TEC and foF2 variations: preliminary results

Investigation of the relationship between TEC and (foF2)2 shows that although they are highly correlated, a «hysteresis» effect exists between them. The slab thickness is greater before than after mid-day for equal cos ?values. Moreover, a comparison of the calculated upper and lower quartiles of variability in TEC, foF2 and Nmax, respectively shows that the variability of TEC lies between those of foF2 and Nmax depending on the level of solar activity

Within-the-hour variability: levels and their probabilities

The study of foF2 data measured every 5-min and of TEC measurements made every 10-min shows that the within-the-hour variability is different in the two parameters. Deciles of this variability for foF2 and for TEC are determined together with the probabilities of exceeding a given level of variability. Furthermore, considering hourly values, it is found that the variability in TEC is like an «intrinsic noise» throughout the day of the order of less than 5% of the hourly value; but at sunrise and often at sunset large values take place. A seasonal dependence is evident. Besides, a within-the-hour variability in foF2 is always present with large values at sunrise or sunset depending on the season, and also during disturbed ionospheric conditions

SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper, we propose SPINN, a distributed inference system that employs synergistic device-cloud computation together with a progressive inference method to deliver fast and robust CNN inference across diverse settings. The proposed system introduces a novel scheduler that co-optimises the early-exit policy and the CNN splitting at run time, in order to adapt to dynamic conditions and meet user-defined service-level requirements. Quantitative evaluation illustrates that SPINN outperforms its state-of-the-art collaborative inference counterparts by up to 2x in achieved throughput under varying network conditions, reduces the server cost by up to 6.8x and improves accuracy by 20.7% under latency constraints, while providing robust operation under uncertain connectivity conditions and significant energy savings compared to cloud-centric execution.Comment: Accepted at the 26th Annual International Conference on Mobile Computing and Networking (MobiCom), 202

Healthier and Independent Living of the Elderly: Interoperability in a Cross-Project Pilot

The ageing of the population creates new heterogeneous challenges for age-friendly living. The progressive decline in physical and cognitive skills tends to prevent elderly people from performing basic instrumental activities of daily living and there is a growing interest in technology for aging support. Digital health today can be exercised by anyone owning a smartphone and parameters such as heart rate, step counts, calorie intake, sleep quality, can be collected and used not only to monitor and improve the individual’s health condition but also to prevent illnesses. However, for the benefits of e-health to take place, digital health data, either Electronic Health Records (EHR) or sensor data from the IoMT, must be shared, maintaining privacy and security requirements but unlocking the potential for research an innovation throughout EU. This paper demonstrates the added value of such interoperability requirements, and a form of accomplishing them through a cross-project pilot

Evaluation of a commercial web-based weight loss and weight loss maintenance program in overweight and obese adults: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Obesity rates in adults continue to rise and effective treatment programs with a broad reach are urgently required. This paper describes the study protocol for a web-based randomized controlled trial (RCT) of a commercially available program for overweight and obese adult males and females. The aim of this RCT was to determine and compare the efficacy of two web-based interventions for weight loss and maintenance of lost weight.</p> <p>Methods/Design</p> <p>Overweight and obese adult males and females were stratified by gender and BMI and randomly assigned to one of three groups for 12-weeks: waitlist control, or basic or enhanced online weight-loss. Control participants were re-randomized to the two weight loss groups at the end of the 12-week period. The basic and enhanced group participants had an option to continue or repeat the 12-week program. If the weight loss goal was achieved at the end of 12, otherwise on completion of 24 weeks of weight loss, participants were re-randomized to one of two online maintenance programs (maintenance basic or maintenance enhanced), until 18 months from commencing the weight loss program. Assessments took place at baseline, three, six, and 18 months after commencing the initial weight loss intervention with control participants repeating the initial assessment after three month of waiting. The primary outcome is body mass index (BMI). Other outcomes include weight, waist circumference, blood pressure, plasma markers of cardiovascular disease risk, dietary intake, eating behaviours, physical activity and quality of life.</p> <p>Both the weight loss and maintenance of lost weight programs were based on social cognitive theory with participants advised to set goals, self-monitor weight, dietary intake and physical activity levels. The enhanced weight loss and maintenance programs provided additional personalized, system-generated feedback on progress and use of the program. Details of the methodological aspects of recruitment, inclusion criteria, randomization, intervention programs, assessments and statistical analyses are described.</p> <p>Discussion</p> <p>Importantly, this paper describes how an RCT of a currently available commercial online program in Australia addresses some of the short falls in the current literature pertaining to the efficacy of web-based weight loss programs.</p> <p>Australian New Zealand Clinical Trials Registry (ANZCTR) number: ACTRN12610000197033</p

Overview of the SMART-BEAR Technical Infrastructure

This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites