Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposalThis work was supported by the research projects TIN2009-07737, INCITE08PXIB262202PR, and TIN2012-32262, the grant BES-2010-040813 FPI-MICINN, and by the grant “Consolidation of Competitive Research Groups, Xunta de Galicia ref. 2010/6”S

Explainable agents adapt to human behaviour

When integrating artificial agents into physical or digital environments that are shared with humans, agents are often equipped with opaque Machine Learning methods to enable adapting their behaviour to dynamic human needs and environment. This brings about agents that are also opaque and therefore hard to explain. In previous work, we show that we can reduce an opaque agent into an explainable Policy Graph (PG) which works accurately in multi-agent environments. Policy Graphs are based on a discretisation of the world into propositional logic to identify states, and the choice of which discretiser to apply is key to the performance of the reduced agent. In this work, we explore this further by 1) reducing a single agent into an explainable PG, and 2) enforcing collaboration between this agent and an agent trained from human behaviour. The human agent is computed by using GAIL from a series of human-played episodes, and kept unchanged. We show that an opaque agent created and trained to collaborate with the human agent can be reduced to an explainable, non-opaque PG, so long as predicates regarding collaboration are included in the state representation, by showing the difference in reward between the agent and its PG. Code is available at https://github.com/HPAI-BSC/explainable-agents-with-humansThis work has been partially supported by EU Horizon 2020 Project StairwAI (grant agreement No. 101017142).Peer ReviewedPostprint (published version

Study of the Time-Temperature-Dependent behaviour of PVB: application to laminated glass elements

The mechanical behavior of laminated glass elements is governed by material properties of the interlayer, the Polyvinyl Butiral (PVB) being the most used interlayer material in these elements. PVB is a viscoelastic material whose mechanical properties (Young’s modulus, shear modulus, etc.) depend mainly on the load application time and the temperature. Thus an adequate mechanical characterization of the PVB must be performed in order to predict the response of laminated glass elements with a good accuracy In this work, PVB specimens were subjected to static relaxation tests and to dynamic experimental tests (frequency domain) at different temperatures from -〖15〗^o C to 〖50〗^o C using a DMTA equipment. Then the curves at different temperatures were related using the William-Landel-Ferry (WLF) Time-Temperature Superposition (TTS) model to obtain the mastercurve of both the time and frequency domain Young’s moduli of the PVB. Finally, a viscoelastic Prony based model was fitted to the experimental data and used, afterwards, to simulate numerically the static and dynamic behaviour of different laminated glass elements at different temperatures. The numerical simulations were compared with the static and dynamic experimental results achieving a good accuracy in both the static deflections and the natural frequencies. With respect to the damping, the discrepancies are less than 22%

Patrón geométrico de pacientes hipertensos que ingresan por insuficiencia cardiaca

