Passengers' perception of airlines' services: addressing systematic and random variation in tastes

This paper investigates heterogeneities in passengers' perceptions of airlines' services considering systematic and random variations in users' tastes. For this purpose, an efficient design of a stated preference survey is carried out, where attributes related to the in-flight travel and to the passenger`s experience before and after the flight are considered. A Random Parameter Multinomial Logit model is estimated with the obtained data, which also considers systematic variations in user tastes. The obtained results show that most of the parameters associated with the considered variables fit better to a normal or uniform distribution, and that part of their variance can be explained by interacting these variables with other variables such as gender, age, travel frequency or income level. The proposed model allows conclusions to be drawn and marketing policies to be implemented that directly aim at certain user segments, in addition to comprehensively explaining passenger behaviour by showing their preferencesThe work was supported in part by: Grant PLEC2021-007824 funded by MICIU/AEI/10.13039/501100011033 and, by the European Union NextGenerationEU/PRTR

An NMR study of hydrofluorocarbon mixed-gas solubility and self-diffusivity in the ionic liquid 1-ethyl-3-methylimidazolium dicyanamide

To date, the design of advanced separation processes, such as the extractive distillation with ionic liquids (ILs), for the separation of common close-boiling refrigerant blends relies almost exclusively on binary equilibrium data obtained for single-gas/solvent systems, thus neglecting the influence of possible mixture effects. In this work, Nuclear Magnetic Resonance (NMR) spectroscopy and pulsed gradient spin echo (PGSE) NMR are pro posed for the sequential assessment of the single and mixed-gas vapor-liquid equilibrium and self-diffusivity of two fluorinated refrigerants, difluoromethane (R-32) and pentafluoroethane (R-125), in the IL 1-ethyl-3-methy imidazolium dicyanamide at 303.1 K and pressures up to 4 bar, either as pure R-32 or using the commercial refrigerant blend R-410A. The results confirmed that the mixed-gas solubility and self-diffusivities were essen tially equal to those obtained with pure feed gas, thus significant mixing effects were not observed for this particular system. However, an increase in the self-diffusion coefficients was observed with the concentration of absorbed gas, which was more significant for the smallest hydrofluorocarbon (R-32) than for R-125. This technique also allowed evaluating the mobility of the IL moieties, which was slightly higher for the IL anion. Moreover, the self-diffusion coefficients of the IL ions also increased with the amount of gas absorbed, yet less markedly than for the refrigerants. Overall, the NMR technique proved to be an accurate method for the rapid screening of possible mixture effects in equilibrium and transport properties of refrigerant and IL systems, thus providing essential information for designing novel advanced separation processes.The authors acknowledge the financial support of MICIU/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR to projects TED2021-129844B-I00 and PID2022-138028OB-I00 (Universidad de Cantabria), and project PID2019-108552GB-I00 (ICTP-CSIC) as well as project LIFE4F-Gases (LIFE20CCM/ES/001748) co-funded by the European Union LIFE programme. F. Pardo thanks the postdoctoral fellowship IJC2020–043134-I “Juan de la Cierva Incorporación”. M. Viar acknowledges the FPU grant (FPU22/04137) awarded by the Spanish Ministry of Education and Professional Training

Low nickel loading carbon microfibers fabricated by electrospinning for the glycerol electrooxidation coupled with the continuous gas-phase CO2 reduction reaction towards formate

The glycerol market is currently experiencing a surplus due to increased biodiesel production,mcreating a demand for innovative approaches for its optimal utilization. Electrochemical valorization, particularly electro-oxidation, emerges as a promising solution for transforming excess glycerol into valuable products. Here, we report the use of carbon microfibers with ultralow nickel content (<5 wt %) to catalyze glycerol oxidation reaction (GOR), coupled with continuous gas-phase CO2 electroreduction to obtain formate. The humidified CO2-fed membrane electrode assembly electrolyzer, devoid of noble metals, efficiently produces oxidized products like lactate at concentrations of 0.144 g L-1 from glycerol and formate solutions reaching up to 100 g L-1 from CO2, surpassing previous methods employing commercial Pt-based materials. This novel approach not only enhances glycerol conversion efficiency but also contributes to sustainable carbon utilization, leading to the production of value added products.The authors gratefully acknowledge financial support through projects PID2019-108136RB-C31 (MCIN/AEI/10.13039/501100011033), PID2022-138491OB-C31 (MICIU/AEI/10.13039/501100011033 and ERDF/EU), TED2021–129810B-C21 (MCIN/AEI/10.13039/501100011033 and European Union Next Generation EU/PRTR), PLEC2022-009398 (MCIN/AEI/10.13039/501100011033 and European Union Next Generation EU/PRTR), PCI2024-155027-2 (MICIU/AEI/10.13039/501100011033/UE) and the “Complementary Plan in the area of Energy and Renewable Hydrogen” funded by Autonomous Community of Cantabria, Spain, and the European Union Next Generation EU/PRTR. The present work is related to CAPTUS Project. This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101118265. We are also grateful for the Bi carbon-supported nanoparticles prepared and provided by the group of Prof. V. Montiel and Dr. José Solla-Gullón from the Institute of Electrochemistry of the University of Alicante

