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Copyright of Investigaciones Historicas is the property of Universidad de Valladolid, Facultad de Filosofia y Letras and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The University of Alcalá in Madrid will organize the "III International Congress on Green Public Procurement and Climate Change" on October 4th and 5th, 2023. The congress aims to receive oral communications on topics related to green public procurement and climate change, as well as tangential aspects such as sustainable food, legislation on climate change, public procurement and depopulation, sustainable infrastructure and energy efficiency, and sustainable or low-emission mobility. The Scientific Committee will review the communications and select the best ones for possible publication in the journal Actualidad Jurídica Ambiental. The deadline for submitting abstracts is May 15th, 2023. [Extracted from the article]

Copyright of Documenta & Instrumenta is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The following paper proposes a novel machine learning approach to the segmentation of urban housing markets. We extract features from globally available satellite imagery using an unsupervised machine learning model called MOSAIKS, and apply a k-means clustering algorithm to the extracted features to identify sub-markets at multiple intra-urban scales within a case study of Madrid (Spain). To systematically explore scale effects on the resulting clusters, the analysis is repeated with varying sizes of satellite image patches. We assess the resulting clusters across scales using several internal cluster-evaluation metrics. Additionally, we use data from online listings portal Idealista to measure the homogeneity of housing prices within the clusters, to understand how well sub-markets can be differentiated by the image features. This paper evaluates the strengths and weakness of the method to identify urban housing sub-markets, a task which is important for planners and policy makers and is often limited by a lack of data. We conclude that the approach seems useful to divide large urban housing markets according to different attributes and scales. [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]

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]

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]

This document is an introduction to a special issue of the journal SERIEs, which is dedicated to honoring the influential economist Manuel Arellano. The special issue follows a conference held in Madrid in 2022, where presentations were given by past and present co-authors of Arellano. Arellano, a renowned econometrician, has made significant contributions to panel data econometrics and empirical microeconomics. The special issue includes a collection of research articles that showcase the breadth and impact of Arellano's work. The document also expresses gratitude to those involved in organizing the conference and the special issue. [Extracted from the article]

The paper attempts to investigate the capacity of agroecological cooperative supermarkets in Spain to promote scaling of food products by means of a double perspective. We first employ a vertical scaling approach to analyze the issues affecting the governance and collective organization of the Cooperative Supermarket Network (CSN), set up in May 2022 and comprising eleven Spanish supermarkets. Secondly, we employ a perspective of horizontal scaling to investigate the potential for increased numbers of members, as well as the geographic and sociodemographic variables at play which limit the abovementioned scaling: to this end we use the case study of the cooperative supermarket La Osa, opened in Madrid in December 2020. We adopt a methodology based on participatory action research throughout the years 2022 and 2023, in which the research team was involved in the real processes of creation and development of the CSN or of La Osa. The study confirms the hypothesis that agroecological cooperative supermarkets constitute a formula for efficient retail distribution for scaling sustainable food in Spain. As opposed to the first-generation options for responsible consumption, these supermarkets appear to contribute to generating significant economies of scale and scope. In terms of vertical scaling, joint provision of services, as well as the gaining of political influence in society, constitute the main advantages in relation to the functioning of the CSN. While the recruitment of new members has heretofore been considered a priority in horizontal scaling, particular emphasis should also be placed on loyalty strategies targeting existing members. [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]

Context. Problem of traffic prediction in a city is closely connected to the tasks of transportations in a city as well as air pollution detection in a city. Modern prediction models have redundant complexity when used for separate stations, require large number of measuring stations, long measurement period when predictions are made hourly. Therefore, there is a lack of method to overcome these constraints. The object of the study is a city traffic. Objective. The objective of the study is to develop a method for traffic prediction, providing models for traffic quantification at measuring stations in the future under data and resource constraints. Method. The method for agent-oriented traffic prediction under data and resource constraints was proposed in the paper. This method uses biLSTM models with input features, including traffic data obtained from agent, representing target station, and other agents, representing informative city stations. These agents are selected by ensembles of decision trees using Random Forest method. Input time period length is proposed to set using autocorrelation data. Results. Experimental investigation was conducted on traffic data taken in Madrid from 59 measuring stations. Models created by the proposed method had higher prediction accuracy with lower values of MSE, MAE, RMSE and higher informativeness compared to base LSTM models. Conclusions. Obtained models as study results have optimal number of input features compared to the known models, do not require complete system of city stations for all roads. It enables to apply these models under city traffic data and resource constraints. The proposed solutions provide high informativeness of obtained models with practically applicable accuracy level. [ABSTRACT FROM AUTHOR]

