Body composition and metabolic syndrome in patients with type 1 diabetes

BACKGROUND In recent years,the prevalence of obesity and metabolic syndrome in type 1 diabetes(T1DM)patients has gradually increased.Insulin resistance in T1DM deserves attention.It is necessary to clarify the relationship between body composition,metabolic syndrome and insulin resistance in T1DM to guide clinical treatment and intervention.AIM To assess body composition(BC)in T1DM patients and evaluate the relationship between BC,metabolic syndrome(MS),and insulin resistance in these indi-viduals.METHODS A total of 101 subjects with T1DM,aged 10 years or older,and with a disease duration of over 1 year were included.Bioelectrical impedance analysis using the Tsinghua-Tongfang BC Analyzer BCA-1B was employed to measure various BC parameters.Clinical and laboratory data were collected,and insulin resistance was calculated using the estimated glucose disposal rate(eGDR).RESULTS MS was diagnosed in 16/101 patients(15.84%),overweight in 16/101 patients(15.84%),obesity in 4/101(3.96%),hypertension in 34/101(33.66%%)and dyslip-idemia in 16/101 patients(15.84%).Visceral fat index(VFI)and trunk fat mass were significantly and negatively correlated with eGDR(both P<0.001).Female patients exhibited higher body fat percentage and visceral fat ratio compared to male patients.Binary logistic regression analysis revealed that significant factors for MS included eGDR[P=0.017,odds ratio(OR)=0.109],VFI(P=0.030,OR=3.529),and a family history of diabetes(P=0.004,OR=0.228).Significant factors for hypertension included eGDR(P<0.001,OR=0.488)and skeletal muscle mass(P=0.003,OR=1.111).Significant factors for dyslipidemia included trunk fat mass(P=0.033,OR=1.202)and eGDR(P=0.037,OR=0.708).CONCLUSION Visceral fat was found to be a superior predictor of MS compared to conventional measures such as body mass index and waist-to-hip ratio in Chinese individuals with T1DM.BC analysis,specifically identifying visceral fat(trunk fat),may play an important role in identifying the increased risk of MS in non-obese patients with T1DM.

Estimation of the Ballistic Effectiveness of 3,4-and 3,5-Dinitro-1-(trinitromethyl)-1H-Pyrazoles as Oxidizers for Composite Solid Propellants

The experimental values of the enthalpy of formation of two isomeric 3,4-and 3,5-dinitro-1-(trinitromethyl)-1H-pyrazoles have been obtained(261.5±5.0and 246.4±6.7kJ/mol for crystalline 3,4-and 3,5-dinitro-1-(trinitromethyl)-1H-pyrazoles,respectively).The ballistic effectiveness of these potential oxidizers in composite solid propellants was studied.It is shown that these two oxidizers may be successfully applied in metal-free compositions or with a small content of metal.For the bottom stage 3,4-dinitro-1-(trinitromethyl)-1H-pyrazole is a bit better than 3,5-dinitro-1-(trinitromethyl)-1H-pyrazole,for the upper stage the both oxidizers show the equal ballistic parameters.These oxidizers allow to create metal-free solid composite propellants with the binder percentage not lower than 19%(volume fraction),with I3spequal to 256.5-257.0sat density equal to 1.72-1.74g/cm^3.

Reliability analysis of a composite laminate using estimation theory

Composite laminates are made up of composite single-plies sequence. The plies generally have the same fiber and resin and their difference in fiber orientation results in a difference in various laminates' strength. Tsai-Hill failure criterion as a limiting state function to analyze structural reliability of a composite laminate and estimation theory in order to estimate statistical parameters of effective stress were utilized to construct probability box. Finally, we used the Monte Carlo simulation and FERUM software to calculate the upper and lower bounds of probability of failure.

Constraints on Estimation of Radius of Double Pulsar PSR J0737-3039A and Its Neutron Star Nuclear Matter Composition

The aspect of formation and evolution of the recycled pulsar（PSR J0737-3039 A/B） is investigated, taking into account the contributions of accretion rate, radius and spin-evolution diagram（- diagram） in the double pulsar system. Accepting the spin-down age as a rough estimate（or often an upper limit） of the true age of the neutron star, we also impose the restrictions on the radius of this system. We calculate the radius of the recycled pulsar PSR J0737-3039 A ranges approximately from 8.14 to 25.74 km, and the composition of its neutron star nuclear matters is discussed in the mass-radius diagram.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

复合LINEX对称损失下Poisson分布参数的Bayes估计和E-Bayes估计

在复合LINEX对称损失函数下,给出Poisson分布参数λ的Bayes估计及E-Bayes估计,并通过数值模拟验证Bayes估计及E-Bayes估计的合理性及优越性.

