Noerdouglas5280

Wearing surgical masks or other similar face coverings can reduce the emission of expiratory particles produced via breathing, talking, coughing, or sneezing. Although it is well established that some fraction of the expiratory airflow leaks around the edges of the mask, it is unclear how these leakage airflows affect the overall efficiency with which masks block emission of expiratory aerosol particles. Here, we show experimentally that the aerosol particle concentrations in the leakage airflows around a surgical mask are reduced compared to no mask wearing, with the magnitude of reduction dependent on the direction of escape (out the top, the sides, or the bottom). Because the actual leakage flowrate in each direction is difficult to measure, we use a Monte Carlo approach to estimate flow-corrected particle emission rates for particles having diameters in the range 0.5-20 μm. in all orientations. From these, we derive a flow-weighted overall number-based particle removal efficiency for the mask. The overall mask efficiency, accounting both for air that passes through the mask and for leakage flows, is reduced compared to the through-mask filtration efficiency, from 93 to 70% for talking, but from only 94-90% for coughing. check details These results demonstrate that leakage flows due to imperfect sealing do decrease mask efficiencies for reducing emission of expiratory particles, but even with such leakage surgical masks provide substantial control.The high-pressure phases of oxyhydroxides (δ-AlOOH, ε-FeOOH, and their solid solution), candidate components of subducted slabs, have wide stability fields, thus potentially influencing volatile circulation and dynamics in the Earth's lower mantle. Here, we report the elastic wave velocities of δ-(Al,Fe)OOH (Fe/(Al + Fe) = 0.13, δ-Fe13) to 79 GPa, determined by nuclear resonant inelastic X-ray scattering. At pressures below 20 GPa, a softening of the phonon spectra is observed. With increasing pressure up to the Fe3+ spin crossover (~ 45 GPa), the Debye sound velocity (vD) increases. At higher pressures, the low spin δ-Fe13 is characterized by a pressure-invariant vD. Using the equation of state for the same sample, the shear-, compressional-, and bulk-velocities (vS, vP, and vΦ) are calculated and extrapolated to deep mantle conditions. The obtained velocity data show that δ-(Al,Fe)OOH may cause low-vΦ and low-vP anomalies in the shallow lower mantle. At deeper depths, we find that this hydrous phase reproduces the anti-correlation between vS and vΦ reported for the large low seismic velocity provinces, thus serving as a potential seismic signature of hydrous circulation in the lower mantle.Advances of soft robotics enabled better mimicking of biological creatures and closer realization of animals' motion in the robotics field. The biological creature's movement has morphology and flexibility that is problematic deportation to a bio-inspired robot. This paper aims to study the ability to mimic turtle motion using a soft pneumatic actuator (SPA) as a turtle flipper limb. SPA's behavior is simulated using finite element analysis to design turtle flipper at 22 different geometrical configurations, and the simulations are conducted on a large pressure range (0.11-0.4 Mpa). The simulation results are validated using vision feedback with respect to varying the air pillow orientation angle. Consequently, four SPAs with different inclination angles are selected to build a bio-mimetic turtle, which is tested at two different driving configurations. The nonlinear dynamics of soft actuators, which is challenging to model the motion using traditional modeling techniques affect the turtle's motion. Conclusively, according to kinematics behavior, the turtle motion path is modeled using the Echo State Network (ESN) method, one of the reservoir computing techniques. The ESN models the turtle path with respect to the actuators' rotation motion angle with maximum root-mean-square error of [Formula see text]. The turtle is designed to enhance the robot interaction with living creatures by mimicking their limbs' flexibility and the way of their motion.Using density-functional theory, we investigate the electronic, magnetic, and hyperfine-interaction properties of the 112-type iron-pnictide compound [Formula see text], which is isostructural to the high-temperature iron-based superconductor [Formula see text]. We show that the band structure of [Formula see text] is similar to that of the 112-type compounds' family, with hole-like and electron-like bands at the Brillouin-zone center and corners, respectively. We demonstrate that the bands near the Fermi level originate mainly from the Fe atoms. The presence of a mixture of ionic and covalent bonding is predicted from the charge-density and atom-resolved density-of-states calculations. There is good agreement between the calculated hyperfine-interaction parameters with those obtained from the [Formula see text]Fe and [Formula see text]Eu Mössbauer measurements. The spatial distribution of atoms in [Formula see text] leads to an in-plane 2D magnetism. Moreover, ab-initio calculations predict the compound's magnetic moment and the magnetic moments of each constituent atom. Also, the density of states profile provides insight into the relative magnitude of these moments. Electronic structure calculations and Fermi surface topology reveal various physical and chemical properties of [Formula see text]. Valence electron density maps indicate the co-existence of a wide range of chemical bonds in this system, and based on structural properties, the transport characteristics are deduced and discussed. A thorough analysis of the atomic structure of [Formula see text] and its role in the bond formation is presented.State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuations effectively. However, traditional GPR methods based on explicit functions require multiple screenings of optimal mean and covariance functions, which results in data scarcity and increased time consumption. In this study, we propose a GPR-implicit function learning, which is a prior knowledge algorithm for calculating mean and covariance functions from a preliminary data set instead of screening. After introducing the implicit function, the average root mean square error (Average RMSE) is 0.0056 F and the average mean absolute percent error (Average MAPE) is 0.6%, where only the first 5% of the data are trained to predict the remaining 95% of the cycles, thereby decreasing the error by more than three times than previous studies.