Comparison of Statistical Models for Regional Crop Trial Analysis

Based on the review and comparison of main statistical analysis models for estimating variety-environment cell means in regional crop trials, a new statistical model, LR-PCA composite model was proposed, and the predictive precision of these models were compared by cross validation of an example data. Results showed that the order of model precision was LR-PCA model > AMMI model > PCA model > Treatment Means (TM) model > Linear Regression (LR) model > Additive Main Effects ANOVA model. The precision gain factor of LR-PCA model was 1.55, increasing by 8.4% compared with AMMI.

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

Quality Management Model for Phase I Clinical Drug Trials:A Structural Equation Model

This study aimed to construct a quality management model for phase I clinical drug trials.A cross-sectional survey was conducted and data were collected from 604 respondents at 69 institutions in China engaged in phase I clinical drug trials.Exploratory and confirmatory factor analyses were used to develop the survey tool.Structural equation modeling was used to construct a quality management model for phase I clinical drug trials.The results showed that the final survey tool had good reliability and validity(Cronbach’sα=0.938,root mean square error of approximation=0.074,comparative fit index=0.962,and Tucker—Lewis index=0.955).The model included five dimensions:government regulation,industry management,medical institution management,research team management,and contract research organization(CRO)management.In total,22 measurement items were obtained.The structural equation model indicated government regulation,industry management,medical institution management,and CRO management significantly affected the quality of phase I clinical drug trials(β=0.195,β=0.331,β=0.279,andβ=−0.267,respectively;P<0.05).Research team management had no effect on the quality of trials(β=0.041,P=0.610).In conclusion,the model is valuable for identifying factors influencing phase I clinical drug trials and guiding quality management practices.

An efficient approach for shadow detection based on Gaussian mixture model

An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate(the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.

Randomized controlled trials on Roy adaptation model nursing in patients with acute myocardial infarction in china: a systematic review

Objective:This study was to evaluate the quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction in China.Methods:We systematically searched the Cnki,Wanfang and Vipdatabases,to get randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction.The search period was from inception to October 2020.According to the Cochrane risk bias assessment tool,the quality of the studies included was appraised.Results:A total of 55 studies were retrieved,and 11 were eventually included in the study.Among the studies included,the first study was published in 2008.The overall quality of the 11 studies included was relatively low.Conclusions:The overall quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction was not high,which would hinder the evidence transformation as well as clinical practice.

Evaluation of the effects of health education interventions for hypertensive patients based on the health belief model

BACKGROUND Hypertension is a major risk factor for cardiovascular disease and stroke,and its prevalence is increasing worldwide.Health education interventions based on the health belief model(HBM)can improve the knowledge,attitudes,and behaviors of patients with hypertension and help them control their blood pressure.AIM To evaluate the effects of health education interventions based on the HBM in patients with hypertension in China.METHODS Between 2021 and 2023,140 patients with hypertension were randomly assigned to either the intervention or control group.The intervention group received health education based on the HBM,including lectures,brochures,videos,and counseling sessions,whereas the control group received routine care.Outcomes were measured at baseline,three months,and six months after the intervention and included blood pressure,medication adherence,self-efficacy,and perceived benefits,barriers,susceptibility,and severity.RESULTS The intervention group had significantly lower systolic blood pressure[mean difference(MD):-8.2 mmHg,P<0.001]and diastolic blood pressure(MD:-5.1 mmHg,P=0.002)compared to the control group at six months.The intervention group also had higher medication adherence(MD:1.8,P<0.001),self-efficacy(MD:12.4,P<0.001),perceived benefits(MD:3.2,P<0.001),lower perceived barriers(MD:-2.6,P=0.001),higher perceived susceptibility(MD:2.8,P=0.002),and higher perceived severity(MD:3.1,P<0.001)than the control group at six months.CONCLUSION Health education interventions based on the HBM effectively improve blood pressure control and health beliefs in patients with hypertension and should be implemented in clinical practice and community settings.

Frequentist Model Averaging and Applications to Bernoulli Trials

In several instances of statistical practice, it is not uncommon to use the same data for both model selection and inference, without taking account of the variability induced by model selection step. This is usually referred to as post-model selection inference. The shortcomings of such practice are widely recognized, finding a general solution is extremely challenging. We propose a model averaging alternative consisting on taking into account model selection probability and the like-lihood in assigning the weights. The approach is applied to Bernoulli trials and outperforms Akaike weights model averaging and post-model selection estimators.

