Development of a Biomimetic Semicircular Canal with MEMS Sensors to Restore Balance

© 2001-2012 IEEE. A third of adults over the age of 50 suffer from chronic impairment of balance, posture, and/or gaze stability due to partial or complete impairment of the sensory cells in the inner ear responsible for these functions. The consequences of impaired balance organ can be dizziness, social withdrawal, and acceleration of the further functional decline. Despite the significant progress in biomedical sensing technologies, current artificial vestibular systems fail to function in practical situations and in very low frequencies. Herein, we introduced a novel biomechanical device that closely mimics the human vestibular system. A microelectromechanical systems (MEMS) flow sensor was first developed to mimic the vestibular haircell sensors. The sensor was then embedded into a three-dimensional (3D) printed semicircular canal and tested at various angular accelerations in the frequency range from 0.5Hz to 1.5Hz. The miniaturized device embedded into a 3D printed model will respond to mechanical deflections and essentially restore the sense of balance in patients with vestibular dysfunctions. The experimental and simulation studies of semicircular canal presented in this work will pave the way for the development of balance sensory system, which could lead to the design of a low-cost and commercially viable medical device with significant health benefits and economic potential

Application of inclusive multiple model for the prediction of saffron water footprint

Applying new approaches in the management of water resources is a vital issue, especially in arid and semi-arid regions. The water footprint is a key index in water management. Therefore, it is necessary to predict its changes for future durations. The soft computing model is one of the most widely used models in predicting and estimating agroclimatic variables. The purpose of this study is to predict the green and blue water footprints of saffron product using the soft computing model. In order to select the most effective variables in prediction water footprints, the individual input was eliminated one by one and the effect of each on the residual mean square error (RMSE) was measured. In the first stage, the Group Method of Data Handling (GMDH) and evolutionary algorithms have been applied. In the next stage, the output of individual models was incorporated into the Inclusive Multiple Model (IMM) as the input variables in order to predict the blue and green water footprints of saffron product in three homogenous agroclimatic regions. Finally, the uncertainty of the model caused by the input and parameters was evaluated. The contributions of this research are introducing optimized GMDH and new ensemble models for predicting BWF, and GWF, uncertainty analysis and investigating effective inputs on the GWF and BWF. The results indicated that the most important variables affecting green and blue water footprints are plant transpiration, evapotranspiration, and yield, since removing these variables significantly increased the RMSE (range=11–25). Among the GMDH models, the best performance belonged to NMRA (Naked Mole Ranked Algorithm) due to the fast convergence and high accuracy of the outputs. In this regard, the IMM has a better performance (FSD=0.76, NSE=0.95, MAE) = 8, PBIAS= 8) than the alternatives due to applying the outputs of several individual models and the lowest uncertainty based on the parameters and inputs of the model (p = 0.98, r = 0.08)

Development of an Ultra-Sensitive and Flexible Piezoresistive Flow Sensor Using Vertical Graphene Nanosheets

© 2020, © 2020, The Author(s). This paper suggests development of a flexible, lightweight, and ultra-sensitive piezoresistive flow sensor based on vertical graphene nanosheets (VGNs) with a mazelike structure. The sensor was thoroughly characterized for steady-state and oscillatory water flow monitoring applications. The results demonstrated a high sensitivity (103.91 mV (mm/s)−1) and a very low-velocity detection threshold (1.127 mm s−1) in steady-state flow monitoring. As one of many potential applications, we demonstrated that the proposed VGNs/PDMS flow sensor can closely mimic the vestibular hair cell sensors housed inside the semicircular canals (SCCs). As a proof of concept, magnetic resonance imaging of the human inner ear was conducted to measure the dimensions of the SCCs and to develop a 3D printed lateral semicircular canal (LSCC). The sensor was embedded into the artificial LSCC and tested for various physiological movements. The obtained results indicate that the flow sensor is able to distinguish minute changes in the rotational axis physical geometry, frequency, and amplitude. The success of this study paves the way for extending this technology not only to vestibular organ prosthesis but also to other applications such as blood/urine flow monitoring, intravenous therapy (IV), water leakage monitoring, and unmanned underwater robots through incorporation of the appropriate packaging of devices.[Figure not available: see fulltext.

Homotopy semi-numerical modeling of non-Newtonian nanofluid transport external to multiple geometries using a revised Buongiorno Model

A semi-analytical solution for the convection of a power-law nanofluid external to three different geometries (i.e. cone, wedge and plate), subject to convective boundary condition is presented. A revised Buongiorno model is employed for the nanofluid transport over the various geometries with variable wall temperature and nano-particle concentration conditions (nonisothermal and non-isolutal). Wall transpiration is included. The dimensional governing equations comprising the conservation of mass, momentum, energy and nanoparticle volume fraction are transformed to dimensionless form using appropriate transformations. The transformed equations are solved using a robust semi-analytical power series method known as the Homotopy analysis method (HAM). The convergence and validation of the series solutions is considered in detail. The variation of order of the approximation and computational time with respect to residual errors for temperature for the different geometries is also elaborated. The influence of thermophysical parameters such as wall temperature parameter, wall concentration parameter for nanofluid, Biot number, thermophoresis parameter, Brownian motion parameter and suction/blowing parameter on the velocity, temperature and nanoparticle volume fraction is visualized graphically and tabulated. The impact of these parameters on the engineering design functions e.g. coefficient of skin fraction factor, Nusselt number and Sherwood number is also shown in tabular form. The outcomes are compared with the existing results from the literature to validate the study. It is found that thermal and solute Grashof numbers both significantly enhance the flow velocity whereas they suppress the temperature and nanoparticle volume fraction for the three different configurations i.e. cone, wedge and plate. Furthermore, the thermal and concentration boundary layers are more dramatically modified for the wedge case, as compared to the plate and cone. This study has substantial applications in polymer engineering coating processes, fiber technology and nanoscale materials processing systems

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In this paper, the viscous and inviscid flow fields of a gas turbine blade cascade are investigated. A two-dimensional CFD solver is developed to simulate the flow field through VKI blade cascade. A high resolution flux difference splitting scheme of Roe is applied to discretize the convective part of Navier–Stokes equations. Baldwin Lomax (BL) model is used to account for turbulent effects on the viscous flow field of the blade cascade. For validation of the code, the flow field was solved by Ansys Fluent commercial software. The flow solution was done by third order flux difference splitting scheme of Roe and k–ω turbulence model. The findings show that the high turbulent and the shock creation in the flow field, lead to the same results in viscous and inviscid flows. Also, the results show that the grid and solver’s focus must be on the precise prediction of the shock effects, when the shock is occurred in the domain

Using Advanced 2D Materials to Closely Mimic Vestibular Hair Cell Sensors

In this work, an ultra-sensitive flow sensor is presented, consisting of vertically grown graphene nanosheets (VGNs) with a mazelike structure and an elastomer (polydimethylsiloxane, PDMS). The VGNs/PDMS piezoresistive flow sensor exhibits great linearity, low-velocity detection threshold (1.127 mm/s) and super-high sensitivity under exposure to stationary flow (0.127 kΩ/(mL/min)). The proposed flow sensor, analogous to hair cells in the vestibular system, was embedded in a 3D-printed lateral semicircular canal, and the sensing performance was studied in response to various physiological movements. This work paves the way for development of physical sensors using novel two-dimensional (2D) materials for various biomedical applications.</p

Biomimetic ultraflexible piezoresistive flow sensor based on graphene nanosheets and PVA hydrogel

Flow sensors play a critical role in monitoring flow parameters, including rate, velocity, direction, and rotation frequency. In this paper, inspired by biological hair cells in the human vestibular system, an innovative flow sensor is developed based on polyvinyl alcohol (PVA) hydrogel nanocomposites with a maze‐like network of vertically grown graphene nanosheets (VGNs). The VGNs/PVA hydrogel absorbs a copious amount of water when immersed in water, making the sensor highly sensitive to tiny stimuli underwater. The sensor demonstrates a high sensitivity (5.755 mV (mm s−1)−1) and extremely low velocity detection (0.022 mm s−1). It also reveals outstanding performance in detecting low‐frequency oscillatory flows down to 0.1 Hz, which make it suitable for many biomedical applications. As one of the potential applications of the sensor, it exhibits excellent performance in mimicking various physiological

Development of Ultrasensitive Biomimetic Auditory Hair Cells Based on Piezoresistive Hydrogel Nanocomposites

With an ageing population, hearing disorders are predicted to rise considerably in the following decades. Thus, developing a new class of artificial auditory system has been highlighted as one of the most exciting research topics for biomedical applications. Herein, a design of a biocompatible piezoresistive-based artificial hair cell sensor is presented consisting of a highly flexible and conductive polyvinyl alcohol (PVA) nanocomposite with vertical graphene nanosheets (VGNs). The bilayer hydrogel sensor demonstrates excellent performance to mimic biological hair cells, responding to acoustic stimuli in the audible range between 60 Hz to 20 kHz. The sensor output demonstrates stable mid-frequency regions (∼4–9 kHz), with the greatest sensitivity as high frequencies (∼13–20 kHz). This is somewhat akin to the mammalian auditory system, which has remarkable sensitivity and sharp tuning at high frequencies due to the “active process”. This work validates the PVA/VGN sensor as a potential candidate to play a similar functional role to that of the cochlear hair cells, which also operate over a wide frequency domain in a viscous environment. Further characterizations of the sensor show that increasing the sound amplitude results in higher responses from the sensor while taking it to the depth drops the sensor outputs due to attenuation of sound in water. Meanwhile, the acoustic pressure distribution of sound waves is predicted through finite element analysis, whereby the numerical results are in perfect agreement with experimental data. This proof-of-concept work creates a platform for the future design of susceptible, flexible biomimetic sensors to closely mimic the biological cochlea