综合E-Bayes估计船舶寿命的研究

为了研究船舶寿命和使用时的可靠度,提出了一种基于试验的可靠度估计方法.针对船舶寿命服从指数分布,根据船舶寿命试验数据,给出了在无失效数据情形下和引进失效信息后船舶失效率的E-Bayes估计,并在此基础上给出了船舶失效率的综合E-Bayes估计.通过对Ⅰ型和Ⅱ型船舶寿命无失效数据进行计算,得到了船舶寿命在无失效数据情形下和引进失效信息后船舶失效率的E-Bayes估计及综合E-Bayes估计.最后得到了Ⅰ型和Ⅱ型船舶寿命可靠度的综合E-Bayes估计.结果表明,综合E-Bayes估计适合船舶寿命可靠性的研究.

Approach to blind estimation of the PN sequence in DS-SS signals with residual carrier

This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.

湿热环境下复合材料含孔层合板静力拉伸性能及工程估算模型

湿热环境下的复合材料结构件力学性能预测对其工程应用具有重要意义。文中针对复合材料层合板静力拉伸性能和强度预测问题,开展6种湿热环境下复合材料含孔层合板的静力拉伸试验,分析其结构失效响应及损伤表征。基于应力场强法建立湿热环境下复合材料含孔层合板工程估算模型,与有限元渐进损伤模型和试验结果进行对比,分析了湿热环境对含孔层合板力学性能和拉伸失效的影响。结果表明,工程估算模型预测结果与有限元及试验结果误差范围较小,可用于预测温度和吸湿率对含孔层合板拉伸失效强度的影响;相比于室温干态,75℃吸湿饱和态下试件拉伸失效强度下降了6.1%;25℃干态和75℃吸湿饱和态下含孔层合板0°铺层出现最为严重的纤维拉伸失效,90°铺层出现最为严重的基体拉伸失效,纤维拉伸失效和基体拉伸失效为层合板主要破坏模式;通过扫描电镜对75℃吸湿饱和态下层合板厚度方向微观形貌进行分析,发现试件0°方向纤维与树脂的脱粘程度加重且出现明显的裂痕,90°方向纤维分布较为齐整,但黏附的树脂较少。

基于复合阵列的矩阵循环重构DOA估计算法

相干源角度估计一直是困扰雷达角度测量的一个重要难题,基于均匀阵列假设提出了模式空间解相干算法,一定程度上解决了相干干扰条件下的目标角度估计问题,但是上述算法对复合阵列的适应性较差。针对复合阵列下相干多源测角问题,本文提出一种基于模式空间循环重构的解相关算法,利用圆阵外其他任意天线赋形空域滤波特性,在信号源数多于阵元数时较好地解决相干源角度估计问题。仿真试验结果表明,该算法能较好地解决复合阵列天线下的相干源测角问题,并且其角分辨性能优于传统算法。

基于改进近端策略优化的空战自主决策研究

针对传统强化学习在空战自主决策应用中信息冗余度高、收敛速度慢等问题,提出一种基于双重观测与复合奖励的近端策略优化空战自主决策算法。设计了以交互信息为主、个体特征信息为辅的双重观测信息,降低战场信息高度冗余对训练效率的影响;设计了结果奖励和过程奖励相结合的复合奖励函数,提高了训练过程收敛速度;采用广义优势函数估计,改进了近端策略优化算法,提高优势函数估计的准确性。仿真结果表明:在对战固定程控对手和矩阵博弈对手实验场景中,该算法决策模型均可根据战场态势准确进行自主决策,完成空战任务。

基于离焦二值显示和条纹投影的复合表面三维测量方法

由于复合表面物体中漫反射部分和镜面部分反射性质不同,受到相机景深的限制,镜面成像的正弦条纹发生离焦,从而引起相位误差。为了实现复合表面物体的高效、高精度测量,本文提出了一种离焦二值结合正弦条纹的复合表面三维形貌测量方法。首先,本文基于刃边法与二次模糊法对系统离焦程度进行分区标定,解决了由于相机倾斜放置导致参考面离焦程度不同的问题。然后,建立二值条纹相位误差模型,确定最佳条纹宽度和离焦量范围。最后,对轻微离焦区域的二值条纹进行离焦补偿,确保相机采集的条纹均在最佳离焦范围内。在此基础上进行三维形貌测量。实验结果表明,所提方法使镜面部分误差从原来的0.033 mm降至0.019 mm,提高了复合表面测量精度。

