Passengers satisfaction with the technologies used in smart airports: an empirical study from a gender perspectiva

This work was supported by Banco Santander-Autonomous University of Madrid − 10th Call for Interuniversity Cooperation Projects UAMSantander-CEAL-AL/2017-07 (Smart Airports: Impact on airport quality and effects on tourist competitiveness

Inclusive education in higher education?

The present study provides partial findings from research currently underway at the University of Seville: Hurdles & Help as Perceived by University Students Disabilities. (Directed by Dr. Anabel Mori~na, project funding: MICINN, I+D+I, ref. EDU 2010-16264). How does the university, as an insti- tution, open doors and/or put hurdles in the way of students with special needs? The present study adopts a qualitative methodological approach. More specifically, biographic-narrative methods are employed to give shape to a series of life sto- ries. A wide range of data gathering techniques were used, including discussion groups, in-depth interviews, classroom observation sessions, pho- tographs, biograms, etc. Data analysis was carried out in two phases. In the first, the focus was on individual life stories. The second phase involved applying comparative data analysis methods to transcriptions of documents generated using aforementioned methods, in line with Miles and Huberman (1994). Maxqda10 data analysis soft- ware was the tool of choice. Results will be dis- cussed with the following questions as a backdrop: Is the University inclusive? We will analyse institu- tional barriers and aids, as perceived by the stu- dents themselves. Architectural and structural hurdles affecting access to university classrooms, infrastructures and other spaces will be assessed here. Finally, we will take a closer look at student expectations with respect to their conception of the ideal university. Is the University an institution that opens or closes its doors to students with dis- abilities? Based on the analysis in the previous sec- tion, a number of conclusions can be reached. The first and foremost is the fact that the students coincided in their opinions, independently of the disability they might have and the courses studied, both when identifying help and barriers. Having said that, the number of barriers identified sur- passed the help

Dominance of phage particles carrying antibiotic resistance genes in the viromes of retail food sources

The growth of antibiotic resistance has stimulated interest in understanding the mechanisms by which antibiotic resistance genes (ARG) are mobilized. Among them, studies analyzing the presence of ARGs in the viral fraction of environmental, food and human samples, and reporting bacteriophages as vehicles of ARG transmission, have been the focus of increasing research. However, it has been argued that in these studies the abundance of phages carrying ARGs has been overestimated due to experimental contamination with non-packaged bacterial DNA or other elements such as outer membrane vesicles (OMVs). This study aims to shed light on the extent to which phages, OMVs or contaminating non-packaged DNA contribute as carriers of ARGs in the viromes. The viral fractions of three types of food (chicken, fish, and mussels) were selected as sources of ARG-carrying phage particles, whose ability to infect and propagate in an Escherichia coli host was confirmed after isolation. The ARG-containing fraction was further purified by CsCl density gradient centrifugation and, after removal of DNA outside the capsids, ARGs inside the particles were confirmed. The purified fraction was stained with SYBR Gold, which allowed the visualization of phage capsids attached to and infecting E. coli cells. Phages with Myoviridae and Siphoviridae morphology were observed by electron microscopy. The proteins in the purified fraction belonged predominantly to phages (71.8% in fish, 52.9% in mussels, 78.7% in chicken sample 1, and 64.1% in chicken sample 2), mainly corresponding to tail, capsid, and other structural proteins, whereas membrane proteins, expected to be abundant if OMVs were present, accounted for only 3.8–21.4% of the protein content. The predominance of phage particles in the viromes supports the reliability of the protocols used in this study and in recent findings on the abundance of ARG-carrying phage particles.This work was supported by the Spanish Ministerio de Ciencia e Innovación (PID2020-113355GB-I00), the Agencia Estatal de Investigación (AEI) and the European regional fund (ERF). The study was partially supported by the Generalitat de Catalunya (2017SGR170). PB-P has a grant from the Spanish Ministry of Economy, Industry and Competitiveness (BES-2017-081296), SM-C has a grant from Colciencias (Republic of Colombia) and LR-R is lecturer of the Serra-Hunter program, Generalitat de Catalunya. MDR-B has a Margarita Salas fellowship from the Spanish Ministerio de Universidades

Polychrony as Chinampas

We study the flow of signals through paths with the following condition: a node emits a signal if two incoming signals from other nodes arrive coincidentally or if it receives an external stimuli. We apply our study to count and describe families of polychrony groups on a line, and we introduce triangular sequences.Comment: 32 pages. We refocus our study on nonlinear signal-flow graphs. We add possible generalizations of our wor

Crecimiento y contenido bioquímico comparativo de larvas en postflexión de sardina de tres zonas del Mediterráneo Occidental (Ebro, bahías de Almaría y Málaga)

