People with quadriplegia have a high risk for respiratory illness, social isolation and depression. Previous research has demonstrated that therapeutic singing interventions can not only improve breathing function and speech loudness, but also improve mood and social connectedness for people with quadriplegia. Face-to-face group attendance is difficult for this population due to difficulties with distance and travel. Online environments offer an accessible and cost-effective solution for people to connect with others without leaving their home. In a two-phase iterative design, we explored and tested different approaches for delivering online music therapy sessions with 12 patients from an inpatient spinal cord injury rehabilitation service. Six participants in Phase 1 trialled different virtual reality headsets and completed a short interview about their experience of the equipment and online singing trials. Outcomes from Phase 1 testing led to the development of a custom-built virtual reality application for online group music therapy sessions with low-latency audio. We tested the acceptability and feasibility of this platform in comparison to face-to-face and teleconference options for music therapy with six different patients. These participants completed three validated questionnaires: System Usability Scale, Quebec User Evaluation of Satisfaction with assistive Technology, and Psychosocial Impact of Assistive Devices Scale, and an interview about their experience. Questionnaire scores were good with mean ratings of 4.4 for Quebec User Evaluation of Satisfaction with assistive Technology, 53 for System Usability Scale and positive mean Psychosocial Impact of Assistive Devices Scale scores of 1.5 for competence, 2 for adaptability and 1.5 for self-esteem. Thematic analysis of post-session qualitative interviews revealed five themes: virtual reality was a positive experience, virtual reality was immersive and transportative, virtual reality reduced inhibitions about singing in front of others, virtual reality may reduce social cues, and the virtual reality equipment was comfortable, accessible and easy to use. Telehealth options, including a custom-designed virtual reality program, with low-latency audio are an acceptable and feasible mode of delivery for therapeutic singing interventions for people with spinal cord injury. Future non-inferiority research is needed to test online delivery modes for music therapy in comparison to face-to-face treatment.

This document investigates the adoption of traditional solutions such as change of off-load and on-load tap changer positions and/or network augmentation to increase the hosting capacity of PV-rich distribution networks considering the new Victorian Volt-Watt and Volt-var settings which mandates that both power quality response modes are enabled. Studies are performed on four fully modelled and significantly different HV feeders (i.e., urban and rural) considering time-series seasonal analyses with growing penetrations of solar PV. Findings show that enabling both Volt-Watt and Volt-var functions with the Victorian settings provides significant benefits to both DNSPs and customers. Voltage rise issues and curtailment are dramatically reduced, making it possible to host 20% of customers without the need for other solutions. Adopting traditional solutions can help increase the solar PV hosting capacity to 40% (excluding HV feeders with long SWER lines). However, beyond 40%, traditional solutions were found to have limited effectiveness in mitigating network issues.

This document presents a smart meter-driven analytical technique proposed by The University of Melbourne to estimate PV hosting capacity in distribution networks. Two significantly different HV feeders, urban and rural, are modelled in detail with growing PV penetrations in a horizon of 5 years to create a large realistic smart meter data set. The analytical technique is then applied to this data set for different PV penetrations. The findings show that the proposed analytical technique provides adequate estimations of PV hosting capacity, making it a faster and simpler alternative to model-based approaches.

This document first presents the process adopted by The University of Melbourne in collaboration with AusNet Services to select the HV feeders that will be modelled and used throughout the project. Then, the selected HV feeders are fully modelled along with their corresponding LV networks using the software OpenDSS.

Schizotypal disorder lies in the schizophrenia spectrum and is widely studied in adult populations. Schizotypal disorder in children (SDc) is less well described. This study examined brain morphological and functional connectivity abnormalities in SDc (12 SDc and 9 typically developing children), focusing on the default mode and executive control brain networks. Results indicated that SDc is associated with reduced grey matter volume (GMV) in superior and medial frontal gyri, and increased resting-state functional connectivity between the superior frontal gyrus and inferior parietal lobule, compared to typically developing children (cluster-level FWE-corrected p < 0.05). The brain structure abnormality (GMV in left superior frontal gyrus) was correlated with clinical symptoms in SDc (r = -0.66, p = 0.026) and functional connectivity abnormality was correlated with extra-dimensional shifting impairments in all participants (r = 0.62, p = 0.011), suggesting their contribution to the underlying mechanisms of clinical presentation. These preliminary results motivate further work to characterize the neural basis of SDc and its significance as a risk factor for later psychosis.

Cryptocurrency has as of late pulled in extensive consideration in the fields of economics, cryptography, and computer science due to it is an encrypted digital currency, peer- to- peer virtual forex produced using codes, and it is much the same as another medium of the trade like real cash. This study mainly focuses to combine the Deep Learning with Data parallelism and Cloud Computing Machine learning engine as “hybrid architecture” to predict new Cryptocurrency prices by using historical Cryptocurrency data. The study has exploited 266,776 of Cryptocurrency prices values from the pilot experiment, and Deep Learning algorithm used for the price prediction. The four hybrid architecture models, namely, (i) standalone PC, (ii) Cloud computing without data parallelism (GPU-1), (iii) Cloud computing with data parallelism (GPU-4), and (iv) Cloud computing with data parallelism (GPU-8) introduced and utilized for the analysis. The performance of each model is evaluated using different performance evaluation parameters. Then, the efficiency of each model was compared using different batch sizes. An experimental result reveals that Cloud computing technology exposes new era by performing parallel computing in IoT to reduce computation time up to 90% of the Deep Learning algorithm-based Cryptocurrencies price prediction model and many other IoT applications such as character recognition, biomedical field, industrial automation, and natural disaster prediction.

To function, mobile phone systems require transmitters that emit and receive radiofrequency signals over an extended geographical area exposing humans in all stages of development ranging from in-utero, early childhood, adolescents and adults. This study evaluates the question of the impact of radiofrequency radiation on living organisms in vitro studies. In this study, we abstract data from 300 peer-reviewed scientific publications (1990–2015) describing 1127 experimental observations in cell-based in vitro models. Our first analysis of these data found that out of 746 human cell experiments, 45.3% indicated cell changes, whereas 54.7% indicated no changes (p = 0.001). Realizing that there are profound distinctions between cell types in terms of age, rate of proliferation and apoptosis, and other characteristics and that RF signals can be characterized in terms of polarity, information content, frequency, Specific Absorption Rate (SAR) and power, we further refined our analysis to determine if there were so e distinct properties of negative and positive findings associated with these specific characteristics. We further analyzed the data taking into account the cumulative effect (SAR × exposure time) to acquire the cumulative energy absorption of experiments due to radiofrequency exposure, which we believe, has not been fully considered previously. When the frequency of signals, length and type of exposure, and maturity, rate of growth (doubling time), apoptosis and other properties of individual cell types are considered, our results identify a number of potential non-thermal effects of radiofrequency fields that are restricted to a subset of specific faster-growing less differentiated cell types such as human spermatozoa (based on 19 reported experiments, p-value = 0.002) and human epithelial cells (based on 89 reported experiments, p-value < 0.0001). In contrast, for mature, differentiated adult cells of Glia (p = 0.001) and Glioblastoma (p < 0.0001) and adult human blood lymphocytes (p < 0.0001) there are no statistically significant differences for these more slowly reproducing cell lines. Thus, we show that RF induces significant changes in human cells (45.3%), and in faster-growing rat/mouse cell dataset (47.3%). In parallel with this finding, further analysis of faster-growing cells from other species (chicken, rabbit, pig, frog, snail) indicates that most undergo significant changes (74.4%) when exposed to RF. This study confirms observations from the REFLEX project, Belyaev and others that cellular response varies with signal properties. We concur that differentiation of cell type thus constitutes a critical piece of information and should be useful as a reference for many researchers planning additional studies. Sponsorship bias is also a factor that we did not take into account in this analysis.

Vehicles have to rely on satellite navigation in an open environment. However, satellite navigation cannot obtain accurate positioning information for vehicles in the interior of underground parking lots, as they comprise a semi-enclosed navigation space. Therefore, vehicular navigation needs to take into consideration both outdoor and indoor environments. Actually, outdoor navigation and indoor navigation require different positioning methods, and it is of great importance to choose a reasonable navigation and positioning algorithm solution for vehicles. Fortunately, the integrated navigation of the Global Positioning System (GPS) and the Micro-Electro-Mechanical System (MEMS) inertial navigation system could solve the problem of switching navigation algorithms in the entrance and exit of underground parking lots. This paper proposes a low cost vehicular seamless navigation technology based on the reduced inertial sensor system (RISS)/GPS between the outdoors and an underground garage. Specifically, the enhanced RISS is a positioning algorithm based on three inertial sensors and one odometer, which could achieve a similar location effect as the full model integrated navigation, reduce the costs greatly, and improve the efficiency of each sensor.

Efficient provision of Video-on-Demand (VoD) services requires that popular videos are stored in a cache close to users. Video popularity (defined by requested count) prediction is, therefore, important for optimal choice of videos to be cached. The popularity of a video depends on many factors and, as a result, changes dynamically with time. Accurate video popularity estimation that can promptly respond to the variations in video popularity then becomes crucial. In this paper, we analyze a method, called Minimal Inverted Pyramid Distance (MIPD), to estimate a video popularity measure called the Inverted Pyramid Distance (IPD). MIPD requires choice of a parameter, $k$ , representing the number of past requests from each video used to calculate its IPD. We derive, analytically, expressions to determine an optimal value for $k$ , given the requirement on ranking a certain number of videos with specified confidence. In order to assess the prediction efficiency of MIPD, we have compared it by simulations against four other prediction methods: Least Recency Used (LRU), Least Frequency Used (LFU), Least Recently/Frequently Used (LRFU), and Exponential Weighted Moving Average (EWMA). Lacking real data, we have, based on an extensive literature review of real-life VoD system, designed a model of VoD system to provide a realistic simulation of videos with different patterns of popularity variation, using the Zipf (heavy-tailed) distribution of popularity and a non-homogeneous Poisson process for requests. From a large number of simulations, we conclude that the performance of MIPD is, in general, superior to all of the other four methods.

To overcome power fading induced by chromatic dispersion in optical fiber communications, optical field recovery is a promising solution for direct detection short-reach applications, such as fast-evolving data center interconnects (DCIs). To date, various direct detection schemes capable of optical field recovery have been proposed, including Kramers-Kronig (KK) and signal-signal beat interference (SSBI) iterative cancellation (IC) receivers. However, they are all restricted to the single sideband (SSB) modulation format, thus conspicuously losing half of the electrical spectral efficiency (SE) compared with double sideband (DSB) modulation. Additionally, SSB suffers from the noise folding issue, requiring a precise optical filter that complicates the receiver design. As such, it is highly desirable to investigate the field recovery of DSB signals via direct detection. In this paper, for the first time, we propose a novel receiver scheme called carrier-assisted differential detection (CADD) to realize optical field recovery of complex-valued DSB signals via direct detection. First, CADD doubles the electrical SE compared with the KK and SSBI IC receivers by adopting DSB modulation without sacrificing receiver sensitivities. Furthermore, by using direct detection without needing a precise receiver optical filter, CADD can employ cost-effective uncooled lasers as opposed to expensive temperature-controlled lasers in coherent systems. Our proposed receiver architecture opens a new class of direct detection schemes that are suitable for photonic integration analogous to homodyne receivers in coherent detection.

Purpose: The photopic negative response (PhNR) of the light-adapted electroretinogram (ERG) holds promise as an objective marker of retinal ganglion cell function. We compared baseline detrending methods to improve PhNR repeatability without compromising its diagnostic ability in glaucoma. Methods: Photopic ERGs were recorded in 20 glaucoma and 18 age-matched control participants. A total of 50 brief, red-flashes (1.6 cd.s/m2) on a blue background (10 photopic cd/m2) were delivered using the RETeval device. Detrending methods compared were: (1) increasing the high-pass filter from 1 to 10 Hz and (2) estimating and removing the trend with an increasing polynomial (order from 1-10) applied to the prestimulus interval, prestimulus and postsignal interval, or the whole ERG signal. Coefficient of repeatability (COR%), unpaired Student's t-test, and area under the receiver operating characteristic curve (AUC) were used to compare the detrending methods. Results: Most detrending methods improved PhNR test-retest repeatability compared to the International Society for Clinical Electrophysiology of Vision (ISCEV) recommended 0.3 to 300 Hz band-pass filter (COR% ± 200%). In particular, detrending with a polynomial (order 3) applied to the whole signal performed the best (COR% ± 44%) while achieving similar diagnostic ability as ISCEV band-pass (AUC 0.74 vs. 0.75, respectively). However, over-correcting with higher orders of processing can cause waveform distortion and reduce diagnostic ability. Conclusions: Baseline detrending can improve the PhNR repeatability without compromising its clinical use in glaucoma. Further studies exploring more complex processing methods are encouraged. Translational Relevance: Baseline detrending can significantly improve the quality of the PhNR.

Malaria elimination is a global public health priority. To fulfil the demands of elimination diagnostics, we have developed an interdigitated electrode sensor platform targeting the Plasmodium falciparum Histidine Rich Protein 2 (PfHRP2) protein in saliva samples. A protocol for frequency-specific PfHRP2 detection in phosphate buffered saline was developed, yielding a sensitivity of 2.5 pg/mL based on change in impedance magnitude of the sensor. This protocol was adapted and optimized for use in saliva with a sensitivity of 25 pg/mL based on change in resistance. Further validation demonstrated detection in saliva spiked with PfHRP2 from clinical isolates in 8 of 11 samples. With a turnaround time of ~2 hours, the label-free platform based on impedance sensors has the potential for miniaturization into a point-of-care diagnostic device for malaria elimination.