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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 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]

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]

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]

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]

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 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.)

Copyright of Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales is the property of Academia Colombiana de Ciencias Exactas, Fisicas y Naturales 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.)

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]

Urban overheating and its ongoing exacerbation due to global warming and urban development lead to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees and vary on the meter scale. Therefore, most numerical thermal comfort studies apply microscale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation and/or mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parameterization BEP-BEM within the Weather Research and Forecasting (WRF) model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood-scale heat stress distributions across whole cities and for timescales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed and its subgrid variability are downscaled as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square, combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature both across the city and over the diurnal cycle. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) significantly impact air quality and atmospheric chemistry, influencing ozone formation and secondary organic aerosol production. Despite their importance, the uncertainties associated with representing VOCs in atmospheric emission inventories are considerable. This work presents a spatiotemporal assessment and evaluation of benzene, toluene, and xylene (BTX) emissions and concentrations in Spain by combining bottom-up emissions, air quality modelling techniques, and ground-based observations. The emissions produced by High-Elective Resolution Modelling Emission System (HERMESv3) were used as input to the Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) chemical transport model to simulate surface concentrations across Spain. Comparing modelled and observed levels revealed uncertainty in the anthropogenic emissions, which were further explored through sensitivity tests. The largest levels of observed benzene and xylene were found in industrial sites near coke ovens, refineries, and car manufacturing facilities, where the modelling results show large underestimations. Official emissions reported for these facilities were replaced by alternative estimates, resulting in varied improvements in the model's performance across different stations. However, uncertainties associated with industrial emission processes persist, emphasising the need for further refinement. For toluene, consistent overestimations in background stations were mainly related to uncertainties in the spatial disaggregation of emissions from industrial-use solvent activities, mainly wood paint applications. Observed benzene levels in Barcelona's urban traffic areas were 5 times larger than the ones observed in Madrid. MONARCH failed to reproduce the observed gradient between the two cities due to uncertainties arising from estimating emissions from motorcycles and mopeds, as well as from different measurement methods and the model's capacity to accurately simulate meteorological conditions. Our results are constrained by the spatial and temporal coverage of available BTX observations, posing a key challenge in evaluating the spatial distribution of modelled levels and associated emissions. [ABSTRACT FROM AUTHOR]

Background: During the educational stage, academic achievement depends on various social, family, and personal factors. Among the latter, executive skills in everyday life play a significant role in dealing with the academic demands of adolescents. Therefore, the aim of this study is to ascertain the effects of executive symptomatology in everyday functioning on academic achievement in adolescents. Method: The study involved 910 students aged between 13 and 15 years (M = 14.09, SD = 0.68) from both public and private schools in the Community of Madrid. The DEX, BDEFS-CA, and BRIEF-SR questionnaires were utilised to assess executive difficulties, while grades in language, mathematics, and natural sciences were used as a measure of academic achievement. Results: The data revealed statistically significant differences in working memory, emotional control, materials organisation, and task completion. In relation to language and natural sciences subjects. In the case of mathematics, emotional control and task completion were significant variables. Conclusion: Our results indicate that certain executive skills that are manifested in everyday life activities can contribute, albeit in a variable way, to academic achievement in the subjects studied. This aspect is relevant insofar as it allows us to develop preventive interventions based on the executive training of these everyday skills. [ABSTRACT FROM AUTHOR]

