Numerical analysis of permeate flux in reverse osmosis by varying strand geometry.

In regions with limited potable water availability, membrane desalination is being employed to filter water using a pressure-driven approach. Because of the high energy consumption required to produce the pressure differential needed for this method, researchers have been trying different geometric designs of spacer filaments to enhance the amount of permeate flux in terms of energy utilization. The purpose of spacer filaments is to support membranes structurally and induce turbulent mixing in spiral wound membrane desalination.

Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface.

The brain-computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. The functional Near-Infrared Spectroscopy (fNIRS) is becoming popular as a non-invasive modality for brain activity detection. The recent trends show that deep learning has significantly enhanced the performance of the BCI systems.

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface.

A passive brain-computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC.

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI).

Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain-machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy.

Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method.

A state-of-the-art brain-computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance.

Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.

Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.

Enhanced Performance for Multi-Forearm Movement Decoding Using Hybrid IMU-sEMG Interface.

Control of active prosthetic hands using surface electromyography (sEMG) signals is an active research area; despite the advances in sEMG pattern recognition and classification techniques, none of the commercially available prosthetic hands provide the user with an intuitive control. One of the major reasons for this disparity between academia and industry is the variation of sEMG signals in a dynamic environment as opposed to the controlled laboratory conditions. This research investigated the effects of sEMG signal variation on the performance of a hand motion classifier due to arm position variation and also explored the effect of static position and dynamic movement strategies for classifier training.

Comparative analysis of classifiers for developing an adaptive computer-assisted EEG analysis system for diagnosing epilepsy.

Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system's incorrect markings can be mentioned and the system should improve its classification by learning from them.

Should bedside sonography be used first to diagnose pneumothorax secondary to blunt trauma?

BACKGROUND.: Our purpose was to evaluate the effectiveness of bedside sonography (US) in the detection of pneumothorax secondary to blunt thoracic trauma. METHODS.

Doppler sonography in renal artery stenosis. An evaluation of intrarenal and extrarenal imaging parameters.

A prospective comparison of different direct and indirect Doppler parameters with angiography was performed to determine the most useful Doppler parameters and threshold values for the detection of significant (> or =60%) renal artery stenosis (RAS). The best combination of parameters was found to be the use of direct parameters of peak systolic velocity (PSV) greater than 180 or 200 cm/s and renal aortic ratio (RAR) greater than 3.0 with a sensitivity and specificity at 92% and 88%, respectively.