Sex‐specific and dose‐response relationships of urinary cobalt and molybdenum levels with glucose levels and insulin resistance in U.S. adults

Growing studies have linked metal exposure to diabetes risk. However, these studies had inconsistent results. We used a multiple linear regression model to investigate the sex-specific and dose-response associations between urinary metals (cobalt (Co) and molybdenum (Mo)) and diabetes-related indicators (fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), homeostasis model assessment for insulin resistance (HOMA-IR), and insulin) in a cross-sectional study based on the United States National Health and Nutrition Examination Survey. The urinary metal concentrations of 1423 eligible individuals were stratified on the basis of the quartile distribution. Our results showed that the urinary Co level in males at the fourth quartile (Q4) was strongly correlated with increased FPG (β = 0.61, 95% CI: 0.17–1.04), HbA1c (β = 0.31, 95% CI: 0.09–0.54), insulin (β = 8.18, 95% CI: 2.84–13.52), and HOMA–IR (β = 3.42, 95% CI: 1.40–5.44) when compared with first quartile (Q1). High urinary Mo levels (Q4 vs. Q1) were associated with elevated FPG (β = 0.46, 95% CI: 0.17–0.75) and HbA1c (β = 0.27, 95% CI: 0.11–0.42) in the overall population. Positive linear dose-response associations were observed between urinary Co and insulin (P_nonlinear = 0.513) and HOMA–IR (P_nonlinear = 0.736) in males, as well as a positive linear dose-response relationship between urinary Mo and FPG (P_nonlinear = 0.826) and HbA1c (P_nonlinear = 0.376) in the overall population. Significant sex-specific and dose-response relationships were observed between urinary metals (Co and Mo) and diabetes-related indicators, and the potential mechanisms should be further investigated.     

岩溶塌陷倾向性等级的KPCA-SVM预测模型

为了快速、有效地预测岩溶塌陷倾向性等级,在统计分析大量观测实例的基础上,选取岩性系数、岩体结构系数、地下水系数、覆盖层系数、地形地貌系数和环境条件系数作为特征指标。利用核主成分分析(KPCA)方法在高维空间提取岩溶塌陷影响因子的主成分,将获取的主成分作为支持向量机(SVM)的特征向量,建立基于KPCA的岩溶塌陷倾向性等级的SVM预测模型。将12组观测数据作为学习样本对模型进行训练。采用回代估计法进行回检,误判率为0。利用训练好的模型对2组待判样本进行预测。结果表明:经KPCA后指标个数减少,相关性降低,SVM运算的复杂度降低。用该模型所得预测结果的准确率为100%。     

Prediction of minimum ignition energy of aerosols using flame kernel modeling combined with flame front propagation theory

Flammable aerosols have created many fire and explosion hazards in the process industry, but the flammability of aerosols has not been fully understood. The minimum ignition energy has been widely used as an indicator for flammability of combustible mixtures, but the amount of experimental data on the minimum ignition energy of aerosols is very limited. In this work, the minimum ignition energy of tetralin aerosols is predicted using an integrated model. The model applies the flame front propagation theory in aerosol systems to the growth of the flame kernel, which was created during the spark discharge in the ignition process. The aerosol minimum ignition energy was defined as the minimum level of energy in the initial flame kernel to maintain the kernel temperature above the minimum ignition temperature of 1073 K specific for tetralin aerosols during the kernel growth. The minimum ignition energy obtained in the model is influenced by the fuel-air equivalence ratio and the size of the aerosol droplets. For tetralin aerosols of 40 μm diameter, E_min decreases significantly from 0.32 mJ to 4.3 × 10 e⁻³ mJ when the equivalence ratio rises from 0.57 to 1.0. For tetralin aerosols of 0.57 equivalence ratio, E_min increases from as 0.09 mJ to 0.32 mJ when the droplet diameter rises from 10 μm to 60 μm. The trends are in agreement with previous experimental observations. The method used in current work has the potential to prediction of the minimum ignition energy of aerosol.     

