Notes

Dendrobium Officinale Polysaccharide Attenuates Insulin Opposition and also Abnormal Lipid Fat burning capacity inside Overweight Rodents.
This paper intends to explore the rationality and feasibility of modeling dispersed submicron particles in air by a kinetic-based method called the unified gas-kinetic scheme (UGKS) and apply it to the simulation of particle concentration under a transverse standing wave. A gas-particle coupling scheme is proposed where the gas phase is modeled by the two-dimensional linearized Euler equations (LEE) and, through the analogous behavior between the rarefied gas molecules and the air-suspended particles, a modified UGKS is adopted to estimate the particle dynamics. The Stokes' drag force and the acoustic radiation force applied on particles are accounted for by introducing a velocity-dependent acceleration term in the UGKS formulation. To validate this methodology, the computed concentration patterns are compared with experimental results in the literature. The comparison shows that the adopted LEE-UGKS coupling scheme could well capture the concentration pattern of suspended submicron particles in a channel. In addition, numerical simulations with varying standing wave amplitudes, different acoustic radiation force to drag force ratios, and mean flow velocities are conducted. Their respective influences on the particle concentration pattern and efficiency are analyzed.In this work, a convolutional neural network (CNN) is applied to recognize acoustic spatial patterns with the aid of acoustic visualization. The acoustic spatial patterns are obtained by the singular value decomposition of an acoustic radiation operator built with the boundary integral equation. It is to explore the powerful capability of the CNN in the image processing by analogously rendering the measured acoustic spatial patterns into images. Due to practical limitations, a higher resolution of an acoustic image is achieved by interpolating the pressure on a coarse grid. Steady-state analysis of acoustic problems is a complex domain problem. The acoustic fields are then supplied into a CNN scheme as two-channel data which are real and imaginary components of the pressure. Random noises and incident waves with varying energy are added to the measured data to simulate influences from uncorrelated and correlated noises, respectively. It is demonstrated that once the CNN scheme is built and trained with adequate data, which is numerically synthesized, the patterns can be more accurately and robustly recognized by comparing it with the cross-correlation based methods. The hierarchical feature representative as well as nonlinear perception makes the proposed method a promising approach for fault diagnosis and condition monitoring based on spatial acoustic measurements.Southern U.S. speech has been the focus of much sociophonetic work. In terms of vowel patterns, Southern speech is often characterized by the Southern Vowel Shift (SVS, involving shifts in /e/, /ɛ/, and /aɪ/), back vowel fronting, and changes in glide dynamics. The SVS, in particular, is said to play a primary role in distinguishing the South as a unique dialect region. However, there have been few investigations of the role of various vowel quality differences in perceptions of Southern accent, particularly across the vowel space beyond /e/, /ɛ/, and /aɪ/, or that ask whether any aggregate speaker-level acoustic measures align with listeners' perceptions, despite some suggestions in the literature to this effect. The current study examines what acoustic cues contribute to non-Southern listeners' evaluations of words spoken by Southerners as sounding more or less Southern accented, looking at a range of vowels from across the vowel space. Results indicate that listeners rate the speakers' productions of /u/ and /ɔ/ as most Southern and that vowel dynamics and speaker-level measures were the acoustic factors most predictive of Southernness ratings. These results together call for further work examining vowel dynamics and a more complete set of vowel categories in perception studies of Southern speech.Editor's Note Readers of this journal are encouraged to submit news items on awards, appointments, and other activities about themselves or their colleagues. Deadline dates for news and notices are 2 months prior to publication.Personal audio provides private and personalized listening experiences by generating sound zones in a shared space with minimal interference between zones. One challenge of the design is to achieve the best performance with a limited number of microphones and loudspeakers. In this paper, two modal domain methods for personal audio reproduction are compared. One is the spatial harmonic decomposition (SHD) based method and the other is the singular value decomposition (SVD) based method. It is demonstrated that the SVD based method provides a more efficient modal domain decomposition than the SHD method for 2.5 dimensional personal audio design. Simulation results show that the SVD based method outperforms the SHD one by up to 10 dB in terms of acoustic contrast and up to 17 dB in terms of reproduction error for a compact arc array with five loudspeakers, while requiring fewer microphones around the zone boundaries. The SVD based method retains the inherent efficiency of optimizing in a modal domain while avoiding the inherent geometric limitations of using SHD basis functions. Thus, this approach is advantageous for applications with flexible system geometries and a small number of loudspeakers and microphones.Acoustic instabilities are frequently the culprit for engine failure. To mitigate these instabilities, an accurate model of the nonlinear acoustic pressure profile of the system is necessary. This study develops a nonlinear model for the acoustic response of an area-contraction. The derivation begins with the unsteady Bernoulli equation which is formed into the pressure drop across the area-contraction. Each acoustic variable is assumed to be