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Simple Summary: In this paper, we present the zooarchaeological and taphonomic study of a new Bronze Age site in the inland of the Iberian Peninsula. This study represents a significant contribution within the studied area, as it analyses the Pista de Motos faunal collection, one of the few representative samples from this period. This paper significantly contributes to completing the scarce information available for the Bronze Archaeology in the Middle Tagus Valley, located inland of the Iberian Peninsula. The Bronze Age zooarchaeological research for the interior and other regions of the Iberian Peninsula is currently limited. Despite several sites with known zooarchaeological profiles from the period, the main issue is that most of these derive from fragmentary and unrepresentative faunal records or are biased profiles from old excavations. New work has yielded novel zooarchaeological results in recent years that could help fill the existing zooarchaeological information gap in the Iberian inland, particularly in the Middle Tagus Valley. However, these projects are in the initial research stages and have not published much of their results. This paper presents the zooarchaeological profile of the Pista de Motos Bronze Age site to help fill this information gap. It analyses the taxonomic representation, skeletal profiles, and human activity patterns associated with faunal use. These observations suggest that animal exploitation at the site followed two primary purposes. One was linked to economic activities, mainly to obtain meat, milk, wool, or animal labour. The other was probably associated with symbolic-ritual practices suggested by the complete animal burials in some excavated units. We contextualise these interpretations with evidence from other Bronze Age sites in the Middle Tagus Valley. Finally, the paper assesses to what extent Pista de Motos is a relevant site for the zooarchaeology of the Bronze Age in the Iberian inland. [ABSTRACT FROM AUTHOR]

In IoT applications, prediction models have fundamental challenges such as real-time processing, producing results with considerable/without delay, and taking action against pattern drift. While existing models can excel when data statistics remain relatively stable, real-time systems may encounter difficulties, particularly when confronted with dynamic shifts in data behavior. Analyzing data streams generated by different IoT applications and detecting complex pattern on the fly has become an open area of research. Complex event processing with adaptivity is a must to get desired features in such models. To address this issue, a comprehensive model for prediction has been proposed in this paper. It consists of two phases: (1) the basic model is constructed using historical data, (2) a fast Fourier transform-based adaptive support vector regression (FFT-ASVR) approach is proposed to predict events embedded in IoT data streams. FFT-ASVR predicts abnormal events by experiencing a change in data streams with real-time model updation. The performance of FFT-ASVR with a similar existing method SVM-RBF is presented using real-time traffic data of Madrid city. The proposed approach has significant improvement in terms of mean absolute percentage error (MAPE) for prediction, is adaptive in nature, and is also capable of handling the issue of pattern drift. [ABSTRACT FROM AUTHOR]

Agent-based epidemiological simulators have been proven to be one of the most successful tools for the analysis of COVID-19 propagation. The ability of these tools to reproduce the behavior and interactions of each single individual leads to accurate and detailed results, which can be used to model fine-grained health-related policies like selective vaccination campaigns or immunity waning. One characteristic of these tools is the large amount of input data and computational resources that they require. This relies on the development of parallel algorithms and methodologies for generating, accessing, and processing large volumes of data from multiple data sources. This work presents a parallel workflow for extending the social modeling of EpiGraph, an agent-based simulator. We have included two novel parallel social generation stages that generate a detailed and realistic social model and one new visualization stage. This work also presents a description of the algorithms used in each stage, different optimization techniques that permit to reduce the application convergence time, and a practical evaluation of large workloads on HPC systems. Results show that this contribution can be efficiently executed in parallel architectures and the results allow to increase the simulation detail level, representing a significant advance in the simulator scenario modeling. As a summary of results, the first contribution of this paper is the development of two models (a spatial and a social one) that assign geographical and socioeconomic indicators to each simulated individual (i.e., agents), reproducing the real social distribution of the city of Madrid. The second contribution presents an improved parallel and distributed algorithm that executes the two aforementioned models using different parallelization strategies and preserving the load balance. [ABSTRACT FROM AUTHOR]

Effective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a timely manner is of vital importance to them. People with diabetes are particularly concerned about suffering a hypoglycemia (low value) event and, moreover, that the event will be prolonged in time. It is crucial to predict hyperglycemia (high value) and hypoglycemia events that may cause health damages in the short term and potential permanent damages in the long term. This paper describes our research on predicting hypoglycemia events at 30, 60, 90, and 120 minutes using machine learning methods. We propose using structured Grammatical Evolution and dynamic structured Grammatical Evolution to produce interpretable mathematical expressions that predict a hypoglycemia event. Our proposal generates white-box models induced by a grammar based on if-then-else conditions using blood glucose, heart rate, number of steps, and burned calories as the inputs for the machine learning technique. We apply these techniques to create three types of models: individualized, cluster, and population-based. They all are then compared with the predictions of eleven machine learning techniques. We apply these techniques to a dataset of 24 real patients of the Hospital Universitario Principe de Asturias, Madrid, Spain. The resulting models, presented as if-then-else statements that incorporate numeric, relational, and logical operations between variables and constants, are inherently interpretable. The True Positive Rate and True Negative Rate metrics are above 0.90 for 30-minute predictions, 0.80 for 60 min, and 0.70 for 90 min and 120 min for the three types of models. Individualized models exhibit the best metrics, while cluster and population-based models perform similarly. Structured and dynamic structured grammatical evolution techniques perform similarly for all forecasting horizons. Regarding the comparison of different machine learning techniques, on the shorter forecasting horizons, our proposals have a high probability of winning, a probability that diminishes on the longer time horizons. Structured grammatical evolution provides advanced forecasting models that facilitate model explanation, modification, and retesting, offering flexibility for refining solutions post-creation and a deeper understanding of blood glucose behavior. These models have been integrated into the glUCModel application, designed to serve people with diabetes. [ABSTRACT FROM AUTHOR]

Place-based accessibility measures communicate the potential interaction with opportunities at a zone that populations can access. Recent research has explored the implications of how opportunities are counted by different accessibility methods. In conventional measures, opportunities are multiply counted if more than one zone offers access to the same opportunity. This multi-count of opportunities leads to values of accessibility that are difficult to interpret. A possible solution to enhance the meaning-making of accessibility results is by constraining the calculations to match a known quantity. This ensures all zonal values sum up to a predetermined quantity (i.e., the total number of opportunities). In this way, each value can be meaningfully related to this total. A recent effort that implements this solution is spatial availability, a singly-constrained accessibility measure. In this paper, we extend spatial availability for use in the case of multiple modes or more generally, heterogeneous population segments with distinct travel behaviors. After deriving a multimodal version of spatial availability, we proceed to illustrate its features using a synthetic example. We then apply it to an empirical example of low emission zones in Madrid, Spain. We conclude with suggestions for future research and its use in evaluating policy interventions. [ABSTRACT FROM AUTHOR]