Cities are complex systems requiring urban design models that balance order and disorder. Collective creativity initiatives engage citizens in these processes, empowering bottom-up approaches that prioritize people and social well-being within urban development. This paper investigates an 'Urban Laboratory' as a case study, examining the potential of collective creativity to address urban complexity. The successful and ongoing project 'El Campo de Cebada' in Madrid, Spain, demonstrates how a community transformed a vacant lot into a vibrant social hub. The phases of this study include case selection, data collection, data analysis, and presentation of the results. This study identifies key enabling factors, including agents, management, social dynamics, infrastructure, and actions. These insights offer a methodological framework for designing future collaborative, resilient, and inclusive urban spaces, addressing the complex needs of communities within our cities. [ABSTRACT FROM AUTHOR]

Copyright of Hipogrifo: revista de literatura y cultura del siglo de oro is the property of Hipogrifo: revista de literatura y cultura del siglo de oro and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

The aim of this paper is to identify potential causal relationships between macroeconomic variables and the stock market in Spain. Numerous articles recognize the influence of macroeconomic variables on the stock market and value this knowledge as essential for good investment management. However, there are very few empirical studies that justify the influence of disaggregated macroeconomic variables on the stock market in Spain and vice versa. This article uses the general index of the Madrid Stock Exchange as a proxy variable of the stock market and numerous macroeconomic variables, analyzing monthly data from January 2001 to December 2020 from various published data sources. A descriptive analysis is carried out and a vector autoregressive model (VAR) is applied. Finally, the causality is analyzed identifying the transmission of effects between them. The results confirm the impact of lagged interest rate, monetary aggregate M1 and unemployment rate on the stock market but also identify new features, such as the influence of the stock market on the interest rate, industrial production index, manufacturing activity index and economic sentiment index. This research is useful for Public Administration to detect possible risks in the economy, and it enables investors to better manage their investments. [ABSTRACT FROM AUTHOR]

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The agri-food system needs to transition into a more balanced system that takes into account economic, social, and environmental factors. Young people are a key demographic group to consider as they are open to new trends of consumption, including sustainable buying practices. Public universities can play a significant role in promoting sustainable and healthy eating habits among students. In this paper, we focus on the perceptions of young people regarding sustainable food in the Madrid Region. We conducted a survey using a questionnaire-based approach among 1940 students in 2022. The results highlight that young consumers are highly concerned about food sustainability. They perceive sustainability as local and non-processed foods. However, this perception varies among young consumers, and we identified five different consumer profiles. Principal component analysis and cluster analysis provide insights into potential actions that universities can take to promote sustainable and healthy eating habits among students. [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]

Copyright of EMPIRIA: Revista de Metodología de Ciencias Sociales is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Librosdelacorte.es is the property of Instituto Universitario "La Corte en Europa" - IULCE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Arq.urb is the property of Arq.urb and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a novel, cost-effective multisensory system designed for animal monitoring in research settings. The system aims to objectively assess animal welfare and discomfort during experiments, addressing the need for affordable monitoring solutions in research laboratories. Developed and validated in compliance with European regulations on animal experimentation and in accordance with the requirements of the Animal Research Centre at the University of Alcala´ in Madrid. The system integrates Raspberry Pi 4 Model B and Arduino Uno boards with various sensors, including temperature, humidity, ammonia, airborne particles, and an RGB camera. A user-friendly web interface allows remote monitoring and management of the system. This innovation promises to improve the efficiency and feasibility of animal research, enabling more precise and ethical experimentation while advancing scientific knowledge and animal welfare. [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]