Regression Model to Analyze Differential Response to Treatment in Randomized Controlled Clinical Trial

The primary aim of clinical trials is to investigate whether a treatment is effective for a particular disease or condition. Randomized controlled clinical trials are considered to be the gold standard for evaluating the effect of a certain intervention. However, in clinical trials, even after randomization, there are situations where the patients differ substantially with respect to the baseline value of the outcome variable. Many a times the response to interventions depends on the baseline values of the outcome variable. When there are baseline-dependent treatment effects, differences among treatments vary as a function of baseline level. Although variation in outcome associated with baseline value is accounted for in ANCOVA, analysis of individual differences in treatment effect is precluded by the homogeneity of regression assumption. This assumption requires that expected differences in outcome among treatments be constant across all baseline levels. To overcome this difficulty, Weigel and Narvaez [7] proposed a regression model for two treatment groups to analyze individual response to treatments in randomized controlled clinical trials. The authors reviewed the model suggested by Weigel and Narvaez and extended further for three or more treatment groups. The utility of the model was demonstrated with real life data from a randomized controlled clinical trial of bronchial asthma.

基于Shadowing模型的无线入侵主机物理定位研究

为了在室内无线局域网环境中及时准确地定位非法入侵主机物理位置,以无线信号强度衰减过程的Shadowing传播模型为基础,研究了在一定的范围内如何合理地布置探测器,如何根据接收到的入侵主机的信号强度进行物理定位的问题.提出的定位方法可以有效地对抗入侵主机随机更改信号发射功率对定位精度的影响.实际测试结果表明,该方法定位误差平均约为2.3 m,可以满足室内环境下定位非法入侵主机的需求.

Distance Measurement Model Based on RSSI in WSN

The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM.

A General Channel Model for Visible Light Communications in Underground Mines

In underground mines, visible light communication(VLC) system is a promising method to realize effective communication,which supports communication and illumination at the same time. Therefore, adequate research of underlying physical propagation phenomenon should be carried out to realize VLC system in underground mines. To design VLC system and evaluate its performance, accurate and efficient channel models, including large-scale fading and scattering characteristics, are needed to be established. However,the characteristics of the underlying VLC channels about fading and scattering have not been sufficiently investigated yet. In this paper, a path loss channel model, based on the recursive model, is proposed precisely. Its path loss exponent is determined by three different trajectories, which is studied in the mining roadway and working face environment. Besides, the shadowing effect for VLC has been modelled by a Bimodal Gaussian distribution in underground mines. Considering the number of transmitters in line-of-sight(Lo S) as well as non-line-of-sight(NLo S) scenarios,our simulation illustrates the fact that, as the curve fitting technique is employed, the path loss displays a linear behavior in log-domain.The path loss expression is derived, it is related to the distance. Finally, root mean square(RMS) delay spread and Mie scattering in underground mines are analyzed.

基于ISM-24Model的重大铁路事故组织致因研究

为探究重大铁路事故深度组织致因机制,结合24Model与解释结构模型(ISM),构建ISM-24Model事故深度组织致因分析框架。首先,基于24Model致因识别程序识别个人及组织因素,然后结合集成决策实验室分析法(DEMATEL)与ISM法构建深度组织致因综合影响矩阵,计算其可达矩阵并最终获取组织因素间的相对重要程度及递阶结构。以台铁"10·21"事故为例进行实证研究,框架共识别出不安全的个人因素31项,组织因素14项。组织因素影响关系及递阶结构的分析结果表明:对安全投入认识不足是导致该事故发生的组织核心因素,而安全培训制度为受到不良影响最集中的组织因素;采取多元化措施整改缺失的组织因素可从根源上避免事故的再次发生;ISM-24Model可有效探讨事故中的深度组织致因作用规律,是24Model的有益补充。

Validation of an Emission Model for a Marine Diesel Engine with Data from Sea Operations

In this study,a model is developed to simulate the dynamics of an internal combustion engine,and it is calibrated and validated against reliable experimental data,making it a tool that can effectively be adopted to conduct emission predictions.In this work,the Ricardo WAVE software is applied to the simulation of a particular marine diesel engine,a four-stroke engine used in the maritime field.Results from the bench tests are used for the calibration of the model.Finally,the calibration of the model and its validation with full-scale data measured at sea are presented.The prediction includes not only the classic engine operating parameters for a comparison with surveys but also an estimate of nitrogen oxide emissions,which are compared with similar results obtained with emission factors.The calibration of the model made it possible to obtain an overlap between the simulation results and real data with an average error of approximately 7%on power,torque,and consumption.The model provides encouraging results,suggesting further applications,such as in the study on transient conditions,coupling of the engine model with the ship model for a complete simulation of the operating conditions,and optimization studies on consumption and emissions.The availability of the emission data during the sea trial and validated simulation results are the strengths and novelties of this work.