Aim: Suicidal behavior is a serious public health problem and a major cause of death among adolescents. Three categories of major risk factors have been identified: psychological factors, stressful life events, and personality traits. Severe and objective stressful life events (SLEs), such as childhood mistreatment or abuse, have been clearly associated with higher rates of suicide risk. However, the relationship between suicide risk and adolescents' perceptions of the SLE impact is not as clear. This paper studies the relationship between SLE impact perception and suicide risk and the possible mediating role of perceived family functioning in this relationship. The need for longer-term or more intense psychological or psychiatric treatment in relation to SLE impact perception is also addressed. Method: One hundred forty-seven adolescents aged 11–17 were consecutively recruited from the Child and Adolescent Mental Health Outpatient Services Department of a general hospital in Madrid, Spain. Self-informed questionnaires were used to assess suicide risk, SLEs, and family functioning. In addition, the clinical records of the participants were consulted to collect information about their treatment histories, including the number of appointments and the duration of follow-up. Results: SLE impact perception correlates significantly with suicide risk, the number of clinical appointments, the duration of treatment, and the perceived level of family functioning. The mediation model of the family functioning perception variable in the relationship between SLE impact perception and suicide risk is significant. The linear regression model of SLE impact perception and family functioning perception on suicide risk is also significant, accounting for 25.7% of the variance. Conclusions: Beyond the clear and proven effect of serious and objective SLEs, the perceived impact of SLEs reported by adolescents is related to an increased risk of suicide and more intense psychological and/or psychiatric follow-up. This relationship is mediated by the perceived level of family functioning. Adolescents' perceptions of their life experiences and perceived family support may be key determinants of suicide risk prevention. [ABSTRACT FROM AUTHOR]

This study formulates a mathematical dynamic Computable General Equilibrium (CGE) model within a rational expectations framework, adhering to neo-classical principles. It emphasizes the significant role of agents' expectations in determining the broader economic trajectory over time. The model combines microeconomic and macroeconomic perspectives by merging the concept of intertemporal choice with savings behavior. Its mathematical foundations are derived and calibrated using data from a social accounting matrix to enhance its simulation capabilities. The paper presents a practical simulation investigating the economic implications of a strategic investment impact within an specific European region, Madrid as the case of study. Such demand shock affects sectors such as electronics, food, pharmaceuticals, and education. The study models the long-term effects of heightened investment and persistent demand-side shocks. The research demonstrates the CGE model's ability to forecast economic shifts toward a new equilibrium after an investment shock, proving its utility for assessing the impacts of extensive environmental policies within a European context. The work's originality lies in its detailed mathematical formulation, contributing to theoretical discourse and practical application in business analytics. [ABSTRACT FROM AUTHOR]

The manner in which individuals worldwide shared art during the most challenging months of the COVID-19 pandemic stands as one of the most significant instances of creative social resistance in recent history. As a collective tool of resistance against emotional trauma, and as a means to foster a sense of community and well-being, the study of this phenomenon offers a compelling avenue for research into creativity and its social functions. This paper presents a descriptive case study of a successful 50-day collective experience within a neighborhood community in Madrid, Spain, during a period when the city, as a notably exceptional case study for research, bore one of the heaviest burdens of COVID-19 in the world. Data were gathered through in-depth personal interviews and direct observations. Applying a connected approach drawing on the fields of the Sociology of Art and Media Studies, three key findings emerge: (1) participants emphasized shared live artistic performances as the primary catalyst for fostering a sense of community, collective resilience, and overall well-being; (2) their sense of togetherness was further bolstered by digital and media support, as recordings of live performances were shared with loved ones living elsewhere, as well as with journalists and on social networks. This network of communication played a pivotal role in connecting individuals; (3) the combined efforts of both initiatives contributed to the development of a more positive individual and shared narrative surrounding the crisis. [ABSTRACT FROM AUTHOR]

Students in their first year of computer science (CS1) at universities typically struggle to grasp fundamental programming concepts. This paper discusses research carried out using a Java-based visual execution environment (VEE) to introduce fundamental programming concepts to CS1 students. The VEE guides beginner programmers through the fundamentals of programming, utilizing visual metaphors to explain and direct interactive tasks implemented in Java. The study's goal was to determine if the use of the VEE in the instruction of a group of 63 CS1 students from four different groups enrolled in two academic institutions (based in Madrid, Spain and Galway, Ireland) results in an improvement in their grasp of fundamental programming concepts. The programming concepts covered included those typically found in an introductory programming course, e.g., input and output, conditionals, loops, functions, arrays, recursion, and files. A secondary goal of this research was to examine if the use of the VEE enhances students' understanding of particular concepts more than others, i.e., whether there exists a topic-dependent benefit to the use of the VEE. The results of the study found that use of the VEE in the instruction of these students resulted in a significant improvement in their grasp of fundamental programming concepts compared with a control group who received instruction without the use of the VEE. The study also found a pronounced improvement in the students' grasp of particular concepts (e.g., operators, conditionals, and loops), suggesting the presence of a topic-dependent benefit to the use of the VEE. [ABSTRACT FROM AUTHOR]

This paper aims to analyse human mobility in a university campus on the outskirts of the Madrid region. Several surveys which were distributed to students for completion during the 2017-2018, 2018-2019, and 2021-2022 courses were examined. Both an exploration of existing transport modes using clustering techniques and a statistical analysis on trip origins, travel times, and distances were performed. Not all municipalities with the highest number of trips were the closest to the university. The clustering analysis identified a lower variability in the use ratio of the transport modes in the 2017-2018 course. The private car, which exhibited a low sharing rate, was the most utilised transport mode. This was followed by public and university transportation. Similarities between the probability distributions of journeys using public and university transports were found. High and moderate correlations between the number of the existing stops and the amount of trips by subway and urban bus were detected. The lowest median values of travel distances corresponded to students, administrative staff, teachers, and researchers who exhibited very similar values. Considering the three analysed academic years as a whole, the most likely travel times were 30–60 minutes. It was detected that a higher gross annual income did not imply higher private car use. Residents in areas with the highest ozone concentrations also exhibited a high use of motorised vehicles. A low familiarisation with car-sharing and car-pooling platforms was also found. Globally, a high level of comfort during the trip was mostly perceived. [ABSTRACT FROM AUTHOR]

Respiratory viruses, such as COVID-19, are spread over time and space based on human-to-human interactions. Human mobility plays a key role in the propagation of the virus. Different types of sensors in smart cities are able to continuously monitor traffic-related human mobility, showing the impact of COVID-19 on traffic volumes and patterns. In a similar way, traffic volumes measured by smart traffic sensors provide a proxy variable to capture human mobility, which is expected to have an impact on new COVID-19 infections. Adding traffic data from smart city sensors to machine learning models designed to estimate upcoming COVID-19 incidence values should provide optimized results compared to models based on COVID-19 data alone. This paper proposes a novel model to extract spatio-temporal patterns in the spread of the COVID-19 virus for short-term predictions by organizing COVID-19 incidence and traffic data as interrelated temporal sequences of spatial images. The model is trained and validated with real data from the city of Madrid in Spain for 84 weeks, combining information from 4372 traffic measuring points and 143 COVID-19 PCR test centers. The results are compared with a baseline model designed for the extraction of spatio-temporal patterns from COVID-19-only sequences of images, showing that using traffic information enhances the results when forecasting a new wave of infections (MSE values are reduced by a 70% factor). The information that traffic data has on the spread of the COVID-19 virus is also analyzed, showing that traffic data alone is not sufficient for accurate COVID-19 forecasting. [ABSTRACT FROM AUTHOR]

In most big cities, public transports are enclosed and crowded spaces. Therefore, they are considered as one of the most important triggers of COVID-19 spread. Most of the existing research related to the mobility of people and COVID-19 spread is focused on investigating highly frequented paths by analyzing data collected from mobile devices, which mainly refer to geo-positioning records. In contrast, this paper tackles the problem by studying mass mobility. The relations between daily mobility on public transport (subway or metro) in three big cities and mortality due to COVID-19 are investigated. Data collected for these purposes come from official sources, such as the web pages of the cities' local governments. To provide a systematic framework, we applied the IBM Foundational Methodology for Data Science to the epidemiological domain of this paper. Our analysis consists of moving averages with a moving window equal to seven days so as to avoid bias due to weekly tendencies. Among the main findings of this work are: a) New York City and Madrid show similar distribution on studied variables, which resemble a Gauss bell, in contrast to Mexico City, and b) Non-pharmaceutical interventions don't bring immediate results, and reductions to the number of deaths due to COVID are observed after a certain number of days. This paper yields partial evidence for assessing the effectiveness of public policies in mitigating the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper presents a life cycle assessment (LCA) analysis of a new, high-concentration photovoltaic (HCPV) technology developed as part of the HIPERION project of hybrid photovoltaics for efficiency record using an integrated optical technology. In the LCA calculations, the production stage of a full module was adopted as a functional unit. SimaPro version 9.00.49, the recent Ecoinvent database (3.8), and the IPCC 2021 GWP 100a environmental model were applied to perform the calculations. The environmental impact of the HCPV panel was determined for constructional data and for recycling of the main elements of the module. The results of the calculations show that recycling of PMMA, rubber, and electronic elements reduced the total carbon footprint by 17%, from 240 to 201 kg CO2-eq. The biggest environmental load was generated by the PV cells: 99.9 kg CO2eq., which corresponds to 49.8% (41.7% without recycling) of the total environmental load due to the large number of solar cells used in the construction. The emission of CO2 over a 25-year lifespan was determined from 17.1 to 23.4 g CO2-eq/kWh (20.4 to 27.9 without recycling), depending on the location. The energy payback time (EPBT) for the analyzed module is 0.87 and 1.19 years, depending on the location and the related insolation factors (Madrid: 470 kWh/m2, Lyon: 344 kWh/m2). The results of the calculations proved that the application of recycling and recovery methods for solar cells can improve the sustainability of the photovoltaic industry. [ABSTRACT FROM AUTHOR]

This paper presents evidence of the significant role that geography plays in customer churn behaviour in online retail. In an urban environment, mimetic behaviours are found to affect nearby individuals. This novel approach is based on the idea that customer churn is not randomly distributed across the map. This paper analyses more than 2,000 spatially georeferenced customers and demonstrates that customers show different patterns when deciding to cease activity, and that other factors besides spatial autocorrelation influence churn probability. Finally, the results prove that including spatial spillover in models improves predictability. This improvement results in substantial economic benefits since marketing managers can consequently reduce their company's loss of customers more effectively. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]

In this paper, we describe an educational experience in the context of the Master's degree that is compulsory in Spain to become a secondary education mathematics teacher. Master's students from two universities in Madrid (Spain) attended lectures that addressed—emphasizing the concourse of a dynamic geometry software package—some historical, didactic and mathematical issues related to linkage mechanisms, such as those arising in the 18th and 19th centuries during the development of the steam engine. Afterwards, participants were asked to provide three different kinds of feedback: (i) working on an assigned group task, (ii) individually answering a questionnaire, and (iii) proposing some classroom activity, imagining it would be addressed to their prospective pupils. All three issues focused on the specific topic of the attended lectures. In the framework of Mason's reflective discourse analysis, the information supplied by the participants has been analyzed. The objective was to explore what they have learned from the experience and what their perception is of the potential interest in linkages as a methodological instrument for their future professional activity as teachers. This analysis is then the basis upon which to reflect on the opportunities (and problems) that this particular bar-joint linkages methodological approach could bring towards providing future mathematics teachers with attractive tools that would contribute to enhancing a STEAM-oriented education. Finally, the students' answers allow us to conclude that the experience was beneficial for these pre-service teachers, both in improving their knowledge on linkages history, mathematics, industrial, technological and artistic applications, and in enhancing the use in the classroom of this very suitable STEAM context. [ABSTRACT FROM AUTHOR]

In this paper we present research into narratives on immigrants and immigration, to identify cases of hate speech on the part of political parties against immigrants on Twitter during the 2019 and 2021 electoral campaigns in the Community of Madrid. Using Critical Discourse Analysis, the results confirm the exponential increase by all parties in discussion on the issue of immigration and that the political agenda and public discourse is increasingly framed by hate speech towards unaccompanied minors and undocumented migrants. [ABSTRACT FROM AUTHOR]

