Background: In December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world.
Methods: To examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April).
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Findings: Our projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66-97) and 24% (13-90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic.
How to cite this article: Hwang, B. et al. Effect of halide-mixing on the switching behaviors of organic-inorganic hybrid perovskite memory. Sci. Rep. 7, 43794; doi: 10.1038/srep43794 (2017).
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This pamphlet lists medications that can cause harm when taken with alcohol and describes the effects that can result. The list gives the brand name by which each medicine is commonly known (for example, Benadryl) and its generic name or active ingredient (in Benadryl, this is diphenhydramine). The list presented here does not include all the medicines that may interact harmfully with alcohol. Most important, the list does not include all the ingredients in every medication.
Mixing alcohol and medicines can be harmful. Alcohol, like some medicines, can make you sleepy, drowsy, or lightheaded. Drinking alcohol while taking medicines can intensify these effects. You may have trouble concentrating or performing mechanical skills. Small amounts of alcohol can make it dangerous to drive, and when you mix alcohol with certain medicines you put yourself at even greater risk. Combining alcohol with some medicines can lead to falls and serious injuries, especially among older people.
Monoamine oxidase inhibitors (MAOIs), such as tranylcypromine and phenelzine, when combined with alcohol, may result in serious heart-related side effects. Risk for dangerously high blood pressure is increased when MAOIs are mixed with tyramine, a byproduct found in beer and red wine
I understand that the innermost do block is in an Array context. I don't know how to "strip off" the Effect from the recursive call to rmEmptyDirs. Putting Array.singleton $ before the call doesn't help. liftEffect has the opposite effect of what I want to do. How do I get this compile?
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A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects.[1][2] These models are useful in a wide variety of disciplines in the physical, biological and social sciences.They are particularly useful in settings where repeated measurements are made on the same statistical units (longitudinal study), or where measurements are made on clusters of related statistical units.[2] Mixed models are often preferred over traditional analysis of variance regression models because of their flexibility in dealing with missing values and uneven spacing of repeated measurements.[3] The Mixed model analysis allows measurements to be explicitly modeled in a wider variety of correlation and variance-covariance structures.
Linear mixed models (LMMs) are statistical models that incorporate fixed and random effects to accurately represent non-independent data structures. LMM is an alternative to analysis of variance. Often, ANOVA assumes the independence of observations within each group, however, this assumption may not hold in non-independent data, such as multilevel/hierarchical, longitudinal, or correlated datasets.
Fixed effects encapsulate the tendencies/trends that are consistent at the levels of primary interest. These effects are considered fixed because they are non-random and assumed to be constant for the population being studied.[5] For example, when studying education a fixed effect could represent overall school level effects that are consistent across all schools.
While the hierarchy of the data set is typically obvious, the specific fixed effects that affect the average responses for all subjects must be specified. Some fixed effect coefficients are sufficient without corresponding random effects where as other fixed coefficients only represent an average where the individual units are random. These may be determined by incorporating random intercepts and slopes.[6][7][8]
A key component of the mixed model is the incorporation of random effects with the fixed effect. Fixed effects are often fitted to represent the underlying model. In Linear mixed models, the true regression of the population is linear, . The fixed data is fitted at the highest level. Random effects introduce statistical variability at different levels of the data hierarchy. These account for the unmeasured sources of variance that affect certain groups in the data. For example, the differences between student 1 and student 2 in the same class, or the differences between class 1 and class 2 in the same school. [6][7][8]
Ronald Fisher introduced random effects models to study the correlations of trait values between relatives.[9] In the 1950s, Charles Roy Hendersonprovided best linear unbiased estimates of fixed effects and best linear unbiased predictions of random effects.[10][11][12][13] Subsequently, mixed modeling has become a major area of statistical research, including work on computation of maximum likelihood estimates, non-linear mixed effects models, missing data in mixed effects models, and Bayesian estimation of mixed effects models. Mixed models are applied in many disciplines where multiple correlated measurements are made on each unit of interest. They are prominently used in research involving human and animal subjects in fields ranging from genetics to marketing, and have also been used in baseball [14] and industrial statistics.[15]The mixed linear model association has improved the prevention of false positive associations. Populations are deeply interconnected and the relatedness structure of population dynamics is extremely difficult to model without the use of mixed models. Linear mixed models may not, however, be the only solution. LMM's have a constant-residual variance assumption that is sometimes violated when accounting or deeply associated continuous and binary traits.[16]
Plane turbulent mixing between two streams of different gases (especially nitrogen and helium) was studied in a novel apparatus. Spark shadow pictures showed that, for all ratios of densities in the two streams, the mixing layer is dominated by large coherent structures. High-speed movies showed that these convect at nearly constant speed, and increase their size and spacing discontinuously by amalgamation with neighbouring ones. The pictures and measurements of density fluctuations suggest that turbulent mixing and entrainment is a process of entanglement on the scale of the large structures; some statistical properties of the latter are used to obtain an estimate of entrainment rates. Large changes of the density ratio across the mixing layer were found to have a relatively small effect on the spreading angle; it is concluded that the strong effects, which are observed when one stream is supersonic, are due to compressibility effects, not density effects, as has been generally supposed.
Xu Liu, Peibo Li, Fei Li, Chao Wang, Xiaolong Yang, Hongbo Wang, Mingbo Sun, Yixin Yang, Dapeng Xiong, Yanan Wang; Effect of kerosene injection states on mixing and combustion characteristics in supersonic combustor at high equivalent ratio. Physics of Fluids 1 January 2024; 36 (1): 013305.
Previous studies have found that the difference in combustion characteristics of gaseous and liquid kerosene injection in supersonic combustor is sensitive to the equivalent ratio. In this paper, the previous work is extended to a high equivalent ratio to gain a deeper understanding of the effect of injection states on combustion performance via numerical computation. The simulation results match well with the experiments and demonstrate that due to the different jet structures, the cavity shear layer of liquid injection penetrates deeply into the cavity, forming two recirculation zones therein. As a result, the majority of droplets enter the cavity and exist at a low streamwise velocity, which is favorable to droplet vaporization and combustion. Therefore, when the liquid fuel is injected at a high equivalent ratio, the fuel residence time increases, the droplet evaporation distance decreases, and the fuel vapor accumulates in the cavity. Compared to the gaseous injection with the same equivalent ratio, the liquid injection exhibits similar mixing efficiency in the cavity but slightly higher mixing efficiency in downstream divergent sections. This unique fuel distribution causes the liquid injection to have a higher combustion efficiency than that of the gaseous injection. The weak advantage in mixing and combustion makes the liquid injection capable of compensating for the effects of the fuel atomization and evaporation on combustion performance. As a result, the combustion structure and static pressure distribution of liquid injection with the high equivalent ratio is similar to those of the gaseous injection. 0852c4b9a8
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