Estimation of ice resistance and sensitivity analysis for an icebreaker

The Lindqvist method is adopted to estimate the ice resistance for an icebreaker.The accuracy of the method is evaluated in a comparison of the calculated results with model test results.In addition to the estimation of ice resistance,a sensitivity analysis based on the Lindqvist method is carried out.A full parametric model developed using CAESES software allows the convenient construction of many new hull lines.The primary factors relevant to ice resistance are embedded as design parameters in the full parametric model.Meanwhile,response surface methodology is adopted to give better insight into new hull lines.Results show that the ice resistance is more sensitive to the rake angle and waterline entrance angle.The aim of the present study is to improve the techniques of designing the hull forms of icebreakers.

Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

Estimation of forest parameters based on TM imagery and statistical analysis

One of the primary forestry research interests lies in estimating forest stand parameters by applying empirical or semi-empirical model to establish the relationship between the forest stand parameters and remote sensing data. Using remote sensing image and the inventory data from 2 compartments in northeast Florida, U.S.A., this paper explored the correlation between forest stand parameters and Landsat TM spectral digital number （DN） value. Results showed that less than 50% of the total variance could be explained by linear regression models with only either a single band or such vegetation indices as vegetation index （VI） or normalized difference vegetation index （NDVI） as predicators. In consequence, multi-linear regression models which synthesized more predicators were introduced to estimate forest parameters. Regression results were tested in terms of the other group of data, and verification showed a better capability of explaining over 75% variance except for forest density. The weakness and further improvement of prediction models were also discussed in the article. This paper is expected to provide a better understanding of the relationship between TM spectral and forest characteristics

Rainfall Estimation using Image Processing and Regression Model on DWR Rainfall Product for Delhi-NCR Region

Observed rainfall is a very essential parameter for the analysis of rainfall,day to day weather forecast and its validation.The observed rainfall data is only available from five observatories of IMD;while no rainfall data is available at various important locations in and around Delhi-NCR.However,the 24-hour rainfall data observed by Doppler Weather Radar(DWR)for entire Delhi and surrounding region(up to 150 km)is readily available in a pictorial form.In this paper,efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products.Firstly,the rainfall at desired locations has been estimated from the precipitation accumulation product(PAC)of the DWR using image processing in Python language.After this,a linear regression model using the least square method has been developed in R language.Estimated and observed rainfall data of year 2018(July,August and September)was used to train the model.After this,the model was tested on rainfall data of year 2019(July,August and September)and validated.With the use of linear regression model,the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019.The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81%for the year 2018.Thus,the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

Uncertainty Analysis in CFD for Resistance

Based on the Latin square design of statistics, the thickness of first boundary layer, the turbulence model and the cell number were taken as the three main factors of uncertainty in CFD （computational fluid dynamics）. Total resistance of hull was calculated and the flow field around the hull was simulated by CFD method. Then, the influence of uncertainty factors on the hull resistance was discussed by regression analysis with trimmed mesh and overset mesh. Through a series of calculation and analysis, the optimal calculation method was put forward, and the relevant parameters of the calculation were determined. Thirdly, the total resistance of different speed was calculated by using these two kinds of grids, which were in good agreement with the experimental results. Finally, according to the ITTC recommended procedures, uncertainty analysis in CFD was carried out with the numerical results of the total resistance by three sets of grids with uniform refinement ratio rG = √2. Then the modified resistance was compared with the experimental result, which improved the accuracy of the resistance prediction.

Wear resistance,hardness,and microstructure of carbide dispersion strengthened high-entropy alloys

(CrFeCoNi)high-entropy alloy(HEA)was reinforced with various contents of WC particles from 5 wt%to 20 wt%,and prepared by powder metallurgy.The mixed powders were compacted under 700 MPa and then sintered at 1200℃in a vacuum furnace for 90 min.Density,phase composition,and microstructure of sintered samples were investigated.Hardness,compressive strength,wear resistance and coefficient of thermal expansion(CTE)were estimated.The results revealed the improvement of the density with the addition of WC.XRD results revealed the formation of new FCC chromium carbide phases.Scanning electron microscopy(SEM)results show a good distribution of the carbide phases over the alloy matrix.The CTE was decreased gradually by increasing the WC content.Compressive strength was improved by WC addition.A mathematical model was established to predict the behavior of the strength of the HEA samples.The hardness of the investigated HEAs was increased gradually with the increasing of WC content about 20.35%.Also,the wear rate of HEA without WC is 1.70×10^(−4)mm^(3)/(N·m),which is approximately 4.5 times the wear rate of 20 wt%WC HEA(3.81×10^(−5)mm^(3)/(N·m)),which means that wear resistance was significantly improved with the increase of WC content.