Understanding the characteristics that shape mobility could help to achieve more sustainable transport systems. A considerable body of scientific studies tries to determine these characteristics at the urban level. However, there is a lack of studies analyzing those factors for the heterogeneous zones existing in the suburbs of big cities. The study presented in this paper intends to fill this gap, in the context of two metropolitan corridors in the Madrid Region. Correlation analyses are used to examine how mobility patterns are affected by socioeconomic and urban form variables. Then, a cluster analysis is carried out to classify the types of zones we may find in the suburbs. Results show that the main characteristics leading towards higher car use are low urban density, few local activities, a high percentage of children, and a low percentage of seniors. As for the variable distance to the city center, it does not explain car use. Moreover, some remote areas have many walking trips. This is well understood in the cluster analysis; there are zones far away from the city center but that are dense and well provided for, which work as self-sufficient urban centers. Results reinforce the theories underlying polycentrism as a solution to the urban sprawl challenge. [ABSTRACT FROM AUTHOR]

The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios. [ABSTRACT FROM AUTHOR]

This paper first presents a fuzzy expert system to identify and classify conventional two‐lane roads based on geometric characteristics. Both fuzzy and neuro‐fuzzy techniques have been used. Fuzzy logic has proved suitable to address this problem, since in this case, there is a variability of input information, and classical rules are not suitable to be used due to the uncertainty introduced by some combinations of the variables. Each road's geometric features were measured by sensors in an equipped vehicle, and are subsequently used to classify the roads according to their real condition. The conventional two‐lane roads used for this research are located in the Madrid Region, in Spain. This intelligent system may be used to update the road database regarding the assigned type to each conventional road, according to their present features and state. Also, a risk identification system has been developed to assess whether a vehicle is driving on a two‐lane road with an inappropriate speed, combining variables such as the former identification model, vehicle type, road longitudinal gradient, the angle covered by each horizontal curve, and the existence or not of an additional traffic lane. A fuzzy risk index is proposed for this approach. This fuzzy model may be useful to detect road sections where safety must be enhanced by revising the speed limit, since less safe situations may arise from travelling at unappropriated speeds. [ABSTRACT FROM AUTHOR]

The performing arts are currently in a critical situation worldwide. Various reports warn that the lack of audience. If we focus on dance, and especially folk dances, the situation is worse. In various countries and continents, folk dances are slowly disappearing. In Spain, we find evidence of the downward trend in terms of the number of attendees to performances of Spanish dance -an art form that is highly valued throughout the world. In a generation marked by technological advancements, the only way for classic performing arts to reach young audiences - or digital natives – is to speak the same language they use with new technologies. This paper presents a study in collaboration with the Spanish National Dance Company, carried out with 877 students (aged from 9 to 12) from 12 different schools in the community of Madrid, Spain. We designed a two-phase experiment. In the first phase, we separated the students into 3 groups: students who played a videogame called “Dancing a Treasure,” those who received a workshop from professional dancers, and a control group. In the second phase that took place two weeks later, the participants attended to a real show of Spanish dance, and we studied how the previous educational approaches affected to the students increase of interest after the show. The experiment demonstrated that the videogame was, at least, as effective in increment interest about dance in younger generations as a workshop taught by expert dance professionals. Thus, in terms of scalability, the videogame is a better option because it can be applied with the same results to larger groups with no additional cost. [ABSTRACT FROM AUTHOR]

The aim of this paper is to facilitate the experimental measurement of acoustic metrics in circular bullrings in accordance with the ISO 3382 standards and to enable the comparison of acoustic measurements and simulations for the various types of bullrings. The proposal analyzes those items in the standard that exert a certain influence on acoustic measurement in bullrings. Based on the identification of the sound sources and audience areas characteristic of bullfights, several locations are proposed, and the most suitable source-receiver combinations are recommended for the description of the acoustic field of bullfighting. Practical considerations are also made, such as typologies and axes, to be taken into account when referencing sources and receivers, as are the environmental conditions in a space strongly influenced by direct sunlight. As an illustration of the procedure, an experimental campaign in the bullring of Las Ventas in Madrid is carried out. The analysis shows that the proposed procedure is highly suitable for this type of building and provides detailed acoustic information regarding the seats for all the sound sources that participate in the spectacle. [ABSTRACT FROM AUTHOR]

The suitability of Madrid as the capital of Spain is analyzed from different perspectives, questioning the belief that this choice was eminently personal or political but lacked economic rationality. The paper analyzes Madrid's advantages over other possible capitals from the point of view of both intrinsic characteristics and those that depend on the transport network, such as the problem of supplies or the impact on the development of the surrounding territory. To deal with these questions it is necessary to consider logistical aspects that require an adjusted view of the existing transport network at that time. Using little-known primary sources and a novel methodology based on Delaunay triangulation, the 16th century Spanish transport network is reconstructed with a much higher level of accuracy than ever before. With this information, two maps are prepared that could be used for logistical analysis from a complex network perspective. The first map evaluates the real effects of the choice using an adjusted representation of the territory whilst the second map aims at avoiding the common fallacy of judging decisions made in the past applying current geographical know-how. This map, constructed with the planimetry of the 16th century, indicates how the somewhat deficient knowledge of Philip II with respect to the geographical reality of the day may have favored the choice of Madrid over Toledo, converting some Mediterranean coastal cities into more attractive options. The choice of Madrid as capital appears to be very reasonable in view of the different criteria used. Regarding supply difficulties, our results depart from traditional inclinations by deliberating the fact that the absence of a port in Madrid does not pose an insuperable problem. The latter is the case given that the advantages of maritime transport are far fewer than those usually considered, with Madrid's geographical position offering significant advantages in terms of road transport. [ABSTRACT FROM AUTHOR]

The Olavide Museum in Madrid, which was founded in the 19th century, preserves one of the most important collections in the world of three-dimensional dermatological models made of polychrome beeswax. These models have been used for the training of numerous generations of dermatologists in Spain. Unfortunately, many of the figures were preserved in precarious conditions during the time that the museum was closed in the middle of the 20th century, and some could not be found after its reopening. In this paper, we show a method for the recovery of a missing model of which only the original plaster cast remains. For this purpose, we use the combination of a structured light scanner and 3D printing, together with traditional techniques, to reproduce a copy of the original cast, in order to prevent its deterioration during the wax casting. As a result of this study, a highly realistic figure was obtained, which represented, in great detail, the small superficial reliefs of the skin lesions, as well as their colour. The conclusion of this research is that it is possible to recreate, with precision, a didactic model in beeswax from its mould, without the need to use the mould in the process, which avoids any risk of deterioration in the process. [ABSTRACT FROM AUTHOR]

Learning environments have become increasingly digital in recent decades, requiring teachers and students to develop general digital competences across all educational systems and stages. This also means that for future teachers, professional digital competencies are a valuable asset that enables them to work with the technologies already fully integrated in schools and embedded in most curricula. This paper describes the use of digital communication technology throughout the different stages of a Service Learning Project, involving 2nd and 3rd year students from the Degree in Primary Education at the Universidad Complutense, Madrid. Students and their teachers involved in the project use specific digital communication tools which favour the interaction and completion of the project goal: supporting literacy programmes in two languages for underprivileged students in two local schools. This paper analyses the tools used in the different stages of the project, the digital competencies they are related to and their suitability for similar Service Learning Projects. [ABSTRACT FROM AUTHOR]

In civil aviation flight path planning, in order to effectively reduce the safety threat caused by the volcanic ash area to the civil aviation flight, factors such as the speed and acceleration of the aircraft in the volcanic ash area must be considered. In this paper, we propose an improved A-star algorithm by adopting the concept of potential collision set and using the velocity vector idea of optimal reciprocal collision avoidance (ORCA). The improved A-star algorithm selects the optimal speed range of the aircraft from the limited elements, obtains the speed and acceleration set of the aircraft in the volcanic ash area, calculates the flight path through the displacement increment, continuously refreshes the global starting point to the target point, and finally obtains the final path planning results by calculating the total cost value. The improved A-star algorithm is used to plan two flight paths from Madrid to Cairo and Algiers to Rome in volcanic ash areas. The verification results show that the improved A-star algorithm optimizes the flight path planning in the volcanic ash area and has the advantages of less search nodes, a small search range, and short computing time. [ABSTRACT FROM AUTHOR]

Background: Chagas disease (CD) has become an emerging global health problem in association with the immigration of individuals from endemic areas (in LatinAmerica) to other countries.Spain is the country in Europe with the highest number of CD cases. Concerning pediatric CD, treatment is not only better tolerated by younger children but also has greater cure possibilities. The aim of this study was to describe clinical and epidemiological aspects of CD in a pediatric population diagnosed of 10 hospitals in the Community of Madrid during the 2004–2018 period, as well as the safety and efficacy of CD treatment on this population. Methodology/Principal findings: A multicenter, retrospective, descriptive study was conducted. The studied population included all identified children under the age of 18 with a diagnosis of CD. Diagnosis was performed with a positive parasitological test (with subsequent confirmation) or confirmed persistence of positive serology beyond 9 months, for children younger than one year-old, and with two different positive serological tests, for children older than one. Fifty-one children were included (59% male; 50.9% born in Spain). All mothers were from Latin America. The median age at diagnosis was 0.7 months for those under one year of age, and 11.08 years for those older than one year-old. Only one case presented a symptomatic course (hydrops faetalis, haemodynamic instability at birth, ascites, anaemia). For 94% treatment was completed. Considering patients who received benznidazole (47), AE were recorded in 48,9%. Among the 32 patients older than one year-old treated with benznidazole, 18 (56.25%) had adverse events whereas in the 15 under one year, 5(33,3%) did. Eigtheen (78.2%) of the patients with benznidazole AE were older than one year-old(median age 11.4 years). Of the patients treated with nifurtimox (9), AE were reported in 3 cases (33,3%). Cure was confirmed in 80% of the children under one year-old vs 4.3% in those older (p<0.001). Loss to follow- up occurred in 35.3% of patients. Conclusions/Significances: Screening programs of CD since birth allow early diagnosis and treatment, with a significantly higher cure rate in children treated before one year of age, with lower incidence of adverse events. The high proportion of patients lost to follow-up in this vulnerable population is of concern. Author summary: Chagas disease, caused by the protozoan Trypanosoma cruzi, has traditionally been confined to endemic locations in South America (with vectorial transmission), but is nowadays emerging in Europe, mainly in Spain, due to immigration. A series of characteristics of this disease condition its management. It is usually asymptomatic (especially in early stages), it can be transmitted from mothers to child (vertical transmission) and treatment is more effective at a younger age or during the early stages of infection. For these reasons early diagnosis and treatment of infected children is a priority. To date, there are few publications on pediatric CD series in non-endemic regions. In this paper we describe the epidemiological and clinical characteristics of 51 children diagnosed with pediatric CD in a non-endemic area, as well as the the safety and efficacy of treatment in this patient population. We found that most patients were asymptomatic at diagnosis. The cure percentage was clearly higher when they were treated before one year-old (80% vs 4.3% in children older than one).Adverse events to treatment have occurred especially in older children, being frequent in this age group (median age 11 years). The high proportion of patients lost to follow-up that occurs in this vulnerable population is also noteworthy (35.3% in our case). We highlight, as a conclusion of our study, the need of carrying out systematic screening for this disease (women of childbearing age, children of mothers with positive serology) to reduce the rate of vertical transmission and to achieve higher cure rates and better tolerance to treatment. [ABSTRACT FROM AUTHOR]

As working conditions change worldwide, employment precarity is increasing, including for groups for whom such conditions are unexpected. This study investigates how members of one such group—educationally advantaged young adults—describe their professional futures in a context of unprecedented employment precarity where their expected trajectories are no longer easily achievable. Using 75 interviews with young university graduates in Madrid, Spain, I find that most young graduates drew on a long-standing cultural narrative, which I call the "achievement narrative," to imagine future stable employment. Simultaneously, most denounced this narrative as fraudulent. To explain this finding, I draw on the concept of hysteresis: the mismatch between beliefs that are dependent on the past conditions that produced them and the available opportunities in the present. I argue that hysteresis can extend into future projections; projected futures can be guided by beliefs based on past conditions more than by lived experiences in the present. Further, I argue that the achievement narrative itself reinforces hysteresis in future projections due to its resonance and institutional support. The paper offers new insights into projected futures and employment precarity by analyzing the future projections of a privileged cohort facing unexpected precarity, further develops the concept of hysteresis, and extends the study of cultural narratives. [ABSTRACT FROM AUTHOR]

