基于贝叶斯序贯方法的微波效应实验设计

提出了一种基于贝叶斯验后概率的序贯检验方法,建立了检验的判别准则,给出了判别准则临界值的计算方法。在给定截尾实验次数的条件下,提出了一种截尾方案,建立了截尾判断方法。将其用于解决验证效应物在微波作用下失效概率达到给定水平的微波效应实验设计问题,给出了相应的实验方案和所需样本量的估计。最后,通过实例对上述方法的应用过程进行了说明,并和现有方法进行了分析比较。     

Associations between Neurocardiovascular Signal Entropy and Physical Frailty

In this cross-sectional study, the relationship between noninvasively measured neurocardiovascular signal entropy and physical frailty was explored in a sample of community-dwelling older adults from The Irish Longitudinal Study on Ageing (TILDA). The hypothesis under investigation was that dysfunction in the neurovascular and cardiovascular systems, as quantified by short-length signal complexity during a lying-to-stand test (active stand), could provide a marker for frailty. Frailty status (i.e., “non-frail”, “pre-frail”, and “frail”) was based on Fried’s criteria (i.e., exhaustion, unexplained weight loss, weakness, slowness, and low physical activity). Approximate entropy (ApEn) and sample entropy (SampEn) were calculated during resting (lying down), active standing, and recovery phases. There was continuously measured blood pressure/heart rate data from 2645 individuals (53.0% female) and frontal lobe tissue oxygenation data from 2225 participants (52.3% female); both samples had a mean (SD) age of 64.3 (7.7) years. Results revealed statistically significant associations between neurocardiovascular signal entropy and frailty status. Entropy differences between non-frail and pre-frail/frail were greater during resting state compared with standing and recovery phases. Compared with ApEn, SampEn seemed to have better discriminating power between non-frail and pre-frail/frail individuals. The quantification of entropy in short length neurocardiovascular signals could provide a clinically useful marker of the multiple physiological dysregulations that underlie physical frailty.     

Complexity of Body Movements during Sleep in Children with Autism Spectrum Disorder

Recently, measuring the complexity of body movements during sleep has been proven as an objective biomarker of various psychiatric disorders. Although sleep problems are common in children with autism spectrum disorder (ASD) and might exacerbate ASD symptoms, their objectivity as a biomarker remains to be established. Therefore, details of body movement complexity during sleep as estimated by actigraphy were investigated in typically developing (TD) children and in children with ASD. Several complexity analyses were applied to raw and thresholded data of actigraphy from 17 TD children and 17 children with ASD. Determinism, irregularity and unpredictability, and long-range temporal correlation were examined respectively using the false nearest neighbor (FNN) algorithm, information-theoretic analyses, and detrended fluctuation analysis (DFA). Although the FNN algorithm did not reveal determinism in body movements, surrogate analyses identified the influence of nonlinear processes on the irregularity and long-range temporal correlation of body movements. Additionally, the irregularity and unpredictability of body movements measured by expanded sample entropy were significantly lower in ASD than in TD children up to two hours after sleep onset and at approximately six hours after sleep onset. This difference was found especially for the high-irregularity period. Through this study, we characterized details of the complexity of body movements during sleep and demonstrated the group difference of body movement complexity across TD children and children with ASD. Complexity analyses of body movements during sleep have provided valuable insights into sleep profiles. Body movement complexity might be useful as a biomarker for ASD.     

Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy

Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)–systolic time interval (STI), RRI–diastolic time interval (DTI), HR-corrected QT interval (QTcI)–STI, QTcI–DTI, Tpeak–Tend interval (TpeI)–STI, TpeI–DTI, Tpe/QT interval (Tpe/QTI)–STI, and Tpe/QTI–DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD—mild-to-moderate CHD group, severe CHD—chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD—CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.     

Significance Support Vector Regression for Image Denoising

As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious noise interference, which leads to a degradation of the denoising effect. For this problem, this paper proposes a significance measurement framework for evaluating the sample significance with sample spatial density information. Based on the analysis of the penalty factor in SVR, significance SVR (SSVR) is presented by assigning the sample significance factor to each sample. The refined penalty factor enables SSVR to be less susceptible to outliers in the solution process. This overcomes the drawback that the SVR imposes the same penalty factor for all samples, which leads to the objective function paying too much attention to outliers, resulting in poorer regression results. As an example of the proposed framework applied in image denoising, a cutoff distance-based significance factor is instantiated to estimate the samples’ importance in SSVR. Experiments conducted on three image datasets showed that SSVR demonstrates excellent performance compared to the best-in-class image denoising techniques in terms of a commonly used denoising evaluation index and observed visual.     

