Estimation of Respiratory Nasal Pressure and Flow Rate Signals Using Different Respiratory Sound Features

BackgroundRespiratory sounds are associated with the flow rate, nasal flow pressure, and physical characteristics of airways. In this study, we aimed to develop the flow rate and nasal flow pressure estimation models for the clinical application, and find out the optimal feature set for estimation to achieve the optimal model performance.MethodsRespiratory sounds and flow rate were acquired from nine healthy volunteers. Respiratory sounds and nasal flow pressure were acquired from twenty-three healthy volunteers. Four types of respiratory sound features were extracted for flow rate and nasal flow pressure estimation using different estimation models. Effects of estimations using these features were evaluated using Bland-Altman analysis, estimation error, and respiratory sound feature calculation time. Besides, expiratory and inspiratory phases divided estimation errors were compared with united estimation errors.ResultsThe personalized logarithm model was selected as the optimal flow rate estimation model. Respiratory nasal flow pressure estimation based on this model was also performed. For the four different respiratory sound features, there is no statistically significant difference in flow rate and pressure estimation errors. LogEnvelope was, therefore, chosen as the optimal feature because of the lowest computational cost. In addition, for any type of respiratory sound feature, no statistically significant difference was observed between divided and united estimation errors (flow rate and pressure).ConclusionRespiratory flow rate and nasal flow pressure can be estimated accurately using respiratory sound features. Expiratory and inspiratory phases united estimation using respiratory sounds is a more reasonable estimation method than divided estimation. LogEnvelope can be used for this united respiratory flow rate and nasal flow pressure estimation with minimum computational cost and acceptable estimation error.     

城市建筑屋顶光伏发电潜力评估方法和模型

建筑屋顶作为闲置的土地资源已成为光伏发电重要的潜在空间,屋顶光伏发电是脱碳电力供应的主要方式,将在实现城市碳中和进程中发挥重要作用。对建筑屋顶光伏发电潜力进行精确评估将有助于分布式光伏的科学规划和合理布局,提升土地利用效率。旨在对建筑屋顶光伏发电潜力影响因素和评估方法,以及光伏发电潜力主要评估模型进行系统性阐述,比较分析不同评估方法的优缺点,总结未来研究的重点方向。现有研究表明,建筑屋顶光伏发电潜力评估已从经验取值发展为定量空间分析,评估尺度、评估精度和评估成本已经成为不同评估方法综合权衡的重点。现有三种评估方法中,采样法计算成本和数据成本较低,但评估结果不确定性较大、精度较低;全面评估法评估精度较高,但数据获取成本和计算成本较高;机器学习法能够高效挖掘大数据潜力,且算法性能显著提升,因而相较于其他方法更适宜大尺度建筑屋顶光伏发电潜力评估。当前建筑屋顶光伏发电潜力评估仍然存在大尺度精细评估缺乏、评估结果不确定性大以及评估模型计算量大等问题。未来研究重点应关注三个方面：1)建立适宜不同区域的高精度简化模型并完善技术潜力评估模型;2)阐明建筑屋顶光伏发电潜力的影响因素,为代表性建筑分类体系...     

Conditional Point Estimation in Adaptive Group Sequential Test Designs

It is well known that point estimates in group sequential designs are biased. This also applies to adaptive designs that enable, e.g., data driven reassessments of group sample sizes. For triangular designs, Whitehead (1986) (Biometrika 73 , 573–581) proposed a bias adjusted estimate. But this estimate is not feasible in adaptive designs although it is in group sequential designs. Furthermore, there is a waste of information because it does not use the information at which stage the trial was stopped. We present a modification which does use this information and which is applicable to adaptive designs. The modified estimate achieves an improvement in group sequential designs and shows similar results in adaptive designs.     

家畜基因组选择研究进展

近年来,随着基因芯片技术的发展与育种技术的进步,动植物的基因组选择成为研究热点。在家畜育种中,基因组选择凭借其准确性高、世代间隔短和育种成本低等优势被应用于各种经济动物的种畜选择中。本文详细介绍了基因分型技术和基因组育种值估计方法(最小二乘法、RR-BLUP法、GBLUP法、ssGBLUP法、贝叶斯A法、贝叶斯B法等),并对这些育种方法选用的标记范围、准确性以及计算速度进行了比较,总结了我国和其他国家基因组选择在种畜选择中的应用情况及存在的问题,展望了目前国内外在基因组选择上的最新研究动态及进展,以期为其他育种工作者进一步了解基因组选择提供参考。     

Estimating the effect of inbreeding on survival

Conservation biologists need to be able to estimate reliably the effects of inbreeding on survival, and need to be able to do so with a range of different data types. Kalinowski and Hedrick described a non-linear maximum likelihood estimation procedure for modelling relationships between survivorship and inbreeding. Although their method is useful for illustrating the concepts involved in modelling such relationships, it is only applicable to simple datasets. We illustrate that the parameter estimates generated by Kalinowski and Hedrick's method are easily obtained using generalized linear modelling procedures available in standard statistical packages, and that these offer several advantages even with simple datasets. We suggest procedures that can be used for modelling relationships between survival and inbreeding with more complex data types, including datasets with multiple and ragged encounters, uncertain detection and random effects.     