Hypertensive heart disease is a complex and variable set of effects that causes the chronic elevation of blood pressure in the heart. We conducted a prospective descriptive study of the patients admitted to the provincial hospital September 17 Sumbe, province of Kwanza Sul, Angola, in the period from 2015 to 2017, with the objective of describing the geometric pattern of hypertensive patients admitted to heart failure. We investigated 97 patients who were admitted to acute ventricular failure. 52.6 % belonged to the female sex, patients under 58 years of age predominated. The mean of the ventricular mass was pathological (271.60 g). The most frequent geometric pattern was concentric remodeling of the left ventricle with 64.9 % and left ventricle diastolic dysfunction grade IV was the predominant with 42 patients. Severe left ventricular systolic dysfunction was the greatest exponent with 27 patients in the entire sample. Finally, those of different degrees of hypertensive heart disease, the grade IV with 61 patients prevailed. We concluded that more than a half of the patients were under 58 years of age and women were more affected. There was a predominance of concentric remodeling of the left ventricle, as well as its diastolic dysfunction. It is relevant that the majority of patients classified in grade IV hypertensive heart disease.La cardiopatía hipertensiva es un complejo y variable conjunto de efectos que provoca en el corazón la elevación crónica de la presión arterial. Realizamos un estudio descriptivo prospectivo de los pacientes que ingresaron en el hospital provincial 17 de septembro, Sumbe provincia de Kwanza Sul, Angola, en el periodo comprendido desde 2015 al 2017, con el objetivo de describir el patrón geométrico de pacientes hipertensos que ingresaron en insuficiencia cardiaca. Se investigaron 97 pacientes que se admitieron con insuficiencia ventricular aguda. Pertenecían al sexo femenino el 52,6 %, predominaron los pacientes menores de 58 años. La media de la masa ventricular fue patológica (271,60 gramos). El patrón geométrico más frecuente fue el remodelado concéntrico del ventrículo izquierdo con un 64,9 % y la disfunción diastólica ventrículo izquierdo grado IV fue la predominante con 42 pacientes. La disfunción sistólica ventrículo izquierdo severa fue la mayor exponente con 27 pacientes de toda la muestra. Finalmente, los de diferentes grados de la cardiopatía hipertensiva, predominó el grado IV con 61 enfermos. Se concluye que más de la mitad de los pacientes eran menores de 58 años y las mujeres estuvieron más afectadas. Hubo un predominio del remodelado concéntrico de ventrículo izquierdo, así como de la disfunción diastólica del mismo. Es relevante que la mayoría de los enfermos clasificaran en el grado IV de la cardiopatía hipertensiva

Assessing the feasibility of cardiopulmonary resuscitation over bulletproof vests: Implications for rapid response by police officers

Introduction The aim was to evaluate the feasibility and quality of chest compressions (CCs) performed by police officers on a victim wearing a bulletproof vest as well as analyze the time taken to remove the vest. Methods Ten officers conducted two 5‐min cardiopulmonary resuscitation (CPR) trials. Trial 1: on a dummy wearing bulletproof vest (CPR‐VEST)/Trial 2: bare‐torso dummy (CPR‐NO). Results CC's quality and the timed vest removal. Results show that CC's quality was a median of 90% in CPR‐VEST and 99% in CPR‐NO ( p  = 0.08). There were significantly poorer results in CPR‐VEST in CC with correct recoil (CPR‐VEST: 12%, CPR‐NO: 10%, and p  = 0.04) and rate (CPR‐VEST: 82%, CPR‐NO: 97%, and p  = 0.01). The bulletproof vest removal time was 9.45 ± 1.33 s. Conclusion The removal of the vest is prioritized to facilitate CPR. CC over the vest is performed with similar quality levels to standard CPR, supporting the immediate initiation of CPR in special circumstances where the vest cannot be removed.Universidade de Vigo/CISU

Optimization of Analytical Ultrasound-Assisted Methods for the Extraction of Total Phenolic Compounds and Anthocyanins from Sloes (Prunus spinosa L.)

Prunus spinosaL. is a shrub that produces berries-sloes-with a high content of phenolic compounds and anthocyanins. Numerous consumer products are made from sloes, such as "Pacharan" liquors or jams. For these reasons, it is crucial to design accurate analytical methods for the extraction of these bioactive compounds in order to promote their consumption and to improve our health. In this study, an ultrasound-assisted extraction method was developed to extract phenolic compounds and anthocyanins from sloes. Several process variables (methanol composition, pH, temperature, cycle, amplitude, and sample-solvent ratio) were optimized based on a Box-Behnken design. The most influential parameters for the extraction of total phenolic compounds were the cycle, the percentage of methanol, and pH of the extraction solvent, with 0.2 s, 67%, and pH 7 as their optimal values, respectively. For total anthocyanins, all parameters were influential, with 44% methanol at pH 2, 48 degrees C, 0.2 s cycles, 30% amplitude, and 1.5 g/20 mL ratio as the optimal values for their corresponding variables. Both methods showed high recoveries, short time use, and high precision (RSD < 5%). In addition, the sloe liqueur "Pacharan" and sloe jams were analyzed to demonstrate the applicability of these methods for the extraction of the real matrix

Autonomous aerial robot for high-speed search and intercept applications

In recent years, high-speed navigation and environment interaction in the context of aerial robotics has become a field of interest for several academic and industrial research studies. In particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research area due to their potential usability in several environments. Nevertheless, SaI tasks involve a challenging development regarding sensory weight, onboard computation resources, actuation design, and algorithms for perception and control, among others. In this work, a fully autonomous aerial robot for high-speed object grasping has been proposed. As an additional subtask, our system is able to autonomously pierce balloons located in poles close to the surface. Our first contribution is the design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with additional sensors enabling the robot to grasp objects at high speeds. The second contribution is a complete software framework consisting of perception, state estimation, motion planning, motion control, and mission control in order to rapidly and robustly perform the autonomous grasping mission. Our approach has been validated in a challenging international competition and has shown outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification

La Ingeniería Automotriz clave para el desarrollo sostenible de Ecuador

El presente texto es una contribución al desarrollo de la sostenibilidad ecuatoriana y mantiene el debate sobre temas del estudio de la Ingeniería Automotriz. El mérito del libro radica en una triple condición: alimenta la investigación académica ecuatoriana, contribuye a llenar el vacío de producción científica automotriz direccionada a las necesidades del Ecuador y reconoce el esfuerzo de los investigadores que se dedican a la producción académica técnica. La Universidad Politécnica Salesiana —en su sede Guayaquil— realizó en 2018, las Segundas Jornadas Científicas de Ingeniería Automotriz; este texto es el producto final de ese evento académico, cuyas memorias técnicas son constituidas por ocho resultados de investigaciones en Ingeniería Automotriz que aportarán desarrollo sostenible al Ecuador en áreas como: el diseño, el control de contaminación, la eficiencia energética y la movilidad. Este recorrido por varias ramas de la Ingeniería Automotriz muestra al lector múltiples aplicaciones y cambios de paradigmas en la industria; no somos solamente consumidores de tecnología, somos también productores de la misma. Este texto da cuenta del desarrollo de la industria automotriz ecuatoriana. Ing. Renato Fierro J. MSc

The SADDEN DEATH Study: Results from a Pilot Study in Non-ICU COVID-19 Spanish Patients

Introduction: The worldwide pandemic, coronavirus disease 2019 (COVID-19) is a novel infection with serious clinical manifestations, including death. Our aim is to describe the first non-ICU Spanish deceased series with COVID-19, comparing specifically between unexpected and expected deaths. Methods: In this single-centre study, all deceased inpatients with laboratory-confirmed COVID-19 who had died from March 4 to April 16, 2020 were consecutively included. Demographic, clinical, treatment, and laboratory data, were analyzed and compared between groups. Factors associated with unexpected death were identified by multivariable logistic regression methods. Results: In total, 324 deceased patients were included. Median age was 82 years (IQR 76–87); 55.9% males. The most common cardiovascular risk factors were hypertension (78.4%), hyperlipidemia (57.7%), and diabetes (34.3%). Other common comorbidities were chronic kidney disease (40.1%), chronic pulmonary disease (30.3%), active cancer (13%), and immunosuppression (13%). The Confusion, BUN, Respiratory Rate, Systolic BP and age ≥65 (CURB-65) score at admission was >2 in 40.7% of patients. During hospitalization, 77.8% of patients received antivirals, 43.3% systemic corticosteroids, and 22.2% full anticoagulation. The rate of bacterial co-infection was 5.5%, and 105 (32.4%) patients had an increased level of troponin I. The median time from initiation of therapy to death was 5 days (IQR 3.0–8.0). In 45 patients (13.9%), the death was exclusively attributed to COVID-19, and in 254 patients (78.4%), both COVID-19 and the clinical status before admission contributed to death. Progressive respiratory failure was the most frequent cause of death (92.0%). Twenty-five patients (7.7%) had an unexpected death. Factors independently associated with unexpected death were male sex, chronic kidney disease, insulin-treated diabetes, and functional independence. Conclusions: This case series provides in-depth characterization of hospitalized non-ICU COVID-19 patients who died in Madrid. Male sex, insulin-treated diabetes, chronic kidney disease, and independency for activities of daily living are predictors of unexpected death