Optimització del procés d'arrencada d'un sistema multiprocessador

L'objectiu d'aquest projecte és el d'optimitzar l'arrencada d'un sistema encastatEl objetivo de este proyecto es el de optimizar el arranque de un sistema empotradoThe aim of this project is to optimaze the boot process with different computin

Effectiveness of a cognitive behavioral intervention in patients with medically unexplained symptoms: cluster randomized trial

BACKGROUND: Medically unexplained symptoms are an important mental health problem in primary care and generate a high cost in health services.Cognitive behavioral therapy and psychodynamic therapy have proven effective in these patients. However, there are few studies on the effectiveness of psychosocial interventions by primary health care. The project aims to determine whether a cognitive-behavioral group intervention in patients with medically unexplained symptoms, is more effective than routine clinical practice to improve the quality of life measured by the SF-12 questionary at 12 month. METHODS/DESIGN: This study involves a community based cluster randomized trial in primary healthcare centres in Madrid (Spain). The number of patients required is 242 (121 in each arm), all between 18 and 65 of age with medically unexplained symptoms that had seeked medical attention in primary care at least 10 times during the previous year. The main outcome variable is the quality of life measured by the SF-12 questionnaire on Mental Healthcare. Secondary outcome variables include number of consultations, number of drug (prescriptions) and number of days of sick leave together with other prognosis and descriptive variables. Main effectiveness will be analyzed by comparing the percentage of patients that improve at least 4 points on the SF-12 questionnaire between intervention and control groups at 12 months. All statistical tests will be performed with intention to treat. Logistic regression with random effects will be used to adjust for prognostic factors. Confounding factors or factors that might alter the effect recorded will be taken into account in this analysis. DISCUSSION: This study aims to provide more insight to address medically unexplained symptoms, highly prevalent in primary care, from a quantitative methodology. It involves intervention group conducted by previously trained nursing staff to diminish the progression to the chronicity of the symptoms, improve quality of life, and reduce frequency of medical consultations. TRIAL REGISTRATION: The trial was registered with ClinicalTrials.gov, number NCT01484223 [http://ClinicalTrials.gov].S

What pulmonologists think about the asthma-COPD overlap syndrome

Some patients with COPD may share characteristics of asthma; this is the so-called asthma-COPD overlap syndrome (ACOS). There are no universally accepted criteria for ACOS, and most treatments for asthma and COPD have not been adequately tested in this population. We performed a survey among pulmonology specialists in asthma and COPD aimed at collecting their opinions about ACOS and their attitudes in regard to some case scenarios of ACOS patients. The participants answered a structured questionnaire and attended a face-to-face meeting with the Metaplan methodology to discuss different aspects of ACOS. A total of 26 pulmonologists with a mean age of 49.7 years participated in the survey (13 specialists in asthma and 13 in COPD). Among these, 84.6% recognized the existence of ACOS and stated that a mean of 12.6% of their patients might have this syndrome. In addition, 80.8% agreed that the diagnostic criteria for ACOS are not yet well defined. The most frequently mentioned characteristics of ACOS were a history of asthma (88.5%), significant smoking exposure (73.1%), and postbronchodilator forced expiratory volume in 1 second/forced vital capacity <0.7 (69.2%). The most accepted diagnostic criteria were eosinophilia in sputum (80.8%), a very positive bronchodilator test (69.2%), and a history of asthma before 40 years of age (65.4%). Up to 96.2% agreed that first-line treatment for ACOS was the combination of a long-acting β-agonist and inhaled steroid, with a long-acting antimuscarinic agent (triple therapy) for severe ACOS. Most Spanish specialists in asthma and COPD agree that ACOS exists, but the diagnostic criteria are not yet well defined. A previous history of asthma, smoking, and not fully reversible airflow limitation are considered the main characteristics of ACOS, with the most accepted first-line treatment being long-acting β-agonist/inhaled corticosteroids

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Exploiting spatio-temporal correlations for energy management policies

Estamos viviendo una nueva era, caracterizada por la omnipresencia de dispositivos inteligentes conectados a la red. En esta era, la emergencia de la llamada Internet of Things (IoT) está transformando profundamente la industria a nivel global, así como la propia vida de los seres humanos. El grado de integración alcanzado, así como su conectividad a través de internet, ha permitido que millones de dispositivos se conecten e interactúen a una escala sin precedentes. Esta diseminación generalizada de dispositivos con potencial de procesado, combinada a su capacidad sensora y comunicativa, está incrementando de forma extraordinaria el volumen de datos capturados. Como consecuencia de la expansión de la IoT, el número de dispositivos conectados esta aumentando exponencialmente y pronto ha de generar un problema de escalabilidad, problema muy ligado a su dependencia energética. Un buen número de dispositivos quedarán integrados en el entorno, en lugares inaccesibles o cuya conexión por cable suponga un coste elevado, convirtiendo la energía en un recurso muy preciado. De hecho, el cambio de baterías de miles de dispositivos es simplemente inconcebible. El coste de mantenimiento y, en general, de cualquier intervención, puede suponer un severo freno al avance de este nuevo paradigma. Por tanto, uno de los retos para hacer sostenible la masiva expansión de dispositivos sensores inalámbricos es reducir su coste en términos energéticos. Claramente, se requieren nuevos métodos para afrontar este cambio. Las correlaciones espacio-temporales son esenciales en muchos campos y, por tanto, es bastante razonable suponer que la información de contexto pueda ser también explotada en este paradigma. Partiendo de esta hipótesis, el presenta trabajo ofrece una aproximación sistemática para definir Políticas de Eficiencia Energética para dispositivos sensores inalámbricos, basada en el análisis de las Correlaciones Espacio-Temporales. Sobre esta idea, esta tesis se estructura en dos partes. En primer lugar se aborda la necesidad de un perfilado energético suficientemente preciso para sensores inalámbricos. Para este fin, se ha formalizado un modelo general de consumo que permite perfilar la gestión de energía en dispositivos integrados. Los resultados obtenidos remarcan la importancia de entender los ciclos de actividad involucrados en las tareas que ejecutan este tipo de dispositivos. La segunda parte, desarrollada en base a éste modelo, demuestra el potencial que ofrece el análisis de correlaciones espacio-temporales como herramienta para definir políticas eficientes de gestión. Esta hipótesis se ha investigado desde tres perspectivas diferentes: a) captación de la energía del entorno, b) compresión de la información y c) análisis de datos de contexto. El análisis realizado y las políticas definidas desde estas tres perspectivas proporcionan importantes reducciones tanto en términos energéticos como de coste. Como conclusión, todos los métodos estudiados han demostrado su validez en la definición y validación de políticas energéticas. Las estrategias propuestas pueden ser de gran ayuda para los ingenieros de aplicación, ya que permiten parametrizar las plataformas y explorar sus diseños en las primeras fases de desarrollo. De esta forma se puede reducir el tiempo de acceso al mercado, a la vez que se asegura un balance óptimo entre coste, funcionalidad y tiempo de vida.We are living in a new era, which is characterized by the omnipresence of smart, networked devices. The developing Internet of Things is profoundly transforming both global industry and human lives. Hardware integration, along with the ability to seamlessly communicate over the internet, has allowed millions of embedded objects to connect and interact on an unprecedented scale. The ubiquitous presence of embedded computing devices, combined with their sensing and communicating capabilities, is increasing the amounts of data captured on a massive scale. As a result of the expanding IoT, the number of connected devices is increasing exponentially and will soon generate a problem of scalability, related mostly to their energy dependence. Many devices will be embedded in the environment, in places that are inaccessible or expensive to connect with wires, making them resource-constrained. Most importantly, battery replacements for thousands of devices are inconceivable. Maintenance and intervention costs can limit the advance of this new paradigm. Therefore, one of the challenges in ensuring the massive expansion of wireless sensing devices is reducing their cost in terms of energy. Clearly, novel methods are required for addressing this change. Spatio-temporal correlations are essential in many different fields. Thus, it is quite reasonable to assume that contextual information can be exploited within this emerging paradigm. Under this hypothesis, the present study provides a systematic approach to defining Energy Efficiency Policies for Wireless Sensor Devices, based on the analysis of Spatio-Temporal Correlations. To this end, the present work is structured in two parts. First, we address the necessity of an accurate energy profiling model for wireless sensing devices. We have formalized a generic consumption model to profile the energy utilization of low-power embedded devices. The obtained results stress the importance of understanding the cycles of operation involved in embedded tasks. The second part of this dissertation demonstrates the applicability of spatio-temporal correlation analysis as a tool for defining energy efficiency policies. This hypothesis has been investigated from three different perspectives: a) energy harvesting, b) data compression and c) contextual data analysis. The correct analysis and policy definition from these three perspectives provides important energy and cost reduction opportunities. In conclusion, all the studied methods proved to be effective for defining and validating energy policies. The proposed strategies help designers to parameterize and customize platforms for their application during the design phases, and hence the time-to-market of new products is reduced while an optimal tradeoff is ensured among cost, functionality and life expectancy

