Examinando por Autor "Simón de Blas, Clara"

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A forecast model applied to monitor crops dynamics using vegetation indices (Ndvi)
(MDPI, 2021) Carreño Conde, Francisco; Sipols, Ana Elizabeth; Simón de Blas, Clara; Mostaza Colado, David
Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation.
A Model to Evaluate the Effect of Urban Road Pricing on Traffic Speed and Congestion in Madrid City Center and Its Surrounding
(2021) Muñoz Miguel, Juan Pedro; García Sipols, Ana Elizabeth; Simón de Blas, Clara; Anguita Rodríguez, Francisca
Currently, traffic intensity in large cities and their surroundings constitute the main unsustainability factor associated with urban transport, leading to significant traffic speed reduction due to high levels of congestion. Road pricing seems to be a measure of transport policy capable of improving efficiency and sustainability in urban transport, reducing traffic intensity and increasing traffic speed, as reflected in the main road pricing indicators currently in operation (Singapore, London, Stockholm, Milan . . . ). Based on the data obtained through a mobility survey applied to a theoretical design of road pricing for the city of Madrid, we developed a traffic speed forecast model using time series analysis, to which we applied the mobility survey results. The research results show that theoretical urban road pricing could imply very significant positive effects in traffic speed increase and congestion reduction, fundamentally in the city center and metropolitan crown, as well as demonstrating positive effects in the improvement of traffic speed in those municipalities furthest from the urban center. Moreover, our findings reveal that road pricing would allow an average traffic speed increase in the protected area of the city center during the operating hours of between 10% and 32.5%: 15.9% in the metropolitan crown, 10% in M-30, and 32.5% in the case of Madrid’s city center.
Actas Día de pi
(Servicio de Publicaciones de la Universidad Rey Juan Carlos, 2021-2023) Duarte Muñoz, Abraham; García, Esther; Simón de Blas, Clara
12 de marzo de 2021, Universidad Rey Juan Carlos, Móstoles.
Anatomic mapping of the collateral branches of the external carotid artery with regard to daily clinical practice
(Elsevier, 2021) Cobiella, R; Quinones, Sara; Aragones, Paloma; Leon, X; Abramovic, A; Vazquez, T; Sanudo, Jose Ramón; Maranillo, Eva; Simón de Blas, Clara; Konschake, Marko
Background: To identify the anatomical variations of the main branches of the external carotid artery (lingual, facial, occipital, ascending pharyngeal and sternocleidomastoid), giving information about the calibers and origins with the aim of creating a new classification useful in clinical practice. Material and methods: 193 human embalmed body-donors were dissected. The data collected were analyzed using the Chi² test. The results of previous studies were reviewed. Results: The majority of the anterior arterial branches (superior thyroid, facial and lingual artery) were observed with an independent origin, respectively, classified as pattern I (80.83%, 156/193). In 17.62% (34/193) a linguofacial trunk, pattern II, has been observed, only in 1,04% (2/193) a thyrolingual trunk, pattern III, has been found and in one case (1/193, 0.52%) one thyrolinguofacial trunk, pattern IV, was found. Depending on the posterior branches (occipital and ascending pharyngeal), four different types could be determined: type a, the posterior arteries originated independently, type b, the posterior arteries originated in a common trunk, type c, the ascending pharyngeal artery was absent, type d, the occipital artery was absent. Conclusion: Anatomical variations in these arteries are relevant in daily clinical practice due to growing applications, e.g., in Interventional Radiology techniques. Knowledge of these anatomical references could help clinicians in the interpretation of the carotid system.
Body Donation, Teaching, and Research in Dissection Rooms in Spain in Times of Covid-19
(wiley, 2021) Manzanares-Cespedes, MC; Dalmau-Pastor, M; Simón de Blas, Clara; Vazquez-Osorio, Maria Teresa
The state of alarm due to Covid-19 pandemic in Spain stopped all educational and most university research activities. The Spanish Anatomical Society (SAE) Consensus Expert Group on Body Donations piloted a study based on a questionnaire to know the status of body donations and dissection activities during the lockdown, as well as the future implications of Covid-19 pandemic for body donation programs and anatomy teaching. The questionnaire results show that Spanish Universities refused body donations and stopped all dissection research and teaching. The Covid-19 expected influence on anatomy teaching was referred to the increase in teaching workforce and resources required to apply the new safety measures to future practical activities, as well as to prepare and adapt teaching material for online-only programs. The application of reinforced safety measures was expected to be perceived by the respondent's students as a gain in teaching quality, while the transformation of the anatomy courses in online-only programs will be perceived as a quality decrease. The respondent's concerns about future institutional implications of the pandemic were related to increased costs of the adaptation of the facilities and the reinforced preventive measures, as well as the eventual decrease in donations. The complete lockdown applied to dissection rooms was not justified by scientific evidence and represented a break of the confidence deposed in the institutions by the donors. A consensus is required for the adoption of a renewed, comprehensive protocol for present and future body donations including the evidence Covid-19 pandemic has contributed to create.