The use of technologies that allow for the utilization of renewable energies wasted around buildings is one of the ways to ensure the decarbonization of the sector. Wastewater from buildings is a renewable source of thermal energy. Groundwater and rainwater are important components of wastewater that flow into sewerage systems. The main objective of this research is to estimate the thermal potential of wastewater for the heating and cooling of buildings. In this paper, an office building with a low-energy system (TABS) was studied for one year to assess the energy contribution of wastewater in a hybrid system that includes geothermal exchangers and a wastewater exchanger. This study shows that wastewater from sewerage systems that flows faster than 5 L/s can make enough heat to power an office building with a power demand of 45 kW (60 W/m2). The energy contribution of wastewater from the sewerage system is more favorable in heating scenarios than in cooling ones, improving the system efficiency by over 22% compared to geothermal systems. Rainwater enhances cooling efficiency by over 14% compared to geothermal systems. This finding could help to establish a predictive method or guidelines for the design and sizing of heat exchangers in sewerage systems. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos de Investigación Geográfica is the property of Universidad de la Rioja, Servicio de Publicaciones and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is a growing interest in finding new ways to address the difficult task of introducing programming to secondary students for the first time to improve students' computational thinking (CT) skills. Therefore, extensive research is required in this field. Worldwide, new ways to address this difficult task have been developed: visual execution environments and approaches by text programming or visual programming are among the most popular. This paper addresses the complex task by using a visual execution environment (VEE) to introduce the first programming concepts that should be covered in any introductory programming course. These concepts include variables, input and output, conditionals, loops, arrays, functions, and files. This study explores two approaches to achieve this goal: visual programming (using Scratch) and text programming (using Java) to improve CT. Additionally, it proposes an AI recommendation model into the VEE to further improve the effectiveness of developing CT among secondary education students. This integrated model combines the capabilities of an AI learning system module and a personalized learning module to better address the task at hand. To pursue this task, an experiment has been carried out among 23 preservice secondary teachers' students in two universities, one in Madrid, Spain, and the other in Galway, Ireland. The overall results showed a significant improvement in the Scratch group. However, when analyzing the results based on specific programming concepts, significance was observed only in the Scratch group, specifically for the Loop concept. [ABSTRACT FROM AUTHOR]

This paper addresses the Multifunctional and Territorialised Agri-Food Systems (MTLAFS) in areas of direct urban influence, focusing on the metropolitan region of Madrid. MTLAFS are contextualised as alternatives to the hegemonic global model of mass production and consumption. They are created by combining two conceptual and theoretical bodies of knowledge that share many elements: the study of Local Agri-Food Systems (LAFS), a critical approach to agri-food economies and the re-territorialisation of agri-food systems. The paper analyses the factors that negatively affect the resilience of LAFS and it describes re-territorialisation strategies that enable MTLAFS to be built. By using a multi-criteria methodology to identify agri-environmental and food governance indicators, the research identifies and describes the characteristics that allow the case studies selected in the region of Madrid to be classified as MTLAFS. This has been carried out by gathering the main discussion points on the fractures and relocation strategies that accentuate the vulnerability or, on the contrary, enhance the resilience of the cases analysed. The paper concludes with some recommendations for strengthening the socio-ecological resilience of MTLAFS by using the systemic basis provided by the agro-urban project. This agro-urban project brings together different public policies, governance tools, territorial and food planning, as well as agrarian practices anchored to each specific territory. All these agrarian practices contribute to the configuration of an alternative territorial agri-food model that ensures food security and a shift towards the sustainable development of our planet. [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]

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]

This paper is an attempt to add knowledge to scholarship in a field that has attracted less attention from researchers. It presents a study of graffiti found on a board located inside a gym in Madrid that was carried out in order to get a grasp of the functions of the graffiti and gain an insight into discursive practices in gyms. Data analysis focused on texts while overlooking the graffiti's non-verbal features and was carried out following Fairclough's (2004, 2013) approach, which emphasizes the study of language as a social practice and encourages a three-fold analysis of discourse that includes action, representation and identification. The study revealed that social agents attempted to act and interact directly or indirectly with fellow gym-goers through graffiti. [ABSTRACT FROM AUTHOR]