Aprendizaje profundo para la segmentación de tumores cerebrales en imagen por resonancia magnética

The advancement of medical imaging techniques has significantly enhanced diagnostic capabilities, particularly in the context of brain tumor detection and analysis. This work presents a detailed study on the application of neural networks for the segmentation of brain tumors using the BraTS dataset. The research focuses on leveraging deep learning methodologies to achieve an accurate, efficient, and reliable model to carry out brain tumor segmentation in MRI scans. This work highlighted the limitations of traditional methods and the transformative potential of neural networks and proposed the development, training and evaluation of a 3D UNet model tailored to handle the complexities of brain tumor segmentation through key aspects as data preprocessing, model architecture, loss and activation functions and validation strategies. The model was evaluated on diverse training and validation datasets encompassing various tumor subregions and situations. Results demonstrated that the proposed model achieved a substantial segmentation accuracy, as evidenced by metrics such as HD95 and DSC as well as graphical evaluation. Validation across a diverse dataset confirmed the model’s robustness and capability to correctly segment 2D and 3D predicted masks. Furthermore, constraints imposed by limited computational resources impacted the ability for the fine-tuning of the model, showing very promising future opportunities for improvement.El avance de las técnicas en imagen médica ha mejorado significativamente las capacidades diagnósticas, particularmente en el contexto de la detección y análisis de tumores cerebrales. Este trabajo presenta un estudio detallado sobre la aplicación de redes neuronales para la segmentación de tumores cerebrales utilizando el conjunto de datos BraTS. La investigación se centra en aprovechar las metodologías de aprendizaje profundo para lograr un modelo preciso, eficiente y fiable que realice la segmentación de tumores cerebrales en resonancias magnéticas. Este trabajo destacó las limitaciones de los métodos tradicionales y el potencial transformador de las redes neuronales, proponiendo el desarrollo, entrenamiento y evaluación de un modelo 3D UNet adaptado para manejar las complejidades de la segmentación de tumores cerebrales a través de aspectos clave como el preprocesamiento de datos, la arquitectura del modelo, las funciones de pérdida y activación, y las estrategias de validación. El modelo fue evaluado en diversos conjuntos de datos de entrenamiento y validación, que abarcan diferentes subregiones tumorales y situaciones. Los resultados demostraron que el modelo propuesto alcanzó una precisión sustancial en la segmentación, como lo evidencian métricas tales como HD95 y DSC, así como una evaluación gráfica. La validación en un conjunto de datos diverso confirmó la robustez del modelo y su capacidad para segmentar correctamente máscaras predichas en 2D y 3D. Además, las limitaciones impuestas por los recursos computacionales restringidos impactaron en la capacidad para afinar el modelo, lo que muestra grandes oportunidades esperanzadoras de mejora.Grado en Físic

Monitorizado del proceso de trefilado mediante adquisición de imágenes y uso de redes neuronales

Este Trabajo de Fin de Grado (TFG) se centra en la monitorización del proceso de trefilado mediante la adquisición de imágenes y su posterior procesamiento utilizando una red neuronal convolucional, específicamente ResNet50, implementada en MATLAB. El objetivo principal es identificar y clasificar imágenes del proceso de trefilado, diferenciando aquellas con defectos de las que no presentan anomalías, así como identificar el tipo de defecto que aparecen en las imágenes. Para llevar a cabo este estudio, se capturaron imágenes de muestras de cables obtenidos mediante el proceso de trefilado. Las imágenes fueron etiquetadas manualmente para entrenar y evaluar la red neuronal. Se utilizó la red neuronal ResNet50, con el objetivo de encontrar la solución óptima. El modelo fue entrenado y ajustado en MATLAB, empleando un conjunto de datos balanceado para evitar sesgos en la clasificación. Los resultados muestran una precisión equilibrada del 98.1% en la detección de defectos y un 98.11% en la clasificación de los mismos, destacando la efectividad de ResNet50 para este tipo de aplicaciones industriales. Además, se discuten las limitaciones del estudio y se proponen mejoras futuras, como la utilización de técnicas de aumento de datos y el empleo de cámaras de mayor resolución. En conclusión, la implementación de un sistema de monitorización basado en la adquisición de imágenes y el procesamiento mediante redes neuronales se presenta como una solución viable y efectiva para la identificación y clasificación de defectos en el proceso de trefilado.This Final Degree Project (TFG) focuses on monitoring the wire drawing process through image acquisition and subsequent processing using a convolutional neural network, specifically ResNet50, implemented in MATLAB. The main objective is to identify and classify images from the wire drawing process, distinguishing those with defects from those without anomalies, as well as identifying the type of defect present in the images. For this study, images of wire samples obtained through the wire drawing process were captured. The images were manually labeled to train and evaluate the neural network. The ResNet50 neural network was used with the goal of finding the optimal solution. The model was trained and fine-tuned in MATLAB, using a balanced dataset to avoid biases in classification. The results show a balanced accuracy of 98.1% in defect detection and 98.11% in defect classification, highlighting the effectiveness of ResNet50 for this type of industrial application. Additionally, the study's limitations are discussed, and future improvements are proposed, such as the use of data augmentation techniques and higher-resolution cameras. In conclusion, the implementation of a monitoring system based on image acquisition and processing using neural networks is presented as a viable and effective solution for defect identification and classification in the wire drawing process.Grado en Ingeniería Mecánic