Background: Xenoestrogens are synthetic or naturally occurring chemicals capable of altering the endocrine system of humans and animals owing to their molecular similarity to endogenous hormones. There is limited data regarding their effects on women´s health. Chronic exposure to xenoestrogens can promote the development of estrogen-related diseases. Objectives: To examine xenoestrogen concentration (TEXB-α) differences between women with leiomyomas or endometriosis and control women, and to study the relationship between the clinical and sociodemographic characteristics of these patients and their xenoestrogen levels. Methods: Prospective case-control study. We selected 221 women who underwent surgery at Quironsalud Madrid University Hospital between 2017 and 2021. The cases included 117 patients: 74 women who underwent surgery for uterine leiomyomas, 21 with endometriosis, and 22 with both pathologies. The control group comprised 104 healthy women who underwent surgical procedures for other reasons. TEXB-α was determined in the omental fat of all patients. Using a questionnaire and reviewing the patients' medical records, we collected sociodemographic data and other relevant variables. Results: A significant majority of study participants (68.8%) had detectable levels of xenoestrogens. We found no association between TEXB-α levels in omental fat and the presence of myomas or endometriosis. In the case group, women living or working in Madrid Community exhibited, on average, 3.12 Eeq pM/g higher levels of TEXB-α compared to those working in other areas (p = 0.030). Women who referred to the use of estrogen-containing hormonal contraceptives had, on average, 3.02 Eeq pM/g higher levels of TEXB-α than those who had never used them (p = 0.022). Conclusions: This study found no association between omental xenoestrogen levels and leiomyomas or endometriosis. However, their presence in most participants and their association with highly polluted areas emphasizes the importance of limiting environmental exposure to these substances. We also identified an association between hormonal contraceptive use and xenoestrogen concentration. [ABSTRACT FROM AUTHOR]

While variation in the southern Peninsular Spanish affricate /tʃ/ has been considered in the context of deaffrication to [ʃ], this study examines an emergent variant [ts] in the context of sociolinguistic identity and style in political speech. Based on a corpus of public speech from Madrid and Andalusia, Spain, this study examines the phonetic and sociolinguistic characteristics of the affricate, finding variation in the quality of the frication portion of the segment through an analysis of segment duration (ms), the center of gravity (Hz), and a categorical identification of realization type. The results suggest that both linguistic variables, like phonetic environment, stress, lexical frequency, and following vowel formant height, as well as extralinguistic variables, like speaker city, gender, political affiliation, and speech context, condition use. Based on these findings, it appears that production of the alveolar affricate [ts] is an incipient sociolinguistic marker in the process of acquiring social meaning. It is particularly associated with female speech and prestige norms that transcend regional identification. This alveolar variant serves as an additional sociolinguistic resource accessible for identity development among politicians and offers insight into ongoing change in the affricate inventory of southern and northern-central Peninsular Spanish. [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]

Copyright of Revista INVI is the property of Universidad de Chile 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 Revista Latinoamericana de Estudios Urbano Regionales is the property of Pontificia Universidad Catolica de Chile 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.)

Innovation in the beer market focuses on research into the different ingredients that make up this popular and sought-after beverage. Some of these innovations have focused on the use of autochthonous ingredients, which bring added value to products, reduce production costs and environmental impact, and provide beers with special organoleptic characteristics. This study focuses on the use of autochthonous ingredients from the Community of Madrid and the design of four recipes that are suited to these ingredients and consumer demand. In terms of the ingredients used, the micromalting of barley grown in the CM was carried out, while hops (Cascade variety for bittering hops and wild hops Rayo verde Tajuña and Torreta Tajuña for aroma) and yeast strains (Saccharomyces cerevisiae G 520 and G 354) were selected from previous studies, which were also sourced from the CM. For the barley malt, parameters such as protein content were determined, which was at the maximum limit but did not affect the final quality of the beers. The content of α- and β-acids in the hops was also determined, being higher in the Cascade variety. Different physicochemical analyses (GC, aromatic compounds, bitterness, colour, SO2, polyphenols, and antioxidant capacity) were carried out to determine the quality of the beers obtained from the four recipes designed, as well as tastings by a panel of experts and consumers. Finally, two recipes were rated the best by a panel of experts and consumers, where the cereal, hop, and fruit flavours stood out for their aromatic profile. [ABSTRACT FROM AUTHOR]