Copyright of Futuro del Pasado: Revista Electrónica de Historia is the property of Futuro del Pasado: Revista Electronica de Historia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In this paper, we pay significant attention to the most vulnerable road users (i.e., people with disabilities) when interacting with cyclists. The special needs of these groups are studied by distributing an online questionnaire about their perception and interaction with cyclists besides conducting an on-road experiment to test the possibility of sharing cycling infrastructure with wheelchair users. In an authentic case study, 2 cyclists and 5 wheelchair users were asked to ride their vehicles on a cycling lane in Madrid, in order to evaluate wheelchair users' interaction with cyclists and reaction to the infrastructure by applying objective and subjective measures. The participants were provided with GPS, a speed sensor, and a head-mounted camera to record the experiment. The results show that people with disabilities feel threatened by cyclists who share the sidewalk with them; the respondents to the questionnaire suggested making the sidewalk free of cyclists to avoid conflict and improve safety. Moreover, the outputs of the experiment show positive feedback from wheelchair users when sharing cycling infrastructure regarding the improvement of speed and safety feeling. However, it is recommended to increase the number of wheelchair users to obtain more reliable and generalizable results. [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]

Copyright of Hipogrifo: revista de literatura y cultura del siglo de oro is the property of Hipogrifo: revista de literatura y cultura del siglo de oro and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Fonseca: Journal of Communication is the property of Revista Fonseca Journal of Communication and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Investigaciones Historicas is the property of Universidad de Valladolid, Facultad de Filosofia y Letras and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Nature-based solutions (NBS) are becoming popular in urban planning and policy making as cost-effective solutions capable of delivering multiple ecosystem services and addressing several societal challenges. So far, however, the cost-effectiveness of urban NBS projects has not been consistently quantified by built environment professionals, who lack user-friendly tools to account for the environmental costs and benefits of NBS. This paper presents a prototype online decision support tool (NBenefit$®) that calculates the negative and positive environmental impacts, externalities, and financial values of planned urban forests over their entire life cycle. NBenefit$ relies on a modelling framework that combines system dynamics, urban ecology, and life cycle thinking approaches, and it is presented as a visual web-based interface. An online map and a grid of cells is used to map the site of intervention, to delineate the size of the urban forest, and to define variations in abiotic, biotic, and management attributes in each site. Outputs are provided by year, for the entire site and NBS life cycle. The potential value of NBenefit$ as a supporting tool was exemplified with the calculation of 48 urban forest archetypes, a few of which were used to set scenarios for a hypothetical urban forest in Madrid (Spain). The results showcase the impact that decisions taken during the planning, design, or management of an NBS project may have on its long-term performance. Future works will expand the scope of NBenefit$, including other types of urban NBS. [ABSTRACT FROM AUTHOR]

While levels of internal mobility have been trending down in many advanced economies, the interregional migration rates of Spanish young adults have increased. This paper analyses the internal movements of the Spanish-born population aged 25-39 between NUTS-2 regions from 1992 to 2018, including sub-periods linked to the Spanish economic context. The analysis incorporates the urban-rural dimension within each region and the educational level of migrants, a variable that has not been included in Spanish internal migration studies. We used flow register data of migration and sociodemographic information from the Labor Force Survey. The results show that migrations between regions have become more unbalanced over time, especially since the 2008 crisis. In addition, a new trend of out-migration from cities in peripheral regions has been detected, which contrasts with the former high level of rural outmigration. A great educational selectivity of out-migrants and a growing internal brain drain have also been found. Moreover, qualified human capital accumulation has been increasing in Madrid. [ABSTRACT FROM AUTHOR]

The aim of this paper is to shed light on the effects of the only privatisation that existed in the street cleaning service in Madrid. Framed within the international processes of privatisation since the 1970s and through a search in historical archives, it analyses the political-administrative and social factors that influenced this privatisation and its consequences on labour relations in the sector. Finally, the hypothesis of the planning of the privatisation of the service, the existence of differences with the political parties in the municipal government of Madrid and the effects of privatisation on the dualisation of working conditions affecting the further development of the service in the different districts of Madrid are tested. JEL Code: H75, I18, N9. [ABSTRACT FROM AUTHOR]