Bilingualism was implemented in the Community of Madrid (Spain) more than ten years ago, through the incorporation of the English language in the teaching methods of certain schools. Since that time, various research projects have been carried out, with the objective of comparing the academic performance of students in bilingual schools with those in non-bilingual schools. The present paper makes use of primary education data from the Department of Education and Research for the Community of Madrid in an effort to analyze whether or not bilingualism results in the relative improvement of educational outcomes achieved in primary schools in the Region of Madrid, in Spain. More specifically, the data used is from sixth grade classrooms, given that, generally speaking, in this grade all schools give a standardized test which measures academic performance in Math, Science and Technology, Spanish Language Arts and English Language Arts. Our assessment makes use of a multinomial logit model, and includes the most common variables found in the research on the determination of educational outcomes (variables related to whether or not schools are bilingual, which is the main focus of this paper), as well as other less common variables considered to be relevant. These include absenteeism, satisfaction levels among families and students, and the percentage of students in second chance programs. The results show that bilingualism does not lower children performance in the subjects taught in English or in the subjects taught in Spanish. Academic performance in Mathematics, Science and Technology, and Spanish Language Arts is similar with respect to those schools which are not bilingual. However, results in English are significantly higher in bilingual schools when compared to non-bilingual schools. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyse whether the evolution of NO2 and PM10 emission levels in the Madrid metropolitan area with heavy traffic are affected by business cycles (comprised of a boom, recession and recovery). Therefore, we explore if economic developments act as automatic environmental stabilizers in an urban context in order to provide guidelines for environmental policymaking in large cities. Considering the continuous nature of the particles that this paper analyses, functional data have been used and the most suitable statistical method has been applied to this information’s typology. The aim is to capture the behaviour of NO2 and PM10 particles throughout the booming years (2001-2005), the recession (2008-2012) and the recovery (2014-2018) periods. The results obtained through the Kendall’s functional Tau statistic have revealed that the similarities and differences found between the booming years, the recession and the recovery prove the effect of the economy’s cyclical fluctuations on pollution levels. This is due to the fact that the negative and close to zero correlations obtained through these periods represent a sign that pollution levels regarding these two particles are significantly different during the phases of business cycles. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning. • Data-driven and physical modelling are merged to predict runoff. • Data-driven algorithm fed on weather variables before the storm starts. • Light GBM and Deep Neural Networks were coupled with Green-Ampt model. [ABSTRACT FROM AUTHOR]

In this paper we discuss emergent cross-cutting themes across a series of educational intervention projects in which practitioners-in-training adopted and adapted in their proposals and work design the logic of ethnographic experimental collaboration (XCOL) and participatory action research (PAR) (Clark, 2010; Estalella & Sánchez-Criado, 2018) perspectives. We were involved in three interventions developed in Madrid (Spain) across formal and informal learning contexts as part of the internship/practicum of future educational psychologists. Our work was designed in response to the identified needs and demands of the internship sites. Yet, as educational interventions, they were explicitly conceptualized and implemented in ways that depart substantially from the common expectations of process-product educational intervention and dominant ways, at least in Spain, of constructing educational accountability (cf. Berliner, 1989; Gage & Needels, 1989). We unpack four themes relevant across the three projects, which emerged from our joint discussions of the three interventions: (a) how "outcomes/results" are reconstructed in XCOL/PAR educational interventions, (b) the transformations in our emergent professional identities, (c) the place of different materialities and expressive media in the work we planned (d) how space-time constraints were construed in our unfolding projects. [ABSTRACT FROM AUTHOR]

The XXth century gateway cities in Europe, such as Madrid, have followed and in-depth population readjustment, due to the arrival of immigrants from all over the world. This readjustment has left traces in the public spaces of the city; and the media have noted this phenomenon as well. This paper aims at analysing the superdiversity present in Lavapiés, through two different and complementary methodologies: the analyses of the Linguistic Landscape and of press corpora. The analysis of the landscape of the territory will be conducted through a corpus of images or Linguistic Cartography that includes samples of multilingualism present in the area. The press corpora contain news stories, interviews and articles on immigration. These analyses will provide a better picture of the area, in terms of diversity. This paper is part of the Post-conference Special Issue on Language and Migration. [ABSTRACT FROM AUTHOR]

The aim of this paper is to explore the political messages painted on Madrid's walls by urban artists from the beginning of this artistic and political movement (the end of the 1990s) to the present day. We developed an extensive field-work that has helped us to create the first compilation of the key political messages of urban art in Madrid, based on an exploratory research approach complemented by the use of social media search. This descriptive and documentary approach was followed while keeping in mind that a wall is a public channel open to all and an outstanding method for expressing any political opinion with the greatest amount of visibility. Over the time span analyzed, the investigation discovered 15 urban artists with political content in their artistic production. The findings indicate that the political theme of these artists' works focuses primarily on condemning the Spanish government for current issues such as corruption or the economic crisis, as well as past events such as the Iraq War. Other recurring themes include social criticism of municipal policies and the living conditions of underprivileged groups such as refugees and beggars, as well as support for feminism and critiques of the rapid gentrification process of certain historic Madrid neighborhoods, such as Malasaña and Lavapiés. [ABSTRACT FROM AUTHOR]

Tourism gentrification, as a complex phenomenon in large urban destinations, is an important topic for future tourism professionals. The aim of this paper is to offer a digital cartography making activity based on the contemporary dynamics of commercial transformation within tourism gentrification contexts in Madrid in order to facilitate its understanding. This innovative teaching activity is in accordance with the sustainable tourism pedagogy approach and follows an active methodology using three different GIS applications: Google Street View, ESRI ArcGIS Desktop and ESRI ArcGIS Online Story Maps application. A 4-phase methodological design is proposed so that the activity is customizable (1- recognition of previous ideas), achievable (2- significant streets’ mapping), collaborative (3- collective idea-sharing) and replicable (4- academic and didactic evaluation). Finally, the results from an initial implementation are brought to light: i) an initial conceptual lack was found, ii) examples that help to see the proposal materialized are shown and iii) expected positive consequences according to the scientific literature are presented. It is concluded that, despite the scarce bibliography, the use of GIS software and Google Street View in tourism higher education is convenient, not only to facilitate the learning of complex territorial phenomena but also to learn digital tools highly valued in the professional field. [ABSTRACT FROM AUTHOR]