Clinical anatomy of the lumbar sinuvertebral nerve with regard to discogenic low back pain and review of literature
(Springer, 2021) Quinones, Sara; Konschake, Marko; Aguilar, Llopis; Simón de Blas, Clara; Aragones, Paloma; Hernandez, LM; Abramovic, A; Tubbs, SR; Bouzad, J; Valderrama-Canales, FJ; Vazquez, Teresa; Sañudo, Jose Ramón
Purpose: Lumbar discogenic diffuse pain is still not understood. Authors describe the sinuvertebral nerve (SVN) as one possible cause. Body-donor studies are rare and controversial. Therefore, the aim was to revisit the origin, course and distribution in a body-donor study. Methods: Six lumbar blocks (3 female, 3 male) aged between 59 and 94 years were dissected. After removal of the back muscles, lamina, dura mater and cauda equina, the anterior vertebral venous plexus, spinal artery and SVN were exposed and evaluated. Results: 43 nerves out of 48 levels could be evaluated. The origin of the SVN was constituted by two roots: a somatic and a sympathetic branch arising from the rami communicantes. In 4/48 intervertebral canals studied (8.3%), we found two SVN at the same level. In 35/48 cases, one SVN was found. In 9/48 cases, no SVN was found. The SVN had a recurrent course below the inferior vertebral notch; in the vertebral canal it showed different patterns: ascending branch (31/43, 72.1%), common branch diverging into two branches (10/43, 23.3%), double ascending branch (1/43, 2.3%) finalizing two levels above and a descending branch (1/43, 2.3%). In 12/43 cases (27.9%) the SVN had ipsilateral connections with another SVN. The distribution ended in the middle of the vertebral body supplying adjacent structures. Conclusion: A thorough understanding of the anatomy of the SVN might lead to significant benefits in therapy of discogenic low back pain. We suggest blocking the SVN at the level of the inferior vertebral notch of two adjacent segments.
Combined social networks and data envelopment analysis for ranking
(Elsevier, 2018) Simón de Blas, Clara; Simón, Jose; Gómez González, Daniel
In this work, we propose a method for ranking efficient decision-making units (DMUs) that uses measures of dominance derived from social network analysis in combination with data envelopment analysis (DEA). For this purpose, a directed and weighted graph is constructed, in which the nodes represent the system's DMUs and the edges represent the relationships between them. The objective is to identify and rank the most important nodes by taking into account the influence or dominance relations between the DMUs. The method uses a weighted HITS algorithm to identify the hubs and the authorities in the network by assigning to each DMU two numbers, the authority weight and the hub weight. Additionally, this method allows for the identification of DMUs whose exclusion from the DEA analysis does not modify the efficiency values obtained for the remaining DMUs.
Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings
(MDPI, 2021) Mendoza-Pittí, Luis; Calderón-Gómez, Huriviades; Gómez-Pulido, Jose Manuel; Vargas-Lombardo, Miguel; Castillo-Sequera, Jose Luis; Simón de Blas, Clara
Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain.
Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
(palgrave macmillan, 2022) Martínez, Andres; Salafranca, Alfonso; Sipols, Ana E.; Simón de Blas, Clara; Van Hengel, Daniel
For many decades, considerable research has been conducted on Market Response models. Mostly without any attempts to validate the results in strictly predictive tasks and often ignoring if the methods comply with the underlying assumptions and conditions, like the method’s ability to outline the broadly accepted effects of advertising actions. This work presents an enhanced method for market response models consistent with the underlying assumptions of such. Our method is based on Distributed Lag Models with the novelty of introducing regularization in its estimation, a cross-validation framework, and hold-out testing, next to present an empirical manner of extracting its effects. This approach allows the construction of models in an exploratory and simple manner, unlocking the possibility of extracting the underlying effects and being suitable for large samples and many variables. Last, we conduct a practical example using real-world data, accompanied by an unprecedented set of empirical explainability assessments next to a high level of predictive capability in similar circumstances to how it would be used for decision-making in a corporate setup.
