Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value(GEBV).This study is a fi rst attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits.The performance of GS models in L.vannamei was evaluated in a population consisting of 205 individuals,which were genotyped for 6 359 single nucleotide polymorphism(SNP)markers by specifi c length amplifi ed fragment sequencing(SLAF-seq)and phenotyped for body length and body weight.Three GS models(RR-BLUP,Bayes A,and Bayesian LASSO)were used to obtain the GEBV,and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes.The mean reliability of the GEBVs for body length and body weight predicted by the dif ferent models was 0.296 and 0.411,respectively.For each trait,the performances of the three models were very similar to each other with respect to predictability.The regression coeffi cients estimated by the three models were close to one,suggesting near to zero bias for the predictions.Therefore,when GS was applied in a L.vannamei population for the studied scenarios,all three models appeared practicable.Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.

Distribution Prediction of Suitable Growth Area for Eucommia ulmoides in China under Climatic Change Background

[ Objective] The research aimed to study distribution prediction of suitable growth area for Eucommia ulmoides in China under climatic change background. [ Method] By using the maximum entropy model and many kinds of climate change scenarios, we predicted current and future distribution pattems of suitable growth area for Eucommia ulmoides in China and its change process. [ Result ] At present, highly suitable growth area of E. ulmoides mainly distributed in Sichuan, Shaanxi and Chongqing, Under climate change background, total suitable growth areas in future three decades all drastically reduced when compared with that at present. It was noteworthy that moderately and highly suitable growth areas of wild E. ulmoides all disappeared, and junction between Shaanxi and Gansu and Taibai Mountain would be stable suitable growth area of wild E. ulmoides. [ Condusioa] The research could provide useful reference data for investigation, protection and sustainable development of the wild E. ulmoides resources.

Studies on stand dynamic growth model for larch in Jilin in China

The stand growth and yield dynamic models for Larch in Jilin Province were developed based on the forest growth theories with the forest continuous inventory data. The results indicated that the developed models had high precision, and they could be used for the updating data of inventory of planning and designing and optimal decision of forest management.

Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nio events? Results from a Theoretical Model

Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ＂spring predictability barrier＂ （SPB） for El Nio events. First, sensitivity experiments were respectively performed to the air-sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation （CNOP） approach was used to study the effect of the optimal mode （CNOP-P） of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nio events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode （i.e. CNOP error） of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nio events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.

Spatial Effects of Varying Model Coefficients in Urban Growth Modeling in Nairobi, Kenya

Urban land-use modeling has gained increased attention as a research topic over the last decade. This has been attributed to advances in remote sensing and computing technology that now can process several models simultaneously at regional and local levels. In this research we implemented a cellular automata (CA) urban growth model (UGM) integrated in the XULU modeling frame-work (eXtendable Unified Land Use Modeling Platform). We used multi-temporal Landsat satellite image sets for 1986, 2000 and 2010 to map urban land-use in Nairobi. We also tested the spatial effects of varying model coefficients. This approach improved model performance and aided in understanding the particular urban land-use system dynamics operating in our Nairobi study area. The UGM was calibrated for Nairobi and predicted development was derived for the city for the year 2030 when Kenya plans to attain Vision 2030. Observed land-use changes in urban areas were compared to the results of UGM modeling for the year 2010. The results indicate that varying the UGM model coefficients simulates urban growth in different directions and magnitudes. This approach is useful to planners and policy makers because the model outputs can identify specific areas within the urban complex which will require infrastructure and amenities in order to realize sustainable development.

Discrete Choice Analysis of Temporal Factors on Social Network Growth

Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.

A New Paradigm for Simulating and Forecasting China's Economic Growth in the Medium and Long Term

Taking the system philosophy of human-earth relationship as the theoretical axis,and under the three-dimensional goals of economic growth,social development,and protection of the ecological environment,this paper constructs the supporting system of China’s economic development.On this basis,guided by the basic principles of system theory and system dynamics,and combined with the theories of other related disciplines,we constructed an economic geography-system dynamics(EG-SD)integrated forecasting model to simulate and quantitatively forecast China’s economic growth in the medium and long term.China’s economic growth will be affected by quantifiable and unquantifiable factors.If the main unquantifiable factors are not taken into account in the simulation and prediction of China’s economic growth in the medium and long term,the accuracy and objectivity of the prediction results will be diminished.Therefore,based on situation analysis(Strengths,Weaknesses,Opportunities,and Threats,SWOT),we combined scenario analysis with the Delphi method,and established a qualitative prediction simulation model(referred to as the S-D compound prediction model)to make up for the shortcomings associated with quantitative simulation predictions.EG-SD and S-D are organically combined to construct a simulation and prediction paradigm of China’s economic growth in the medium and long term.This paradigm not only realizes the integration of various forecasting methods and the combination of qualitative and quantitative measures,but also realizes the organic combination of unquantifiable and quantifiable elements by innovatively introducing fuzzy simulation of system dynamics,which renders the simulation and prediction results more objective and accurate.

Prediction model for austenite grains growth during reheating process in Ti micro-alloyed cast steel by coupling precipitates dissolution and coarsening behavior

A combined model to predict austenite grains growth of titanium micro-alloyed as-cast steel during reheating process was established.The model invoIves the behaviors of austenite grains growth in continuous heating process and isothermal soaking process,and the variation of boundary pinning efficiency caused by the dissolution and coarsening kinetics of sec on d-phase particles was also con sidered into the model.Furthermore,the experimental verificatio ns were performed to examine the prediction power of the model.The results revealed that the mean austenite grains size increased with the increase in reheating temperature and soaking time,and the coarsening temperature of austenite grains growth was 1423 K under the current titanium content.In addition,the reliability of the predicted results in continuous heating process was validated by continuous heating experimenls.Moreover,an optimal regression expression of austenite grains growth in isothermal soaking process was obtained based on the experimental results.The compared results indicated that the combined model in conjunction with precipitates dissolution and coarsening kinetics had good reliability and accuracy to predict the austenite grains growth of titanium micro-alloyed casting steel during reheating process.

Development and Prospect of Process Models and Simulation Methods for Atomic Layer Deposition

Thin film deposition is one of the most important processes in IC manufacturing. In this paper, several typical models and numerical simulation methods for thin film deposition and atomic layer deposition are introduced. Several modeling methods based on the characteristics of atomic layer deposition are introduced, it includes geometric method, cellular automata and multiscale simulation. The principle of each model and simulation method is explained, and their advantages and disadvantages are analyzed. Finally, the development direction of thin film deposition and atomic layer deposition modeling is prospected, and some modeling ideas are also provided.

Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model

Background:Many studies have modeled and predicted the spread of COVID-19(coronavirus disease 2019)in the U.S.using data that begins with the first reported cases.However,the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S.Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic.Methods:We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S.from January 22 to April 6,2020,and reconstructed the epidemic using a 5-parameter logistic growth model.We fitted our model to data from a 2-week window(i.e.,from March 21 to April 4,approximately one incubation period)during which large-scale testing was being conducted.With parameters obtained from this modeling,we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection.Results:The data fit the model satisfactorily.The estimated daily growth rate was 16.8%overall with 95%CI:[15.95,17.76%],suggesting a doubling period of 4 days.Based on the modeling result,the tipping point at which new cases will begin to decline will be on April 7th,2020,with a peak of 32,860 new cases on that day.By the end of the epidemic,at least 792,548(95%CI:[789,162,795,934])will be infected in the U.S.Based on our model,a total of 12,029 cases were not detected between January 22(when the first case was detected in the U.S.)and April 4.Conclusions:Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented.Beyond informing public health decision-making,our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic.

温室无土栽培切花月季生长发育预测模型及其验证

【目的】建立一个可以预测温室无土栽培切花月季生长发育时期及收获期的模型,为切花月季生产过程中的环境因子调控提供理论支持。【方法】以生长周期差异明显的3个主栽切花月季品种‘洛神’‘欢乐颂’和‘粉红雪山’为试验材料,无土栽培种植于曲靖市马龙区的塑料温室大棚中,于2021—2022年收集5期的生长发育数据和同期的光照辐射及温度数据。通过分析切花月季的生长周期特征,构建基于生理辐热积(Physiological product of thermal effectiveness and PAR,PTEP)的切花月季生长发育时期预测模型,并使用独立数据对构建的生长模型进行验证。【结果】切花月季在修剪到萌芽、萌芽到现蕾以及现蕾到收获这3个生长发育阶段所需的生理辐热积分别为22.08、29.41和38.89 MJ/m^(2);本研究所构建的切花月季生长发育时期预测模型基于生理辐热积,在切花月季的各个生长发育阶段,模型的模拟预测值与实测值表现出良好的一致性。1∶1线性回归标准误差(RMSE)分别为0.7、6.5和9.4 d,显示出模型预测的准确性。【结论】通过考虑光照辐射与温度的综合影响,构建的模型能够预测切花月季在不同生长发育阶段的时间点,以及切花产品的收获期。基于该模型,种植者可以更精准地调节温室内的光照与温度,从而在一定程度上调控切花月季产品的生产周期。研究结果将为温室无土栽培切花月季的生产提供科学依据,同时也将为种植者制定切实可行的生产和技术支持。

基于池塘圈养条件的大口黑鲈生长特征与模型构建

为掌握池塘圈养条件下大口黑鲈养殖周期的生长特征变化规律,测定体质量为(16.3±4.9)~(424.9±27.2)g生长周期内大口黑鲈的体长、全长、吻长、眼径、头长、尾柄长、头高、体高、尾柄高、体宽和体质量生长特征参数,分析其生长特征参数之间的相关性,分别建立基于支持向量回归(SVR)、径向基神经网络(RBF)和随机森林回归(RF)的体质量预测模型,将预测值与实测值拟合确定最佳模型;并运用模型拟合的方法建立各个生长特征参数的最佳生长模型。结果显示:体质量与生长特征参数均呈极显著相关性;基于支持向量回归(SVR)的体质量预测模型预测效果最佳,预测模型的决定系数R^(2)为0.996,均方根误差为9.004,平均绝对误差为6.598;体质量与体长呈幂函数关系W=0.0127×L^(3.224),决定系数R^(2)为0.977;全长、体长、吻长和头长的最佳生长模型为Logistic模型,头高、体高、眼径和体宽最佳生长模型为Von Bertalanffy模型,体质量、尾柄长和尾柄高最佳生长模型为Gompertz模型;在养殖周期内大口黑鲈肥满度在2.26%~2.93%波动。以上结果表明,可以利用生长模型和体质量预测模型预测掌握圈养条件下大口黑鲈的生长过程,并通过精准投喂达最佳养殖效果。

掺建筑垃圾水泥稳定碎石力学强度增长规律与预测模型

本文研究了建筑垃圾再生集料掺量对水泥稳定碎石(CSM)力学强度的影响,以强度最高或掺量最大为原则提出了再生集料最佳掺量和配比,并在最优掺量下研究了再生集料水泥稳定碎石(CSMRA)的力学强度增长规律,提出了CSRA力学强度的预测模型,并进行了可靠性验证。结果表明:CSRA抗压强度随再生细集料掺量增加先增大后减小,随再生粗集料掺量增加而降低,再生集料最大掺量为70%(质量分数);所建立的力学强度增长方程和预测模型的相关系数均大于0.98,预测值与实测值误差不大于14.0%,表明在确定水泥掺量、集料类型、矿料配比及7 d强度后,该模型可准确预测CSRA在其他养生龄期时的力学强度。

子痫前期并发胎儿生长受限的风险预测列线图模型构建与验证

目的探讨子痫前期并发胎儿生长受限(FGR)的影响因素,并依此建立列线图预测模型。方法回顾性分析郑州人民医院2018年12月至2022年12月收治的168例子痫前期患者的临床资料,按照2∶1的比例将其分为建模组(112例)和验证组(56例),并根据是否并发FGR将建模组分为并发组(49例)和未并发组(63例)。采用多因素logistic回归分析法分析子痫前期并发FGR的影响因素,并采用R3.4.3软件包绘制列线图模型,采用Bootstrap法进行内部验证;绘制受试者工作特征(ROC)曲线对列线图预测子痫前期并发FGR的效能进行分析,采用决策曲线分析法(DCA)验证模型的临床净获益率。结果并发组发病孕周<34周、羊水过少、收缩压≥160 mmHg、胎儿脐动脉收缩压与舒张压比值(S/D)升高、24 h尿蛋白定量≥2.0 g占比以及血红蛋白(HB)、谷草转氨酸(AST)、血尿酸(UA)、尿素氮(BUN)、肌酐、D-二聚体(D-D)水平高于未并发组(P<0.05),白蛋白、凝血酶原时间(PT)水平低于未并发组(P<0.05);经logistic回归分析可知,发病孕周<34周、羊水过少、S/D比值升高、24 h尿蛋白定量≥2.0 g、BUN和D-D水平升高是子痫前期并发FGR的危险因素(P<0.05),白蛋白是其保护因素(P<0.05)。依据以上影响因素构建子痫前期并发FGR的列线图模型,经Bootstrap法进行内部验证,其一致性指数为0.825,校正曲线和标准曲线拟合度较好;ROC曲线结果显示,建模组列线图预测子痫前期并发FGR的曲线下面积(AUC)、灵敏度、特异度分别为0.862、83.67%、87.30%;验证组列线图预测子痫前期并发FGR的AUC、灵敏度、特异度分别为0.830、80.77%、83.33%;DCA提示列线图模型进行风险评估可获得满意的净收益。结论发病孕周<34周、羊水过少、S/D比值升高、24 h尿蛋白定量≥2.0 g、BUN和D-D水平升高均是子痫前期并发FGR的危险因素,白蛋白是其保护因素,且基于此构建的列线图模型临床应用价值较高,可为临床筛选子痫前期并发FGR高危患者提供参考。