Life Estimation of Reinforced Concrete Members after Fire

To solve the problem of life estimation of reinforced concrete （RC） members after fire, an analysis is made of the resistance of RC members after fire. On basis of the resistance, the life of RC members after fire is analyzed by using JC （Jukes and Cantor） method. Then the calculation models for the resistance and the life estimation of RC members after fire are put forward, and an example analysis proves their reliability and accuracy.

Estimation of Above Ground Biomass in Forests Using Alos Palsar Data in Kericho and Aberdare Ranges

Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.

Survival Analysis of Logistics Service Providers: An Empirical Study of Chengdu, Area in China

This paper worked on a sample of 6791 logistics establishments registered in Chengdu, China over the period 1984-2016 to understand the survival status of logistics service providers (LSPs) by non-parametric Kaplan-Meier estimation, together with Cox proportional hazard regression model, to identify factors affecting the failure of LSPs. In particular, it studies the interaction effect between LSPs’ size and entry timing and location. The empirical results show that: 1) Regarding the survival time, 1365 of the 6791 sample LSPs exited from the market by 2017. The exit rate is 20.1%, and the average life of the 6791 LSPs is about 6 years. 2) The survival of LSPs depends on their typology, ownership structure. And there is no significant difference in the probability of survival for both independent LSPs and logistics branches after controlling the effects of other variables. 3) Location and entry timing also play an important role in the survival of small-scale LSPs, but these factors cannot explain large-scale LSPs’ failure.

3D Human Pose Estimation from a Monocular Image Using Model Fitting in Eigenspaces

Generally, there are two approaches for solving the problem of human pose estimation from monocular images. One is the learning-based approach, and the other is the model-based approach. The former method can estimate the poses rapidly but has the disadvantage of low estimation accuracy. While the latter method is able to accurately estimate the poses, its computational cost is high. In this paper, we propose a method to integrate the learning-based and model-based approaches to improve the estimation precision. In the learning-based approach, we use regression analysis to model the mapping from visual observations to human poses. In the model-based approach, a particle filter is employed on the results of regression analysis. To solve the curse of the dimensionality problem, the eigenspace of each motion is learned using Principal Component Analysis (PCA). Finally, the proposed method was estimated using the CMU Graphics Lab Motion Capture Database. The RMS error of human joint angles was 6.2 degrees using our method, an improvement of up to 0.9 degrees compared to the method without eigenspaces.

Small area estimation with subgroup analysis

In this article,a new unit level model based on a pairwise penalised regression approach is proposed for problems in small area estimation(SAE).Instead of assuming common regression coefficients for all small domains in the traditional model,the new estimator is based on a subgroup regression model which allows different regression coefficients in different groups.The alternating direction method of multipliers(ADMM)algorithm is used to find subgroups with different regression coefficients.We also consider pairwise spatial weights for spatial areal data.In the simulation study,we compare the performances of the new estimator with the traditional small area estimator.We also apply the new estimator to urban area estimation using data from the National Resources Inventory survey in Iowa.

Neural Network for Estimating Daily Global Solar Radiation Using Temperature, Humidity and Pressure as Unique Climatic Input Variables

Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m2] in the validation dataset.

