Enseñanzas Técnicas

Mining patient data from heterogeneous sources for decision making on administration of non invasive mechanical ventilation in intensive care units.

2026-06-02T00:02:03Z

[EN]This paper addresses the problem of decision making regarding the administration of non invasive mechanical ventilation in intensive care units. The great number of factors to take into account, its heterogeneity and diverse origin make very difficult this process. In order to facilitate this task we propose the application of data mining methods to extract knowledge from the wide and complex information available. The aim is to find out the factors influencing the success/failure of NIMV and to predict the results in future patients. These methods have not been previously applied in this field in spite of the good results obtained in other medical areas. In this work a comparative study of different algorithms has been carried out using a wide spectrum of data obtained during 6 years about 389 patients that received treatment with NIMV. The results reveal that some multiclasifiers can be useful tools for helping physicians in the choice of the best action.

Machine Learning Methods for Mortality Prediction of Polytraumatized Patients in Intensive Care Units – Dealing with Imbalanced and High-Dimensional Data

2026-06-02T00:02:02Z

[EN]The aim of this study is the prediction of death of polytraumatized patients based on epidemiological, clinical and health treatment variables by means of data-mining methods. The main problems to be addressed were high dimensionality and imbalanced data. Since the techniques usually used to deal with these drawbacks, as feature selection methods and sampling strategies respectively, did not provided satisfactory results, the aim of the study was to find out the data mining algorithms showing the best behavior in this kind of scenarios. The study was carried out with data from 497 patients diagnosed with severe trauma who were hospitalized in the Intensive Care Unit (ICU) of the University Hospital of Salamanca. The results of the study reveal the better behavior of multiclassifiers as compared with simple classifiers in contexts of high dimensionality and imbalanced datasets, without the need to resort to undersampling and oversampling strategies, which can lead to the loss of valuable data and overfitting problems respectively.

Computational study of the effect of forced exercise (swim stress) on the NADPH- diaphorase activity in the supraoptic nucleus

2026-06-02T00:02:02Z

[EN]Nitric oxide synthesizing neurons of the hypothalamus show changes following a variety of experimental and pathological conditions, such as salt loading, hypotension, cholestasis, water deprivation, and several types of stress including immobilization, exposure to low temperature and changes of environment. Recently, our group has shown that forced exercise (swim stress) is an additional stressor for the nitrergic neurons located in the paraventricular nucleus of the hypothalamus. On the other hand it is currently well-known, by means of histochemical (NADPH-diaphorase), immunohistochemical and hybridization methods, that the supraoptic nucleus contains an important nitrergic population of neurons. In the present study, the effect of forced exercise on these neurons was investigated in a computational study of the NADPH-diaphorase positive population of the supraoptic nucleus. A significant increase in the number of positive neurons was observed following forced swimming, especially after 30 and 40 min. These data indicate: 1.– The influence of stress on the NADPH-diaphorase-activity in the supraoptic nucleus. 2.– The involvement of hypothalamic nitric oxide synthesizing-neurons in the response to different types of acute stressors. 3.– The excellence of a combination of morphological and computational tecniques for the detection of changes in plasticity of the hypothalamic neurons.

Predictors of the post-stroke status in the discharge from the hospital. Importance in nursing