Improvement of Glacial Lakes Detection under Shadow Environment Using ASTER Data in Himalayas, Nepal

The detection of glacial lake change in the Himalayas, Nepal is extremely significant since the glacial lake change is one of the crucial indicators of global climate change in this area, where is the most sensitive area of the global climate changes. In the Hima- layas, some of glacial lakes are covered by the dark mountains＇ shadow because of their location. Therefore, these lakes can not be de- tected by conventional method such as Normalized Difference Water Index （NDWI）, because the reflectance feature of shadowed glacial lake is different comparing to the ones which are located in the open flat area. The shadow causes two major problems： 1） glacial lakes which are covered by shadow completely result in underestimation of the number of glacial lakes; 2） glacial lakes which are partly iden- tified are considered to undervalue the area of glacial lakes. The aim of this study is to develop a new model, named Detection of Shadowed Glacial Lakes （DSGL） model, to identify glacial lakes under the shadow environment by using Advanced Space-borne Ther- mal Emission and Reflection Radiometer （ASTER） data in the Himalayas, Nepal. The DSGL model is based on integration of two dif- ferent modifications of NDWI, namely NDWls model and NDWIshe model. NDWI~ is defined as integration of the NDWI and slope analysis and used for detecting non-shadowed lake in the mountain area. The NDWIshe is proposed as a new methodology to overcome the weakness of NDWI~ on identifying shadowed lakes in highly elevated mountainous area such as the Himalayas. The first step of the NDWIshe is to enhance the data from ASTER 1B using the histogram equalization （HE） method, and its outcome product is named AS- TERho. We used the ASTERhe for calculating the NDWIhc and the NDWIshe. Integrated with terrain analysis using Digital Elevation Model （DEM） data, the NDWIshe can be used to identify the shadowed glacial lakes in the Himalayas. NDWIs value of 0.41 is used to identify the glacier lake （NDWI~ 〉 0.41）, and 0.3 of NDWIshe is used to identify the shadowed glacier lake （NDWIsho 〈 0.3）. The DSGL model was proved to be able to classify the glacial lakes more accurately, while the NDWI model had tendency to underestimate the presence of actual glacial lakes. Correct classification rate regarding the products from NDWI model and DSGL model were 57% and 99%, respectively. The results of this paper demonstrated that the DSGL model is promising to detect glacial lakes in the shadowed en- vironment at high mountains.

Inhomogeneous Shadowing Effect in High-Energy p-A Drell-Yan Process:

Having studied the initial state energy loss versus nuclear shadowing for the Drell-Yan dimuon pairproduction in the color string model,the inhomogeneous shadowing effect is considered in this paper.We find thatthe inhomogeneous shadowing effect does amend the rate of energy loss per unit path length,-dE/dz.Finally,thetheoretical results for the Drell-Yan differential cross-section ratios are compared with the E772 and E866 data.It isfound that the theoretical results are in good agreement with the experimental data.

Development and Influencing Factors of Compliance Behaviors of Investigators in Clinical Trials

The development and influencing factors of compliance behavior of investigators in clinical trials were explored. According to literature review, a hypothetical model of development of compliance behavior of investigators in clinical trials was established, and the influencing factors of compliance behavior of investigators and their interrelationships were studied based on questionnaire survey of five hundred investigators sampled randomly from one hundred clinical trial institutions in China. Cron- bach＇s alpha coefficient and structural equation modeling were adopted to empirically analyze the re- suits. Six variables in the hypothetical model were included： compliance behavior of investigators, credibility of clinical trial, capability of government regulation, quality control of sponsor, quality con- trol of clinical institution and compliance intention of investigators. Empirical analysis showed that the compliance behavior of investigators in clinical trial was directly affected by compliance intention of investigators, quality control of sponsor and quality control of clinical institution. In addition, credibility of clinical trial and capability of government regulation indirectly affected the compliance behavior of investigators in clinical trial through influencing the compliance intention of investigators, quality con- trol of sponsor and quality control of clinical institution. Quality control of sponsor was affected by credibility of clinical trial and capability of government regulation while quality control of clinical in- stitutinn wan only influenced by capability of government regulation.