Improved Performance of On-line Atom Trapping in Flame Furnace Atomic Absorption Spectrometry by Chemical Vapor Generation: Determination of Cadmium in High-Salinity Water Samples

ABSTRACT

On-line atom trapping inside a nickel flame furnace using chemical vapor generation for sample introduction was proposed for the determination of trace cadmium by flame atomic absorption spectrometry (AAS). Cadmium volatile species was generated upon reaction with potassium borohydride and then flushed into a flame furnace for on-line trapping by a flow of nitrogen carrier gas. The middle part of the flame furnace, where the carrier gas impacts, is cooled by the gas flow, and this provides a fine strategy for on-line atom trapping for the purpose of preconcentration. A stainless steel plate is put on the top of the flame burner in the middle to form a flame-free zone, which also greatly lowers the temperature of the flame furnace and facilitates the atom-trapping process. Due to the introduction of chemical vapor generation, matrix effect was greatly alleviated compared with direct pneumatic nebulization for on-line atom trapping in flame furnace AAS. With trapping time of 35 s, the current approach achieved an excellent limit of detection of 20 ng L^?1. The proposed method was successfully applied for the quantification of cadmium in high-salinity samples.     

Isotope Studies of Hydrogen and Oxygen in Ground Ice - Experiences with the Equilibration Technique

Abstract

Equilibration technique suitable for a large amount ofsamples is described for hydrogen and oxygen isotope analyses of ground ice, especially ice wedges, including the sampling strategy and the analytical procedure as well as the calibration of the Finnigan MAT Delta-S mass spectrometer in June, 1999. Since for future analyses of ice wedges, a higher sampling resolution with limited sample volume is required, the limit of the equliibration technique for small water samples size of between 0.05 and 5 ml was checked. For water samples smaller than 1ml, corresponding to a molar ratio [H₂O]/[H₂] of smaller than 0.994, a balance correction has to be applied. The experimental errors due to partial evaporation during evacuation, the balance calcultion of the isotope equilibration process, the linearity as well as memory effects of the mass spectrometer for smaples with large differences in δ¹⁸O and δD are tackled in this paper. In the polar regions of Northern Siberia without Late Pleistocene and Holocene glaciation, ground ice is used as an archive for paleoclimate studies. First results of stable isotope measurements on ice wedges clearly show a shift towards heavier isotopes and thus warmer winter temperatures as well as a change in the source of the precipitation between Late Pleistocene and Holocene. These results indicate the high potential of ground ice for paleoclimate studies.     

15N/14N Analysis of Amino Acids with GC-C-IRMS - Methodical Investigations and Ecotoxicological Applications

Abstract

In order to perform the ¹⁵N/¹⁴N analysis of amino acids using a gas chromatograph and isotope ratio mass spectrometer linked up via a combustion interface (GC-C-IRMS), the amino acids must be derivatized. Tert-butyldimethylsilylation is examined using various techniques (direct conversion to nitrogen gas, ConFlo-IRMS [1], GC-C-IRMS [2]) and subsequently applied to isotopic characterization of amino acids from wheat protein hydrolyzate obtained from plants exposed to ozone. The method provides a reliable tool for studying ecotoxicological effects on plants at a molecular level in addition to the investigation of the natural variations of different N fractions.     

Magnetic properties of carbon nanosheets

Two-dimensional carbon nanosheets have been fabricated using inductively coupled radio frequency plasma-enhanced chemical vapour deposition. The structural properties of the nanosheets have been characterised using atomic force microscopy, scanning electron microscopy and X-ray diffractometer. The magnetisation of the samples was studied using vibrating sample magnetometer. The magnetisation of the nanosheets was found to be diamagnetic for fast synthesis processes (30 and 60 min). On the other hand, the nanosheets exhibited a weak ferromagnetic response for the slow (120 min) synthesis process. Energy dispersive spectrometry and atomic absorption spectroscopy confirmed that the magnetisation exhibited by the carbon nanosheets was an intrinsic property and that it was not due to contamination from the substrate. Raman spectroscopy studies revealed that the ferromagnetic carbon nanosheets have a higher ratio (1.20) of graphite peak (I _G) to disordered peak (I _D) than normally expected (0.75–0.90). Available data indicated that the magnetisation was due to the presence of structural disorders.     

Sample Preparation for the Determination of Metals in Food Samples Using Spectroanalytical Methods—A Review

Abstract

The present article gives an overview of recent publications and modern techniques of sample preparation for food analysis employing atomic and inorganic mass spectrometric techniques, such as flame atomic absorption spectrometry, chemical vapor generation atomic absorption and atomic fluorescence spectrometry, graphite furnace atomic absorption spectrometry, inductively coupled plasma optical emission spectrometry, and inductively coupled plasma mass spectrometry. Among the most frequently applied sample preparation techniques for food analysis are dry ashing, usually with the addition of an ashing aid, and acid digestion, preferably with the assistance of microwave energy. Slurry preparation, particularly with the assistance of ultrasound, is increasingly used to reduce acid consumption and sample preparation time. Direct analysis of solid samples is gaining importance in the field of food analysis as it offers the highest sensitivity, avoids the use of acids and other aggressive reagents, makes possible the analysis of micro‐samples, and can be applied for fast screening analysis, e.g., of fresh meat.