Smarter grid through collective intelligence: user awareness for enhanced performance

This paper examines the scenario of a university campus, and the impact on energy consumption of the awareness of building managers and users (lecturers, students and administrative staff).Peer ReviewedPostprint (published version

El monumento turriforme como forma de autorrepresentación de las élites locales

Para el hombre romano el concepto de la muerte se entendía desde una óptica particular.Tras la muerte y celebración del ritual funerario, se enterraba al difunto en una sepultura que podía adoptar gran variedad de formas, encaminadas a mostrar el poder y el prestigio del difunto y de su familia. De ahí la importancia de la elección del lugar -en las vías de entrada a la ciudad o en los cruces de caminos-para asegurarse la visibilidad de la tumba y con ello mantener la memoria del difunto. El monumento del tipo “aedicula” fue uno de los tipos mas utilizados por las élites locales como símbolo de su riqueza y poder.For Roman the concept of death was known from a particular perspective. After the death and the celebration of the funeral ceremony, the diseased was buried in a grave which can adopt a big variety of forms, aimed to show the power and prestige of the diseased and his family. From that point on the importance of the election of the place -in the entrance roads to the cities or in roads intersections-to be sure that the tomb is visible and, with it, to keep the memory of the diseased. “Aedicula” type of monument is one of the mostly used between the local elites as symbol of their wealth and strength.Departamento de Prehistoria, Arqueología, Antropología Social y Ciencias y Técnicas HistoriográficasGrado en Histori

Material multimèdia per a l'aprenentatge autònom en l'àmbit de la construcció

Peer Reviewe

Face comparison in forensics:A deep dive into deep learning and likelihood rations

This thesis explores the transformative potential of deep learning techniques in the field of forensic face recognition. It aims to address the pivotal question of how deep learning can advance this traditionally manual field, focusing on three key areas: forensic face comparison, face image quality assessment, and likelihood ratio estimation. Using a comparative analysis of open-source automated systems and forensic experts, the study finds that automated systems excel in identifying non-matches in low-quality images, but lag behind experts in high-quality settings. The thesis also investigates the role of calibration methods in estimating likelihood ratios, revealing that quality score-based and feature-based calibrations are more effective than naive methods. To enhance face image quality assessment, a multi-task explainable quality network is proposed that not only gauges image quality, but also identifies contributing factors. Additionally, a novel images-to-video recognition method is introduced to improve the estimation of likelihood ratios in surveillance settings. The study employs multiple datasets and software systems for its evaluations, aiming for a comprehensive analysis that can serve as a cornerstone for future research in forensic face recognition

Factors affecting rework costs in construction

Rework adversely impacts the performance of building projects. In this study, data were analyzed from 788 construction incidents in 40 Spanish building projects to determine the influence of project and managerial characteristics on rework costs. Finally, regression analysis was used to understand the relationship between the contributing factors, and to determine a model for rework prediction.Interestingly, the rework prediction model showed that only the original contract value (OCV) and the project location in relation to the company’s headquarters contribute to the regression model. The Project type, the Type of organization, the Type of contract and the original contract duration (OCD) which represents the magnitude and complexity of a project, were represented by the OCV. This model for rework prediction based on original project conditions enables strategies to be put in place prior to the start of construction, to minimize uncertainties and reduce the impact on project cost and schedule, and thus improve productivity.Peer ReviewedPostprint (author's final draft

Modelling indoor air carbon dioxide concentration using grey-box models

Predictive control is the strategy that has the greatest reported benefits when it is implemented in a building energy management system. Predictive control requires low-order models to assess different scenarios and determine which strategy should be implemented to achieve a good compromise between comfort, energy consumption and energy cost. Usually, a deterministic approach is used to create low-order models to estimate the indoor CO2 concentration using the differential equation of the tracer-gas mass balance. However, the use of stochastic differential equations based on the tracer-gas mass balance is not common. The objective of this paper is to assess the potential of creating predictive models for a specific room using for the first time a stochastic grey-box modelling approach to estimate future CO2 concentrations. First of all, a set of stochastic differential equations are defined. Then, the model parameters are estimated using a maximum likelihood method. Different models are defined, and tested using a set of statistical methods. The approach used combines physical knowledge and information embedded in the monitored data to identify a suitable parametrization for a simple model that is more accurate than commonly used deterministic approaches. As a consequence, predictive control can be easily implemented in energy management systems.Peer ReviewedPostprint (author's final draft

The potential of patient-derived organoids in precision medicine of biliary tract cancer

Organoids; Precision; Biliary tract cancerOrganoides; Medicina de precisión; Cáncer de vías biliaresOrganoides; Medicina de precisió; Càncer de vies biliarsChemotherapy resistance in biliary tract cancer (BTC) presents a major clinical hurdle. Ren et al.1 developed and characterized an extensive collection of BTC patient-derived organoid (PDO) models, enabling advanced investigation of chemotherapy response prediction.This work is supported by grants from the EU Transcan-3 project (SIMMBAP), Instituto de Salud Carlos III (PMP22/00054 and PI20/00898) awarded to T.M., and grants from Asociación Española Contra el Cáncer (AECC), Ministerio de Ciencia e Innovación de España (RYC2020-029098-I and PID2019-108008RJ-I00), and FERO Foundation awarded to T.V.T

Likelihood Ratios for Deep Neural Networks in Face Comparison

In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparency in the methods is essential. Consequently, state-of-the-art free software was preferred over commercial software. Three different open-source automated systems chosen for their availability and clarity were as follows: OpenFace, SeetaFace, and FaceNet; all three based on convolutional neural networks that return a distance (OpenFace, FaceNet) or similarity (SeetaFace). The returned distance or similarity is converted to a likelihood ratio using three different distribution fits: parametric fit Weibull distribution, nonparametric fit kernel density estimation, and isotonic regression with pool adjacent violators algorithm. The results show that with low-quality frontal images, automated systems have better performance to detect nonmatches than investigators: 100% of precision and specificity in confusion matrix against 89% and 86% obtained by investigators, but with good quality images forensic experts have better results. The rank correlation between investigators and software is around 80%. We conclude that the software can assist in reporting officers as it can do faster and more reliable comparisons with full-frontal images, which can help the forensic expert in casework