基于贝叶斯估计和数据融合的复合材料结构冲击定位

到达时刻(TOA)是用于冲击定位的关键特征之一,目前随着信号处理技术的不断发展,在文献中可找到多种时频模型进行测量,但是在选择上通常依赖于人的经验。此外,考虑到测量模型的系统误差和测量噪声引起的不确定性,传统基于TOA的冲击定位方法存在不足。为此,本文提出了一种基于贝叶斯估计和数据融合的复合材料结构冲击定位方法。首先,应用多种不同的时频模型来获取冲击响应信号的TOA数据;然后根据TOA数据测量误差的不确定性,应用贝叶斯定理构建冲击位置的后验概率密度函数;再采用马尔可夫链蒙特卡罗采样方法估计冲击位置参数的后验分布,并用正态分布进行拟合;最后,对不同TOA数据获得的冲击位置概率分布进行融合,利用融合后的概率分布对冲击源的位置做最终决策。通过复合材料加筋板上的落锤撞击实验验证了本文方法的可行性,平均定位误差仅为0.94 cm,相比于传统基于TOA的冲击定位方法可靠性和准确性更高,且在鲁棒性和定位时间方面同样具有优势。

多特征下室内声源定位的复合模型粒子滤波

为提高混响噪声下声源定位的精度和稳健性,提出多特征复合模型粒子滤波算法。该算法以麦克风接收信号的多特征构建似然函数,采用卷积神经网络提取多假设时延估计图像的深度特征,建立基于支持向量回归的时延估计模型;引入波束输出能量融合机制,弥补单特征不能同时抑制噪声和混响的缺陷。针对说话人运动随机性的问题建立声源跟踪的复合模型,改善说话人跟踪系统的鲁棒性。仿真和实测结果表明:在复合模型跟踪下,多特征算法比可控响应功率时延估计算法位置平均均方根误差减少83%以上;在多特征观测下,复合模型比郎之万模型和随机行走模型位置平均均方根误差减少46%以上;新算法实现了对复杂环境下随机运动声源的有效跟踪。

济南市不同类型燃煤供暖企业废气组分特征及排放估算

利用稀释通道采样法于2022年12月对济南市热电厂、热源厂、电厂3种类型冬季燃煤供暖典型企业废气细颗粒物进行采样,结合在线监测数据分析了SO_(2)、NO_(x)、PM_(2.5)和NH3排放情况及PM_(2.5)化学组分特征,并估算了济南市冬季3种类型供暖企业PM_(2.5)主要组分排放量。结果表明:3种类型燃煤供暖企业SO_(2)、NO_(x)、PM_(2.5)和NH3排放浓度均值范围分别为1.01-15.0、18.6-39.8、0.592-2.37、0.125-2.00 mg∙m^(-3),排放浓度差异较大,其中使用含硫率较低的燃煤供暖热源厂SO_(2)排放浓度明显低于热电厂和电厂。热电厂和电厂SO_(2)、NO_(x)、PM_(2.5)排放浓度均满足国家超低排放限值要求,热源厂满足山东省地方燃煤锅炉排放标准限值要求。PM_(2.5)组分特征分析结果表明不同供暖类型企业排放主要组分存在较大差异,其中热电厂主要为SO_(4)^(2−)(11.8%)、OC(17.1%),热源厂主要为Ca^(2+)(11.6%)、OC(33.5%),电厂主要为SO_(4)^(2−)(23.0%)、OC(18.6%),3类企业OC占比均远高于国内其他地区,这可能与本地调查企业煤质及环保设施运行工况等有关,热电厂和电厂高的SO_(4)^(2−)占比与燃料含硫率较高有关。经估算,2022-2023年供暖季济南市3种类型供暖企业排放烟气PM_(2.5)组分中水溶性离子总量最多,排放总量为3.74×10^(4) kg,是碳组分的2.07倍,是无机元素的5.18倍,其中水溶性离子排放总量以电厂最多,排放量为3.01×10^(4) kg,占比为80.5%;热源厂水溶性离子排放量是热电厂的4.14倍,无机元素排放量以电厂最高,排放量为4.96×103 kg,热源厂的水溶性离子(0.605 kg∙m^(-3)烟气)、碳组分(0.715 kg∙m^(-3)烟气)、无机元素(0.161 kg∙m^(-3)烟气)均呈现高排放特征,建议进一步加强对冬季热源厂PM_(2.5)中碳组分及水溶性离子的管控,关注脱硝设施的稳定运行。

复合地形场景下的米波雷达超分辨测高方法

文中重点研究一种典型“平坦+斜坡”复合地形场景下超分辨测高问题。根据阵列反射径回波遮蔽情况,将回波场景分成无遮蔽、全遮蔽和部分遮蔽等三种情况。传统的基于最大似然超分辨测高方法,无法适用于阵列反射径回波部分被遮蔽的场景。文中设计了一种自适应最大似然测高方法。首先,通过上下阵子阵分割,并行构建回波模型,采用模型匹配度预估反射点遮蔽边界大致位置;然后,基于阵列通道数据幅度差异,判别反射点遮蔽边界准备位置;最后,构建新的回波模型估计目标高度。仿真结果表明,该方法能够有效实现复合地形场景下米波雷达超分辨测高。

