Analysis of synchronous localization systems for UAVs urban applications

[EN] Unmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform these tasks without the need of human intervention, which demands a robust and accurate localization system for achieving a safe and efficient autonomous navigation. Nevertheless, currently used satellite-based localization systems like GPS are insufficient for high-precision applications, especially in harsh scenarios like indoor and deep urban environments. In these contexts, Local Positioning Systems (LPS) have been widely proposed for satisfying the localization requirements of these vehicles. However, the performance of LPS is highly dependent on the actual localization architecture and the spatial disposition of the deployed sensor distribution. Therefore, before the deployment of an extensive localization network, an analysis regarding localization architecture and sensor distribution should be taken into consideration for the task at hand. Nonetheless, no actual study is proposed either for comparing localization architectures or for attaining a solution for the Node Location Problem (NLP), a problem of NP-Hard complexity. Therefore, in this paper, we propose a comparison among synchronous LPS for determining the most suited system for localizing UAVs over urban scenarios. We employ the Cràmer–Rao-Bound (CRB) for evaluating the performance of each localization system, based on the provided error characterization of each synchronous architecture. Furthermore, in order to attain the optimal sensor distribution for each architecture, a Black-Widow-Optimization (BWO) algorithm is devised for the NLP and the application at hand. The results obtained denote the effectiveness of the devised technique and recommend the implementation of Time Difference Of Arrival (TDOA) over Time of Arrival (TOA) systems, attaining up to 47% less localization uncertainty due to the unnecessary synchronization of the target clock with the architecture sensors in the TDOA architecture.S

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

[EN] Background: Eating disorders affect an increasing number of people. Social networks provide information that can help. Objective: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. Methods: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. Results: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer–based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). Conclusions: Bidirectional encoder representations from transformer–based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder–related tweets.S

Diseño de un modelo de evaluación y desarrollo docente en una universidad privada.

La universidad que quiera responder al principio de excelencia académica, debe conocer de manera exhaustiva el desarrollo e impacto de la actividad docente, investigadora y de gestión de los profesores que en ella desempeñan su labor profesional. Para ello es necesario establecer un procedimiento riguroso que proporcione información válida y suficiente acerca de los procesos que contribuyen a lograr los objetivos estratégicos de la universidad, los medios que propician la mejora continua de los protocolos de enseñanza-aprendizaje, el modo en que se implican los responsables en el ejercicio del liderazgo en la organización, el compromiso de los profesores, los métodos de identificación de necesidades de formación y desarrollo y las maneras en que se incentiva la calidad esperada y percibida y la excelencia docente (reconocimiento). De esta manera podrán tomarse decisiones convenientes y oportunas para mejorar los aspectos críticos detectados y potenciar aquellos cuya valoración ha sido positiva. Para validar una propuesta de este tipo, en la UFV se ha implementado un sistema de desarrollo y evaluación del desempeño docente que tiene la pretensión de superar algunas de las limitaciones del Programa DOCENTIA, adecuando el desarrollo de las competencias profesionales de los profesores a las expectativas organizacionales expresadas en las líneas estratégicas de la universidad. Para ello, define las competencias, acciones y comportamientos profesionales y personales relevantes para el cumplimiento de la Misión universitaria, integrando las misiones y objetivos en un sistema de mejora profesional y personal acompañado por el director académico, recompensando la excelencia en términos estratégicos y organizativos y permitiendo que los sistemas de delegación, de autonomía y de dirección de los profesores se ajusten, se redefinan y se adapten al sistema de gobierno de la universidad.pre-print707 K

Revista complutense de educación

Resumen basado en el de la publicaciónTítulo, resumen y palabras clave también en inglésSe establece un procedimiento riguroso que proporcione información válida y suficiente acerca de los procesos que contribuyen a lograr los objetivos estratégicos de la universidad, los medios que propician la mejora continua de los protocolos de enseñanza-aprendizaje, el modo en que se implican los responsables en el ejercicio del liderazgo en la organización, el compromiso de los profesores, los métodos de identificación de necesidades de formación y desarrollo y las maneras en que se incentiva la calidad esperada y percibida y la excelencia docente (reconocimiento). Para validar una propuesta así, en la Universidad Francisco de Vitoria (en adelante UFV) se ha implementado un sistema de desarrollo y evaluación del desempeño docente que tiene la pretensión de superar algunas de las limitaciones del Programa DOCENTIA, adecuando el desarrollo de las competencias profesionales de los profesores a las expectativas organizacionales expresadas en las líneas estratégicas de la universidad. Se define las competencias, acciones y comportamientos profesionales y personales relevantes para el cumplimiento de la misión universitaria, integrando las misiones y objetivos en un sistema de mejora profesional y personal acompañado por el director académico, recompensando la excelencia en términos estratégicos y organizativos, y permitiendo que los sistemas de delegación, de autonomía y de dirección de los profesores se ajusten, se redefinan y se adapten al sistema de gobierno de la universidad.ES

Determining the severity of Parkinson’s disease in patients using a multi task neural network