Accurate clock discipline for long-term synchronization intervals

Efficient spectrum usage and optimized energy consumption directly depend on the radio duty cycle of the communicating devices. Tight synchronization of communicating nodes enables optimal orchestration of the access to the medium as nodes turn their radios on precisely when needed. Achieving synchronization, however, requires a reference time source to synchronize with and a periodic discipline mechanism to cope with the inherent clock drift. Packet-based synchronization or external time sources, such as GPS, are usually required to achieve that goal. When low-power operation is combined with bandwidth limitations and scale, as in the case of low power wide area networks, the overhead of such approaches is not affordable and advanced clock discipline mechanisms are required. In this paper, we propose a novel adaptive mechanism to discipline clocks in order to guarantee a 1ppm drift with minimal communication overhead

Accurate clock discipline for long-term synchronization intervals

Efficient spectrum usage and optimized energy consumption directly depend on the radio duty cycle of the communicating devices. Tight synchronization of communicating nodes enables optimal orchestration of the access to the medium as nodes turn their radios on precisely when needed. Achieving synchronization, however, requires a reference time source to synchronize with and a periodic discipline mechanism to cope with the inherent clock drift. Packet-based synchronization or external time sources, such as GPS, are usually required to achieve that goal. When low-power operation is combined with bandwidth limitations and scale, as in the case of low power wide area networks, the overhead of such approaches is not affordable and advanced clock discipline mechanisms are required. In this paper, we propose a novel adaptive mechanism to discipline clocks in order to guarantee a 1ppm drift with minimal communication overhead

Optimització del procés d'arrencada d'un sistema multiprocessador

L'objectiu d'aquest projecte és el d'optimitzar l'arrencada d'un sistema encastatEl objetivo de este proyecto es el de optimizar el arranque de un sistema empotradoThe aim of this project is to optimaze the boot process with different computin

