Parameter prediction for cash flow forecasting models

The paper describes the application of the DHSS formula to 27 completed construction projects comprising four types - steel-framed low rise buildings, new build housing developments, housing refurbishment projects, and multi-house ‘pre-paint’ maintenance contracts. Application of the formula to individual projects indicates that the ‘best’ parameter values offer a ten-fold improvement over the published values based on project size. Similar results occur when using the best parameter values for other two parameter models.

Various approaches are considered in attempting to predict the best parameter values of the models based on known characteristics of the project. A multiple linear regression with project value, duration, and type independent variables is shown not to produce any significant improvement on standard DHSS formula predictions. However, a reduction in the number of independent variables by cross validation produces an approximately 25% improvement on standard DHSS formula forecasts outside the data base. Examination of the models derived from this analysis indicate the type of project to be of major importance.     

Modelling air pollution for epidemiologic research--part II: predicting temporal variation through land use regression

Over recent years land use regression (LUR) has become a frequently used method in air pollution exposure studies, as it can model intra-urban variation in pollutant concentrations at a fine spatial scale. However, very few studies have used the LUR methodology to also model the temporal variation in air pollution exposure. The aim of this study is to estimate annual mean NO₂ and PM₁₀ concentrations from 1996 to 2008 for Greater Manchester using land use regression models. The results from these models will be used in the Manchester Asthma and Allergy Study (MAAS) birth cohort to determine health effects of air pollution exposure.The Greater Manchester LUR model for 2005 was recalibrated using interpolated and adjusted NO₂ and PM₁₀ concentrations as dependent variables for 1996-2008. In addition, temporally resolved variables were available for traffic intensity and PM₁₀ emissions. To validate the resulting LUR models, they were applied to the locations of automatic monitoring stations and the estimated concentrations were compared against measured concentrations.The 2005 LUR models were successfully recalibrated, providing individual models for each year from 1996 to 2008. When applied to the monitoring stations the mean prediction error (MPE) for NO₂ concentrations for all stations and years was -0.8 μg/m³ and the root mean squared error (RMSE) was 6.7 μg/m³. For PM₁₀ concentrations the MPE was 0.8 μg/m³ and the RMSE was 3.4 μg/m³.These results indicate that it is possible to model temporal variation in air pollution through LUR with relatively small prediction errors. It is likely that most previous LUR studies did not include temporal variation, because they were based on short term monitoring campaigns and did not have historic pollution data. The advantage of this study is that it uses data from an air dispersion model, which provided concentrations for 2005 and 2010, and therefore allowed extrapolation over a longer time period.     

传感器输出信号通用线性处理电路的研究

根据电量输出传感器测量物理量的范围,平分二段,分别建立拟合回归方程,并用电路实现之.这种线性处理方法具有通用性.以K型热电偶传感器为实验例子,未经线性处理前,理论线性度为3.2%,线性处理后,理论线性度为0.015%.测量数据标准差为0.00138.结果表明,该方法测量效果良好.     

组合支护结构下土钉内力监测的回归分析

侨福花园广场深基坑工程基坑深达21.675m,采用上部复合土钉墙、下部地下连续墙的组合支护体系,按照信息化施工要求对土钉内力进行了长时间监测,根据监测曲线,运用回归分析方法建立了土钉内力非线性回归模型,得出了土钉内力与时间的相互关系,并得到了分别适合于中短期以及中长期土钉内力与时间相互关系的较为理想的回归函数。监测结果和数据分析表明,土钉内力监测中应用回归分析是完全可行的,有助于提高深基坑工程领域信息化施工的准确性和预见性。     

Activated Hepatic Stellate Cells in Hepatocellular Carcinoma: Their Role as a Potential Target for Future Therapies

Hepatocellular carcinoma (HCC) is a global healthcare challenge, which affects more than 815,000 new cases every year. Activated hepatic stellate cells (aHSCs) remain the principal cells that drive HCC onset and growth. aHSCs suppress the anti-tumor immune response through interaction with different immune cells. They also increase the deposition of the extracellular matrix proteins, challenging the reversion of fibrosis and increasing HCC growth and metastasis. Therapy for HCC was reported to activate HSCs, which could explain the low efficacy of current treatments. Conversely, recent studies aimed at the deactivation of HSCs show that they have been able to inhibit HCC growth. In this review article, we discuss the role of aHSCs in HCC pathophysiology and therapy. Finally, we provide suggestions for the experimental implementation of HSCs in HCC therapies.     