The different degrees on Tourism seek to train experts capable of managing changes and stimulating the necessary improvements to be able to maintain and enhance the dynamic tourism sector. This paper provides the design of a teaching activity for students of tourism degree. With it, it is intended that students, through collaborative work methodology and through the use of geospatial technologies, especially the GIS Story Map, acquire essential competencies in their training and are capable of creating a tourist product according to the characteristics of the destination. The didactic experience consists of creating an original and innovative tourist route in Madrid. This way, a collaborative map and a series of individually presented routes are obtained that are co-evaluated by the students themselves. The results of a satisfaction survey about the activity answered by the participants are also presented. It appears that the activity has been considered positive, since the students claim to have acquired skills of great interest in their training as future professionals, while feeling motivated. We conclude by pointing out that it is necessary to include this type of initiative in university education and that this methodology can be extrapolated to other subjects and places. [ABSTRACT FROM AUTHOR]

Although mapping long-term scenarios can be considered crucial in decision-making, its inclusion in planning processes remains a challenge. This paper aims to gain insights into this by showing a participatory mapping method that creates a dialogue and interaction space between stakeholders and experts. The research was based on three 2050 visions, taking as a case study the eastern sector of the metropolitan area of Madrid, Spain. The method consisted of conducting a participatory mapping workshop involving urban planners, transport engineers, environmental consultants, and property developers. Those experts mapped transformations in urban land use for each 2050 scenario. The results evidenced differences between the three 2050 scenarios, highlighting the usefulness of the participatory mapping workshop to represent the amount and nature of growth in urban land use and organisation of the transport network. [ABSTRACT FROM AUTHOR]

Evaluating spatial inequalities using a single walking accessibility measure is quite challenging. In response, the paper proposes combining two accessibility measures (real and potential) to provide additional insights into the identification and mapping of spatial inequalities. The municipality of Getafe in the Madrid Metropolitan Area, Spain serves as a case study. A questionnaire, administered via face-to-face interviews, recorded the resident's walking preferences for reaching in-store retail. A gravity-based model was used to calculate real and potential accessibilities, which were combined to map four accessibility places that originate spatial inequalities: advantageous, moderately advantageous, moderately disadvantageous, and disadvantageous. The results suggest that potential accessibility values are higher than real accessibility values, and the final map shows the city centre residents (mostly seniors) benefit from the advantageous accessibility places. Disadvantageous places are mainly found in the city's periphery, where younger people live. [ABSTRACT FROM AUTHOR]

In this paper, we assess the drivers of office rental prices in the municipality of Madrid with a sample of 4,721 offices in March, 2020. The estimation was performed using the decision tree approach, which was built with a random forest algorithm. This technique allows us to capture the strong nonlinear component in the relation between price and its drivers, mainly geospatial location. Through a stratified analysis, we find out that the willingness to pay high rent in the center of Madrid is a feature of particular relevance to medium-sized offices. For different reasons, we also find out some office clusters located far from the city center with high rent for both large and small offices. [ABSTRACT FROM AUTHOR]

Gender stereotyping remains a pervasive issue in society. Gender stereotypes are cognitive structures containing socially shared knowledge and expectations about women and men. Research has found that the dimensions evaluation (sweetheart vs. bitch) and traditionality (businessman vs. stay-at-home dad) have high explanatory power for identifying gender stereotypes. As a pilot study, the current paper investigates the traditionality and evaluation perceptions of expressions for women and men in English and Spanish, analysing them in the framework of Conceptual Metaphor and Metonymy Theory. In an online questionnaire, university students in London and Madrid rated 20 expressions for women (e.g. Eng. bitch, Spa. princesa) and men (e.g. Eng. player, Spa. cabrón), previously produced by themselves in single brainstorming sessions. The results indicate the existence of gender stereotypes, especially regarding promiscuity, and a correlation between traditionality and evaluation. Surprisingly and contrary to previous research, female participants produce more promiscuous subtypes than male participants. [ABSTRACT FROM AUTHOR]

Background: The aim of this paper is to analyze the differences in the coordination of chronic illness care between the different public hospital management models coexisting in the Spanish region of Madrid (25 hospitals) during the period 2013-2017.Methods: The performance of hospitals might be affected by the characteristics of the population they serve and, therefore, this information should be taken into account when estimating efficiency measures. For this purpose, we apply the nonparametric Data Envelopment Analysis (DEA) conditioned to some contextual variables and adapted to a dynamic framework, so that we can assess hospitals during a five-year period. The outputs considered are preventable hospitalizations, readmissions for heart failure and readmissions for chronic obstructive pulmonary disease, whereas the inputs considered are the number of beds, personnel (physicians and other healthcare professionals) and total expenditure on goods and services.Results: The results suggest that the level of efficiency demonstrated by the public-private collaboration models of hospital management is higher than traditionally managed hospitals throughout the analyzed period. Nevertheless, we notice that efficiency differences among hospitals are significantly reduced when contextual factors were taken into account.Conclusions: Hospitals managed under public-private collaboration models are more efficient than those under traditional management in terms of chronic illness care coordination, being this difference attributable to more agile and flexible management under the collaborative models. [ABSTRACT FROM AUTHOR]

Improving the management of the urban water cycle can reduce greenhouse gas emissions and contribute to climate change mitigation in cities. Stormwater in urban areas has traditionally been collected and conveyed to sewage treatment plants, a practice that has continued with the need to manage increased superficial flow. This paper presents the Low Impact Development (LID) measures to improve water management in the new urban development of Valdebebas in Madrid, and the associated benefits. Stormwater was considered a sub cycle; by shortening the water cycle the local use of rainwater was achieved while reducing energy costs and greenhouse gas emissions for off-site treatment. The article analyses the urban water cycle from a preventive approach. It considers how urban design features such as topography and pavement selection were used to convey surface water from impermeable areas to permeable ones (vegetated or permeable pavements) and the infiltration of water into engineered soil, reducing the need for off-site conveyance infrastructure, and the amount of water discharged into municipal treatment installations. Moreover, the captured water increases soil water content available for plants and trees which play an important role in absorbing CO 2 . Water exceeding soil capacity is collected in the subsurface Sustainable Drainage system (SuDS), including infiltration boxes where water is temporarily stored before moving through the soil toward ground water reserves. This paper describes the approaches and criteria used to design a large scale SuDS infrastructure in the district of Valdebebas, in northeastern Madrid (Spain), and analyses its performance, followed by a comparison with the predictions under three climate change scenarios, which consider not only the variation in peak flows and runoff, but also the reduction in GHG emissions. Additionally, it presents a sensitivity analysis for the main variables in the design of SuDS, and a multicriteria analysis, to compare the different drainage systems. [ABSTRACT FROM AUTHOR]