Exploiting spatio-temporal correlations for energy management policies

Estamos viviendo una nueva era, caracterizada por la omnipresencia de dispositivos inteligentes conectados a la red. En esta era, la emergencia de la llamada Internet of Things (IoT) está transformando profundamente la industria a nivel global, así como la propia vida de los seres humanos. El grado de integración alcanzado, así como su conectividad a través de internet, ha permitido que millones de dispositivos se conecten e interactúen a una escala sin precedentes. Esta diseminación generalizada de dispositivos con potencial de procesado, combinada a su capacidad sensora y comunicativa, está incrementando de forma extraordinaria el volumen de datos capturados. Como consecuencia de la expansión de la IoT, el número de dispositivos conectados esta aumentando exponencialmente y pronto ha de generar un problema de escalabilidad, problema muy ligado a su dependencia energética. Un buen número de dispositivos quedarán integrados en el entorno, en lugares inaccesibles o cuya conexión por cable suponga un coste elevado, convirtiendo la energía en un recurso muy preciado. De hecho, el cambio de baterías de miles de dispositivos es simplemente inconcebible. El coste de mantenimiento y, en general, de cualquier intervención, puede suponer un severo freno al avance de este nuevo paradigma. Por tanto, uno de los retos para hacer sostenible la masiva expansión de dispositivos sensores inalámbricos es reducir su coste en términos energéticos. Claramente, se requieren nuevos métodos para afrontar este cambio. Las correlaciones espacio-temporales son esenciales en muchos campos y, por tanto, es bastante razonable suponer que la información de contexto pueda ser también explotada en este paradigma. Partiendo de esta hipótesis, el presenta trabajo ofrece una aproximación sistemática para definir Políticas de Eficiencia Energética para dispositivos sensores inalámbricos, basada en el análisis de las Correlaciones Espacio-Temporales. Sobre esta idea, esta tesis se estructura en dos partes. En primer lugar se aborda la necesidad de un perfilado energético suficientemente preciso para sensores inalámbricos. Para este fin, se ha formalizado un modelo general de consumo que permite perfilar la gestión de energía en dispositivos integrados. Los resultados obtenidos remarcan la importancia de entender los ciclos de actividad involucrados en las tareas que ejecutan este tipo de dispositivos. La segunda parte, desarrollada en base a éste modelo, demuestra el potencial que ofrece el análisis de correlaciones espacio-temporales como herramienta para definir políticas eficientes de gestión. Esta hipótesis se ha investigado desde tres perspectivas diferentes: a) captación de la energía del entorno, b) compresión de la información y c) análisis de datos de contexto. El análisis realizado y las políticas definidas desde estas tres perspectivas proporcionan importantes reducciones tanto en términos energéticos como de coste. Como conclusión, todos los métodos estudiados han demostrado su validez en la definición y validación de políticas energéticas. Las estrategias propuestas pueden ser de gran ayuda para los ingenieros de aplicación, ya que permiten parametrizar las plataformas y explorar sus diseños en las primeras fases de desarrollo. De esta forma se puede reducir el tiempo de acceso al mercado, a la vez que se asegura un balance óptimo entre coste, funcionalidad y tiempo de vida.We are living in a new era, which is characterized by the omnipresence of smart, networked devices. The developing Internet of Things is profoundly transforming both global industry and human lives. Hardware integration, along with the ability to seamlessly communicate over the internet, has allowed millions of embedded objects to connect and interact on an unprecedented scale. The ubiquitous presence of embedded computing devices, combined with their sensing and communicating capabilities, is increasing the amounts of data captured on a massive scale. As a result of the expanding IoT, the number of connected devices is increasing exponentially and will soon generate a problem of scalability, related mostly to their energy dependence. Many devices will be embedded in the environment, in places that are inaccessible or expensive to connect with wires, making them resource-constrained. Most importantly, battery replacements for thousands of devices are inconceivable. Maintenance and intervention costs can limit the advance of this new paradigm. Therefore, one of the challenges in ensuring the massive expansion of wireless sensing devices is reducing their cost in terms of energy. Clearly, novel methods are required for addressing this change. Spatio-temporal correlations are essential in many different fields. Thus, it is quite reasonable to assume that contextual information can be exploited within this emerging paradigm. Under this hypothesis, the present study provides a systematic approach to defining Energy Efficiency Policies for Wireless Sensor Devices, based on the analysis of Spatio-Temporal Correlations. To this end, the present work is structured in two parts. First, we address the necessity of an accurate energy profiling model for wireless sensing devices. We have formalized a generic consumption model to profile the energy utilization of low-power embedded devices. The obtained results stress the importance of understanding the cycles of operation involved in embedded tasks. The second part of this dissertation demonstrates the applicability of spatio-temporal correlation analysis as a tool for defining energy efficiency policies. This hypothesis has been investigated from three different perspectives: a) energy harvesting, b) data compression and c) contextual data analysis. The correct analysis and policy definition from these three perspectives provides important energy and cost reduction opportunities. In conclusion, all the studied methods proved to be effective for defining and validating energy policies. The proposed strategies help designers to parameterize and customize platforms for their application during the design phases, and hence the time-to-market of new products is reduced while an optimal tradeoff is ensured among cost, functionality and life expectancy