Physiological and Pathological Remodeling of Cerebral Microvessels

There is growing evidence that the remodeling of cerebral microvessels plays an important role in plastic changes in the brain associated with development, experience, learning, and memory consolidation. At the same time, abnormal neoangiogenesis, and deregulated regulation of microvascular regression, or pruning, could contribute to the pathogenesis of neurodevelopmental diseases, stroke, and neurodegeneration. Aberrant remodeling of microvesselsis associated with blood–brain barrier breakdown, development of neuroinflammation, inadequate microcirculation in active brain regions, and leads to the dysfunction of the neurovascular unit and progressive neurological deficits. In this review, we summarize current data on the mechanisms of blood vessel regression and pruning in brain plasticity and in Alzheimer’s-type neurodegeneration. We discuss some novel approaches to modulating cerebral remodeling and preventing degeneration-coupled aberrant microvascular activity in chronic neurodegeneration.     

基于小波多分辨率分析的时变结构参数识别研究

提出一种小波多分辨率分析的最优尺度选择方法,并将其应用于结构时变物理参数的识别。首先,从函数空间剖分的角度引入WMRA对时变参数进行多分辨率近似展开,将振动微分方程转化成多元线性回归方程,根据时变参数的频率范围及采样频率、线性方程组的个数等确定分解层数取值范围;其次,利用赤池信息准则（AIC）寻求最优分解尺度,为增强数据的稳定性,采用正交最小二乘算法（OLS）代替传统最小二乘算法（LS）对模型中小波系数进行估计并重构时变参数;最后,分别以突变和连续变化的两种时变参数的5层剪切框架模型进行数值模拟。分析结果表明：在预先确立的分解尺度范围内,采用无噪声干扰的响应信号进行识别时,识别精度随着分解尺度的增加而增加;采用噪声干扰的测量信号进行识别时,识别精度与分解尺度的增加无必然联系;通过选择适当的分解尺度,能够准确识别时变参数、提高方法的计算效率并保证很好的抗噪性能。     

基于PLS-LDA和拉曼光谱快速定性识别食用植物油

以6种食用油共计23个样本为分析对象,采用偏最小二乘线性判别分析法（PLS-LDA）和拉曼光谱进行单一种类（橄榄油、花生油和玉米油）食用油快速定性检测,通过自适应迭代惩罚最小二乘法（airPLS）对拉曼信号进行背景扣除,以及蒙特卡洛无信息变量消除法筛选波长变量,不但有效减少了波长点数,降低了建模运算量,而且提高了单一种类食用油的识别率,使得总体识别率均高于90%,并在此基础上进一步提出了采用PLS-LDA进行多种类食用油识别的检测流程。实验结果表明PLS-LDA在食用油定性识别检测中具有较好的应用前景和可行性,该方法也可为定性检测食品及农产品品质提供借鉴。      

带Hermite插值条件的最小二乘估计

颜宁生(颜宁生. 带Hermite插值条件的最小二乘估计[J].大学数学学报: 2011, 27(5): 80- 84.)研究了带Hermite插值条件的最小二乘估计。本文在此基础上,进一步考虑提高函数的光滑度即插值点存在二阶导数的插值,并提出了带二阶Hermite插值条件的最小二乘拟合问题,进而给出了带二阶Hermite插值条件的最小二乘拟合的拟合曲线的具体表达式。最后利用Lingo建模语言设计了求解带二阶Hermite插值条件的最小二乘拟合的拟合曲线参数的程序。     

热带钢轧机平坦度控制补偿策略

在带钢热连轧平坦度控制系统中,平坦度实测信号通常包含精轧后带钢横向温差引起的附加干扰.针对鞍钢ASP1700热连轧机组,利用红外热像仪测量精轧后带钢横向温度场.通过具体分析温差附加干扰对带钢平坦度控制的影响,采用最小二乘法拟合得到带钢横向温度场分布规律,建立了平坦度控制目标设定的温差补偿模型.