Late larval stages of sardine (16-23 mm) were sampled during the 2003 spawning season in their nursery grounds located off the Ebro river mouth, on the Catalan coast, and in two bays of the Alborán Sea coasts, the Bay of Almería and the Bay of Málaga. The daily growth analysis of each sampled population revealed faster growth in the Ebro sardine larvae than in both of the Alboran Sea larval populations. This fact is supported by their greater content with age of DNA, RNA and protein. However, the significantly higher carbohydrate content of the Bay of Almería sardine larvae and a higher Fulton’s index, indicative of energy storage of individuals, in both of the sardine populations sampled in the Bays of Almería and Málaga show evident differences in the daily growth of the Alborán Sea larvae from those originating in the Ebro region. Late larval growth in the Alborán Sea sardine tends to favour an increase in body mass rather than in body length. This study hypothesises that the productivity pulses off the Alboran Sea coasts induced by the north and northwestern wind regimes may be responsible for the growth pulses observed in the otolith microstructure.Estados avanzados de larvas de sardina (de 16 a 23 mm) fueron muestreados durante el invierno de 2003 en zonas de alevinaje situadas en la desembocadura del río Ebro, en la costa catalana, y en las bahías de Almería y de Málaga, en las costas del Mar de Alborán. El análisis del crecimiento diario de cada población larvaria muestreada, evidencia un mayor crecimiento en las larvas procedentes del Ebro respecto a las nacidas en el Mar de Alborán. Esta observación se corrobora con un mayor incremento con la edad de los contenidos en DNA, RNA y proteínas de la población larvaria de sardina del río Ebro. Sin embargo, un contenido significativamente mayor de carbohidratos en la población de Almería, así como un mayor índice de Fulton, indicador de energía almacenada en un individuo, en ambas poblaciones del Mar de Alborán, evidencia una estrategia diferenciada de crecimiento de estas poblaciones larvarias con respecto a las procedentes del río Ebro. En consecuencia, las larvas de sardina del Mar de Alborán muestran una tendencia a crecer más en masa que en longitud, como lo evidencia el crecimiento relativo del peso seco, DNA, RNA y proteínas en relación con su longitud estándar. En este estudio se plantea la hipótesis de que los pulsos productivos en el Mar de Alborán, inducidos por el régimen de vientos de norte y poniente, pueden ser responsables de los pulsos de crecimiento larvario, como se evidencia en las microestructuras de los otolitos

Criterios de Implementación ISO 14001 2015 Caso de Estudio Cemex Premezclados

ImágenesEn la elaboración del caso de estudio se presenta un análisis de cada uno de los procesos que se realizan en la elaboración de aditivos químicos para la adición de concreto, cemento y mortero de la empresa CEMEX (Planta Aditivos). Lo primero que debemos tener claro es cuál es el significado de los aditivos: estos son químicos naturales o manufacturados que se adicionan al concreto antes o durante el mezclado del mismo. El objetivo de los aditivos químicos en la industria de la construcción es proporcionar características especiales a otros materiales de construcción, y se clasifican en productos derivados de las materias orgánicas y / o sintéticas. Las familias de aditivos más comunes y que tienen mayor participación en el mercado son Plasticantes y Superplasticantes (los cuales brindan fluidez al concreto), Acelerantes y retardantes (asignan propiedades de aceleración o retardo de procesos químicos) y otros. (Cemex, 2019) La producción de aditivos químicos para la industria de la construcción es una actividad fundamental para la implantación de proyectos de construcción en general. Sin embargo, cuando esta actividad no cuenta con una gestión ambiental bien implementada y eficazmente llevada a la práctica, afecta el medio ambiente debido al uso intensivo de energía, recurso hídrico y materias primas. Así mismo, CEMEX Planta Aditivos ha realizado una gestión para el control y la detección de impactos ambientales que se producen con la producción de Aditivos para el sector, la calidad se rige bajo la normatividad legal aplicable para el sector Químico y Constructor. (Cemex, 2019).In the preparation of the case study, an analysis of each of the processes carried out in the preparation of chemical additives for the addition of concrete, cement and mortar from the company CEMEX (Plant Additives) is presented. The first thing we must be clear about is what the additives mean: these are natural or manufactured chemicals that are added to the concrete before or during mixing. The objective of chemical additives in the construction industry is to provide special characteristics to other construction materials, and they are classified into products derived from organic and / or synthetic materials. The most common families of additives with the largest market share are Plasticizers and Superplasticizers (which provide fluidity to concrete), Accelerators and retarders (assign properties of acceleration or retardation of chemical processes) and others. (Cemex, 2019) The production of chemical additives for the construction industry is a fundamental activity for the implementation of construction projects in general. However, when this activity does not have well-implemented and effectively implemented environmental management, it affects the environment due to the intensive use of energy, water resources and raw materials. Likewise, CEMEX Plant Additives has carried out a management for the control and detection of environmental impacts that occur with the production of Additives for the sector, the quality is governed under the applicable legal regulations for the Chemical and Construction sector. (Cemex, 2019)

Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

[EN] Background The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. Methods A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. Results The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. Conclusion Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2018/1, and by Carlos III Institute of Health under the project DTS15/00080Pérez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Pollán, M.; Perez-Gómez, B.; Salas-Trejo, D.; Casals, M.... (2019). Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Computer Methods and Programs in Biomedicine. 177:123-132. https://doi.org/10.1016/j.cmpb.2019.05.022S12313217

Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)-YNet model for the segmentation step. This architecture includes networks to model each radiologist's noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose "for presentation" mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist's label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.This research was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed by nomination to Valencian technological innovation centres under project expedient IMDEEA/2021/100. It was also supported by grants from Instituto de Salud Carlos III FEDER (PI17/00047).S

A deep learning framework to classify breast density with noisy labels regularization

Background and objective: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. Methods: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. Results: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. Conclusions: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2021/1.S