Evaluation of two unstructured mathematical models for the penicillin G fedbatch fermentation

The mathematical model for the penicillin G fed-batch fermentation proposed by Heijnen et al. (1979) is compared with the model of Bajpai & Reuß (1980). Although the general structure of these models is similar, the difference in metabolic assumptions and specific growth and production kinetics results in a completely different behaviour towards product optimization. A detailed analysis of both models reveals some physical and biochemical shortcomings. It is shown that it is impossible to make a reliable estimation of the model parameters, only using experimental data of simple constant glucose feed rate fermentations with low initial substrate amount. However, it is demonstrated that some model parameters might be key factors in concluding whether or not altering the substrate feeding strategy has an important influence on the final amount of product.It is illustrated that feeding strategy optimization studies can be a tool in designing experiments for parameter estimation purposes.     

基于可见光植被指数的面向对象湿地水生植被提取方法

利用ESP分割工具确定最佳分割尺度,通过多尺度分割算法创建最优分割影像,基于微型无人机影像数据生成可见光植被指数,从一系列可见光植被指数中选取一组最优植被指数,建立决策树规则,利用隶属度函数对研究区自动分类,生成水生植被分布图.结果表明: 监督分类法的总体精度为53.7%,面向对象分类法总体精度为91.7%,与基于像元的监督分类法相比,面向对象分类法显著改善了影像分类结果,并大大提高了水生植被提取精度,监督分类法的Kappa系数为0.4,而面向对象分类法的Kappa系数为0.9.这表明利用微型无人机数据生成的可见光植被指数结合面向对象分类方法提取水生植被在该研究区是可行的,并能够应用到其他类似区域.     

Application of Numerical Methods to Elasticity Imaging

Elasticity imaging can be understood as the intersection of the study of biomechanical properties, imaging sciences, and physics. It was mainly motivated by the fact that pathological tissue presents an increased stiffness when compared to surrounding normal tissue. In the last two decades, research on elasticity imaging has been an international and interdisciplinary pursuit aiming to map the viscoelastic properties of tissue in order to provide clinically useful information. As a result, several modalities of elasticity imaging, mostly based on ultrasound but also on magnetic resonance imaging and optical coherence tomography, have been proposed and applied to a number of clinical applications: cancer diagnosis (prostate, breast, liver), hepatic cirrhosis, renal disease, thyroiditis, arterial plaque evaluation, wall stiffness in arteries, evaluation of thrombosis in veins, and many others. In this context, numerical methods are applied to solve forward and inverse problems implicit in the algorithms in order to estimate viscoelastic linear and nonlinear parameters, especially for quantitative elasticity imaging modalities. In this work, an introduction to elasticity imaging modalities is presented. The working principle of qualitative modalities (sonoelasticity, strain elastography, acoustic radiation force impulse) and quantitative modalities (Crawling Waves Sonoelastography, Spatially Modulated Ultrasound Radiation Force (SMURF), Supersonic Imaging) will be explained. Subsequently, the areas in which numerical methods can be applied to elasticity imaging are highlighted and discussed. Finally, we present a detailed example of applying total variation and AM-FM techniques to the estimation of elasticity.     

An X-ray spectrum estimation method from transmission measurement combined with scatter correction

PurposeConventional x-ray spectrum estimation methods from transmission measurement often lead to inaccurate results when extensive x-ray scatter is present in the measured projection. This study aims to apply the weighted L1-norm scatter correction algorithm in spectrum estimation for reducing residual differences between the estimated and true spectrum.MethodThe scatter correction algorithm is based on a simple radiographic scattering model where the intensity of scattered x-ray is directly estimated from a transmission measurement. Then, the scatter-corrected measurement is used for the spectrum estimation method that consists of deciding the weights of predefined spectra and representing the spectrum as a linear combination of the predefined spectra with the weights. The performances of the estimation method combined with scatter correction are evaluated on both simulated and experimental data.ResultsThe results show that the estimated spectra using the scatter-corrected projection nearly match the true spectra. The normalized-root-mean-square-error and the mean energy difference between the estimated spectra and corresponding true spectra are reduced from 5.8% and 1.33 keV without the scatter correction to 3.2% and 0.73 keV with the scatter correction for both simulation and experimental data, respectively.ConclusionsThe proposed method is more accurate for the acquisition of x-ray spectrum than the estimation method without scatter correction and the spectrum can be successfully estimated even the materials of the filters and their thicknesses are unknown. The proposed method has the potential to be used in several diagnostic x-ray imaging applications.