The design of emission abatement measures to effectively reduce high ground-level ozone (O3) concentrations in urban areas is very complex. In addition to the strongly non-linear chemistry of this secondary pollutant, precursors can be released by a variety of sources in different regions, and locally produced O3 is mixed with that transported from the regional or continental scales. All of these processes depend also on the specific meteorological conditions and topography of the study area. Consequently, high-resolution comprehensive modeling tools are needed to understand the drivers of photochemical pollution and to assess the potential of local strategies to reduce adverse impacts from high tropospheric O3 levels. In this study, we apply the Integrated Source Apportionment Method (ISAM) implemented in the Community Multiscale Air Quality (CMAQ v5.3.2) model to investigate the origin of summertime O3 in the Madrid region (Spain). Consistent with previous studies, our results confirm that O3 levels are dominated by non-local contributions, representing around 70 % of mean values across the region. Nonetheless, precursors emitted by local sources, mainly road traffic, play a more important role during O3 peaks, with contributions as high as 25 ppb. The potential impact of local measures is higher under unfavorable meteorological conditions associated with regional accumulation patterns. These findings suggest that this modeling system may be used in the future to simulate the potential outcomes of specific emission abatement measures to prevent high- O3 episodes in the Madrid metropolitan area. [ABSTRACT FROM AUTHOR]

In this study, a total of 34 Spanish minority varieties were studied during four seasons from 2020 to 2023, and their behavior was characterized according to their main phenological stages (bud break, bloom, veraison, and maturity) and complete cycle. We focused on the varieties prospected in the central Spanish plateau and conserved in the "El Encín" grapevine collection, aiming to identify the potential for adaptation of these varieties and for them to be considered by winegrowers as an alternative to current climate change conditions. The growing degree days required for the expression of the phenological stage, and the duration of each stage, were compiled. Characteristics of oenological interest were also monitored, such as °Brix, pH, and titratable acidity in must at the time of harvest. This study was carried out in years with atypical snowfall and cold spells (winter 2021), as well as with heat waves (summer 2022), with average temperatures 3–5 °C higher than normal and absolute maximum temperatures over 40 °C. Both cases also exceeded records of historical series (1957–2019). Veraison has been identified as the stage most susceptible to damage from high temperatures, in addition to the maturation and duration of the complete cycle. The varieties were classified into five groups according to the duration of each phenological stage (very early, early, medium, late, and very late). Some varieties with late or very late maturation and with must characteristics of 20–23 °Brix, 3–5 g/L of titratable acidity, and pH 3.5–4.5, as well as others, retained stability in their phenological periodicity and must quality. The results suggest that special attention should be given to thoroughly evaluating these varieties, and that the strategies contemplated should be considered as a feasible cultivation alternative in viticulture to mitigate the effects of climate change. [ABSTRACT FROM AUTHOR]

The variety of types that appear in kidney stones makes it necessary to describe them to try to understand their formation. The crystalline phases recognized in stones are classified according to their chemical composition. For this reason, a study using X-ray microdiffraction, Raman spectroscopy, SEM/EDX analysis, and cathodoluminescence (CL) is recommended, to identify their composition. In the present study, this was carried out on kidney stones collected from surgeries in hospitals from Spain. The samples were collected in two representative urban centers: one from a large city, Madrid; the other with a mostly rural population, in order to contrast the possible compositions of the kidney stones. In large cities, struvite appears more frequently, although calcium oxalates are very common in both areas. An attempt was made to provide a guide to the most frequent phases, accompanied by four analysis methods that make their identification possible. [ABSTRACT FROM AUTHOR]

