No abstract

Introduction Implementation of telemedicine has been shown to improve health outcomes, such as body mass index (BMI). However, it is unclear whether telemedicine is useful alongside traditional weight-management programmes for adolescents with complex obesity. The objective was to evaluate implementation of the Ontario Telemedicine Network (OTN), a videoconferencing programme, as an adjunctive tool to face-to-face counselling within the setting of an established interdisciplinary obesity treatment programme. Methods Our observational cohort included two groups of adolescents enrolled in a clinical obesity-management programme over a two year period. Adolescents ( n = 50) in group 1 attended both in-person and virtual visits (OTN group), and adolescents ( n = 50) in group 2 received only in-person visits (comparison group). Within the OTN group, satisfaction survey responses were compared between patients and healthcare professionals. Change in BMI per month, paediatric quality of life scores, session attendance and demographic variables were compared between groups. Results OTN subjects averaged 4.9 telehealth visits per adolescent over the two year programme. Both OTN and comparison groups had similar changes in BMI ( p = 0.757), with increases over time ( p = 0.042). Paediatric quality of life scores in both groups improved over time compared to baseline ( p < 0.001), with higher scores for children compared to parental-reported child scores ( p = 0.008). Both adolescents and healthcare professionals using the OTN were similarly satisfied with their experience. Conclusion Adjunctive use of the OTN within the setting of a weight-management programme is feasible, well accepted by families and healthcare providers, and led to similar outcomes compared to usual care.

No abstract

Biochar, a stable, carbon-rich solid produced during biomass pyrolysis, has been widely used in soil conditioning. A promising option to alleviate the problem of sludge management. In this study, Cr, Ni, Zn, Cd, As, Pb and Cu were studied owing to their relatively high content in the sludge and their potential to cause environmental damage. Sludge-biochars were produced at different temperatures

This paper proposes a high-speed low-cost VLSI system capable of on-chip online learning for classifying address-event representation (AER) streams from dynamic vision sensor (DVS) retina chips. The proposed system executes a lightweight statistic algorithm based on simple binary features extracted from AER streams and a Random Ferns classifier to classify these features. The proposed system’s characteristics of multi-level pipelines and parallel processing circuits achieves a high throughput up to 1 spike event per clock cycle for AER data processing. Thanks to the nature of the lightweight algorithm, our hardware system is realized in a low-cost memory-centric paradigm. In addition, the system is capable of on-chip online learning to flexibly adapt to different in-situ application scenarios. The extra overheads for on-chip learning in terms of time and resource consumption are quite low, as the training procedure of the Random Ferns is quite simple, requiring few auxiliary learning circuits. An FPGA prototype of the proposed VLSI system was implemented with 9.5~96.7% memory consumption and <11% computational and logic resources on a Xilinx Zynq-7045 chip platform. It was running at a clock frequency of 100 MHz and achieved a peak processing throughput up to 100 Meps (Mega events per second), with an estimated power consumption of 690 mW leading to a high energy efficiency of 145 Meps/W or 145 event/μJ. We tested the prototype system on MNIST-DVS, Poker-DVS, and Posture-DVS datasets, and obtained classification accuracies of 77.9%, 99.4% and 99.3%, respectively. Compared to prior works, our VLSI system achieves higher processing speeds, higher computing efficiency, comparable accuracy, and lower resource costs.