The permanent museography project for the Cuatro Caminos metro station in Madrid features two historical synthesis murals whose main methodological instrument is architectural drawing. In addition, they offer narrative proposals that use resources of the avant-garde comic, together with simultaneous representations of space and time in which the chronological sense of the narrative is even manipulated. This paper analyses the two murals from the conceptual framework of the comic book in terms of the combination of the simultaneous vision of the graphic medium and the partial visions of its contents. Moreover, it ponders on the use of these resources when transmitting complex realities in heritage dissemination works. [ABSTRACT FROM AUTHOR]

Copyright of Research in Education & Learning Innovation Archives (REALIA) is the property of Research in Education & Learning Innovation Archives (REALIA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Very unhealthy air quality is consistently connected with numerous diseases. Appropriate extreme analysis and accurate predictions are in rising demand for exploring potential linked causes and for providing suggestions for the environmental agency in public policy strategy. This paper aims to model the spatial and temporal pattern of both moderate and extremely poor PM 10 concentrations (of daily mean) collected from 342 representative monitors distributed throughout mainland Spain from 2017 to 2021. We first propose and compare a series of Bayesian hierarchical generalized extreme models of annual maxima PM 10 concentrations, including both the fixed effect of altitude, temperature, precipitation, vapour pressure and population density, as well as the spatio-temporal random effect with the Stochastic Partial Differential Equation (SPDE) approach and a lag-one dynamic auto-regressive component (AR(1)). Under WAIC, DIC and other criteria, the best model is selected with good predictive ability based on the first four-year data (2017–2020) for training and the last-year data (2021) for testing. We bring the structure of the best model to establish the joint Bayesian model of annual mean and annual maxima PM 10 concentrations and provide evidence that certain predictors (precipitation, vapour pressure and population density) influence comparably while the other predictors (altitude and temperature) impact reversely in the different scaled PM 10 concentrations. The findings are applied to identify the hot-spot regions with poor air quality using excursion functions specified at the grid level. It suggests that the community of Madrid and some sites in northwestern and southern Spain are likely to be exposed to severe air pollution, simultaneously exceeding the warning risk threshold. [ABSTRACT FROM AUTHOR]

The aim of this paper is to conduct a revenue management study, generating a theoretical model that establishes the relationship between the factors of a Short-Term Rental apartment offered on the Airbnb marketplace or similar and its optimal rental price set when the property is first put on the market, considering not only the characteristics defined in the platform listing but also the sociodemographic characteristics of the area in which the apartment is located. The research process was structured in six phases as case study for the technology transfer model. First, research planning was conducted to estimate the time, cost, and suitability of the research topic. Second, the study design was determined to establish a technology transfer model focusing on the theory of mixed revenue management. Third, data collection about the city of Madrid was extracted from two technological databases, namely SeeTransparent based mainly on Airbnb (28 internal characteristics of the apartment) and Deskmind Research (9 sociodemographic variables of the area in which the apartment is located). Fourth, the data were prepared to create a new descriptive variable of the apartments based on geolocation. Fifth, the analysis of this study was applied to explore the correlation between the price charged per night, the 28 internal characteristics of the apartments, and the 9 sociodemographic variables of their surrounding areas. Sixth, with this integrated database, the information was transformed into multivariate inferential statistics through Exploratory Factor Analysis and Multiple Linear Regression, creating a technology transfer model (big data algorithm) that allows revenue managers to set the price of an apartment based on known information, prior to having a history of market reactions. This research process and model consider some of the factors affecting the psychological behavior of tourism consumers. Practical implications of the findings indicate that the size/capacity of the apartments used for Short-Term rentals largely determines the initial rental price set (72%). The equipment offered by the apartments has a moderate impact (18%), and the sociodemographic characteristics of the surrounding area have a minor influence (11%). [ABSTRACT FROM AUTHOR]