应用Open Meta-Analysis软件实现诊断准确性试验的Meta分析

Open Meta-Analysis软件是用于二分类数据、连续型数据以及诊断数据Meta分析的开放软件,该软件提供了四种模型来执行诊断准确性试验的Meta分析,即诊断随机效应模型、倒方差混合效应模型、双变量模型和分层综合受试者工作特征曲线法,其中前两者为单变量模型,只能执行单个指标合并,而后两者为双变量模型,能够对灵敏度和特异度之间的负相关性进行综合分析。本文以实例就Open Meta-Analysis软件实现诊断准确性试验Meta分析做相关简述。

Epidata Entry-Epidata Analysis软件联用在医院处方分析评价中的应用

目的探讨不合格处方分析评价的新技术、新方法。方法运用Epidata Entry软件进行数据录入,通过编写check文件,对录入数据和流程进行控制,联用Epidata Analysis软件对不合格处方进行分析。结果 Epidata Entry软件便于不合格处方的录入和管理,Epidata Analysis软件便于不合格处方的分析,两软件联用可实现不合格处方数字化管理,方法可靠。结论该方法应用于医院处方分析评价,具有简单、易学、方便、实用、几乎不需任何费用等优点。

Ecotect Analysis软件采光模拟准确性分析

文章首先制作实体模型,将其置入人工天穹进行采光实验,改变天空照度及窗洞口位置,测量室内采光照度;再进行计算机建模,模型尺寸与实验模型一致,并基于Ecotect Analysis软件进行采光模拟实验;最后将两者数据进行对比,比较结果表明模拟数据与实验差异集中分布在20%附近,证明了Ecotect Analysis采光模拟数据具有可信性,可基于其进行建筑采光优化设计。

EpiData Entry-EpiData Analysis软件联用在药学信息处理中的应用

目的为EpiData软件更好地运用于医院药学信息处理提供参考。方法简单介绍运用EpiData Entry对门诊退药等药学信息数据进行录入,通过编写check文件对录入数据和流程进行控制,联用EpiData Analysis软件对药学信息进行分析。结果 EpiData Entry软件便于药学信息的录入和管理,EpiData Analysis便于信息的分析。结论 EpiData Entry-EpiDataAnalysis软件便于药学信息的处理与分析,是一种适合医院药学部门使用的信息化手段。

基于Ecotect Analysis软件的集装箱建筑透明围护结构优化设计

文章首先对大连民族大学建筑学院实践教育基地集装箱建筑中的一个房间进行实地尺寸测量,然后在Ecotect Analysis软件中进行1:1建模,从热湿环境及光环境两个方面分析采用现有材质窗时室内温度、太阳辐射、窗自身得热量等的分布,以及室内空调系统制冷制热功耗情况,根据模拟结果给出透明围护结构的优化建议,并对比优化前后室内热湿环境、光环境的变化。

Stenosis Analysis软件在冠脉支架植入中的应用探索

目的探讨Stenosis Analysis软件在冠脉支架植入中的使用价值。方法通过目测判断测量包线与被测血管段的边缘吻合是否良好,来判断狭窄测量的准确性;利用完全打开的植入血管内的支架,其于狭窄段血管近端正常血管的吻合情况,来判断Stenosis Analysis软件在选择支架口径中的准确性。结果冠脉血管重度狭窄时,Stenosis Analysis软件测量精度差;轻度、中度狭窄时测量较准确;利用Stenosis Analysis软件提供的测量数值选择的支架口径与狭窄段近端正常血管吻合度很高。结论 Stenosis Analysis软件测量重度狭窄时,误差较大;对于轻、中度狭窄及正常血管的测量较精确,有临床使用价值。

TSAnalysis软件在辽宁地区面波频散提取中的应用

介绍了在辽宁地区使用基于图像分析的双台相速度频散曲线提取方法进行相速度频散提取的工作,并选取了TSAnalysis软件操作实例详细展示提取过程。通过质量控制和筛选,获得了15~120s周期范围内的1100条独立双台路径相速度频散资料。

Network Analysis软件在浙江电网的应用

简要介绍了NetworkAnalysis软件的工作流程及硬件配置 ,分析了NA软件存在的问题及其解决方法 ,并介绍了NA软件的正确性及其校核方法 。

利用Advanced Aircraft Analysis软件进行飞机设计

在飞机设计领域,Advanced Aircraft Analysis提供了全面的、面向工程实际的设计技术。本文将详细介绍这款软件的特点、应用范围以及设计效果。

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

Lagrangian coherent structure analysis on transport of Acetes chinensis along coast of Lianyungang,China

Spatial heterogeneity or“patchiness”of plankton distributions in the ocean has always been an attractive and challenging scientific issue to oceanographers.We focused on the accumulation and dynamic mechanism of the Acetes chinensis in the Lianyungang nearshore licensed fishing area.The Lagrangian frame approaches including the Lagrangian coherent structures theory,Lagrangian residual current,and Lagrangian particle-tracking model were applied to find the transport pathways and aggregation characteristics of Acetes chinensis.There exist some material transport pathways for Acetes chinensis passing through the licensed fishing area,and Acetes chinensis is easy to accumulate in the licensed fishing area.The main mechanism forming this distribution pattern is the local circulation induced by the nonlinear interaction of topography and tidal flow.Both the Lagrangian coherent structure analysis and the particle trajectory tracking indicate that Acetes chinensis in the licensed fishing area come from the nearshore estuary.This work contributed to the adjustment of licensed fishing area and the efficient utilization of fishery resources.

Semi-analytical solution for drained expansion analysis of a hollow cylinder of critical state soils

The expansion of a thick-walled hollow cylinder in soil is of non-self-similar nature that the stress/deformation paths are not the same for different soil material points.As a result,this problem cannot be solved by the common self-similar-based similarity techniques.This paper proposes a novel,exact solution for rigorous drained expansion analysis of a hollow cylinder of critical state soils.Considering stress-dependent elastic moduli of soils,new analytical stress and displacement solutions for the nonself-similar problem are developed taking the small strain assumption in the elastic zone.In the plastic zone,the cavity expansion response is formulated into a set of first-order partial differential equations(PDEs)with the combination use of Eulerian and Lagrangian descriptions,and a novel solution algorithm is developed to efficiently solve this complex boundary value problem.The solution is presented in a general form and thus can be useful for a wide range of soils.With the new solution,the non-self-similar nature induced by the finite outer boundary is clearly demonstrated and highlighted,which is found to be greatly different to the behaviour of cavity expansion in infinite soil mass.The present solution may serve as a benchmark for verifying the performance of advanced numerical techniques with critical state soil models and be used to capture the finite boundary effect for pressuremeter tests in small-sized calibration chambers.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Application of Isogeometric Analysis Method in Three-Dimensional Gear Contact Analysis

Gears are pivotal in mechanical drives,and gear contact analysis is a typically difficult problem to solve.Emerging isogeometric analysis(IGA)methods have developed new ideas to solve this problem.In this paper,a threedimensional body parametric gear model of IGA is established,and a theoretical formula is derived to realize single-tooth contact analysis.Results were benchmarked against those obtained from commercial software utilizing the finite element analysis(FEA)method to validate the accuracy of our approach.Our findings indicate that the IGA-based contact algorithmsuccessfullymet theHertz contact test.When juxtaposed with the FEA approach,the IGAmethod demonstrated fewer node degrees of freedomand reduced computational units,all whilemaintaining comparable accuracy.Notably,the IGA method appeared to exhibit consistency in analysis accuracy irrespective of computational unit density,and also significantlymitigated non-physical oscillations in contact stress across the tooth width.This underscores the prowess of IGA in contact analysis.In conclusion,IGA emerges as a potent tool for addressing contact analysis challenges and holds significant promise for 3D gear modeling,simulation,and optimization of various mechanical components.

Preliminary electromagnetic analysis of the COOL blanket for CFETR

The supercritical CO_(2)cOoled Lithium-Lead(COOL)blanket has been designed as one advanced blanket candidate for the Chinese Fusion Engineering Test Reactor(CFETR).This work focuses on the electromagnetic(EM)loads(Maxwell force and Lorentz force)acting on the COOL blanket,which are important mechanical loads in further structural analysis of the COOL blanket.A 3D electromagnetic analysis is performed using the ANSYS finite element method to obtain EM loads on the COOL blanket in this study.At first,the magnetic scalar potential(MSP)method is used to obtain the magnetic field and the Maxwell force on the COOL blanket.Then,the magnetic vector potential(MVP)method is performed during a plasma disruption event to get the eddy current distribution.At last,a multi-step method is adopted for the calculation of the Lorentz force and the torque.The maximum Lorentz forces of inboard and outboard blanket structural components are 5624 kN and 2360 kN respectively.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Geometric prior guided hybrid deep neural network for facial beauty analysis

Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis

Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

Evaluation of the seismic behavior of reinforced concrete structures with flat slab-column gravity frame and shear walls through nonlinear analysis methods

This paper presents an investigation of the seismic behavior of reinforced concrete(RC)structures in which shear walls are the main lateral load-resisting elements and the participation of flat slab floor systems is not considered in the seismic design procedure.In this regard,the behavior of six prototype structures(with different heights and plan layouts)is investigated through nonlinear static and time history analyses,implemented in the OpenSees platform.The results of the analyses are presented in terms of the behavior of the slab-column connections and their mode of failure at different loading stages.Moreover,the global response of the buildings is discussed in terms of some parameters,such as lateral overstrength due to the gravity flat slab-column frames.According to the nonlinear static analyses,in structures in which the slab-column connections were designed only for gravity loads,the slab-column connections exhibited a punching mode of failure even in the early stages of loading.However,the punching failure was eliminated in structures in which a minimum transverse reinforcement recommended in ACI 318(2019)was provided in the slabs at joint regions.Furthermore,despite neglecting the contribution of gravity flat slab-column frames in the lateral load resistance of the structures,a relatively significant overstrength was imposed on the structures by the gravity frames.