苦瓜种质资源耐热性评价及其鉴定指标的筛选

本研究以27个苦瓜自交系4叶1心的幼苗为材料,预培养1 d后,置于39℃高温培养,观察其高温胁迫72 h后的热害情况,通过计算热害指数(TDG)筛选评价出高、中、低耐热性的苦瓜种质;从中选取6个不同耐热性的自交系,测定39℃胁迫0 h和48 h的株高(PH)、茎粗(SD)、鲜物质量(SFW)和干物质量(SDW)等形态学指标以及叶片相对叶绿素含量(SPAD)、电导率(REL)、过氧化物酶活性(POD)、过氧化氢酶活性(CAT)、超氧化物歧化酶活性(SOD)、丙二醛含量(MDA)、脯氨酸含量(Pro)和可溶性糖含量(SS)等生理指标;主成分分析上述12个指标的耐热系数(α),综合评价6个自交系材料的耐热性;利用逐步回归分析法建立耐热性评价数学模型获得苦瓜耐热性鉴定指标体系。结果表明:以TDG为依据,27个苦瓜自交系中有耐热型3个,中等耐热型18个,热敏型6个;6个不同耐热性材料的综合评价结果与热害指数鉴定结果基本一致;耐热性的评价数学模型为:Y=-0.426+0.049X1-0.011X2-0.009X3-0.018X4,利用建立的最优回归方程预测供试材料的耐热性,预测值与Y值基本一致。说明SFW(X1)、POD活性(X2)、CAT活性(X3)和SS含量(X4)这4个指标的耐热系数可以快速准确地评价苦瓜耐热性差异,可用作苦瓜耐热性鉴定指标。本研究筛选出苦瓜耐热种质并构建苦瓜耐热性评价体系,为海南夏季耐热苦瓜新品种选育提供依据。

Robust Interval State Estimation for Distribution Systems Considering Pseudo-measurement Interval Prediction

Interval state estimation(ISE)can estimate state intervals of power systems according to confidence intervals of predicted pseudo-measurements,thereby analyzing the impact of uncertain pseudo-measurements on states.However,predicted pseudo-measurements have prediction errors,and their confidence intervals do not necessarily contain the truth values,leading to estimation biases of the ISE.To solve this problem,this paper proposes a pseudo-measurement interval prediction framework based on the Gaussian process regression(GPR)model,thereby improving the prediction accuracy of pseudo-measurement confidence intervals.Besides,a weight assignment strategy for improving the robustness of weighted least squares(WLS)ISE is proposed.This strategy quantifies the deviation between the pseudo-measurement intervals and their estimated intervals and assigns smaller weights to the pseudo-measurement intervals with larger deviations,thereby improving the estimation accuracy and robustness of the ISE.This paper adopts the data from the supervisory control and data acquisition(SCADA)system of the New York Independent System Operator(NYISO).It verifies the advantages of the GPR method for pseudo-measurement interval prediction by comparing it with the quantile regression and neural network methods.In addition,this paper demonstrates the effectiveness of the proposed weight assignment strategy through the IEEE 14-bus case.Finally,the differences in the estimation accuracy and the bad data identification between the robust interval state estimation and deterministic state estimation are discussed.

早胜牛母牛体重与体尺性状相关回归分析

为了阐明不同月龄早胜牛母牛体重与体尺指标之间的关系,以建立不同月龄早胜牛的体重估测模型,试验分别测定了12月龄(233头)、18月龄(514头)和24月龄(797头)共1544头早胜牛母牛的体重(Y)、体高(X_(1))、体斜长(X_(2))、胸围(X_(3))和管围(X_(4))指标,并进行了相关分析、通径分析和逐步回归分析。结果表明:12,18,24月龄早胜牛母牛体重与体尺指标均呈极显著正相关(P<0.01),其中均与胸围的相关性最高,相关系数分别为0.947,0.848,0.908;12,18,24月龄早胜牛母牛体尺指标间均呈极显著正相关(P<0.01);12,18,24月龄早胜牛母牛体尺指标对体重的直接影响由大到小均依次为胸围(0.677,0.409,0.574)、体斜长(0.188,0.394,0.378)、体高(0.134,0.255,0.048)和管围(0.057,-0.015,-0.009);12月龄早胜牛母牛体斜长对体重的间接影响最大,胸围对体重的间接影响最小;18,24月龄早胜牛母牛体高对体重的间接影响最大,管围对体重的间接影响最小;建立了12,18,24月龄早胜牛母牛体重预测多元线性回归模型,分别为Y=93.023+0.717X_(1)+0.229X_(2)+0.171X_(3)+0.285X_(4)、Y=107.257+0.652X_(1)+0.571X_(2)+0.448X_(3)、Y=-245.280+2.430X_(1)+1.519X_(2)+0.182X_(3),3个模型的R2分别为0.925,0.872,0.880,拟合度较高。说明可通过测定早胜牛母牛胸围、体斜长和体高指标,利用上述不同月龄的体重回归模型估测其体重。