基于核密度估计的清代中国自然灾害时空分布特征

基于《清史·灾赈志》中的历史灾害信息,提取清代（1644-1911年）自然灾害共24 537县次,重建逐年灾害频次序列,以核密度估计法对5类主要灾害（水灾、旱灾、蝗灾、疫灾、冷害）的空间分布特征进行分析。结果表明:清代自然灾害频次序列没有明显的趋势性特征,而体现为阶段性波动,基于灾害频次30年滑动平均值可提取出4个峰值时段（1644-1673、1721-1750、1812-1841和1872-1901年）;相比于现代,清代灾害类型更加集中在农业灾害,最为多发的是水灾和旱灾,合计占总数的近80%,其次是蝗灾、疫灾和冷害;灾害最为多发的高风险区分布在黄淮海平原和长江三角洲,前者集中了水灾、旱灾、蝗灾的极端多发区,后者除了水旱多发,还是疫灾和冷害的极端多发区; 4个峰值时段的灾种类型组合和灾害多发区均有明显差异,时空变化受到致灾因子、暴露度和脆弱性因素的共同作用。上述工作有助于更好地认识历史灾害发生规律,对于当前及未来的防灾减灾工作具有重要参考价值。     

On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data

We present a new method for estimating a distribution of dispersal displacements (a dispersal kernel) from mark-recapture data. One conventional method of calculating the dispersal kernel assumes that the distribution of displacements are Gaussian (e.g. resulting from a diffusion process) and that individuals remain within sampled areas. The first assumption prohibits an analysis of dispersal data that do not exhibit the Gaussian distribution (a common situation); the second assumption leads to underestimation of dispersal distance because individuals that disperse outside of sampling areas are never recaptured. Our method eliminates these two assumptions. In addition, the method can also accommodate mortality during a sampling period. This new method uses integrodifference equations to express the probability of spatial mark-recapture data; associated dispersal, survival, and recapture parameters are then estimated using a maximum likelihood method. We examined the accuracy of the estimators by applying the method to simulated data sets. Our method suggests designs for future mark-recapture experiments. Received: January 2004 / Revised: July 2005     

Kernel-based home range method for data with irregular sampling intervals

Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations.     

基于支持向量机的巢湖富营养化程度评价研究

由于湖泊富营养化程度影响因素多,评价因素与富营养化等级之间关系复杂而且具有非线性特征。支持向量机是由Vapnik等人提出的建立在统计学习理论基础上的一种新的机器学习方法,由于其使用结构风险最小化原则代替经验风险最小化原则,解决了一些神经网络遗留的问题,又由于其应用了核函数思想,它可以较好地解决非线性问题,利用支持向量机多类分类算法,构建巢湖富营养化程度评价模型,取得较好的结果。     

Molecular docking analysis on antifungal properties of acid condensate produced from palm kernel shell

The rise in the number of fungi that resisted antifungal action is of serious concern nowadays. In this study, the potential of acid condensate (AC) produced from microwave-assisted pyrolysis of palm kernel shell (PKS) was investigated for its antifungal properties through molecular docking evaluation. The phenolic-rich AC was determined for its chemical compositions using the GC–MS analysis where compounds with the highest phenolics content were further evaluated (using the Autodock Tools 1.5.7) for its potential enzymes/protein binding properties. From the GC–MS analysis, catechol, guaiacol and syringol were present at highest percentages. This directly correlates with results obtained from the molecular docking works where all these ligands managed to bind (indicated by H-bond, π-stacking, hydrophobic interaction) with some of the amino acid at the active sites which indicate its potential to inhibit substrate binding of this enzyme. As a conclusion, this study demonstrated the potential use of AC from agricultural biomass such as PKS as a natural-based antifungal agent that can reduce environmental and health impacts.     

基于SVR的船舶溢油事故预测

船舶溢油事故的预测,是确定所需应急资源多少的重要依据;但船舶溢油事故具有小样本性,传统预测方法不再适用。针对船舶溢油事故的小样本性,该文选择SVR模型对其进行预测;SVR算法的关键是核函数的选择及自由参数的确定,针对上述问题该文分别采用交叉验证法、遗传算法;并在matlab软件上建立SVR模型,最后将该模型应用于上海港水域船舶溢油事故的预测。     

前沿技术进步、技术效率和区域经济差距

采用1998-2009年中国30个省市的人均GDP与当年全国人均GDP的差值作为区域经济差距指标,运用核密度估计方法观察了我国区域经济差距的变动状况。从核密度图可知,我国区域经济呈现出收敛的态势;通过随机前沿模型从全要素生产率中分解出前沿技术进步和技术效率,利用1998-2009年28个省市的面板数据,检验了全要素生产率对区域经济差距的影响。实证结果显示,前沿技术进步能够显著的缩小区域经济差距,其中对于东部地区的效果较为显著,技术效率则能够扩大东部地区的经济差距,对于中部和西部地区的影响不显著。政策含义是缩小东部地区经济差距的主要途径是加大对科技创新领域的投入,加强对欠发达地区的技术支持;对于中部地区和西部地区而言,则应该是加大市场化水平、对外开放水平和地方支出比重。