Background: Healthcare professionals traditional education reflects constraints to face the complex needs of people with chronic diseases in primary care settings. Since more innovative and practical solutions are required, Virtual Community of Practices (vCoP) seem to better respond to learning updates, improving professional and organizational knowledge. However, little is known about the value created in vCoPs as social learning environments. The objective of this project was to explore the value creation process of a gamified vCoP ("e-mpodera vCoP") aimed at improving the knowledge and attitudes of primary healthcare professionals (PCPs) (nurses and general practitioners) to the empowerment of people with chronic conditions. Methods: A framework analysis assessed the value creation process using a mixed methods approach. The framework provided awareness about knowledge and usefulness in a learning community through five cycles: (1) immediate value, (2) potential value, (3) applied value, (4) realized value, and (5) reframing value. Quantitative data included vCoP analytics such as logins, contributions, points, badges, and performance metrics. Qualitative data consisted of PCPs' forum contributions from Madrid, Catalonia, and Canary Islands over 14 months. Results: A total of 185 PCPs had access to the e-mpodera vCoPs. The vCoP showed the dynamic participation of 146 PCPs, along 63 content activities posted, including a total of 3,571 contributions (including text, images, links to webpages, and other files). Regarding the value creation process, the e-mpodera vCoP seems to encompass a broad spectrum of value cycles, with indicators mostly related to cycle 1 (immediate value – activities and interactions) and cycle 2 (potential value – knowledge capital); and to a lesser extent for cycle 3 (applied value – changes in practice) and for cycle 4 (realized value – performance improvement). The presence of indicators related to cycle 5 (reframing value), was minimal, due to few individual redefinitions of success. Conclusion: To reach a wider range of value possibilities, a combination of learning objectives, competence framework, challenged-based gamified platform, and pathway model of skill development seems crucial. However, additional research is required to gain clearer insights into organizational values, professionals' lifelong educational needs in healthcare, and the long-term sustainability of performance improvement. Trial registration: ClinicalTrials.gov, NCT02757781. Registered on 02/05/2016. [ABSTRACT FROM AUTHOR]

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers' safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver's perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one's driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving. [ABSTRACT FROM AUTHOR]

This article describes the objects in the barniz de Pasto collection at the Museo de América, Madrid. The barniz de Pasto technique will be described, as well as the historical documentary sources that have previously discussed this varnish. The article will also mention the historical reasons why Spain is the European country in which the greatest number of objects decorated with varnish have been found, in both religious and private collections. The main body of the article discusses all the barniz de Pasto objects held in the Museum collection, focuses on the history of their arrival at the Museum, and investigates their possible origin, with the help of ample photographs. The final section examines the Museum's three most recent acquisitions, completed in the second half of 2022, in detail. [ABSTRACT FROM AUTHOR]

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Simple Summary: The rise of drug-resistant bacteria, particularly in animals, poses a major challenge in veterinary medicine. Antibiotic development lags behind the increasing resistance. To tackle this, alternative therapies have been explored, such as the use of natural products and plant extracts. This study evaluates the laboratory efficacy of plant derivatives of the Alliaceae group (which includes garlic and onion) as antimicrobial agents, with encouraging results. Although further research is needed, these findings suggest a potential role for these natural compounds in veterinary medicine. Background: The increase of multi-resistant bacteria, especially Staphylococcus spp. and Enterobacteriaceae, constitutes a challenge in veterinary medicine. The rapid growth of resistance is outpacing antibiotic discovery. Innovative strategies are needed, including the use of natural products like Allium species (Allium sativum L. and Allium cepa L.), which have been used empirically for centuries to treat infectious diseases in humans and farm and aquaculture animals due to their antibacterial properties. Methods: This study aimed to evaluate the in vitro activity of two Allium-derived compounds, propyl propane thiosulfinate (PTS) and propyl propane thiosulfonate (PTSO), against multi-resistant Staphylococcus spp. (n = 30) and Enterobacteriaceae (n = 26) isolated from dogs referred to a veterinary teaching hospital in Madrid. Results and Discussion: The results indicated the in vitro efficacy of PTSO/PTS against the tested bacterial strains, and 56.7% of Staphylococcus pseudintermedius and 53.8% of Enterobacteriaceae showed sensitivity to PTS and PTSO compared with classic antibiotics. In addition, 50% of S. pseudintermedius strains resistant to erythromycin, ibofloxacin, difloxacin and orbifloxacin and 50% of Enterobacteriaceae strains resistant to tetracycline and doxycycline were sensitive to PTS and PTSO. Although studies are needed to verify their efficacy in vivo, the combined use of PTS and PTSO exhibits promise in enhancing bacterial sensitivity against S. pseudintermedius and Enterobacteriaceae infections, providing a first insight into the potential of both compounds in veterinary practice. [ABSTRACT FROM AUTHOR]