The Directive (EU) 2024/1760 on due diligence directive and decent work: special reference to migrations and child labour in value chains

A raíz de la aprobación de la Directiva (UE) 2024/1760 sobre diligencia debida a las empresas, nos acercamos a un intento europeo de hacer efectiva esa asunción de una mayor responsabilidad social por parte de las empresas que deberán observar el respeto a los derechos humanos en todos los estadios de la cadena de actividades. Así una empresa ubicada en España, con proveedores en diferentes partes del planeta donde las regulaciones o mecanismos de control nacionales no ofrezcan una efectiva protección a los trabajadores o donde se constate que existe trabajo forzoso, trabajo infantil o cualquiera de las variedades de esclavitud, deberá actuar rápidamente al respecto. En este espacio se tratará de reflexionar sobre la necesidad de regularización de la actividad empresarial y de la implementación de leyes laborales sólidas a partir del mandato normativo de la Directiva Europea.Following the approval of Directive (EU) 2024/1760 on corporate due diligence, we are approaching a European attempt to make effective this assumption of greater social responsibility by companies that must observe respect for human rights at all stages of the chain of activities. Thus a company located in Spain, with suppliers in different parts of the planet where national regulations or control mechanisms do not offer effective protection to workers or where it is found that forced labour, child labour or any variety of slavery exist, must act quickly in this regard. This space will try to reflect on the need to regularize business activity and implement solid labour laws based on the mandate of the European Directive

Effects of a specific tax on sweetened beverages: an industrial economy approach

Muchas personas no son conscientes de los problemas que generan en su salud un consumo excesivo de bebidas con alto contenido en azúcar. Comenzar a padecer obesidad, diabetes tipo 2 o algún problema cardiovascular, son algunos de los problemas que causan en las personas el consumo habitual de este grupo de bebidas. En países como México, Francia o Chile ya se han llevado a cabo, con éxito, políticas públicas con el fin de reducir el consumo de las bebidas con alto contenido en azúcar. Este trabajo pretende ver los efectos de un impuesto al consumo en el sector de bebidas azucaradas en España. A partir de la facturación de las principales empresas de este sector en España, hemos supuesto la existencia de un mercado oligopolista, con una empresa líder que presenta una elevada cuota de mercado y varias empresas seguidoras. Tras desarrollar los modelos teóricos de Cournot y de Stackelberg para evaluar en cuál de los dos modelos el impuesto tiene un mayor impacto, se ha obtenido que, dado el tipo de mercado y sus características, el modelo de Stackelberg ofrece mejores resultados. Así, si los consumidores ven reducidas sus posibilidades de compra, es probable que opten por dejar de invertir en ese tipo de bebidas con alto contenido en azúcar y reduzcan su consumoMany people are unaware of the health problems caused by excessive consumption of high-sugar beverages. Developing obesity, type 2 diabetes, or cardiovascular issues are some of the health risk associated with the regular consumption of this type of drink. In countries such as Mexico, France and Chile, public policies have already been successfully implemented to reduce the consumption of high-sugar beverages.This study aims to analyze the effects of a consumption tax on the sugary drinks sector in Spain. Based on the revenue of the leading companies in this sector in the country, we have assumed the existence of an oligopolistic market, with leading company holding a high market share and several follower companies. After developing the theoretical models of Cournot and Stackelberg to evalue in which of the two models the tax has a greater impact, it has been determinated that, given the market type and its characteristics, the Stackelberg model yields better results. Thus, if consumers see their purchasing options reduced, they are likely to stop investing in this type of high-sugar beverages and decrease their consumptionGrado en Economí

