Explore the vast range of titles available.

We develop an expression for the Li-Keiper coefficients λn in terms of k-blocks partitions, to begin with, for low values of n. The k-blocks partitions are given in terms of our cluster functions φn and the main point of this work lies in the emergence of an alternating sequence of values converging toward values of λn near the true values, i.e., increasing the index k of the blocks one obtains an increasing range of positivity of the Li-Keiper coefficients. With the contribution of k = 1 and k = 2 blocks, positivity of the λn is reached already until n = 26-27. The treatment is given here until k = 4 blocks up to n = 30. λn are all found to be positive.

The global annual death toll due to drowning is of the order of 105. Rescue and search operations in urban rivers show a low rate of success. Operational computational drift models have been developed for marine environments but not for the case of river drowning. In the latter case, no scale separation occurs between the body and flow length scales. To model them, three hydrodynamic force coefficients of representative bodies, such as drag, side and lift coefficients, are needed. So far, their value was not characterized for the typical positioning of the body of a drowning victim. In this work, we used full-scale laboratory experiments to identify the range of value of these hydrodynamic coefficients based on 249 tests conducted in a wind tunnel. Observations in the air can be transferred to water environment thanks to flow similarity. For the typical body positioning of a drowning victim, the drag coefficient was found to vary in the range 0.5–1.2. Changing the yaw angle of the body, induces variations in the drag coefficient by about 50%. Considering loose clothes instead of tight clothes leads to an increase in the drag coefficient by about 30%, whereas adding a backpack has a limited influence (less than 5%). With the available experimental setup, it has been difficult to detect distinctive patterns and trends for the side and lift coefficients. This study is part of a multidisciplinary effort for developing scientific knowledge and technologies contributing to a reduction of drowning-induced fatalities in rivers.

With the help of the improved tanh-function method, some exact variable separation solutions for a (2+1)-dimensional variable coefficient BroeraKaup system in water waves are found. The detailed investigation indicates that these seemly independent variable separation solutions actually depend on each other. Based on the exact variable separation solution, completely and noncompletely elastic interactions between multi-valued foldons and anti-foldons are studied analytically and graphically.

Sr doped BaZn sub(2)Sb sub(2) polycrystalline materials with nominal compositions of Ba sub(1-x)Sr sub(x)Zn sub(2)Sb sub(2) (x = 0.0, 0.25, 0.5) were prepared by synthesizing single crystals using a Sn-flux method followed by vacuum melting. The materials were characterized by powder X-ray diffraction and scanning electron microscopy equipped with electron energy dispersive spectroscopy, respectively. The electrical conductivity and Seebeck coefficient of the materials from room temperature to 773 K were measured. The electrical conductivity of all the materials decreased with increasing temperature and turned to increase with increasing temperature when temperature is above ~600 K, while the Seebeck coefficient increased with increasing temperature and turned to decrease with increasing temperature when temperature is above ~580 K. The electrical conductivity and Seebeck coefficient both increased after doping Sr into BaZn sub(2)Sb sub(2). The maximum power factor for the sample with nominal composition of Ba sub(0.5)Sr sub(0.5)Zn sub(2)Sb sub(2) reached 10.67 mu W cm super(-1) K super(-2), which was about five times as high as that of the pure BaZn sub(2)Sb sub(2).

