Numerical investigation of spatially nonhomogeneous acoustic agglomeration using sectional algorithm

<p>In the simulation of acoustic agglomeration, the conventional temporal model assumes spatial homogeneity in aerosol properties and sound field, which is often not the case in real applications. In this article, we investigated the effects of spatial nonhomogeneity of sound field on the acoustic agglomeration process through a one-dimensional spatial sectional model. The spatial sectional model is validated against existing experimental data and results indicate lower requirements on the number of sections and better accuracy. Two typical cases of spatial nonhomogeneous acoustic agglomeration are studied by the established model. The first case involves acoustic agglomeration in a standing wave field with spatial alternation of acoustic kernels from nodes to antinodes. The good agreement between the simulation and experiments demonstrates the predictive capability of the present spatial sectional model for the standing-conditioned agglomeration. The second case incorporates sound attenuation in the particulate medium into acoustic agglomeration. Results indicate that sound attenuation can influence acoustic agglomeration significantly, particularly at high frequencies, and neglecting the effects of sound attenuation can cause overprediction of agglomeration rates. The present investigation demonstrates that the spatial sectional method is capable of simulating the spatially nonhomogeneous acoustic agglomeration with high computation efficiency and numerical robustness and the coupling with flow dynamics will be the goal of future work.</p> <p>Copyright © 2018 American Association for Aerosol Research</p

Exposure to the live or freshly slaughtered poultry from a poultry market of control-persons and their household members.

<p>Exposure to the live or freshly slaughtered poultry from a poultry market of control-persons and their household members.</p

Univariate exact conditional logistic regression analysis of potential exposures and risk factors for influenza A (H7N9) virus human infection — Zhejiang Province, China, 2013.

<p>*The following exposures and factors were evaluated but were not statistically associated with disease onset: Slaughtering poultry, contact with dead poultry, visiting lake/park/pond/paddy field.</p

Exact conditional logistic regression analysis of exposures and risk factors for influenza A(H7N9) virus human infection, by urban or rural residence — Zhejiang Province, China, March – 2013.

#<p>NS = Association not statistically significant (p>0.05).</p

Characteristics of case- and control-persons in the study of risk factors for influenza A (H7N9) virus human infections — Zhejiang Province, China, 2013.

<p>Characteristics of case- and control-persons in the study of risk factors for influenza A (H7N9) virus human infections — Zhejiang Province, China, 2013.</p

Onset time of 44 confirmed cases of influenza A (H7N9) virus human infection — Zhejiang Province, China, 2013.

<p>Onset time of 44 confirmed cases of influenza A (H7N9) virus human infection — Zhejiang Province, China, 2013.</p

Logistic regression models fitting results of the association between tea categories and cognitive impairment ¹.

<p>¹Binary logistic regression analysis was used to calculate ORs and 95% CIs for tea categories related to cognitive impairment which assessed with CCM, with non-consumption group treated as reference.</p><p>² P value were tested by logistic regressions in which tea category was treated as categorical variable.</p><p>³ Crude model.</p><p>⁴ Adjusted for age, sex, race, education, marriage, tea consumption volume and tea concentration.</p><p>⁵Adjusted for variables in model 2 plus physical examinations (BMI, WHR, SBP, DBP), family status (family income, have children or not) and disease situation (history of present illness and family history of hypertension, diabetes, CHD, AD, PD).</p><p>⁶ Adjusted for variables in model 3 plus behavioral risk factors (cigarette smoking, alcohol consumption, and physical activities), dietary intake (vegetables, fruits, meat, fish, beans, milk).</p><p>⁷Adjusted for variables in model 4 plus nutrition supplement, depression and ADL.</p><p>Logistic regression models fitting results of the association between tea categories and cognitive impairment <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137781#t004fn001" target="_blank">¹</a>.</p

Logistic regression models fitting results of the association between tea concentration and cognitive impairment ¹.

<p>¹Binary logistic regression analysis was used to calculate ORs and 95% CIs for cognitive impairment related with tea concentration which assessed with CCM, with non-consumption group treated as reference.</p><p>² P value were determined by logistic regressions in which tea concentration was treated as non-ordinal categorical variable.</p><p>³Crude model.</p><p>⁴ Adjusted for age, sex, race, education, marriage, tea consumption volume and tea categories.</p><p>⁵ Adjusted for variables in model 2 plus physical examinations (BMI, WHR, SBP, DBP), family status (family income, have children or not) and disease situation (history of present illness and family history of hypertension, diabetes, CHD, AD, PD).</p><p>⁶ Adjusted for variables in model 3 plus behavioral risk factors (cigarette smoking, alcohol consumption, and physical activities), dietary intake (vegetables, fruits, red meat, fish, beans, milk).</p><p>⁷Adjusted for variables in model 4 plus nutrition supplement, depression and ADL.</p><p>Logistic regression models fitting results of the association between tea concentration and cognitive impairment <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137781#t005fn001" target="_blank">¹</a>.</p

Characteristics of study participants by volume of tea consumption.

<p>¹ Based on ANOVA, chi-square test or Kruskal-Wallis test.</p><p>² Under the CCM of cognitive impairment.</p><p>³ Under the commonly used MMSE cut-off worldwide of cognitive impairment.</p><p>Characteristics of study participants by volume of tea consumption.</p

Frequency of tea consumption among study participants.

<p>Frequency of tea consumption among study participants.</p