Evaluation of Various Dynamic Issues During Transient Operation of Turbocharged Diesel Engine with Special Reference to Friction Development

Copyright © 2007 SAE International The modeling of transient turbocharged diesel engine operation appeared in the early seventies and continues to be in the focal point of research, due to the importance of transient response in the everyday operating conditions of engines. The majority of research has focused so far on issues concerning thermodynamic modeling, as these directly affect heat release predictions and consequently performance and pollutants emissions. On the other hand, issues concerning the dynamics of transient operation are often disregarded or over-simplified, possibly for the sake of speeding up program execution time. In the present work, an experimentally validated transient diesel engin

Feature extraction based on bio-inspired model for robust emotion recognition

Emotional state identification is an important issue to achieve more natural speech interactive systems. Ideally, these systems should also be able to work in real environments in which generally exist some kind of noise. Several bio-inspired representations have been applied to artificial systems for speech processing under noise conditions. In this work, an auditory signal representation is used to obtain a novel bio-inspired set of features for emotional speech signals. These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions. Neural models were trained as classifiers and results were compared to the well-known mel-frequency cepstral coefficients. Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system. The results were also validated in a speaker independent scheme and with two emotional speech corpora.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Primary care professionals’ experiences during the first wave of the COVID-19 pandemic in Greece: a qualitative study

Background: The coronavirus outbreak (COVID-19) tested health care systems worldwide. This qualitative study aimed to explore and understand the experiences, beliefs and concerns of Primary Care Professionals (PCPs) regarding the preparedness and response of primary care to the first wave of the pandemic in Greece, a country where a public structured primary care system has been developing. Methods: We conducted semi-structured telephone interviews with 33 PCPs (General Practitioners, community General Internal Medicine Specialists, community Paediatricians and nurses) recruited from all regions of Greece after the first wave of the pandemic (June 2020). Interviews were transcribed verbatim, data were anonymised and analysed. Thematic analysis was applied developing a conceptual framework. Results: Four main themes were identified: a) Primary care unit adaptation and issues faced during the pandemic; b) Management of suspected COVID-19 cases; c) Management of non-suspected cases; d) Consequences of the pandemic. In the first phase of the pandemic, remote management of suspected cases and their referral to the hospital were preferred as a result of a shortage of personal protective equipment and inaccessibility to coronavirus testing in primary care. Due to the discontinuation of regular medical services and the limited in-person contact between doctors and patients, chronic disease management and prevention programmes were left behind. Social and emotional consequences of the pandemic, such as workplace stigma, isolation and social seclusion, deriving from fear of viral transmission, as well as burnout symptoms and exhaustion were commonly experienced among PCPs. Positive consequences of the pandemic were considered to be the recognition of the importance of an empowered public healthcare system by citizens and the valuable insight, knowledge and experience professionals gained in times of crisis. Conclusions: Primary care has a key role to play during and after the pandemic by using its information infrastructure to identify at-risk groups, detect new cases of COVID-19, provide care according to needs, and carry out vaccination programmes. Central coordination and empowerment of primary care will increase its effectiveness, via public awareness, holistic patient management, and unburdening of hospitals

Robust Human Pose Tracking For Realistic Service Robot Applications

Robust human pose estimation and tracking plays an integral role in assistive service robot applications, as it provides information regarding the body pose and motion of the user in a scene. Even though current solutions provide high-accuracy results in controlled environments, they fail to successfully deal with problems encountered under real-life situations such as tracking initialization and failure, body part intersection, large object handling and partial-view body-part tracking. This paper presents a framework tailored for deployment under real-life situations addressing the above limitations. The framework is based on the articulated 3D-SDF data representation model, and has been extended with complementary mechanisms for addressing the above challenges. Extensive evaluation on public datasets demonstrates the framework's state-of-the-art performance, while experimental results on a challenging realistic human motion dataset exhibit its robustness in real life scenarios

The changing epidemiology of the ageing thalassaemia populations: A position statement of the Thalassaemia International Federation

Therapeutic advances in β-thalassaemia have gradually lead to a significant improvement in prognosis over the past few decades. As a result, patients living in areas where disease-specific programmes offering access to modern therapy are in place experience a new era of prolonged survival that tends to reach that of the normal population. This ageing thalassaemia population, however, faces a new spectrum of comorbidities resulting from increasing age that may jeopardise the advances in prognosis provided by current therapy and thus poses new challenges in diagnosis, monitoring and treatment. In this position paper of the Thalassaemia International Federation, we review the changing epidemiology and clinical spectrum of patients with β-thalassaemia and propose actions to be undertaken in order to address the emerging spectrum of comorbidities resulting from ageing. © 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Lt

The influence of oxygenated fuels on transient and steady-state engine emissions

Highlights\ud \ud •A custom test was designed to investigate transient engine operation.\ud \ud •Waste cooking biodiesel (primary fuel) and triacetin (highly oxygenated additive) were used.\ud \ud •Turbocharger lag caused an overshoot in NOx, PM and PN emissions.\ud \ud •Oxygenated fuels showed higher PN and lower PM overshoots compared to diesel.\ud \ud •Oxygenated fuels emitted smaller particles during transient compared to diesel.\ud \ud \ud This research studies the influence of oxygenated fuels on transient and steady-state engine performance and emissions using a fully instrumented, 6-cylinder, common rail turbocharged compression ignition engine. Beside diesel, the other tested fuels were based on waste cooking biodiesel (primary fuel) with triacetin (highly oxygenated additive). A custom test was designed in this study to investigate the engine performance and emissions during steady-state, load acceptance and acceleration operation modes. Furthermore, to study the engine performance and emissions during a whole transient cycle, a legislative cycle (NRTC), which contains numerous discrete transient modes, was utilised. In this paper, the turbocharger lag, engine power, NOx, PM, PN and PN size distribution were investigated. During steady-state operation, compared to diesel, the oxygenated fuels showed lower indicated power, while they showed higher values during turbocharger lag and acceleration. Also, during acceleration and load increase modes, NOx, PM and PN peaked over the steady-state counterpart, also, the accumulation mode count median diameter moved toward the larger particle sizes. Increasing the fuel oxygen content increased the indicated specific NOx and PN maximum overshoot, while engine power, PM, PN and PM maximum overshoot decreased. Also, the accumulation mode count median diameter moved toward the smaller particle sizes

Understanding of human behavior with a robotic agent through daily activity analysis

Personal assistive robots to be realized in the near future should have the ability to seamlessly coexist with humans in unconstrained environments, with the robot’s capability to understand and interpret the human behavior during human–robot cohabitation significantly contributing towards this end. Still, the understanding of human behavior through a robot is a challenging task as it necessitates a comprehensive representation of the high-level structure of the human’s behavior from the robot’s low-level sensory input. The paper at hand tackles this problem by demonstrating a robotic agent capable of apprehending human daily activities through a method, the Interaction Unit analysis, that enables activities’ decomposition into a sequence of units, each one associated with a behavioral factor. The modelling of human behavior is addressed with a Dynamic Bayesian Network that operates on top of the Interaction Unit, offering quantification of the behavioral factors and the formulation of the human’s behavioral model. In addition, light-weight human action and object manipulation monitoring strategies have been developed, based on RGB-D and laser sensors, tailored for onboard robot operation. As a proof of concept, we used our robot to evaluate the ability of the method to differentiate among the examined human activities, as well as to assess the capability of behavior modeling of people with Mild Cognitive Impairment. Moreover, we deployed our robot in 12 real house environments with real users, showcasing the behavior understanding ability of our method in unconstrained realistic environments. The evaluation process revealed promising performance and demonstrated that human behavior can be automatically modeled through Interaction Unit analysis, directly from robotic agents