The validated influence of urban biophysical structure on environmental processes within urban areas has heightened the emphasis on studies examining morphological patterns to determine precise locations and underlying causes of urban climate conditions. The present study aims to characterise morphological patterns describing the distribution of Land Surface Temperature (LST) based on a prior classification of biophysical variables, including urban density (building intensity and average height), surface characteristics, shortwave solar radiation (broadband albedo), and seasonal variations in vegetation cover (high, medium, and low levels), retrieved from multisource datasets. To describe the distribution of LST, the variables were calculated, classified, and subsequently, analysed individually and collectively concerning winter and summer LST values applied in an urban neighbourhood in Madrid, Spain. The results from the analytical approaches (observation, correlations, and multiple regressions) were compared to define the morphological patterns. The selection of areas resulting from the morphological patterns with the most unfavourable LST values showed agreement of up to 89% in summer and up to 70% for winter, demonstrating the feasibility of the methods applied to identify priority areas for intervention by season. Notably, low and high vegetation levels emerged as pivotal biophysical characteristics influencing LST distribution compared to the other characteristics, emphasising the significance of integrating detailed seasonal vegetation variations in urban analyses. [ABSTRACT FROM AUTHOR]

Copyright of Geopolitica(s): Revista de Estudios Sobre Espacio y Poder 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.)

Copyright of Geopolitica(s): Revista de Estudios Sobre Espacio y Poder 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.)

Starting from the contribution of such thinkers as the famous Giordano Bruno (1583) and the great mathematician and physicist Henri Poincaré (1889) and the surprising discovery of the meteorologist Edward Lorenz (1963), we consider the expansion of the mathematics of chaos in this article, paying attention to topology, qualitative geometry, and Catastrophe Theory, on the one hand, and addressing the possibilities derived from the new Computer Science as Quantum Algorithms and the advances in Artificial Intelligence, on the other. We especially highlight the section on computing chaos, which we consider to be new calculation and analysis instruments, such as machine learning and its algorithm called reservoir computing, through which we can know the dynamics of a chaotic system. With past data, with equations like Karamoto–Sivashinsky, one can improve predictions of the system eight times further ahead than in previous methods. Integrating the machine learning approach and traditional model-based prediction, one could obtain accurate predictions twelve Lyapunov times. As we know, in the framework of chaos theory, it is habitually accepted that the idea of long-term prediction seems impossible because we live under a veil of uncertainty. But with technological advances, the landscape begins to change, both in chaos theory and in its applications, especially in the field of economics, to which we devote particular attention, carrying out as an example the analysis of the evolution of the Madrid Stock Exchange in the 2006–2013 crisis. Above all this, a reflection of a general nature is necessary to enlighten us on the possibility of opening a new horizon. [ABSTRACT FROM AUTHOR]

Limited evidence has verified if ultrasound imaging (US) can detect post-exercise muscle damage based on size, shape, and brightness metrics. This study aimed to analyze the correlation between creatine kinase (CK) concentration and (as a biomarker of muscle damage) changes in US gray-scale metrics after an exercise-induced muscle damage protocol. An observational study was conducted at a private university lab located in Madrid. Twenty-five untrained and asymptomatic volunteers were enrolled in this study. Baseline demographic data and body composition metrics were collected. In addition, the rectus femoris US data and CK concentration were assessed at baseline and after inducing muscle damage (24 and 48 h later). After calculating time differences for all the outcomes, the correlation between the changes observed with US and biomarkers was assessed. Significant CK concentration increases were found 24 h (p = 0.003) and 48 h (p < 0.001) after exercise. However, no significant changes in muscle size, shape, or brightness were found in any location (p > 0.05 for all). In addition, no significant associations were found between CK changes and US changes (p > 0.05 for all). Gray-scale US is not a sensitive tool for detecting muscle damage, as a protocol of exercise-induced muscle damage confirmed with CK produced no significant gray-scale US changes after 24 or 48 h. In addition, US and CK changes after 24 and 48 h were not associated with each other. [ABSTRACT FROM AUTHOR]

Copyright of Virtual Archaeology Review is the property of Virtual Archaeology Review 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.)