中医疗法对慢性心力衰竭患者利尿剂抵抗的影响

目的探讨中医疗法对慢性心力衰竭患者利尿剂抵抗的影响,为该病患者利尿剂抵抗的预防及诊疗提供参考。方法回顾性收集2020年1月—2022年12月在上海市浦东新区光明中医医院住院的慢性心力衰竭患者资料,将患者分为发生利尿剂抵抗组和未发生利尿剂抵抗组,比较2组一般资料、中医治疗情况,采用Lasso回归和Logistic回归分析中医疗法预防利尿剂抵抗的效果及影响因素。结果符合入组标准慢性心力衰竭患者419例,其中发生利尿剂抵抗组154例,未发生利尿剂抵抗组265例,2组均使用中西医标准治疗。2组患者使用中药涂擦、耳针疗法、灸法、手指点穴、穴位敷贴、中药包熨烫和中医定向透药疗法的比例差异均有统计学意义(P均<0.05),应用耳穴压丸、推拿和中药塌渍疗法的比例差异均无统计学意义(P均>0.05)。Logistic回归(模型3)分析结果显示,中药涂擦(OR=0.608,95%CI:0.377~0.979)、耳针疗法(OR=0.458,95%CI:0.268~0.782)、灸法疗法(OR=0.621,95%CI:0.403~0.956)、手指点穴疗法(OR=0.624,95%CI:0.405~0.962)、穴位敷贴疗法(OR=0.612,95%CI:0.399~0.938)、中药包熨烫疗法(OR=0.599,95%CI:0.373~0.961)和中医定向透药疗法(OR=0.552,95%CI:0.349~0.873)是利尿剂抵抗发生的保护因素,与模型1和模型2结果一致。结论中医疗法能够有效预防慢性心力衰竭患者利尿剂抵抗的发生,应积极发挥中医优势,为慢性心力衰竭的临床研究提供循证医学证据。

Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods

Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effective alternative to taking traffic counts. This is because traditional traffic counts are expensive and impractical for low priority roads. The purpose of this paper is to present the development of two alternative means of cost- effectively estimating traffic volumes for low volume roads in Wyoming and to make recommendations for their implementation. The study methodology involves reviewing existing studies, identifying data sources, and carrying out the model development. The utility of the models developed were then verified by comparing actual traffic volumes to those predicted by the model. The study resulted in two regression models that are inexpensive and easy to implement. The first regression model was a linear regression model that utilized pavement type, access to highways, predominant land use types, and population to estimate traffic volume. In verifying the model, an R^2 value of 0.64 and a root mean square error of 73.4% were obtained. The second model was a logistic regression model that identified the level of traffic on roads using five thresholds or levels. The logistic regression model was verified by estimating traffic volume thresholds and determining the percentage of roads that were accurately classified as belonging to the given thresholds. For the five thresholds, the percentage of roads classified correctly ranged from 79% to 88%. In conclusion, the verification of the models indicated both model types to be useful for accurate and cost-effective estimation of traffic volumes for low volume Wyoming roads. The models developed were recommended for use in traffic volume estimations for low volume roads in pavement management and environmental impact assessment studies.