Selective separation of La(III) and Ce(III) using hollow fiber membranes: influence of pH and extractant systems

The selective separation of adjacent rare earth elements (REEs), such as La(III) and Ce(III), is a critical challenge in hydrometallurgy due to their similar chemical properties. This work evaluates the performance of non-dispersive solvent extraction (NDSX) using hollow fiber (HF) membranes for this purpose. Initial solvent extraction (SX) equilibrium experiments with Cyanex® 272 in kerosene determined that the aqueous phase’s optimal pH for selectivity is 5.6, achieving a selectivity of αCe/La =12.7. NDSX experiments demonstrated enhanced selectivity αCe/La = 34 after 120 min, benefiting from the additional mass transfer resistance provided by the HF membrane. Maintaining a constant pH of 5.0 with NaOH improved extraction rates but slightly reduced selectivity to αCe/La = 26. Experiments using 1,1,1-trifluoro-2,4-pentanedione (HTFAC) in the ionic liquid (IL) [Omim][Tf2n] as the receiving phase showed lower extraction rates but achieved comparable selectivity values (αCe/La = 22) in just 20 min, thanks to the IL’s viscosity limiting La(III) extraction. The impact of HF membrane design was also assessed; increasing the membrane’s surface area significantly improved extraction rates but reduced selectivity due to reduced mass transfer resistance. These results demonstrate the potential of NDSX systems for selective REE separation, particularly by leveraging controlled mass transfer and operating conditions. However, further work is needed to optimize system design. The findings highlight the advantages of NDSX over traditional SX, offering a promising pathway for sustainable and efficient REE processing.This research was funded by the Project Fondecyt 1211234 from the National Agency for Research and Development (ANID) and Dicyt 051911QM_PAP from the University of Santiago de Chile. Felipe Olea thanks the National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL/2019—21191785

Courbe jacobienne de feuilletages singuliers

Topological properties of the jacobian curve J F,G of two foliations F and G are described in terms of invariants associated to the foliations. The main result gives a decomposition of the jacobian curve J F,G which depends on how similar are the foliations F and G. The similarity between foliations is codified in terms of the Camacho-Sad indices of the foliations with the notion of collinear point or divisor. Our approach allows to recover the results concerning the factorization of the jacobian curve of two plane curves and of the polar curve of a curve or a foliation.Nous décrivons des propriétés topologiques de la courbe jacobienne J F,G de deux feuilletages F et G en termes des invariants associés aux feuilletages. Le resultat principal donne une décomposition de la courbe jacobienne J F,G qui dépend de la similitude des feuilletages F et G Cette similitude entre les feuilletages est codifiée en termes des indices de Camacho-Sad des feuilletages avec la notion de point ou diviseur colinéaire. Notre approche permet de récupérer les résultats concernant la factorisation de la courbe jacobienne de deux courbes planes et de la courbe polaire d'une courbe ou d'un feuilletage.The author is supported by the Spanish research project PID2019-105621GBI00/AEI/10.13039/501100011033 funded by the Agencia Estatal de Investigación– Ministerio de Ciencia e Innovación

Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case

Flooding is the most frequent natural hazard in Aotearoa New Zealand and the second most costly after earthquakes. It will change in frequency and intensity, becoming more extreme as climate change impacts are realised. The main inundation driver is heavy rainfall. In this study, flood-inducing heavy rainfall is characterised locally by applying synoptic climatological techniques, using the study case of Aotearoa New Zealand. Extending on previous work in the field, a new set of 49 daily weather types (DWTs) is proposed for New Zealand, based on mean sea level pressure (MLSP) and 500hPa geopotential height (500GH) (predictor variables). The role of the DWTs, the large-scale climatic patterns (LSCPs) known to influence rainfall variability, and the wind conditions (as an additional explanatory variable since they play an essential role in the development of these events) as heavy rainfall and flooding (predictand variables) drivers is investigated using the Wairewa catchment (Little River, Canterbury) as the study site. Heavy rainfall is represented through its temporal and spatial features, based on two rainfall datasets (a rain gauge and a gridded product obtained by the Weather Research and Forecasting (WRF) numerical model). Useful relationships are found between the predictor and the predictand variables. Also, the predictor variables' temporal variability (interannual and intra-annual variability, seasonality) plays a key role, translating to the temporal variability of heavy rainfall and flooding. The proposed synoptic climatological approach provides qualitative and quantitative value, displaying the range of weather and climatic configurations leading to different types of storms and flooding and helping in their identification and understandingThis work was supported by New Zealand Government via the Ministry for Business, Innovation and Employment (MBIE), contract C01X2014 and the Projects MyFlood (PLEC2022-009362) and HyBay (PID2022-141181OB-I00), from the Spanish Ministry of Science and Innovation