An ultrasound-guided percutaneous electrical nerve stimulation regimen devised using finite element modeling promotes functional recovery after median nerve transection

Percutaneous electrical nerve stimulation of an injured nerve can promote and accelerate peripheral nerve regeneration and improve function.When performing acupuncture and moxibustion,locating the injured nerve using ultrasound before percutaneous nerve stimulation can help prevent further injury to an already injured nerve.However,stimulation parameters have not been standardized.In this study,we constructed a multi-layer human forearm model using finite element modeling.Taking current density and activated function as optimization indicators,the optimal percutaneous nerve stimulation parameters were established.The optimal parameters were parallel placement located 3 cm apart with the injury site at the midpoint between the needles.To validate the efficacy of this regimen,we performed a randomized controlled trial in 23 patients with median nerve transection who underwent neurorrhaphy.Patients who received conventional rehabilitation combined with percutaneous electrical nerve stimulation experienced greater improvement in sensory function,motor function,and grip strength than those who received conventional rehabilitation combined with transcutaneous electrical nerve stimulation.These findings suggest that the percutaneous electrical nerve stimulation regimen established in this study can improve global median nerve function in patients with median nerve transection.

A NEW METHOD OF MOVING OBJECT DETECTION AND SHADOW REMOVING

This paper presents an adaptive method of objects and shadows detection in video streams. Models of background are firstly set up and adaptively updated in Hue Saturation Intensity (HSI) color space to detect motion regions. Then, detection errors are dealt with by motion continuity and velocity consistency. Finally, cast shadows are removed by the generic properties of luminance, chrominance and gradient density. Experimental results and their evaluation are presented to verify the effectiveness of this new method.

Additive mixed models to study the effect of tree age and climatic factors on stem radial growth of Eucalyptus trees

The effect of tree age and climatic variables on stem radial growth of two hybrid clones of Eucalyptus was determined using longitudinal data from eastern South Africa.The stem radius of was measured weekly as the response variable.In addition to tree age,average weekly temperature,solar radiation,relative humidity and wind speed were simultaneously recorded with total rainfall at the site.An additive mixed effects model that incorporates a non-parametric smooth function was used.The results of the analysis indicate that the relationship between stem radius and each of the covariates can be explained by nonlinear functions.Models that account for the effect of clone and season together with their interaction in the parametric part of the additive mixed model were also fitted.The interaction between clone and season was not significant in all cases.For analyzing the joint effect all the covariates,additive mixed models that included two or more covariates were fitted.A significant effect of tree age was found in all cases.Although tree age was the key determinant of stem radial growth,weather variables also had a significant effect that was dependent on season.

Development and Validation of National Cotton Cultivar Registration Index Model in Yangtze River Valley

[Objective] Based on the cotton variety high yielding potential, fiber quality traits, disease resistance, and early maturity characters, a cultivar registration index model was developed to simplify the tedious calculation process in national cotton registration procedure, and thus to enhance the practical application of cultivar regis- tration index in cotton breeding ancl cotton recommending. [Method] By means of correlation analysis, partial correlation analysis and path analysis methods, the cor- relation of cotton main properties and their effects on cultivar registration index were explored using the dataset of national cotton regional trials in Yangtze River Valley during 1996-2013. The cultivar registration index model was constructed with step- wise regression statistical technique to ascertain the quantitative relationship of main characters with cultivar registration index, and the regional cotton trial dataset in 2013 was used to validate the model. [Result] Several characters with larger deter- minants to cultivar registration index were screened out,ie. lint yield increase ratio, pro-frost yield ratio, verticillium wilt index, fiber strength, fusarium wilt index and mi- cronaire value. The cultivar registration index model defined the functional relation- ship of cultivar registration index with the selected main characters, among which lint yield increase ratio, fiber strength and micronaire value contributed most to culti- var registration index. The model validation with regional cotton trials in 2013 indi- cated the root mean square error, RMSE was only 2.77, and the variation coeffi- cient was 6.77%, which confirmed the model prediction effect was quite perfect. [Conclusion] The developed cultivar registration index model was reliable enough to simulate the complicated scoring system in cultivar registration procedure, also sim- plified cotton registration process, and enhanced the practicability of the cultivar reg- istration index.