by the end of this century the increase in average temperature could be around 4.0°C. In this context of global warming the ice deposits of the Pyrenean caves are undergoing a significant regression. Among this type of caves, that on the Cotiella Massif in the Southern Pyrenees is one of the southernmost studied in Europe. These types of caves house microbial communities which have so far been barely explored, and therefore their study is necessary. In this work, the microbial communities of the Pyrenean ice cave A294 were identified using metabarcoding techniques. In addition, research work was carried out to analyze how the age and composition of the ice affect the composition of the bacterial and microeukaryotic populations. Finally, the in vivo effect of climate change on the cellular machinery that allow microorganisms to live with increasing temperatures has been studied using proteomic techniques

Predicción de la producción y rendimiento de frijol, con modelos de redes neuronales artificiales y datos climáticos

The state of Zacatecas ranks first in the production of rainfed beans in Mexico. Due to the economic and food security repercussions, it is important to predict yields, production and harvested area, as well as to know the climatological variables that have the greatest effect on bean cultivation. The objectives of the present work were 1) to develop ANN models for the prediction of the harvested area, yields and production of rainfed beans in the state of Zacatecas, using data on maximum and minimum air temperature, precipitation and evaporation during the period 1988-2019. 2) to determine the input variables that have the greatest influence on bean production and yield through sensitivity analysis. Due to the limited availability of climatic data, the Climatol library of the R statistical package was used to fill in missing data. The results show that the RNA models capture the influence of climate on bean production, with an overall efficiency of 0.89 for Rto and 0.86 for SC. The production was estimated using the outputs, Rto and SC, from RNA models and an R2 =0.80 was obtained. According to the sensitivity analysis, Evaporation of the cycle is the most important variable in predicting yield, while precipitation in August (Pp_Ago) and minimum temperature (Tmin) had a greater influence on production.  El estado de Zacatecas ocupa el primer lugar en la producción de frijol de temporal en México. Debido a las repercusiones económicas y de seguridad alimentaria, es importante la predicción de los rendimientos, producción y superficie cosechada, igualmente, conocer las variables climatológicas que mayor efecto tienen en el cultivo de frijol. Los objetivos del presente trabajo fueron 1) desarrollar modelos de redes neuronales artificiales RNA para la predicción de la superficie cosechada (SC), los rendimientos (Rto) y la producción (P) de frijol de temporal en el estado de Zacatecas, empleando datos de temperatura máxima y mínima del aire, precipitación y evaporación durante el periodo 1988-2019. 2) realizar un análisis de sensibilidad para determinar las variables de entrada que tienen mayor influencia en la producción y rendimiento de frijol. Debido a la limitada disponibilidad de datos climáticos, se usó la librería Climatol del paquete estadístico R, para el llenado de datos faltantes. Los resultados muestran que los modelos de RNA son capaces de detectar la influencia del clima en la producción de frijol. La eficiencia global en los modelos RNA fue de 0.89 para Rto y 0.86 para SC.  La producción se estimó con los modelos de RNA para Rto y SC y se obtuvo un R2 =0.80. De acuerdo al análisis de sensibilidad, la evaporación del ciclo del cultivo (Eva) es la variable más importante en la predicción del rendimiento, mientras que la precipitación de agosto (Pp_Ago) y la temperatura mínima (Tmin) influyeron más en la producción

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools