Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems

Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions

iGPS capability study

This report presents the results of testing of the Metris iGPS system performed by the National Physical Laboratory (NPL) and the University of Bath (UoB), with the assistance of Metris, and Airbus at Airbus, Broughton in March 2008. The aim of the test was to determine the performance capability of the iGPS coordinate metrology system by comparison with a reference measurement system based on multilateration implemented using laser trackers. A network of reference points was created using SMR nests fixed to the ground and above ground level on various stands. The reference points were spread out within the measurement volume of approximately 10 m ´ 10 m ´ 2 m. The coordinates of each reference point were determined by the laser tracker survey using multilateration. The expanded uncertainty (k=2) in the relative position of these reference coordinates was estimated to be of the order of 10 µm in x, y and z. A comparison between the iGPS system and the reference system showed that for the test setup, the iGPS system was able to determine lengths up to 12 m with an uncertainty of 170 µm (k=2) and coordinates with an uncertainty of 120 µm in x and y and 190 µm in z (k=2)

Non-fluorinated sol-gel processing of hydrophobic coating on cotton fabric

376-384In this study, the importance of wash durability tests for providing efficient hydrophobic surfaces through nonfluorinated sol-gel processes has been investigated. Various concentrations of methyltriethoxysilane (MTEOS) and trimethylchlorosilane (TMCS) have been used to deposit SiO2 nanoparticles on a cotton fabric in a two-step sol-gel process. The effects of catalyst type on the hydrophobic properties of fabric are investigated by using ammonia or hydrochloric acid in the sol-gel procedure. Surface characteristics, such as morphology and wettability are evaluated by image processing techniques and contact angle measurements. Air permeability, mechanical strength and wash durability tests are also performed to investigate the physical/mechanical properties of the samples. The results show that SiO2 nanoparticles are well dispersed on cotton fabrics with a contact angle of 149o. It is revealed that the fabric coated with 1 mL of MTEOS and ammonia catalyst exhibits the highest breathability and strength. Moreover, the first stage of the sol-gel technique is sufficient to provide super hydrophobicity. However, the results of the durability test indicate that sol-gel technique does not provide suitable wash durability that should be considered in future investigations

A randomized controlled clinical trial evaluating quality of life when using a simple acupressure protocol in women with primary dysmenorrhea

Objective: To evaluate a simple acupressure protocol in LIV3 and LI4 acupoints in women with primary dysmenorrhea. Methods: This paper reports a randomized, single blinded clinical trial. 90 young women with dysmenorrhea were recruited to three groups to receive 20 minutes acupressure every day in either LIV3 or LI4, or placebo points. Acupressure was timed five days before menstruation for three successive menstrual cycles. On menstruation, each participant completed the Wong Baker faces pain scale, and the quality of life short form -12 (QOL SF-12). Results: Intensity and duration of pain between the three groups in the second and third cycles during the intervention (p<0.05) differed significantly. Significant differences were seen in all domains of QOL except for mental health (p=0.4), general health (p=0.7) and mental subscale component (p=0.12) in the second cycle, and mental health (p=0.9), and mental subscale component (p=0.14) in the third cycle. Conclusion: Performing the simple acupressure protocol is an effective method to decrease the intensity and duration of dysmenorrhea, and improve the QOL. Key words: Dysmenorrhea, acupressure, quality of life Registration ID in IRCT: IRCT2016052428038N

A modified deep learning weather prediction using cubed sphere for global precipitation

Deep learning (DL), a potent technology to develop Digital Twin (DT), for weather prediction using cubed spheres (DLWP-CS) was recently proposed to facilitate data-driven simulations of global weather fields. DLWP-CS is a temporal mapping algorithm wherein time-stepping is performed through U-NET. Although DLWP-CS has shown impressive results for fields, such as temperature and geopotential height, this technique is complicated and computationally challenging for a complex, non-linear field, such as precipitation, which depends on other prognostic environmental co-variables. To address this challenge, we modify the DLWP-CS and call our technique “modified DLWP-CS” (MDLWP-CS). In this study, we transform the architecture from a temporal to a spatio-temporal mapping (multivariate setup), wherein precursor(s) of precipitation can be used as input. As a proof of concept, as a first simple case, a 2-m surface air temperature is used to predict precipitation using MDLWP-CS. The model is trained using hourly ERA-5 reanalysis and the resulting experimental findings are compared to two benchmark models, viz, the linear regression and an operational numerical weather prediction model, which is the Global Forecast System (GFS). The fidelity of MDLWP-CS is much better compared to linear regression and the results are equivalent to GFS output in terms of daily precipitation prediction with 1 day lag. These results provide an encouraging framework for an efficient DT that can facilitate speedy, high fidelity precipitation predictions.</jats:p

Verification of the indoor GPS system, by comparison with calibrated coordinates and by angular reference

This paper details work carried out to verify the dimensional measurement performance of the Indoor GPS (iGPS) system; a network of Rotary-Laser Automatic Theodolites (R-LATs). Initially tests were carried out to determine the angular uncertainties on an individual R-LAT transmitter-receiver pair. A method is presented of determining the uncertainty of dimensional measurement for a three dimensional coordinate measurement machine. An experimental procedure was developed to compare three dimensional coordinate measurements with calibrated reference points. The reference standard used to calibrate these reference points was a fringe counting interferometer with the multilateration technique employed to establish three dimensional coordinates. This is an extension of the established technique of comparing measured lengths with calibrated lengths. The method was found to be practical and able to establish that the expanded uncertainty of the basic iGPS system was approximately 1 mm at a 95% confidence level. Further tests carried out on a highly optimized version of the iGPS system have shown that the coordinate uncertainty can be reduced to 0.25 mm at a 95% confidence level

Guillain�Barré syndrome as a parainfectious manifestation of SARS-CoV-2 infection: A case series

The global SARS-CoV-2 pandemic posed an unprecedented challenge to almost all fields of medicine and Neurology is not an exception. Collecting information about its complications and related conditions will help clinicians to become more confident in managing this disease. Guillain-Barre Syndrome (GBS) is mostly described as a post-infectious phenomenon and its occurrence during acute phase of illness is of interest. GBS has recently been reported during the active phase of COVID-19 for the first time. Severity and fast progression of GBS associated with COVID-19 have also been shown in recent studies. Here we report three cases of GBS during the active phase of COVID-19 with severe symptoms and fast progression to quadriplegia and facial diplegia over 2 days, which led to death in one case due to severe autonomic dysfunction. We suggest SARS-CoV-2 might be associated with rather a severe, rapidly progressive and life-threatening phenotype of GBS. © 2020 Elsevier Lt

Service workload patterns for QoS-driven cloud resource management

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support a continuous approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction technique that combines a workload pattern mining approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are additional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log smoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these challenges