Nonlinear growth generates age changes in the moments of the frequency distribution: the example of height in puberty

Higher moments of the frequency distribution of child height and weight change with age, particularly during puberty, though why is not known. Our aims were to confirm that height skewness and kurtosis change with age during puberty, to devise a model to explain why, and to test the model by analyzing the data longitudinally. Heights of 3245 Christ's Hospital School boys born during 1927-1956 were measured twice termly from 9 to 20 years (n = 129 508). Treating the data as independent, the mean, standard deviation (SD), skewness, and kurtosis were calculated in 40 age groups and plotted as functions of age t. The data were also analyzed longitudinally using the nonlinear random-effects growth model H( t) = h( t - epsilon) + alpha, with H( t) the cross-sectional data, h( t) the individual mean curve, and epsilon and alpha subject-specific random effects reflecting variability in age and height at peak height velocity (PHV). Mean height increased monotonically with age, while the SD, skewness, and kurtosis changed cyclically with, respectively, 1, 2, and 3 turning points. Surprisingly, their age curves corresponded closely in shape to the first, second, and third derivatives of the mean height curve. The growth model expanded as a Taylor series in e predicted such a pattern, and the longitudinal analysis showed that adjusting for age at PHV on a multiplicative scale largely removed the trends in the higher moments. A nonlinear growth process where subjects grow at different rates, such as in puberty, generates cyclical changes in the higher moments of the frequency distribution

A Sparse Spike Deconvolution Algorithm Based on a Recurrent Neural Network and the Iterative Shrinkage-Thresholding Algorithm

Conventional sparse spike deconvolution algorithms that are based on the iterative shrinkage-thresholding algorithm (ISTA) are widely used. The aim of this type of algorithm is to obtain accurate seismic wavelets. When this is not fulfilled, the processing stops being optimum. Using a recurrent neural network (RNN) as deep learning method and applying backpropagation to ISTA, we have developed an RNN-like ISTA as an alternative sparse spike deconvolution algorithm. The algorithm is tested with both synthetic and real seismic data. The algorithm first builds a training dataset from existing well-logs seismic data and then extracts wavelets from those seismic data for further processing. Based on the extracted wavelets, the new method uses ISTA to calculate the reflection coefficients. Next, inspired by the backpropagation through time (BPTT) algorithm, backward error correction is performed on the wavelets while using the errors between the calculated reflection coefficients and the reflection coefficients corresponding to the training dataset. Finally, after performing backward correction over multiple iterations, a set of acceptable seismic wavelets is obtained, which is then used to deduce the sequence of reflection coefficients of the real data. The new algorithm improves the accuracy of the deconvolution results by reducing the effect of wrong seismic wavelets that are given by conventional ISTA. In this study, we account for the mechanism and the derivation of the proposed algorithm, and verify its effectiveness through experimentation using theoretical and real data

Production, bleaching and characterization of pulp from Stipa tenacissima

Alfa grass pulping was successfully performed in hydro-organic acid medium under mild conditions (107°C, atmospheric pressure, cooking time: 3 h). Use of an acetic acid/formic acid/water mixture as pulping liquor was perfectly suitable for selective isolation of pulp, lignin, and hemicelluloses. The unbleached pulp obtained in good yield was first delignified by peroxyacids in organic acid medium and then bleached with hydrogen peroxide in a basic medium to give pulp offering good physico-chemical and mechanical characteristics

Improved measurement of the reactor antineutrino flux and spectrum at Daya Bay

published_or_final_versio

Measurement of electron antineutrino oscillation based on 1230 days of operation of the Daya Bay experiment

published_or_final_versio

Improved Search for a Light Sterile Neutrino with the Full Configuration of the Daya Bay Experiment

published_or_final_versio

Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay

published_or_final_versio

Impact of an Innovative Financing and Payment Model on Tuberculosis Patients’ Financial Burden: is Tuberculosis Care More Affordable for the Poor?

Background: In response to the high financial burden of health services facing tuberculosis (TB) patients in China, the China-Gates TB project, Phase II, has implemented a new financing and payment model as an important component of the overall project in three cities in eastern, central and western China. The model focuses on increasing the reimbursement rate for TB patients and reforming provider payment methods by replacing fee-for-service with a case-based payment approach. This study investigated changes in out-of-pocket (OOP) health expenditure and the financial burden on TB patients before and after the interventions, with a focus on potential differential impacts on patients from different income groups

Synthesis and Growth Mechanism of Ni Nanotubes and Nanowires

Highly ordered Ni nanotube and nanowire arrays were fabricated via electrodeposition. The Ni microstructures and the process of the formation were investigated using conventional and high-resolution transmission electron microscope. Herein, we demonstrated the systematic fabrication of Ni nanotube and nanowire arrays and proposed an original growth mechanism. With the different deposition time, nanotubes or nanowires can be obtained. Tubular nanostructures can be obtained at short time, while nanowires take longer time to form. This formation mechanism is applicable to design and synthesize other metal nanostructures and even compound nanostuctures via template-based electrodeposition