Emotional Perception in 11S (USA) and 11M (Spain) Advertisement
(Universidad de Navarra, 2020-09-30) Martínez-Pastor, Esther; Simón de Blas, Clara
The aim of this paper is to contrast whether cognitive memory and emotional recall related to a tragic event and exposure to advertisements that evoke such an experience generates a negative emotional change in the target. We performed an experiment that analyzes emotional changes derived from advertisements featured in the national press ten years after terrorist attacks. We chose the attacks: September 11, 2001, in New York City, and March 11, 2004, in Madrid and analyze the cognitive recall in a set of Spanish and United States focus groups. The results show a significant emotional change in the respondents after the advertisement visualization that is more strongly linked to the recall of a negative event than to the advertisement creativity.
Evaluation of stress and anxiety levels on science and engineering undergraduate students in spain when facing written assessments guides for positive interventions
(Elsevier, 2024-11) Simón de Blas, Clara; Rojas, Karina; García Sipols, Ana E.; Hernández Alonso, Sonia; Castellanos, María Eugenia; Cano, Javier; Córdoba, Claudia
Anxiety and stress disorders are increasingly common, especially among undergraduate students, significantly affecting their family, social, and academic lives. The isolation from restrictive measures during the COVID-19 pandemic in Spain has further exacerbated mental health issues. The disruption of in-person teaching has also impacted traditional learning and evaluation processes, increasing stress and anxiety levels in students. Based on this background, this study aims to analyze the incidence of these disorders among undergraduates and their relationship with various academic, demographic, and family factors, considering the influence of COVID-19. Our results were obtained from a survey conducted among first- and second-year URJC students who are enrolled in an experimental degree program. The statistical analysis provides guidelines for positive interventions to increase student motivation, which further leads to academic success. Results show that women exhibit higher levels of stress and a greater prevalence of anxiety compared to men. The study highlights the influence of specific factors on anxiety levels among students, proposing direct lines of action that enhance positive feelings concerning academic tasks
Extracranial Course of the Facial Nerve Revisited
(Wiley, 2019) Martínez Pascual, Paula; Maranillo, Eva; Vázquez, Teresa; Simón de Blas, Clara; Lasso, Jose María; Sañudo, Jose Ramón
Introduction: The extrapetrous course of the facial nerve has been a matter of study and debate since XIX century. Two different classifications have been classically proposed and widely accepted by most of the authors. Nevertheless, there are reported cases which do not fit in any of those. The aim of this study is to propose a new and useful classification. Material and methods: We have used 23 embalmed Caucasian adult cadavers (11 male and 12 female) belonging to the Bodies Donation and Dissecting Rooms Centre of the University Complutense of Madrid. The extra-petrous facial nerve was dissected in the possible specimens resulting in 38 facial nerves. The studied parameters were length, diameter of divisions, terminal branches, and nerve connections. Results: In every specimen two main divisions were found, temporofacial and cervicofacial. They divided into five terminal branches from cranial to caudal: temporal, zygomatic, buccal, marginal or mandibular, and cervical. Based on the comparison with previous proposed classifications, we have unified the patterns in 12 types being the most frequent types the type 3 (eight cases, 21.05%), with connections between temporal, zygomatic and buccal branches and the type 8 (eight cases, 21.05%), a complex network between temporal, zygomatic, buccal, and mandibular branches. The number of terminal branches was so variable. Conclusion: We propose a new 12-patterned classification which summarizes the previous ones. However, we consider that a good study of the number of terminal branches, connections between branches or with other cranial nerves are more useful for surgeons to avoid injuries to the facial nerve during surgery than complex classifications. Anat Rec, 302:599-608, 2019. © 2018 Wiley Periodicals, Inc.
Motor imagery EEG signal classification with a multivariate time series approach
(2023) Velasco, Ivan; Sipols, Ana Elizabeth; Simón de Blas, Clara; Pastor, Luis; Bayona, Sofia
Background: Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysis Results: Applying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables. Conclusions: This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.
Multi-criteria Forecast Combination Method with Nonlinear Programming for time series prediction models
(Elsevier, 2024-11-01) Generoso Gutierrez, Oscar; Simón de Blas, Clara; Garcia Sipols, Ana E.