长三角地区半糯粳稻外观品质的评价

【目的】由于半糯米具有云雾状半透明的外观特性,目前行业中缺乏行之有效的评价方法,因此,构建半糯米外观品质评价方法,并对半糯米外观品质进行等级分类,为半糯米产业的规范与发展提供技术支持。【方法】参照GB/T 15682—2008《粮油检验稻谷、大米蒸煮食用品质感官评价方法》,利用人工评价方式获取半糯粳稻的外观品质评分,同时,测定半糯米的各类外观指标,包括垩白、透明度、粒型、色空间相关指标。使用皮尔森相关性系数、最大相关性最小冗余性、竞争自适应重加权采样、交集特征选取方法分析垩白、透明度、粒型、色空间相关指标与半糯米外观感官评分的关系,筛选出能决定半糯米外观品质的核心指标用于构建半糯米外观评分估测模型,并进行验证。最后使用此模型估测长三角地区半糯米外观品质的整体情况并按照估测分数的优劣进行等级划分。【结果】通过比较4种模型的估测精度与稳定性,发现交集特征选取方法筛选出的垩白米透明度(X_(4))、整体透明度(X_(6))、垩白度(X_(9))、L*(X_(10))这4个指标构建的半糯米外观评分估测模型Y=5.68+0.17X_(4)+0.19X_(6)-0.03X_(9)-0.12X_(10)的准确度与稳定性最高,同时,此模型包含的指标数最少,其模型验证的决定系数(R^(2))为0.86,模拟均方根误差(RMSE)为0.32。在使用此模型估测长三角地区3个稻作区绝大部分半糯米的外观分后,发现长三角地区半糯米材料前20%、20%—50%、50%—90%、最后10%的外观评分范围分别是大于0.23、-0.48—0.23、-1.68—-0.48、小于-1.68,在实际应用中可以代表半糯米外观品质的4个等级:一级、二级、三级、等外。同时发现不同外观等级半糯米的粒型指标、垩白指标、透明度指标、色空间指标存在显著梯度差异,但其数值范围之间存在不同程度的交叉。【结论】利用垩白米透明度(X_(4))、整体透明度(X_(6))、垩白度(X_(9))、L*(X_(10))这4个指标可以建立有效的半糯米外观估测模型:Y=5.68+0.17X_(4)+0.19X_(6)-0.03X_(9)-0.12X_(10)。参照长三角地区半糯米各外观等级的外观评分范围,可以快速通过外观估测评分确定单个半糯米材料外观的优劣情况。通过外观估测模型对半糯米进行综合评价,有利于兼顾半糯米不同外观表型,能够准确反映半糯米外观的实际情况。

腰痛患者抗阻训练的剂量-反应关系的Meta分析

目的:探讨抗阻训练(RT)治疗慢性非特异性腰痛(CNSLBP)的临床疗效,通过分析提供RT的剂量与腰部功能改善的关系,以及影响结果最显著的剂量区间。方法:计算机检索2022年12月前CNKI、维普数据库、万方数据库、PubMed、MEDLINE、Embase、Web of Science、Cochrane对照试验注册中心发表的RT治疗CNLBP的随机对照试验。对纳入文献进行筛选,资料提取,质量评价后,采用Stata 14软件进行Meta分析,Meta回归分析以及亚组分析。结果:共纳入13篇RCT,19项结果。RT对腰部功能改善有显著影响[SMD=-1.01,95%CI(-1.42,-0.60),P<0.01]。每组次数(P=0.026)对腰部功能改善影响显著。训练次数10~12个/组(SMD=-2.38),训练周期为9~12周(SMD=-1.68),训练频率1~2次/周(SMD=-1.08),训练组数为1组(SMD=-1.96),训练时长30~39min(SMD=-0.89),训练强度大于70%1RM(SMD=-2.12),组间休息0~30s(SMD=-0.92)对腰部功能改善更有效。结论:RT可以显著改善患者腰部功能受限。未来的研究应特别关注训练变量的详细描述,以便深入分析CNSLBP在RT后的剂量-反应关系。

红茶菌液水蜜桃果酒发酵动力学模型建立及抗氧化性分析

该试验以水蜜桃和红茶菌液为原料,酿制红茶菌液水蜜桃果酒,探究其发酵过程中菌体生长、基质消耗、产物生成的动力学变化并分析相关性,并对其两种抗氧化性物质以及三种自由基清除率进行测定并分析其相关性。结果表明:SGompertz模型可以较好的反映酵母菌生长的变化,Boltzmann模型对乙醇生成变化的拟合度最佳,DoseResp模型能够较好的反映还原糖消耗的变化,三个模型的相关系数R2均>0.98,表明实验值和模型预测值的拟合度很高,反映果酒在发酵过程中的动力学特征;抗氧化性测定结果表明:随发酵时间的变化总体上均呈现先上升再下降的趋势,在发酵第192小时均达到最高值,此时总酚和黄酮质量浓度分别为3802 mg/mL和549 mg/mL,+DPPH、ABTS和羟基自由基清除率分别为86.73%、81.87%和82.45%,综合考虑发酵192h可以作为主发酵结束的依据。该研究揭示了红茶菌液水蜜桃酒发酵过程中代谢产物及抗氧化活性的变化规律,初步判定所建立的模型有效,可为红茶菌液水蜜桃的工业化生产提供理论依据。