2026-06-02T00:02:01Z

[ES]A menudo, por parte del paciente y de la familia, se solicita a los profesionales de enfermería que predigan los factores que influyen en el estado post-ictus. Se han realizado numerosos estudios para determinar los factores que influyen en el estado neurológico post-ictus en el momento del alta hospitalaria. Sin embargo, las técnicas de aprendizaje automático no se han utilizado para este propósito. Con el objetivo de obtener reglas de asociación del pronóstico neurológico, se ha llevado a cabo un doble análisis, tanto clínico como con técnicas de aprendizaje automático, de las posibles asociaciones de factores que influyen en el estado neurológico de los pacientes post-ictus. El algoritmo Apriori detectó varias reglas de asociación con alta confianza (≥ 95%), con el siguiente patrón: En pacientes en el rango de edad de 50-80 años, la asociación de un NIHSS entre 11 y 15 puntos (NIHSS intermedio/bajo), junto con la trombectomía, conduce a la recuperación ad integrum al alta. Con la técnica de remuestreo SMOTE, se alcanzó el 100% de confianza para la asociación de NIHSS elevado (>20) y afectación de las arterias carótida y basilar, con pronóstico nefasto (exitus). Estas reglas confirman, por primera vez con aprendizaje automático, la importancia de la asociación de algunos predictores, en el pronóstico post-ictus. El conocimiento por parte de las enfermeras de estas reglas puede mejorar los resultados del ictus. Adicionalmente, el papel de la enfermería en los programas de educación sobre los factores de riesgo, y pronóstico de un ictus se torna imprescindible.; [EN]Nurses are often asked to predict factors that influence post-stroke outcome by the patient and family. Many studies have been carried out in order to determine the factors that influence the neurological status of the post-stroke patient at the moment of the discharge from the hospital. However, machine learning techniques have not been used for this purpose. Therefore, with the objective of obtaining association rules of neurological prognosis, a double analysis, both clinical and with machine learning techniques of the possible associations of factors that influence the neurological status of the post-stroke patients has been carried out. The Apriori algorithm detected several association rules with high confidence (≥ 95%), from which the following pattern: In patients in the age range of 50-80 years, the association of a NIHSS between 11 and 15 points (intermediate/low NIHSS), along with thrombectomy, leads to recovery ad integrum at discharge. With the SMOTE resampling technique, the 100% confidence was reached for the association of high NIHSS (>20) and involvement of the carotid and basilar arteries, with a dire prognosis (exitus). These rules confirm, for the first time with machine learning, the importance of the association of some predictors, in the post-stroke prognosis. The knowledge by the nurses of these association rules can successfully improve stroke outcome. In addition, the role of nurses in education programs that teach knowledge of risk factors and stroke prognosis becomes essential.

Prognostic factors associated with mortality in patients with severe trauma: From prehospital care to the Intensive Care Unit

2026-06-02T00:01:59Z

[EN]Objective To identify factors related to mortality in adult trauma patients, analyzing the clinical, epidemiological and therapeutic characteristics at the pre-hospital levels, in the Emergency Care Department and in Intensive Care. Design A retrospective, longitudinal descriptive study was carried out. Statistical analysis was performed using SPSS, MultBiplot and data mining methodology. Setting Adult multiple trauma patients admitted to the Salamanca Hospital Complex (Spain) from 2006 to 2011. Main variables of interest Demographic variables, clinical, therapeutic and analytical data from the injury site to ICU admission. Evolution from ICU admission to hospital discharge. Results A total of 497 patients with a median age of 45.5 years were included. Males predominated (76.7%). The main causes of injury were traffic accidents (56.1%), precipitation (18.4%) and falls (11%). The factors with the strongest association to increased mortality risk (p < 0.05) were age > 65 years (OR 3.15), head injuries (OR 3.1), pupillary abnormalities (OR 113.88), level of consciousness according to the Glasgow Coma Scale ≤ 8 (OR 12.97), and serum lactate levels > 4 mmol/L (OR 9.7). Conclusions The main risk factors identified in relation to the prognosis of trauma patients are referred to the presence of head injuries. Less widely known statistical techniques such as data mining or MultBiplot also underscore the importance of other factors such as lactate concentration. Trauma registries help assess the healthcare provided, with a view to adopt measures for improvement.; [ES]Objetivo Identificar los factores relacionados con la mortalidad de los pacientes adultos politraumatizados, analizar las características clínicas, epidemiológicas y terapéuticas en los niveles prehospitalario, Servicio de Urgencias y Cuidados Intensivos. Diseño Estudio retrospectivo, longitudinal y descriptivo. Análisis estadístico a través del programa SPSS, MultBiplot y la metodología de minería de datos. Ámbito Pacientes adultos politraumatizados ingresados en el Complejo Hospitalario de Salamanca entre los años 2006 y 2011. Variables de interés principales Variables demográficas, clínicas, terapéuticas y analíticas desde el lugar del accidente hasta el ingreso en la UCI. Variables evolutivas durante el ingreso en la UCI y hasta el alta hospitalaria. Resultados Se incluyó a 497 pacientes, con una mediana de edad 45,5 años. Predominio de varones (76,7%). La causa principal del traumatismo fueron los accidentes de tráfico (56,1%), precipitaciones (18,4%) y caídas (11%). Los factores con mayor asociación a un incremento del riesgo de mortalidad (p < 0,05) fueron la edad > 65 años (OR 3,15), el traumatismo craneoencefálico (OR 3,1), las alteraciones pupilares (OR 113,88), el nivel de consciencia según la escala de Glasgow ≤ 8 (OR 12,97) y las cifras de lactato > 4 mmol/L (OR 9,7). Conclusiones Los principales factores de riesgo identificados en relación con el pronóstico de los pacientes politraumatizados son los relacionados con la presencia de traumatismo craneoencefálico. Mediante la utilización de distintas técnicas estadísticas menos conocidas como la minería de datos o el MultBiplot también se destaca la importancia de otros factores como el lactato. Los registros de traumatismos ayudan a conocer la asistencia sanitaria realizada para poder establecer medidas de mejora.