The scattering of charged particles by oxygen ion cyclotron harmonic (OCH) waves in the inner magnetosphere is investigated by evaluating the relevant quasi‐linear diffusion coefficients. Recent studies demonstrated that OCH waves are oxygen ion Bernstein modes and their complex kinetic dispersion relation has made it challenging to assess their role in scattering charged particles. The present study calculates the quasi‐linear diffusion coefficients for the scattering of electrons and ions by OCH waves using their kinetic dispersion relation. The results show that OCH waves can effectively scatter electrons between ∼100 eV and 100s keV via Landau resonance. They are also capable of heating cold helium and oxygen ions through cyclotron resonances. Specially, it is found that the 4th harmonic of OCH waves can lead to effective heating of helium ions, while oxygen ions would interact more efficiently with lower harmonics of OCH waves. Plain Language Summary Oxygen ion cyclotron harmonic (OCH) waves observed in the inner magnetosphere often have multiple spectral peaks at harmonics of the local oxygen ion cyclotron frequency. They have been shown to be excited by hot oxygen ion loss‐cone or ring/ring‐like distributions and follow a complicated kinetic dispersion relation for oxygen ion Bernstein waves. Since OCH waves cannot be described by the relatively simple cold plasma dispersion relation, it has been difficult to calculate their diffusion coefficients in scattering charged particles in quasi‐linear theory. The present study numerically solves the kinetic dispersion relation for OCH waves and then uses the results to calculate the corresponding quasi‐linear diffusion coefficients for electrons and ions. The diffusion coefficients obtained show that OCH waves can effectively interact with ∼100 eV to 100s keV electrons and are capable of heating cold helium and oxygen ions. Thus, OCH waves have their own unique contribution to the particle dynamics in the inner magnetosphere. Key Points Quasi‐linear diffusion coefficients are evaluated for particle scattering by oxygen ion cyclotron harmonic (OCH) waves for the first time OCH waves can scatter electrons in a wide energy range (∼100 eV–100s keV) via Landau resonance OCH waves are capable of heating cold helium and oxygen ions through cyclotron resonance

To obtain a thorough knowledge of PM2. 5 chemical composition and its impact on aerosol optical properties across China, existing field studies conducted after the year 2000 are reviewed and summarized in terms of geographical, interannual and seasonal distributions. Annual PM2. 5 was up to 6 times the National Ambient Air Quality Standards (NAAQS) in some megacities in northern China. Annual PM2. 5 was higher in northern than southern cities, and higher in inland than coastal cities. In a few cities with data longer than a decade, PM2. 5 showed a slight decrease only in the second half of the past decade, while carbonaceous aerosols decreased, sulfate (SO42−) and ammonium (NH4+) remained at high levels, and nitrate (NO3−) increased. The highest seasonal averages of PM2. 5 and its major chemical components were typically observed in the cold seasons. Annual average contributions of secondary inorganic aerosols to PM2. 5 ranged from 25 to 48 %, and those of carbonaceous aerosols ranged from 23 to 47 %, both with higher contributions in southern regions due to the frequent dust events in northern China. Source apportionment analysis identified secondary inorganic aerosols, coal combustion and traffic emission as the top three source factors contributing to PM2. 5 mass in most Chinese cities, and the sum of these three source factors explained 44 to 82 % of PM2. 5 mass on annual average across China. Biomass emission in most cities, industrial emission in industrial cities, dust emission in northern cities and ship emission in coastal cities are other major source factors, each of which contributed 7–27 % to PM2. 5 mass in applicable cities. The geographical pattern of scattering coefficient (bsp) was similar to that of PM2. 5, and that of aerosol absorption coefficient (bap) was determined by elemental carbon (EC) mass concentration and its coating. bsp in ambient condition of relative humidity (RH)  =  80 % can be amplified by about 1.8 times that under dry conditions. Secondary inorganic aerosols accounted for about 60 % of aerosol extinction coefficient (bext) at RH greater than 70 %. The mass scattering efficiency (MSE) of PM2. 5 ranged from 3.0 to 5.0 m2 g−1 for aerosols produced from anthropogenic emissions and from 0.7 to 1.0 m2 g−1 for natural dust aerosols. The mass absorption efficiency (MAE) of EC ranged from 6.5 to 12.4 m2 g−1 in urban environments, but the MAE of water-soluble organic carbon was only 0.05 to 0.11 m2 g−1. Historical emission control policies in China and their effectiveness were discussed based on available chemically resolved PM2. 5 data, which provides the much needed knowledge for guiding future studies and emissions policies.

High-spatial-resolution and long-term climate data are highly desirable for understanding climate-related natural processes. China covers a large area with a low density of weather stations in some (e.g., mountainous) regions. This study describes a 0.5′ (∼ 1 km) dataset of monthly air temperatures at 2 m (minimum, maximum, and mean proxy monthly temperatures, TMPs) and precipitation (PRE) for China in the period of 1901–2017. The dataset was spatially downscaled from the 30′ Climatic Research Unit (CRU) time series dataset with the climatology dataset of WorldClim using delta spatial downscaling and evaluated using observations collected in 1951–2016 by 496 weather stations across China. Prior to downscaling, we evaluated the performances of the WorldClim data with different spatial resolutions and the 30′ original CRU dataset using the observations, revealing that their qualities were overall satisfactory. Specifically, WorldClim data exhibited better performance at higher spatial resolution, while the 30′ original CRU dataset had low biases and high performances. Bicubic, bilinear, and nearest-neighbor interpolation methods employed in downscaling processes were compared, and bilinear interpolation was found to exhibit the best performance to generate the downscaled dataset. Compared with the evaluations of the 30′ original CRU dataset, the mean absolute error of the new dataset (i.e., of the 0.5′ dataset downscaled by bilinear interpolation) decreased by 35.4 %–48.7 % for TMPs and by 25.7 % for PRE. The root-mean-square error decreased by 32.4 %–44.9 % for TMPs and by 25.8 % for PRE. The Nash–Sutcliffe efficiency coefficients increased by 9.6 %–13.8 % for TMPs and by 31.6 % for PRE, and correlation coefficients increased by 0.2 %–0.4 % for TMPs and by 5.0 % for PRE. The new dataset could provide detailed climatology data and annual trends of all climatic variables across China, and the results could be evaluated well using observations at the station. Although the new dataset was not evaluated before 1950 owing to data unavailability, the quality of the new dataset in the period of 1901–2017 depended on the quality of the original CRU and WorldClim datasets. Therefore, the new dataset was reliable, as the downscaling procedure further improved the quality and spatial resolution of the CRU dataset and was concluded to be useful for investigations related to climate change across China. The dataset presented in this article has been published in the Network Common Data Form (NetCDF) at https://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and https://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m (Peng, 2019b) and includes 156 NetCDF files compressed in zip format and one user guidance text file.

A fully connected neural network (NN) is used to develop a subgrid-scale (SGS) model mapping the relation between the SGS stresses and filtered flow variables in a turbulent channel flow at $Re_\\tau = 178$. A priori and a posteriori tests are performed to investigate its prediction performance. In a priori test, an NN-based SGS model with the input filtered strain rate or velocity gradient tensor at multiple points provides highest correlation coefficients between the predicted and true SGS stresses, and reasonably predicts the backscatter. However, this model provides unstable solution in a posteriori test, unless a special treatment such as backscatter clipping is used. On the other hand, an NN-based SGS model with the input filtered strain rate tensor at single point shows an excellent prediction capability for the mean velocity and Reynolds shear stress in a posteriori test, although it gives low correlation coefficients between the true and predicted SGS stresses in a priori test. This NN-based SGS model trained at $Re_\\tau = 178$ is applied to a turbulent channel flow at $Re_\\tau = 723$ using the same grid resolution in wall units, providing fairly good agreements of the solutions with the filtered direct numerical simulation (DNS) data. When the grid resolution in wall units is different from that of trained data, this NN-based SGS model does not perform well. This is overcome by training an NN with the datasets having two filters whose sizes are bigger and smaller than the grid size in large eddy simulation (LES). Finally, the limitations of NN-based LES to complex flow are discussed.

This article is concerned with feature screening and variable selection for varying coefficient models with ultrahigh-dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency. To enhance the finite sample performance of the proposed procedure, we further develop an iterative feature screening procedure. Monte Carlo simulation studies were conducted to examine the performance of the proposed procedures. In practice, we advocate a two-stage approach for varying coefficient models. The two-stage approach consists of (a) reducing the ultrahigh dimensionality by using the proposed procedure and (b) applying regularization methods for dimension-reduced varying coefficient models to make statistical inferences on the coefficient functions. We illustrate the proposed two-stage approach by a real data example. Supplementary materials for this article are available online.

Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F 1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F 1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F 1 score in evaluating binary classification tasks by all scientific communities.