The aim of this paper is to contrast whether cognitive memory and emotional recall related to a tragic event and exposure to advertisements that evoke such an experience generates a negative emotional change in the target. We performed an experiment that analyzes emotional changes derived from advertisements featured in the national press ten years after terrorist attacks. We chose the attacks: September 11, 2001, in New York City, and March 11, 2004, in Madrid and analyze the cognitive recall in a set of Spanish and United States focus groups. The results show a significant emotional change in the respondents after the advertisement visualization that is more strongly linked to the recall of a negative event than to the advertisement creativity. [ABSTRACT FROM AUTHOR]

This paper provides evidence about the relevant role of geography on spatial local networks in trade credit between industrial reduced size companies and distin- guishing the spatial behavior according to the firms' size and technological intensity. Using data from a survey of small and medium companies in the municipality of Madrid (Spain), we find significant spatial effects in trade credit between spatially close industrial companies. Whereas prior work has examined the temporal dynamics in trade credit with aggregated information, the spatial dimension with firm level data has not been considered before in trade credit studies and is the aim of this study. We find significant results for the spatio-temporal networks between geographically close companies which cause interaction effects in the proposed models by concluding that firms' surrounding local characteristics impact the issue of trade credit for each company. [ABSTRACT FROM AUTHOR]

We talk to first and last authors of "Checkpoint-mediated DNA polymerase ε exonuclease activity curbing counteracts resection-driven fork collapse," Grazia Pellicanò and Rodrigo Bermejo, about their paper in this issue of Molecular Cell , the critical role of their collaborators, and the research in the Bermejo lab based in Madrid. [ABSTRACT FROM AUTHOR]

Abstract Climate change is expected to increase the frequency and duration of hot weather and its associated adverse health effects. In dense urban areas, these phenomena will be exacerbated by the Urban Heat Island (UHI) effect and indoor overheating. This paper assesses population exposure and vulnerability to high summer temperatures by exploring the geospatial connection between the UHI, housing energy efficiency and overheating risk, and social vulnerability indicators, such as income and the elderly population. Focusing on Madrid and London, two European cities with strong UHIs but contrasting drivers of indoor heat risk, the spatial distribution of selected indicators were analysed by means of Geographical Information Systems, and areas with the highest vulnerability towards summer energy poverty were identified. It was found that while 'hot and vulnerable' areas are present in both Madrid and London, there are significant differences in climate, socioeconomic distribution and housing between the two cities. In warmer climates such as Madrid, energy poverty—traditionally defined by wintertime heating—requires its definition to be broadened to include summertime cooling needs; in the context of climate change and urban warming trends, this may soon also be the case in northern cities such as London. [ABSTRACT FROM AUTHOR]

The grandstands of La Zarzuela Racecourse in Madrid designed by the engineer E. Torroja and the architects C. Arniches and M. Dominguez are one of the world’s most outstanding concrete constructions of the first half of the 20th century. This paper describes the design competition and the conceptual design process that led to their construction and carries out a critical assessment of the grandstands from the perspective of Structural Art. By doing so, the paper helps to develop the discipline of structural criticism, and points out lessons learned from their design and construction process which should not be forgotten at the present time, such as: (a) the importance of design competitions; (b) the fact that the pursuit of economy and efficiency can actually improve the quality and aesthetics of the design; (c) the importance of combining logical reasoning based on technical criteria with imagination to achieve excellence in structural design; and (d) the importance of estimating structural behavior by simple formulas and of learning from both existing structures and experimental models. [ABSTRACT FROM AUTHOR]

The increase of road traffic in large cities during the last years has produced that long and short-term traffic flow forecasting is a critical need for the authorities. The availability of good traffic flow prediction methods is a must to make informed decisions concerning (punctual) traffic congestions. Previous work has shown that the accuracy of these methods decreases if we consider urban traffic and long-term predictions. In this paper we present a hybrid model, combining a Convolutional Neural Network and a Bidirectional Long–Short-Term Memory network, and apply it to long-term traffic flow prediction in urban routes. This model combines the capability of CNN to extract hidden valuable features from the input model and the capability of BiLSTM to understand the temporal context. In order to assess the usefulness of our model, we considered four streets of the city of Madrid with different characteristics and compared the results of our proposed model with the ones obtained by eight widely used baseline models. The results show that our hybrid model outperforms the baseline models with respect to three metrics commonly used in regression: mean absolute error, root mean squared error and accuracy. [ABSTRACT FROM AUTHOR]

Accessibility is an essential concept widely used to evaluate the impact of transport and land-use strategies in urban planning and policy making. Accessibility is typically evaluated by using separately a transport model or a land-use model. This paper embeds two accessibility indicators (i.e., potential and adaptive accessibility) in a land use and transport interaction (LUTI) model in order to assess transport policies implementation. The first aim is to define the adaptive accessibility, considering the competition factor at territorial level (e.g. workplaces and workers). The second aim is to identify the optimal implementation scenario of policy measures using potential and adaptive accessibility indicators. The analysis of the results in terms of social welfare and accessibility changes closes the paper. Two transport policy measures are applied in Madrid region: a cordon toll and increase bus frequency. They have been simulated through the MARS model (Metropolitan Activity Relocation Simulator, i.e. LUTI model). An optimisation procedure is performed by MARS for maximizing the value of the objective function in order to find the optimal policy implementation (first best). Both policy measures are evaluated in terms of accessibility. Results show that the introduction of the accessibility indicators (potential and adaptive) influence the optimal value of the toll price and bus frequency level, generating different results in terms of social welfare. Mapping the difference between potential and adaptive accessibility indicator shows that the main changes occur in areas where there is a strong competition among different land-use opportunities. [ABSTRACT FROM AUTHOR]

The 2nd Computational Methods for the Immune System function Workshop has been held in Madrid in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, from December 3 to 6, 2018. The workshop has been obtained 100% more submissions in respect to the first edition, highlighting a growing interest for the treated topics. The best papers (9) have been selected for extension in this special issue, with themes about immune system and disease simulation, computer-aided design of novel candidate vaccines, methods for the analysis of immune system involved diseases based on statistical methods, meta-heuristics and game theory, and modelling strategies for improving the simulation of the immune system dynamics. [ABSTRACT FROM AUTHOR]