[EN] Parkinson’s disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson’s severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson’s disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson’s Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson’s disease or non-severe Parkinson’s disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson’s outperforming the state-of-the-art proposals.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Assessment of an educational intervention to improve healthy life habits in children living in vulnerable socioeconomic conditions

Producción CientíficaNutritional condition impacts academic performance and cognitive development. In Peru, the prevalence of chronic undernutrition in children is 6.9%, increasing the risk of mortality and morbidity. This study aimed to develop an educational intervention to achieve an improvement in the healthy habits of children in a primary education school in Lima who live in vulnerable socioeconomic conditions. We conducted a prospective quasi-experimental pre-test and post-test study of an educational intervention. The information was collected through the adaptation of the WHO questionnaire “Global School-based Student Health Survey” (GSHS), with anthropometric variables, socioeconomic level, hygiene and eating habits. One hundred eight students from 5 to 13 years old from Arenitas del Mar School in Lima (Peru) participated. The educational intervention improved eating habits. Fruit and vegetable consumption 3 or more times/day (50.9%) increased after the educational intervention (49% vs. 62.9%,) p < 0.0001. There was an improvement in hygiene habits, such as the frequency of handwashing with soap (32.4% vs. 63.9%) and the frequency of weekly bathing 4–6 times/week (25% vs. 47.5%) p < 0.0001. The educational intervention promoted better healthy living behaviors, eating habits and hygiene. This kind of initiative is a crucial tool to establish healthy living habits

Assessment of risk factors associated with cardiovascular diseases in overweight women

Producción CientíficaThe assessment of anthropometric variables has been shown to be useful as a predictor of cardiovascular risk in overweight and obese patients. The aim of this study was to determine the usefulness of the relationship between breast volume and body mass index as an indicator of cardiovascular risk in premenopausal women with overweight and mild obesity. A prospective observational study of 93 premenopausal women was performed. Evaluation of anthropometric measures included age, body mass index, waist and hip circumferences, breast projection, and ptosis. Cardiovascular risk factors were evaluated using the Framingham cardiovascular risk score, the triglycerides/HDL cholesterol ratio and the waist-hip ratio. Ninety-three women were included, with a mean 36.4 ± 7.5 years. Mean BMI was 27.3 ± 1.9 kg/m2, waist-to-Hip ratio was 0.8 ± 0.07, and mammary volume was 1045 ± 657.4 cm3. Mean body fat mass was 30.6 + 3.6% and mean visceral fat was 6.6 + 3.2%. The mean triglycerides to HDL ratio was 1.7 ± 0.8 and waist-to-hip ratio was 0.8 ± 0.07. Breast volume related to body mass index can be used as a predictor of cardiovascular risk in premenopausal women who are overweight and mildly obese

Impact of Nursing Methodology training sessions on completion of the Virginia Henderson assessment record

Producción CientíficaThe Virginia Henderson model, integrated in the computer application GACELA Care, helps to standardise the nursing assessment and establish precise and personalised nursing diagnoses. The aim was to determine the extent of completion of the initial patient assessment record after nurses following a training programme on nursing methodology. A quasi-experimental, retrospective, randomised, observational, single-group study was performed in two stages: pre-training and post-training. Voluntary training sessions were held for the nurses that work with GACELA Care. The completion of the initial patient assessment using the needs of Virginia Henderson and the Norton scale was evaluated before and after the training sessions. Completion of the needs of Virginia Henderson in the initial patient assessment increased from 94.2% to 100% (p = 0.014). Completion of “hygiene/skin” increased significantly from 83.3% to 95.8% (pre-training and post-training, respectively). The remaining needs did not show statistical significance. Recording of the Norton scale increased from 63.13% to 92.5% (p < 0.001). The training sessions on nursing methodology have improved the completion of records and inclusion of normal characteristics, defining characteristics and risk factors, and improving pressure ulcer risk assessment through the Norton scale

Improve Quality of Service for the Internet of Things using Blockchain & Machine Learning Algorithms.

[EN] The quality of service (QoS) parameters in IoT applications plays a prominent role in determining the performance of an application. Considering the significance and popularity of IoT systems, it can be predicted that the number of users and IoT devices are going to increase exponentially shortly. Therefore, it is extremely important to improve the QoS provided by IoT applications to increase their adaptability. Majority of the IoT systems are characterized by their heterogeneous and diverse nature. It is challenging for these systems to provide high-quality access to all the connecting devices with uninterrupted connectivity. Considering their heterogeneity, it is equally difficult to achieve better QoS parameters. Artificial intelligence-based machine learning (ML) tools are considered a potential tool for improving the QoS parameters in IoT applications. This research proposes a novel approach for enhancing QoS parameters in IoT using ML and Blockchain techniques. The IoT network with Blockchain technology is simulated using an NS2 simulator. Different QoS parameters such as delay, throughput, packet delivery ratio, and packet drop are analyzed. The obtained QoS values are classified using different ML models such as Naive Bayes (NB), Decision Tree (DT), and Ensemble, learning techniques. Results show that the Ensemble classifier achieves the highest classification accuracy of 83.74% compared to NB and DT classifiers.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL