585 resultados para Risc


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This article deals with several international instruments which provide legal guarantees for media diversity, which is essential for the promotion of cultural diversity. Based on several articles of the Convention of cultural diversity, the General Comment of the Committee on Economic, Social and Cultural Rights No. 21 on the right to take part in cultural life, as well as the work of the UN Independent Expert on Cultural Rights, this article aims to identify legal tools for the establishing of measures promoting cultural diversity in the media. This article looks at the case study of Honduran Garifuna community radios. It emphasizes the importance of taking into account the economic aspects of cultural and communicational rights.

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This paper deals with the conceptions of the different school actors about the meaning and the implications of mediation in their schools, drawing on data from a qualitative approach carried out as part of a wider project to map mediation perspectives and practices in Catalonia. The authors analyze the scope of the situations regarded as suitable or unsuitable for the introduction of restorative practices, as well as the resistance to change in the practice of conflict resolutions and in the democratization of school culture.

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Reliability has emerged as a critical design constraint especially in memories. Designers are going to great lengths to guarantee fault free operation of the underlying silicon by adopting redundancy-based techniques, which essentially try to detect and correct every single error. However, such techniques come at a cost of large area, power and performance overheads which making many researchers to doubt their efficiency especially for error resilient systems where 100% accuracy is not always required. In this paper, we present an alternative method focusing on the confinement of the resulting output error induced by any reliability issues. By focusing on memory faults, rather than correcting every single error the proposed method exploits the statistical characteristics of any target application and replaces any erroneous data with the best available estimate of that data. To realize the proposed method a RISC processor is augmented with custom instructions and special-purpose functional units. We apply the method on the proposed enhanced processor by studying the statistical characteristics of the various algorithms involved in a popular multimedia application. Our experimental results show that in contrast to state-of-the-art fault tolerance approaches, we are able to reduce runtime and area overhead by 71.3% and 83.3% respectively.

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NCD Risk Factor Collaboration (NCD-RisC)

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El presente trabajo analiza la distribución de los niveles de resiliencia en cuidadores no profesionales (familiares) de personas mayores dependientes, y explora las características que poseen los cuidadores altamente resilientes. Para ello se administró la escala CD-RISC a una muestra de 111 cuidadores. En función de las puntuaciones obtenidas, se distinguieron dos grupos: alta vs. moderada o baja resiliencia. Se analizaron las diferencias entre ambos grupos en las diversas variables que afectan al estrés del cuidador. Aunque hubo diferencias en el estado cognitivo del receptor de cuidado, las mayores diferencias se obtuvieron en las denominadas variables mediadoras, observando en los cuidadores con alta resiliencia mayores puntuaciones en: satisfacción con el apoyo social recibido, autoeficacia para controlar los pensamientos negativos, autoestima, autocuidado y extraversión; y siendo estas diferencias estadísticamente significativas. Asimismo los cuidadores altamente resilientes tendieron a manifestar una menor preocupación por los problemas del mayor, y presentaron menores niveles de sintomatología depresiva y ansiosa. Este estudio pone de manifiesto la importancia de la resiliencia como factor de protección en el ámbito del estrés crónico, incorporando aspectos positivos de la adaptación a los modelos teóricos de cuidado, los cuales tradicionalmente se han venido centrando en el desarrollo de sintomatología

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Predicting risk of adverse healthcare outcomes is important to enable targeted delivery of interventions. The Risk Instrument for Screening in the Community (RISC), designed for use by public health nurses (PHNs), measures the one-year risk of hospitalisation, institutionalisation and death in community-dwelling older adults according to a five-point global risk score: from low (score 1,2), medium (3) and high (4,5). We examined the inter-rater reliability (IRR) of the RISC between student PHNs (n=32) and expert raters using six cases (two low, medium and high-risk), scored before and after RISC training. Correlations increased for each adverse outcome, statistically significantly for institutionalisation (r=0.72 to 0.80,p=0.04) and hospitalisation, (r=0.51 to 0.71,p<0.01) but not death. Training improved accuracy for low-risk but not all high-risk cases. Overall, the RISC showed good IRR, which increased after RISC training. That reliability reduced for some high-risk cases suggests that the training programme requires adjustment to further improve IRR.

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Background: Raised blood pressure is an important risk factor for cardiovascular diseases and chronic kidney disease. We estimated worldwide trends in mean systolic and mean diastolic blood pressure, and the prevalence of, and number of people with, raised blood pressure, defined as systolic blood pressure of 140 mm Hg or higher or diastolic blood pressure of 90 mm Hg or higher. Methods: For this analysis, we pooled national, subnational, or community population-based studies that had measured blood pressure in adults aged 18 years and older. We used a Bayesian hierarchical model to estimate trends from 1975 to 2015 in mean systolic and mean diastolic blood pressure, and the prevalence of raised blood pressure for 200 countries. We calculated the contributions of changes in prevalence versus population growth and ageing to the increase in the number of adults with raised blood pressure. Findings: We pooled 1479 studies that had measured the blood pressures of 19·1 million adults. Global age-standardised mean systolic blood pressure in 2015 was 127·0 mm Hg (95% credible interval 125·7–128·3) in men and 122·3 mm Hg (121·0–123·6) in women; age-standardised mean diastolic blood pressure was 78·7 mm Hg (77·9–79·5) for men and 76·7 mm Hg (75·9–77·6) for women. Global age-standardised prevalence of raised blood pressure was 24·1% (21·4–27·1) in men and 20·1% (17·8–22·5) in women in 2015. Mean systolic and mean diastolic blood pressure decreased substantially from 1975 to 2015 in high-income western and Asia Pacific countries, moving these countries from having some of the highest worldwide blood pressure in 1975 to the lowest in 2015. Mean blood pressure also decreased in women in central and eastern Europe, Latin America and the Caribbean, and, more recently, central Asia, Middle East, and north Africa, but the estimated trends in these super-regions had larger uncertainty than in high-income super-regions. By contrast, mean blood pressure might have increased in east and southeast Asia, south Asia, Oceania, and sub-Saharan Africa. In 2015, central and eastern Europe, sub-Saharan Africa, and south Asia had the highest blood pressure levels. Prevalence of raised blood pressure decreased in high-income and some middle-income countries; it remained unchanged elsewhere. The number of adults with raised blood pressure increased from 594 million in 1975 to 1·13 billion in 2015, with the increase largely in low-income and middle-income countries. The global increase in the number of adults with raised blood pressure is a net effect of increase due to population growth and ageing, and decrease due to declining age-specific prevalence. Interpretation: During the past four decades, the highest worldwide blood pressure levels have shifted from high-income countries to low-income countries in south Asia and sub-Saharan Africa due to opposite trends, while blood pressure has been persistently high in central and eastern Europe.

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Introducción: La artritis reumatoide (AR) es una enfermedad autoinmune-inflamatoria, que compromete las articulaciones diartrodiales. Tiene una importante repercusión sistémica que incluye la depresión; por lo tanto, tiene un severo impacto sobre la calidad de vida. Es posible que mecanismos de defensa, tales como la resiliencia, puedan amortiguar dicho impacto. Metodología: estudio de corte transversal, multicéntrico (análisis inicial dentro del grupo AR, con muestra no probabilística de 66 pacientes, posterior selección aleatoria simple de 16 pacientes de la muestra inicial y selección de 16 individuos sanos pareados). Posteriormente, se comparó la resiliencia entre sujetos con AR y sujetos sanos, mediante las escalas RS y CD-RISC25. Adicionalmente, se aplicaron las escalas EEAE, EADZ, SF-36 y PANAS. Los datos fueron evaluados mediante el coeficiente de correlación de Spearman, las pruebas U Mann-Whitney, Kruskall-Wallis, T de Student y análisis de varianza. Resultados: se encontraron diferencias significativas en las estrategias de afrontamiento no espirituales en grupos de resiliencia baja, media y alta; diferencias en las medianas de resiliencia en los grupos de depresión por EAZD en los pacientes. No se encontraron resultados significativos en las variables clínicas de la AR ni en la comparación con sujetos sanos. Conclusiones: el uso de estrategias de afrontamiento no espirituales y la ausencia de depresión, se asoció a mayores niveles de resiliencia en los pacientes con AR, por lo cual, los componentes emocionales y cognitivos se asocian a la resiliencia.

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Organic Light-Emitting Diodes (OLEDs) technology has matured over recent years, reaching the commercialization level and being used in various applications. The required efficiency can be achieved by transforming triplet excitons into singlet states via Reverse InterSystem Crossing (RISC), which a general mechanism called thermally activated delayed fluorescence (TADF). Two prototypical molecules in the field, 2CzBN and 4CzBN, Carbazole Benzonitrile (donor-acceptor) derivatives, possess similar energy gap between singlet and triplet (∆EST, a key parameter in the RISC rate), but different TADF performance. In this sense, other parameter must be considered to explain these different behaviors. In this work, we theoretically investigate 2CzBN and 4CzBN and address the problem of how flexible donor-acceptor (D-A) or donor-acceptor-donor (D-A-D) molecular architectures affect the nature of excited state, and the oscillator strength. Furthermore, we analyze the RISC rates as a function of the conformation of the carbazole side groups, considering the S0, S1, T1 and T2 states. The oscillator strength of 4CzBN is higher than of 2CzBN, which, in turn, is almost vanishing, resulting in only 4CzBN being a TADF active molecule. We also note the presence of a second triplet state T2 lower in energy than S1, and that the reorganization energies, associated to the RISC processes involving T1 and T2, are both important factor in differentiating the rates in 2CzBN and 4CzBN. However, the 4CzBN RISC rate from T2 to S1 is surprisingly high with respect to the one from T1 to S1, although, according to EL-Sayed rules, since T2 (CT/LE) is more similar to S1 (CT) than in 2CzBN (LE, CT), this transition should be less favored. These insights are important to understand the photophysics of the TADF process and to design novel TADF emitters based on the benzo-carbazole architecture.

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The technology of Organic Light-Emitting Diodes has reached such a high level of reliability that it can be used in various applications. The required light emission efficiency can be achieved by transforming the triplet excitons into singlet states through Reverse InterSystem Crossing (RISC), which is the main process of a general mechanism called thermally activated delayed fluorescence (TADF). In this thesis, we theoretically analyzed two carbazole-benzonitrile (donor-acceptor) derivatives, 2,5-di(9H-carbazol-9-yl)benzonitrile (p-2CzBN) and 2,3,4,5,6-penta(9H-carbazol-9-yl)benzonitrile (5CzBN), and addressed the problem of how donor-acceptor (D-A) or donor-acceptor-donor (D-A-D) flexible molecular architectures influence the nature of the excited states and the emission intensity. Furthermore, we analyzed the RISC rates as a function of the conformation of the carbazole lateral groups, considering the first electronic states, S0, S1, T1 and T2, involved in TADF process. The two prototype molecules, p-2CzBN and 5CzBN, have a similar energy gap between the first singlet and triplet states (∆EST, a key parameter in the RISC rate), but different TADF performances. Therefore, other parameters must be considered to explain their different behavior. The oscillator strength of p-2CzBN, never tested as emitter in OLEDs, is similar to that of 5CzBN, which is an active TADF molecule. We also note that the presence of a second T2 triplet state, lower in energy than S1 only in 5CzBN, and the reorganization energies, associated with RISC processes involving T1 and T2, are important factors in differentiating the rates in p-2CzBN and 5CzBN. For p-2CzBN, the RISC rate from T2 to S1 is surprisingly higher than that from T1 to S1, in disagreement with El-Sayed rules, due to a large reorganization energy associated to the T1 to S1, process; while the contrary occurs for 5CzBN. These insights are important for designing new TADF emitters based on the benzo-carbazole architecture.

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Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

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Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

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Photoplethysmography (PPG) sensors allow for noninvasive and comfortable heart-rate (HR) monitoring, suitable for compact wearable devices. However, PPG signals collected from such devices often suffer from corruption caused by motion artifacts. This is typically addressed by combining the PPG signal with acceleration measurements from an inertial sensor. Recently, different energy-efficient deep learning approaches for heart rate estimation have been proposed. To test these new solutions, in this work, we developed a highly wearable platform (42mm x 48 mm x 1.2mm) for PPG signal acquisition and processing, based on GAP9, a parallel ultra low power system-on-chip featuring nine cores RISC-V compute cluster with neural network accelerator and 1 core RISC-V controller. The hardware platform also integrates a commercial complete Optical Biosensing Module and an ARM-Cortex M4 microcontroller unit (MCU) with Bluetooth low-energy connectivity. To demonstrate the capabilities of the system, a deep learning-based approach for PPG-based HR estimation has been deployed. Thanks to the reduced power consumption of the digital computational platform, the total power budget is just 2.67 mW providing up to 5 days of operation (105 mAh battery).