稀疏监测样本下的复合材料固化过程热源分布动态估计

碳纤维增强树脂基复合材料(CFRP)具有优异的综合性能,已成为航空航天高端装备减重增效的优选材料。固化是实现复材构件成形承载的关键工艺环节,固化过程中的构件温度场直接决定了构件的成形质量与力学性能,如何精确、动态的反求复材构件表面的热源分布,是实现温度场精准调控的基础。然而实际的固化工艺需在构件表面贴附透气毡、真空袋等辅助材料,难以直接监测构件表面的温度场,仅能引入若干个光纤测温点获取稀疏的温度样本,给热源分布这一高维标量场的重构带来挑战。为此,提出一种基于高斯混合分布模型(GMM)的固化过程热源分布动态估计方法,引入高斯模糊与面内热扩散等效性这一物理先验,建立了基于高斯模糊的温度场演变模型,进而利用GMM中的多个高斯分布表征固化过程中的热源分布,将高维场重构难题转化为若干高斯分布参数的优化求解问题。通过仿真实验验证了本文方法的可行性与有效性,能够实现固化过程中热源分布的精确动态估计。

内外分幅的整体复合式高速公路交通指路标志布设方法

整体复合式高速公路具有特殊的道路结构和交通组织模式,所需传递的信息量较大。为提升指路标志引导效率,避免信息过载,改善整体复合式高速公路交通运行的安全性,针对该场景指路标志的布设展开研究。以广深高速公路为例,通过分析内外分幅交通组织模式下整体复合式高速公路特征,明确了指路标志的布设原则并据此提出了基于内外幅功能和基于车道功能的信息指引方法以及适用于复合式高速公路的指路信息分级方式。借助驾驶模拟技术,进行了指路标志的信息阈值和版面设计试验。先后测得不同信息数量指路标志驾驶人目标搜寻时间和被试人员驶出互通出口过程中横向偏移量、速度、加速度以及标志注视时间。信息阈值试验中,使用稳健估计理论处理目标信息搜寻时间,对比可供信息搜寻时间表,认为整体复合式高速公路指路标志信息阈值为8条。版面设计试验中,依据提出的2种信息指引方法和信息分级方式绘制的互通出口指路标志均能正确引导车辆到达目的地,但基于内外幅功能的指引方法信息传递效率更高,使得内外幅换道提前约250 m,驾驶速度提高5.88%,加速度标准差减小45.77%,并且缩短了驾驶人注视标志的时间。研究表明:在内外分幅的整体复合式高速公路中,使用基于内外幅功能的信息指引方法能提高指路标志视认性,有效引导交通运行,推荐整体复合式高速公路采用基于内外幅功能的信息指引方法布设指路标志。

Composite Quantile Estimation for Kink Model with Longitudinal Data

Kink model is developed to analyze the data where the regression function is two-stage piecewise linear with respect to the threshold covariate but continuous at an unknown kink point.In quantile regression for longitudinal data,kink point where the kink effect happens is often assumed to be heterogeneous across different quantiles.However,the kink point tends to be the same across different quantiles,especially in a region of neighboring quantile levels.Incorporating such homogeneity information could increase the estimation efficiency of the common kink point.In this paper,we propose a composite quantile estimation approach for the common kink point by combining information from multiple neighboring quantiles.Asymptotic normality of the proposed estimator is studied.In addition,we also develop a sup-likelihood-ratio test to check the existence of the kink effect at a given quantile level.A test-inversion confidence interval for the common kink point is also developed based on the quantile rank score test.The simulation studies show that the proposed composite kink estimator is more efficient than the single quantile regression estimator.We also illustrate the proposed method via an application to a longitudinal data set on blood pressure and body mass index.

流数据下的复合分位数回归

随着互联网的发展,数据规模急剧增长,但有限的内存只能存储一小批数据,因此在不访问历史数据的情况下进行分析是非常有必要的,流数据分析也因而引起了广泛关注。同时复合分位数回归因其鲁棒性和全面性,在许多领域得到应用,但由于传统复合分位数回归是基于内存可容纳完整数据的条件,因此在流数据环境中实现复合分位数回归是非常有挑战的。针对流数据提出了一种可更新的复合分位数回归方法,可以随着数据的到达,使用当前数据和历史数据的汇总统计量来更新估计量。在理论上证明提出的可更新估计量与使用完整数据得到的估计量是渐近等价的。最后通过模拟研究验证了所提出方法的有效性。