A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

Incorporating prediction models in the SelfLet framework: a plugin approach

A complex pervasive system is typically composed of many cooperating \emph{nodes}, running on machines with different capabilities, and pervasively distributed across the environment. These systems pose several new challenges such as the need for the nodes to manage autonomously and dynamically in order to adapt to changes detected in the environment. To address the above issue, a number of autonomic frameworks has been proposed. These usually offer either predefined self-management policies or programmatic mechanisms for creating new policies at design time. From a more theoretical perspective, some works propose the adoption of prediction models as a way to anticipate the evolution of the system and to make timely decisions. In this context, our aim is to experiment with the integration of prediction models within a specific autonomic framework in order to assess the feasibility of such integration in a setting where the characteristics of dynamicity, decentralization, and cooperation among nodes are important. We extend an existing infrastructure called \emph{SelfLets} in order to make it ready to host various prediction models that can be dynamically plugged and unplugged in the various component nodes, thus enabling a wide range of predictions to be performed. Also, we show in a simple example how the system works when adopting a specific prediction model from the literature

Impact of the HeartMath Self-Management Skills Program on Physiological and Psychological Stress in Police Officers

This study explored the impact on a group of police officers from Santa Clara County, California of the HeartMath stress and emotional self-management training, which provides practical techniques designed to reduce stress in the moment, improve physiological and emotional balance, increase mental clarity and enhance performance and quality of life.This study provides evidence that practical stress and emotional self-management techniques can reduce damaging physiological and psychological responses to both acute and chronic stress in police, and positively impact a variety of major life areas in a relatively short period of time. In particular, results show that application of these interventions can produce notable improvements in communication difficulties at work and in strained family relationships, two areas that are well recognized to be major sources of stress for police

E-democracy: exploring the current stage of e-government

Governments around the world have been pressured to implement e-Government programs in order to improve the government-citizen dialogue. The authors of this article review prior literature on such efforts to find if they lead to increased democratic participation ("e-Democracy") for the affected citizens, with a focus on the key concepts of transparency, openness, and engagement. The authors find that such efforts are a starting point toward e-Democracy, but the journey is far from complete

High-Intensity Variable Stepping Training in Patients With Motor Incomplete Spinal Cord Injury: A Case Series

Background and Purpose: Previous data suggest that large amounts of high-intensity stepping training in variable contexts (tasks and environments) may improve locomotor function, aerobic capacity, and treadmill gait kinematics in individuals poststroke. Whether similar training strategies are tolerated and efficacious for patients with other acute-onset neurological diagnoses, such as motor incomplete spinal cord injury (iSCI), is unknown. Individuals with iSCI potentially have greater bilateral impairments. This case series evaluated the feasibility and preliminary short- and long-term efficacy of highintensity variable stepping practice in ambulatory participants for more than 1 year post-iSCI. Case Series Description: Four participants with iSCI (neurological levels C5-T3) completed up to 40 one-hour sessions over 3 to 4 months. Stepping training in variable contexts was performed at up to 85% maximum predicted heart rate, with feasibility measures of patient tolerance, total steps/session, and intensity of training. Clinical measures of locomotor function, balance, peak metabolic capacity, and gait kinematics during graded treadmill assessments were performed at baseline and posttraining, with more than 1-year follow-up. Outcomes: Participants completed 24 to 40 sessions over 8 to 15 weeks, averaging 2222 ± 653 steps per session, with primary adverse events of fatigue and muscle soreness. Modest improvements in locomotor capacity where observed at posttraining, with variable changes in lower extremity kinematics during treadmill walking. Discussion: High-intensity, variable stepping training was feasible and tolerated by participants with iSCI although only modest gains in gait function or quality were observed. The utility of this intervention in patients with more profound impairments may be limited

Pinpointing brainstem mechanisms responsible for autonomic dysfunction in Rett syndrome:therapeutic perspectives for 5-HT1A agonists

Rett syndrome is a neurological disorder caused by loss of function of methyl-CpG-binding protein 2 (MeCP2). Reduced function of this ubiquitous transcriptional regulator has a devastating effect on the central nervous system. One of the most severe and life-threatening presentations of this syndrome is brainstem dysfunction, which results in autonomic disturbances such as breathing deficits, typified by episodes of breathing cessation intercalated with episodes of hyperventilation or irregular breathing. Defects in numerous neurotransmitter systems have been observed in Rett syndrome both in animal models and patients. Here we dedicate special attention to serotonin due to its role in promoting regular breathing, increasing vagal tone, regulating mood, alleviating Parkinsonian-like symptoms and potential for therapeutic translation. A promising new symptomatic strategy currently focuses on regulation of serotonergic function using highly selective serotonin type 1A (5-HT1A) biased agonists. We address this newly emerging therapy for respiratory brainstem dysfunction and challenges for translation with a holistic perspective of Rett syndrome, considering potential mood and motor effects

BIOTEX-biosensing textiles for personalised healthcare management.

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting