The current study presents about the effect of cellulose nanofibers (CNFs) filler on the thermal and dynamic mechanical analysis (DMA) of epoxy composites as a function of temperature. In this study hand lay-up method was used to fabricate CNF reinforced Epoxy nanocomposites with CNF loading of 0.5%, 0.75%, and 1% into epoxy resin. The obtained thermal and DMA results illustrates that thermal stability, char content, storage modulus (E'), loss modulus (E") and glass transition temperature (Tg) increases for all CNF/epoxy nanocomposites compared to the pure epoxy. Thermal results revealed that 0.75% offers superior resistance or stability towards heat compared to its counterparts. In addition, 0.75% CNF/epoxy nanocomposites confers highest value of storage modulus as compared to 0.5% and 1% filler loading. Hence, it is concluded that 0.75% CNFs loading is the minimal to enhance both thermal and dynamic mechanical properties of the epoxy composites and can be utilized for advance material applications where thermal stability along with renewability are prime requirements.
The Chêneau brace has proven its effectiveness in treating the adolescent idiopathic scoliosis patients. However, no studies reported on the analysis of interface pressure in double-curve adolescent idiopathic scoliosis patients. In this study, we evaluated the interface pressure of the Chêneau brace action in double-curve adolescent idiopathic scoliosis patient treatment. A total of 72 (60 girls and 12 boys) patients aged 10 years and above participated in the study. The F-Socket transducers (9811E) were used to evaluate the pressure on the right thoracic and left thoracolumbar curves between normal and maximum strap tension and variation in these interface pressures with other tasks. Each patient was asked to do nine different tasks corresponding to daily activities, and the interface pressures for each activity were recorded for both normal and maximum tension. The resultant mean peak pressure in double-curve adolescent idiopathic scoliosis was higher for right thoracic curves than left thoracolumbar curves in all tasks. The pressure significantly increased at the task of maximal inspiration (p
In this paper, we propose an easy-to-implement passive liquid valve (PLV) for the microfluidic compact-disc (CD). This valve can be implemented by introducing venting chambers to control the air flow of the source and destination chambers. The PLV mechanism is based on equalizing the main forces acting on the microfluidic CD (i.e., the centrifugal and capillary forces) to control the burst frequency of the source chamber liquid. For a better understanding of the physics behind the proposed PLV, an analytical model is described. Moreover, three parameters that control the effectiveness of the proposed valve, i.e., the liquid height, liquid density, and venting chamber position with respect to the CD center, are tested experimentally. To demonstrate the ability of the proposed PLV valve, microfluidic liquid switching and liquid metering are performed. In addition, a Bradford assay is performed to measure the protein concentration and evaluated in comparison to the benchtop procedure. The result shows that the proposed valve can be implemented in any microfluidic process that requires simplicity and accuracy. Moreover, the developed valve increases the flexibility of the centrifugal CD platform for passive control of the liquid flow without the need for an external force or trigger.
This research has proved that mechanomyographic (MMG) signals can be used for evaluating muscle performance. Stimulation of the lost physiological functions of a muscle using an electrical signal has been determined crucial in clinical and experimental settings in which voluntary contraction fails in stimulating specific muscles. Previous studies have already indicated that characterizing contractile properties of muscles using MMG through neuromuscular electrical stimulation (NMES) showed excellent reliability. Thus, this review highlights the use of MMG signals on evaluating skeletal muscles under electrical stimulation. In total, 336 original articles were identified from the Scopus and SpringerLink electronic databases using search keywords for studies published between 2000 and 2020, and their eligibility for inclusion in this review has been screened using various inclusion criteria. After screening, 62 studies remained for analysis, with two additional articles from the bibliography, were categorized into the following: (1) fatigue, (2) torque, (3) force, (4) stiffness, (5) electrode development, (6) reliability of MMG and NMES approaches, and (7) validation of these techniques in clinical monitoring. This review has found that MMG through NMES provides feature factors for muscle activity assessment, highlighting standardized electromyostimulation and MMG parameters from different experimental protocols. Despite the evidence of mathematical computations in quantifying MMG along with NMES, the requirement of the processing speed, and fluctuation of MMG signals influence the technique to be prone to errors. Interestingly, although this review does not focus on machine learning, there are only few studies that have adopted it as an alternative to statistical analysis in the assessment of muscle fatigue, torque, and force. The results confirm the need for further investigation on the use of sophisticated computations of features of MMG signals from electrically stimulated muscles in muscle function assessment and assistive technology such as prosthetics control.
This paper presents a review of the properties of fresh concrete including workability, heat of hydration, setting time, bleeding, and reactivity by using mineral admixtures fly ash (FA), silica fume (SF), ground granulated blast furnace slag (GGBS), metakaolin (MK), and rice husk ash (RHA). Comparison of normal and high-strength concrete in which cement has been partially supplemented by mineral admixture has been considered. It has been concluded that mineral admixtures may be categorized into two groups: chemically active mineral admixtures and microfiller mineral admixtures. Chemically active mineral admixtures decrease workability and setting time of concrete but increase the heat of hydration and reactivity. On the other hand, microfiller mineral admixtures increase workability and setting time of concrete but decrease the heat of hydration and reactivity. In general, small particle size and higher specific surface area of mineral admixture are favourable to produce highly dense and impermeable concrete; however, they cause low workability and demand more water which may be offset by adding effective superplasticizer.
Tendons are adapted to carry large, repeated loads and are clinically important for the maintenance of musculoskeletal health in an increasing, actively ageing population, as well as in elite athletes. Tendons are known to adapt to mechanical loading. Also, their healing and disease processes are highly sensitive to mechanical load. Computational modelling approaches developed to capture this mechanobiological adaptation in tendons and other tissues have successfully addressed many important scientific and clinical issues. The aim of this review is to identify techniques and approaches that could be further developed to address tendon-related problems. Biomechanical models are identified that capture the multi-level aspects of tendon mechanics. Continuum whole tendon models, both phenomenological and microstructurally motivated, are important to estimate forces during locomotion activities. Fibril-level microstructural models are documented that can use these estimated forces to detail local mechanical parameters relevant to cell mechanotransduction. Cell-level models able to predict the response to such parameters are also described. A selection of updatable mechanobiological models is presented. These use mechanical signals, often continuum tissue level, along with rules for tissue change and have been applied successfully in many tissues to predict in vivo and in vitro outcomes. Signals may include scalars derived from the stress or strain tensors, or in poroelasticity also fluid velocity, while adaptation may be represented by changes to elastic modulus, permeability, fibril density or orientation. So far, only simple analytical approaches have been applied to tendon mechanobiology. With the development of sophisticated computational mechanobiological models in parallel with reporting more quantitative data from in vivo or clinical mechanobiological studies, for example, appropriate imaging, biochemical and histological data, this field offers huge potential for future development towards clinical applications.
A set of coupled particle and thermal transport equations is used to study a formation and sustainability of an edge transport barrier (ETB) in tokamak plasmas based on two-field bifurcation. The two transport equations are numerically solved for spatio-temporal profiles of plasma pressure and density. The plasma core transport includes both neoclassical and turbulent effects, where the latter can be suppressed by flow shear mechanism. The flow shear, approximated from the force balance equation, is proportional to the product of pressure and density gradients, resulting in non-linearity behaviors in this calculation. The main thermal and particle sources are assumed to be localized near plasma center and edge, respectively. It is found that the fluxes versus gradients regime illustrates bifurcation nature of the plasma. This picture of the plasma implies hysteresis properties in fluxes versus gradients space. Hence, near marginal point, the perturbation in thermal or particle sources can trigger an L-H transition. Due to hysteresis, the triggered H-mode can be sustained and the central plasma pressure and density can be enhanced.
Six impact energy values, ranging from 2.5 J to 10 J, were applied to study the impact properties of neat epoxy and bamboo composites, while six impact energy values, ranging from 10 J to 35 J, were applied on bamboo/glass hybrid composites. Woven glass fibre was embedded at the outermost top and bottom layer of bamboo powder-filled epoxy composites, producing sandwich structured hybrid composites through lay-up and molding techniques. A drop weight impact test was performed to study the impact properties. A peak force analysis showed that neat epoxy has the stiffest projectile for targeting interaction, while inconsistent peak force data was collected for the non-hybrid composites. The non-hybrid composites could withstand up to 10 J, while the hybrid composites showed a total failure at 35 J. It can be concluded that increasing the filler loading lessened the severity of damages in non-hybrid composites, while introducing the woven glass fibre could slow down the penetration of the impactor, thus lowering the chances of a total failure of the composites.
The present paper is based on a recent success of the second-order stochastic fluctuation theory in describing time autocorrelations of equilibrium and nonequilibrium physical systems. In particular, it was shown to yield values of the related deterministic parameters of the Langevin equation for a Couette flow in a microscopic molecular dynamics model of a simple fluid. In this paper we find all the remaining constants of the stochastic dynamics, which then is simulated numerically and compared directly with the original physical system. By using these data, we study in detail the accuracy and precision of a second-order Langevin model for nonequilibrium physical systems theoretically and computationally. We find an intriguing relation between an applied external force and cumulants of the resulting flow fluctuations. This is characterized by a linear dependence of an athermal cumulant ratio, an apposite quantity introduced here. In addition, we discuss how the order of a given Langevin dynamics can be raised systematically by introducing colored noise.
In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.