Notes

Medical along with laboratory characteristics involving hemophagocytic lymphohistiocytosis brought on by simply Leishmania infantum contamination.
As an important part of high-speed train (HST), the mechanical performance of bogies imposes a direct impact on the safety and reliability of HST. It is a fact that, regardless of the potential mechanical performance degradation status, most existing fault diagnosis methods focus only on the identification of bogie fault types. However, for application scenarios such as auxiliary maintenance, identifying the performance degradation of bogie is critical in determining a particular maintenance strategy. In this article, by considering the intrinsic link between fault type and performance degradation of bogie, a novel multiple convolutional recurrent neural network (M-CRNN) that consists of two CRNN frameworks is proposed for simultaneous diagnosis of fault type and performance degradation state. Specifically, the CRNN framework 1 is designed to detect the fault types of the bogie. Meanwhile, CRNN framework 2, which is formed by CRNN Framework 1 and an RNN module, is adopted to further extract the features of fault performance degradation. It is worth highlighting that M-CRNN extends the structure of traditional neural networks and makes full use of the temporal correlation of performance degradation and model fault types. The effectiveness of the proposed M-CRNN algorithm is tested via the HST model CRH380A at different running speeds, including 160, 200, and 220 km/h. The overall accuracy of M-CRNN, i.e., the product of the accuracies for identifying the fault types and evaluating the fault performance degradation, is beyond 94.6% in all cases. This clearly demonstrates the potential applicability of the proposed method for multiple fault diagnosis tasks of HST bogie system.This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network (SNN) using spike-timing-dependent plasticity (STDP) for object recognition with MuST. MuST extracts the features contained in both the spatial and temporal information of AER event flow, and forms an informative and compact feature spike representation. We show not only how MuST exploits spikes to convey information more effectively, but also how it benefits the recognition using SNN. The recognition process is performed in an unsupervised manner, which does not need to specify the desired status of every single neuron of SNN, and thus can be flexibly applied in real-world recognition tasks. The experiments are performed on five AER datasets including a new one named GESTURE-DVS. Extensive experimental results show the effectiveness and advantages of the proposed approach.The use of haptic technology has recently become essential in Human-Computer Interaction to improve performance and user experience. Mid-air tactile feedback co-located with virtual touchscreen displays have a great potential to improve the performance in dual-task situations such as when using a phone while walking or driving. The purpose of this study is to investigate the effects of augmenting virtual touchscreen with mid-air tactile feedback to improve dual-task performance where the primary task is driving in a simulation environment and the secondary task involves interacting with a virtual touchscreen. Performance metrics included primary task performance in terms of velocity error, deviation from the middle of the road, number of collisions, and the number of off-road glances, secondary task performance including the interaction time and the reach time, and quality of user experience for perceived difficulty and satisfaction. Results demonstrate that adding mid-air tactile feedback to virtual touchscreen resulted in statistically significant improvement in the primary task performance (the average speed error, spatial deviation, and the number of off-road glances), the secondary task (reach time), and the perceived difficulty. These results provide a great motivation for augmenting virtual touchscreens with mid-air tactile feedback in dual-task human-computer interaction applications.Tactile displays based on friction modulation offer wide-bandwidth forces rendered directly on the fingertip. However, due to a number of touch conditions (e.g., normal force, skin hydration) that result in variations in the friction force and the strength of modulation effect, the precision of the force rendering remains limited. In this paper we demonstrate a closed-loop electroadhesion method for precise playback of friction force profiles on a human finger and we apply this method to the tactile rendering of several textiles encountered in everyday life.In this paper we report on a wireless optical communication system designed for biomedical applications like the transcutaneous optical biotelemetry links in brain machine interfaces. The system employs an optical UWB pulsed coding architecture that allowed to achieve data rate up to 300 Mbps. With respect to the state-of-the-art of these systems, the proposed solution makes use of sub-nanosecond laser pulses to obtain very high bit rate transmissions together with an overall reduced power consumption. The transmitter contains a pulsed semiconductor laser and the receiver a fast response time Si photodiode. The analogue laser driver and the photodiode conditioning circuit have been fabricated by using commercially available discrete components. In particular, the laser driver produces the current signals needed to generate the laser pulses while the photodiode readout circuit converts the photo-generated current pulses into voltage pulses by maintaining unaltered the system effective frequency response and the time domain characteristics. On the other hand, an FPGA board has been employed to implement the digital blocks that allow for the data coding/decoding and for the data pre- and post-processing procedures. A series of experimental measurements have been accomplished for a complete characterization of the proposed system by using a dermal sample of cleaned porcine skin located between the transmitter and the receiver. The communication system has been able to operate at data rates up to 300 Mbps with a BER lower than 1010 and a power consumption less than 37 pJ/bit.The knee joint performs a significant amount of positive or negative mechanical work during gradient walking, and targeted assistance during periods of high mechanical work could yield strong human augmentation benefits. This paper explores the biomechanical effects of providing knee extension assistance during the early stance phase of the gait cycle using a powered unilateral knee exoskeleton during gradient walking on able-bodied subjects. Twelve subjects walked on 15% gradient incline and decline surfaces with the exoskeleton providing knee extension assistance during the early stance phase of the gait cycle. For both incline and decline walking, the exoskeleton assistance reduced the muscle activation of the knee extensors on the assisted leg (p less then 0.05). However, only approximately half the individuals responded to exoskeleton assistance positively by reducing their metabolic cost of walking for both incline and decline tasks. The results indicate that, unlike the individuals who did respond, the individuals who did not respond to the assistance may have penalized their metabolic cost by their biomechanical compensatory behaviors from the unassisted leg.Stroke survivors usually experience paralysis in one half of the body, i.e., hemiparesis, and the upper limbs are severely affected. Continuous monitoring of hemiparesis progression hours after the stroke attack involves manual observation of upper limb movements by medical experts in the hospital. Hence it is resource and time intensive, in addition to being prone to human errors and inter-rater variability. read more Wearable devices have found significance in automated continuous monitoring of neurological disorders like stroke. In this paper, we use accelerometer signals acquired using wrist-worn devices to analyze upper limb movements and identify hemiparesis in acute stroke patients, while they perform a set of proposed spontaneous and instructed movements. We propose novel measures of time (and frequency) domain coherence between accelerometer data from two arms at different lags (and frequency bands). These measures correlate well with the clinical gold standard of measurement of hemiparetic severity in stroke, the National Institutes of Health Stroke Scale (NIHSS). The study, undertaken on 32 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length ( less then 10 minutes) accelerometry data to identify hemiparesis through leave-one-subject-out cross-validation based hierarchical discriminant analysis. The results indicate that the proposed approach can distinguish between controls, moderate and severe hemiparesis with an average accuracy of 91%.Major depressive disorder (MDD) has shown to negatively impact physical recovery in a variety of medical events (e.g., stroke and spinal cord injuries). Yet depression assessments, which are typically subjective in nature, are seldom considered to develop or guide rehabilitation strategies. The present study developed a predictive depression assessment technique using functional near-infrared spectroscopy (fNIRS) that can be rapidly integrated or performed concurrently with existing physical rehabilitation tasks. Thirty-one volunteers, including 14 adults clinically diagnosed with MDD and 17 healthy adults, participated in the study. Brain oxyhemodynamic (HbO) responses were recorded using a 16-channel wearable continuous-wave fNIRS device while the volunteers performed the Grasp and Release Test in four 16-minute blocks. Ten features, extracted from HbO signals, from each channel served as inputs to XGBoost and Random Forest algorithms developed for each block and combination of successive blocks. Top 5 common features resulted in a classification accuracy of 92.6%, sensitivity of 84.8%, and specificity of 91.7% using the XGBoost classifier. This study identified mean HbO, full width half maximum and kurtosis, as specific neuromarkers, for predicting MDD across specific depression-related regions of interests (i.e., dorsolateral and ventrolateral prefrontal cortex). Our results suggest that a wearable fNIRS head probe monitoring specific brain regions, and limiting extraction to few features, can enable quick setup and rapid assessment of depression in patients. The overarching goal is to embed predictive neurotechnology during post-stroke and post-spinalcord-injury rehabilitation sessions to monitor patients' depression symptomology so as to actively guide decisions about motor therapies.Multiphase flows exhibit a large realm of complex behaviors such as bubbling, glugging, wetting, and splashing which emerge from air-water and water-solid interactions. Current fluid solvers in graphics have demonstrated remarkable success in reproducing each of these visual effects, but none have offered a model general enough to capture all of them concurrently. In contrast, computational fluid dynamics have developed very general approaches to multiphase flows, typically based on kinetic models. Yet, in both communities, there is dearth of methods that can simulate density ratios and Reynolds numbers required for the type of challenging real-life simulations that movie productions strive to digitally create, such as air-water flows. In this paper, we propose a kinetic model of the coupling of the Navier-Stokes equations with a conservative phase-field equation, and provide a series of numerical improvements over an existing kinetic-based solver to offer a general multiphase flow solver. The resulting algorithm is embarrassingly parallel, conservative, far more stable than current solvers even for real-life conditions, and general enough to capture the typical multiphase flow behaviors.
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