La huella de Federico Cantero Villamil en la sociedad e industria de Castilla y León (1874-1946). Recuperación y difusión de su legado

2026-05-08T11:50:53Z

[ES] Compendio de publicaciones, ponencias, exposiciones, conferencias en torno a la figura de Federico Cantero Villamil. Libro realizado por una treintena de colaboradores del ámbito académico universitario, pilotos de aviación y directores de museos nacionales. Han participado y colaborado en la redacción del documento los profesores de la USAL: , Santiago Manuel López García, María Ascensión Rodríguez Esteban y Pedro Hernández Ramos; [EN] A compilation of publications, presentations, exhibitions, and conferences about Federico Cantero Villamil. This book was compiled by some thirty contributors from the academic field, aviation pilots, and directors of national museums.

An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images

2026-04-28T00:02:46Z

[EN]In recent years, maintenance work on public transport routes has drastically decreased in many countries due to difficult economic situations. The various studies that have been conducted by groups of drivers and groups related to road safety concluded that accidents are increasing due to the poor conditions of road surfaces, even affecting the condition of vehicles through costly breakdowns. Currently, the processes of detecting any type of damage to a road are carried out manually or are based on the use of a road vehicle, which incurs a high labor cost. To solve this problem, many research centers are investigating image processing techniques to identify poor-condition road areas using deep learning algorithms. The main objective of this work is to design of a distributed platform that allows the detection of damage to transport routes using drones and to provide the results of the most important classifiers. A case study is presented using a multi-agent system based on PANGEA that coordinates the different parts of the architecture using techniques based on ubiquitous computing. The results obtained by means of the customization of the You Only Look Once (YOLO) v4 classifier are promising, reaching an accuracy of more than 95%. The images used have been published in a dataset for use by the scientific community.

Semi-automatic roof modelling from indoor laser-acquired data

2026-04-09T00:01:14Z

[EN] Roof modelling provides useful information for energy analysis, but the methodologies traditionally applied are based on data acquired through aerial vehicles. This requirement makes necessary two data acquisition campaigns: one from indoors and another from outdoors. However, most energy studies can be performed using regularized and simplified models where most of the information of the exhaustive acquisitions is not used. Therefore, this paper proposes a semi-automatic procedure for the 3D modelling of roofs using indoor point clouds, reducing the acquisition campaigns to the indoors campaign. The methodology is based on the hypothesis that surfaces have no thickness, which makes the algorithm especially useful in industrial environments where there are no false ceilings and therefore, the contribution of the roof in the energy behaviour of the building is more important. The methodology is tested on six different scenarios, obtaining their regularized models with relative errors lower than 2% in ideal conditions.

3D-Printed SMC Core Alternators: Enhancing the Efficiency of Vortex-Induced Vibration (VIV) Bladeless Wind Turbines

2026-04-09T00:01:07Z

[EN] This study investigates the application of soft magnetic composite (SMC) materials in alternator core manufacturing for bladeless wind turbines operating under the principle of vortex-induced vibration (VIV), employing additive manufacturing (AM) technologies. Through a comparative analysis of alternator prototypes featuring air, SMC, and iron cores, the investigation aims to evaluate the performance of SMC materials as an alternative to the most commonly used material (iron) in VIV BWT, by assessing damping, resonance frequency, magnetic hysteresis, and energy generation. Results indicate that while alternators with iron cores exhibit superior energy generation (peaking at 3830 mV and an RMS voltage of 1019 mV), those with SMC cores offer a promising compromise with a peak voltage of 1150 mV and RMS voltage of 316 mV, mitigating eddy current losses attributed to magnetic hysteresis. Notably, SMC cores achieve a damping rate of 60%, compared to 67% for air cores and 59% for iron cores, showcasing their potential to enhance the efficiency and sustainability of bladeless wind turbines (BWTs). Furthermore, the adaptability of AM in optimizing designs and accommodating intricate shapes presents significant advantages for future advancements. This study underscores the pivotal role of innovative materials and manufacturing processes in driving progress towards more efficient and sustainable renewable energy solutions.

Optimizing Bladeless Wind Turbines: Morphological Analysis and Lock-In Range Variations

2026-04-08T00:01:47Z

[EN] This study presents a comprehensive exploration centred on the morphology and surface structure of bladeless wind turbines (BWTs) aimed at optimizing their wind energy harvesting capability. Unlike conventional wind technology where vortex-induced vibration (VIV) is seen as problematic due to aeroelastic resonance, this effect becomes advantageous in BWT energy harvesters, devoid of frictional contact or gears. The primary objective of this study is to develop an optimal BWT design for maximizing energy output. Specifically, this study delves into optimizing the energy performance of these VIV wind energy harvesters, investigating how the geometry (shape and roughness) influences their operating range, known as Lock-In range. The results demonstrate how variations in geometry (convergent, straight, or divergent) can shift the Lock-In range to different Reynolds numbers (Re), modelled by the equation: Re (max Lock-In) = 0.30 α + 4.06. Furthermore, this study highlights the minimal impact of roughness within the considered test conditions.

Introduction of active thermography and automatic defect segmentation in the thermographic inspection of specimens of ceramic tiling for building façades

2026-04-08T00:01:46Z

[EN] InfraRed Thermography (IRT) has proven to be a valuable diagnostic tool due to its real-time, remote, and non-destructive operation yielding accurate detection of the position of defect areas in building façade ceramic tiling. Ceramic tiles coating building façades are widely used throughout the world because of their technical and aesthetic characteristics. However, the detachment of ceramic tiles and the water infiltration in deep layers are still common problems. So, this paper proposes active infrared thermography as a thermographic acquisition mode, in contrast to the common use of passive thermography, and segmentation of defect areas and automation in the thermal image processing as added values never before proposed in the ceramic tiling thermographic inspection. For that, specimens of ceramic tiling for building façades were tested under different laboratory conditions, with inserted corks (simulating detachments), and by injecting water into holes drilled in the back surfaces (simulating water infiltration), as defects. Good results have been obtained in all the tests, both in dry and wet conditions in the specimens and for surfaces with homogeneous and heterogeneous surface properties, serving the introduction of this workflow for a first and fast inspection in ceramic tiling building façades. Future research will work with the fine-tuning phase of the methodology by applying it to real case studies.

Introduction of the combination of thermal fundamentals and Deep Learning for the automatic thermographic inspection of thermal bridges and water-related problems in infrastructures

2026-04-08T00:01:44Z

[EN] Infrastructure inspection is fundamental to keep its service performance at the highest level. For that, special attention should be paid to the most severe defects in order to be able to subsequently mitigate or even eliminate them. Therefore, this paper introduces the combination of an automatic thermogram pre-processing algorithm and a Deep Learning (DL) model, Mask R-CNN, applied to thermal images acquired from different infrastructures (buildings, heritage sites and civil infrastructures) with water-related problems and thermal bridges. The pre-processing algorithm developed is based on thermal fundamentals. As an output, the thermal contrast between defect and defect-free areas is increased in each image. Then, Mask R-CNN is trained using the pre-processing algorithm outputs as input dataset to automatically detect, segment and classify each defect area. The training process of Mask R-CNN is improved by the prior application of the proposed pre-processing algorithm in terms of time. This shows the capacity of thermal fundamentals to improve the performance of the DL models for their application to the InfraRed Thermography (IRT) field. In addition, DL models are introduced for the first time in the thermographic inspection of water-related problems and thermal bridges when inspecting an infrastructure.