Improving prediction computation for time series analysis is still a challenge. Finding a method that combines the benefits of different methodologies is still an open problem. Besides the very efficient prediction combination techniques proposed, there is still a lack of procedures that jointly consider error measure combinations and model constraints. In this work, we propose a new forecast combination procedure based on multi-criteria methods that allows the assignment of weights to different error measures in the objective function and the incorporation of constraints. A real case from the pharmaceutical industry for the sale of a probiotic product is presented to illustrate the performance of the proposal. This method is capable of considering different error measures and non distance based errors, is enriched by the consideration of constraints that consider desirable properties of the solution and is robust with respect to different time series characteristics such as trends, seasonality, etc. Results shows similar accuracy to the best known forecasting methods to date
Non‑linear Cointegration Test, Based on Record Counting Statistic
(Springer, 2023-11-27) Atil, Lynda; Fellag, Hocine; Sipols, Ana E.; Santos Martín, M.T; Simón de Blas, Clara
Traditional tests fail to detect the presence of nonlinearities in series that are cointegrated, so in this paper a new procedure for cointegration tests is proposed by modifying the two-step Engle and Granger (EG) test (Engle and Granger in Econometrica 55:251–276, 1987), incorporating the RUR and the FB-RUR test of Aparicio et al. (J Time Ser Anal 27:545–576, 2006). The statistics of these non-parametric tests, which are constructed as functions of order statistics, endow the test with desirable properties such as invariance to non-linear transformations of the series and robustness to the presence of significant parameter shifts. As no prior estimation of the cointegrating parameter is required, the new tests lead to parameter-free asymptotic null distributions. Monte Carlo simulations are used to analyze the test properties and evaluate the power at different sample sizes. The robustness of the procedure is tested by performing a comparison of different tests of cointegration in real exchange rate relationships. These tests are able to find evidence of cointegration while standard cointegration tests fail to detect it.
The urban transport companies in Spain: analysis of efficiency with data envelopment analysis
(Emerald, 2024-12-16) Flores Ureba, Sandra; Simón de Blas, Clara; Sanchez Toledano, Joaquín; Sanchez de Lara, Miguel Angel
Purpose – This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size. Design/methodology/approach – This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change. Findings – Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years. Originality/value – This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.
Time series forecasting methods in emergency contexts
(2023) Villoria Hernandez, Pablo; Mariñas Collado, Irene; García Sipols, Ana Elizabeth; Simón de Blas, Clara; Rodríguez Sánchez, María Cristina
The key issues in any fire emergency are recognising fire hotspots, locating the emergency intervention team (EI), following the evolution of the fire, and selecting the evacuation path. This leads to the study and development of HelpResponder, a solution capable of detecting the focus of interest in hostile spaces derived from fire due to high temperatures without visibility. A study is conducted to determine which model best predicts measured CO2 levels. The variables used are temperature, humidity, and air quality, obtained from sensors installed in a fire tower. The statistical methods applied, namely ARIMAX, KNN, SVM, and TBATS, allow the adjustment and modelling of the variables. Explanatory variables with temporal structure are incorporated into SVM, a new improvement proposal. Moreover, combining different models showed the best efficiency in forecasting. In fact, another contribution of our work lies in offering a small-scale prediction system that is specifically designed to save batteries. The system has been tested and validated in a hostile environment (building), simulating real emergency situations. The system has been tested and validated in several hostile environments, simulating real emergency situations. It can help firefighters respond faster in an emergency. This reduces the risks associated with the lack of information and improves the time for tactical operations, which could save lives.
Time Series of Quad-Pol C-Band Synthetic Aperture Radar for the Forecasting of Crop Biophysical Variables of Barley Fields Using Statistical Techniques
(MDPI, 2022-01-27) Sipols, Ana E.; Valcarce Diñeiro, Ruben; Santos Martín, María Teresa; Sánchez, Nilda; Simón de Blas, Clara
This paper aims to both fit and predict crop biophysical variables with a SAR image series by performing a factorial experiment and estimating time series models using a combination of forecasts. Two plots of barley grown under rainfed conditions in Spain were monitored during the growing cycle of 2015 (February to June). The dataset included nine field estimations of agronomic parameters, 20 RADARSAT-2 images, and daily weather records. Ten polarimetric observables were retrieved and integrated to derive the six agronomic and monitoring variables, including the height, biomass, fraction of vegetation cover, leaf area index, water content, and soil moisture. The statistical methods applied, namely double smoothing, ARIMAX, and robust regression, allowed the adjustment and modelling of these field variables. The model equations showed a positive contribution of meteorological variables and a strong temporal component in the crop’s development, as occurs in natural conditions. After combining different models, the results showed the best efficiency in terms of forecasting and the influence of several weather variables. The existence of a cointegration relationship between the data series of the same crop in different fields allows for adjusting and predicting the results in other fields with similar crops without re-modelling.