time-harmonic and is written as the sum of a steady and fundamental term. Using a Fourier transformation, nonlinear expressions for the pressure drop and impedance are derived as functions of the steady and acoustic velocity. These expressions capture the nonlinearity of the acoustic response when the flow can reverse out of the orifice, i.e., the amplitude of the mean velocity is less than the amplitude of the oscillating acoustic velocity. This impedance model is verified by archive quality acoustic response data from a previous study.This study focuses on the two-dimensional (2-D) finite-difference time-domain (FDTD) formulations to investigate the acoustic wave propagation in elastomers contained in a fluid region under different thermal conditions. The developed FDTD formulation is based on a direct solution of the time-domain wave equation and the Havriliak-Negami (H-N) dynamic mechanical response of the elastomers. The H-N representation, including double fractional derivative operators, can be accurately transferred from the frequency-domain to the time-domain by using Riemann-Liouville theory and the Grunwald-Letnikov operator for fractional derivative approximations. Since the Williams-Landel-Ferry shift function is related to the relaxation time for different thermal conditions, the proposed scheme represents a simple and accurate prediction of acoustic wave propagation for varying thermal conditions. The pulse-wave propagation in a viscous fluid field is simulated by investigating the Navier-Stokes equations. The acoustic properties of different elastomers in a variety of temperatures are obtained by means of the proposed FDTD formulation and validated by a good agreement with the experimental data over a wide frequency range. Additionally, the 2-D examples relevant to wave propagation in different elastomers contained in a fluid field are implemented. The proposed FDTD formulation can be used to predict 2-D acoustic wave propagation in different thermal conditions accurately.Dynamic binaural rendering of Ambisonics considering head movements gives a highly realistic sensation to listeners owing to the precise localization and the presence of dynamic cues. Dealing with a head movement is often achieved in the spherical harmonic domain by multiplying Ambisonic signals by a Wigner D-matrix (WDM) with the aim of rotating signals in the opposite direction to the head movement. However, for a vertical rotation, the system requires an enormous computational cost owing to the structure of the WDM, whose number of block diagonal elements increases with the spherical harmonic order of Ambisonics. In this paper, a method is introduced to reduce the computational cost related to the vertical rotation by approximating a WDM with a banded WDM generated from the truncated sum of a power series expression of the WDM. By using an analytically derived upper bound of the approximation error, two methods are devised to determine the minimum bandwidth which archives the maximum computational cost reduction under the user-preferred threshold. The experimental results show that there is a trade-off between the approximation error and the computational cost and that these methods are applicable to the use case of interest, i.e., dynamic binaural rendering of Ambisonics.American National Standards (ANSI Standards) developed by Accredited Standards Committees S1, S2, S3, S3/SC 1, and S12 in the areas of acoustics, mechanical vibration and shock, bioacoustics, animal bioacoustics, and noise, respectively, are published by the Acoustical Society of America (ASA). read more In addition to these standards, ASA publishes a catalog of Acoustical American National Standards. To browse the latest copy of our catalog, visit our website at https//acousticalsociety.org/download-catalog/.Comments are welcomed on all material in Acoustical Standards News.This Acoustical Standards News section in JASA, as well as the national catalog of Acoustical Standards and other information on the Standards Program of the Acoustical Society of America, are also available via the ASA Standards home page https//acousticalsociety.org/acoustical-society-standards/.A method based on a convolutional neural network for the automatic classification of odontocete echolocation clicks is presented. The proposed convolutional neural network comprises six layers three one-dimensional convolutional layers, two fully connected layers, and a softmax classification layer. Rectified linear units were chosen as the activation function for each convolutional layer. The input to the first convolutional layer is the raw time signal of an echolocation click. Species prediction was performed for groups of m clicks, and two strategies for species label prediction were explored the majority vote and maximum posterior. Two datasets were used to evaluate the classification performance of the proposed algorithm. Experiments showed that the convolutional neural network can model odontocete species from the raw time signal of echolocation clicks. With the increase in m, the classification accuracy of the proposed method improved. The proposed method can be employed in passive acoustic monitoring to classify different delphinid species and facilitate future studies on odontocetes.Simultaneous measurements of wind velocity and pressure fluctuations were conducted in a wind tunnel to investigate the wind noise source inside compact spherical open celled porous windscreens. The existing outdoor wind noise models are found to be inadequate to predict the wind noise inside a wind tunnel. This paper proposes a model to predict the interior stagnation pressure, which agrees with the wind noise measured inside the windscreen within a bandwidth, where the exterior turbulence-turbulence interaction pressure overestimates the wind noise level. The limitations of the proposed model and other potential sources for wind noise inside porous windscreens are discussed.
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