Advancing renewable hydrogen deployment: A web geographic information system and Artificial Intelligent approach to site optimization

2026-04-08T00:01:39Z

[EN] Renewable hydrogen is an emerging solution to the need for decarbonization of the current society, with local deployments being at the core of most implementations. It is currently in early stage of implementation, so there are not many previous experiences to standardize decision-making and the most relevant criteria. However, the lack of experts in the field of renewable hydrogen makes it difficult to design an optimal value chain. For this reason, this paper proposes a specific framework based on Geographic Information Systems, Multi-Criteria Decision Analysis and Intelligent Optimization (specifically two Genetic Algorithms denoted as Methods A and B) for the decision-making regarding the selection of optimal sites for the implementation of the renewable energy value chain from a holistic perspective; that is, considering topographic, economic, social, environmental, and demand criteria. The proposed framework is validated through the comparison of its results with those of the most extended methods in the state of the art. The results show that the application of the proposed framework implies an increase in accuracy in the determination of the locations with the highest Land Suitability Index for the renewable hydrogen value chain. Specifically, an increase in accuracy of 1.35 % (Method A) and 3.25 % (Method B) is observed with respect to the most widely used method in the literature: Analytic Hierarchy Process. Spain has been selected as a case study to validate the applicability of the proposed framework, which has facilitated the identification the optimal municipalities for the local implementation of renewable hydrogen in the country. It has been demonstrated that of the 60 projects in advanced levels of development, 87% (50 projects) have a high level of Land Suitability Index placing them in the first quartile of the ranking. In terms of investment, these projects represent around €468 million (87.7 %). It can therefore be concluded that the renewable hydrogen financing strategy of Spain can be slightly improved with the results of the proposed framework.

Centrales Termoeléctricas de Biomasa Vol.2

2026-04-08T00:01:05Z

[ES] La biomasa, Tipos de biomasa, Procesos de transformación de la biomasa, La valorización de la biomasa mediante combustión, Las centrales termoeléctricas de biomasa, El mercado eléctrico, Situación actual y marco legislativo, Pretratamiento de la biomasa, La caldera de combustión de biomasa, El ciclo agua-vapor, La turbina de vapor, El generador, Sistemas eléctricos de alta, media y baja tensión, Sistemas auxiliares.; [EN] Biomass, Types of biomass, Biomass transformation processes, Biomass valorization through combustion, Biomass thermal power plants, The electricity market, Current situation and legislative framework, Biomass pretreatment, The biomass combustion boiler, The water-steam cycle, The steam turbine, The generator, High, medium and low voltage electrical systems, Auxiliary systems.

Centrales Térmoeléctricas de Biomasa Vol.1

2026-04-08T00:01:03Z

Impacto Medioambiental de las Centrales Térmicas de Ciclo Combinado/Características Constructivas (Centrales Térmicas de Ciclo Combinado Vol.1. Principios de Funcionamiento. Principales Equipos y Sistemas)

2026-04-08T00:01:03Z

[ES] El libro CENTRALES TÉRMICAS DE CICLO COMBINADO trata de exponer al lector los principios de funcionamiento y los principales equipos y sistemas que componen una central de ciclo combinado. A los largo del libro se describen con todo detalle las posibles configuraciones, los equipos principales, las turbinas de gas, las turbinas de vapor, la caldera de recuperación, el ciclo agua-vapor y los sistemas auxiliares. Dedica especialmente más de 100 páginas a describir el generador y los sistemas eléctricos de alta, media y baja tensión de la central. El libro no da nada por sabido, tiene un marcado carácter práctico y pretende convertirse en una guía de referencia incluso para personal no técnico.; [EN] The book COMBINED CYCLE POWER PLANTS aims to explain to the reader the operating principles and the main equipment and systems that make up a combined cycle power plant. Throughout the book, the possible configurations, main equipment, gas turbines, steam turbines, heat recovery steam generator, steam-water cycle, and auxiliary systems are described in detail. It dedicates over 100 pages specifically to describing the generator and the high, medium, and low voltage electrical systems of the plant. The book assumes no prior knowledge, has a distinctly practical focus, and intends to become a reference guide even for non-technical personnel.