高温高压水管道热疲劳试验设计与数值模拟

核电厂压力容器启停堆和正常运行时,温度压力会在一定范围内发生波动,随着波动次数的增加,会对设备和管道造成严重疲劳损伤。依托北京某高校高温高压腐蚀实验站亚临界水汽环境结构材料试验装置双回路,设计了一种承压管道高通量热疲劳试验方法。基于该试验方法,完成了瞬态水温变化对管道疲劳裂纹扩展的影响研究。结果表明,疲劳裂纹扩展速率随冷却速率降低而逐渐降低。结合有限元数值模拟和三维裂纹扩展分析软件建立了阶梯管道结构的热疲劳裂纹扩展速率模型,并用试验结果对模型进行了修正。

Prediction of fatigue crack growth rate for smallsized CIET specimens based on low cycle fatigue properties

Based on experiments of low cycle fatigue for 5083-H112 aluminum alloy, two energybased predictive models have been introduced to predict the fatigue crack growth behaviors of traditional Compact Tension（CT） and small-sized C-shaped Inside Edge-notched Tension（CIET）specimens with different thicknesses and load ratios. Different values of the effective stress ratio U are employed in the theoretical fatigue crack growth models to correct the effect of crack closure.Results indicate that the two predictive models show different capacities of predicting the fatigue crack growth behaviors of CIET and CT specimens with different thicknesses and load ratios.The accuracy of predicted results of the two models is strongly affected by the method for determination of the effective stress ratio U. Finally, the energy-based Shi＆Cai model with crack closure correction by means of Newman＇s method is highly recommended in prediction of fatigue crack growth of CIET specimens via low cycle fatigue properties.

基于集成学习算法和WOFOST模型的小麦生长模拟分析与产量预测

针对传统单一作物生长模型和机器学习模型在预测上的限制,将WOFOST模型与灌溉模型结合,利用集成学习算法建立多模型耦合系统(WOFOST耦合模型),选用美国航空航天局(NASA)1990—2020年数据进行模拟试验,选取2006年、2018年展示试验成果。结果表明,WOFOST耦合模型的小麦叶面积指数、总生物量均高于WOFOST模型,WOFOST耦合模型更贴近实际生产活动。耦合算法的MAE、MSE均低于Bagging、Boosting、Stacking算法,分别为2.836、7.581,R~2均高于Bagging、Boosting、Stacking算法,高达0.942。WOFOST耦合模型更全面和准确地模拟作物生长状态,提高产量预测的准确性与可信度。

HER2阴性乳腺癌化疗患者复发转移的影响因素及风险预测模型构建

目的探讨人表皮生长因子受体-2(HER2)阴性乳腺癌化疗患者复发转移的影响因素,并构建风险预测模型。方法回顾性分析180例接受新辅助化疗的HER2阴性乳腺癌患者病历资料和随访资料,根据2年内复发转移情况将患者分为复发转移组(35例)和未复发转移组(145例)。采用Logistic回归分析HER2阴性乳腺癌化疗患者复发转移的影响因素,依据回归分析结果构建风险预测模型,计算一致性指数(C-index)检验模型准确性。绘制受试者工作曲线(ROC),以曲线下面积(AUC)检验风险预测模型对HER2阴性乳腺癌化疗患者复发转移的预测价值。结果复发转移组和未复发转移组在月经状态、临床分期、p53蛋白、Ki-67、VEGF方面比较,差异有统计学意义(P<0.05)。Logistic回归分析显示,月经状态、临床分期、p53蛋白、Ki-67、VEGF是HER2阴性乳腺癌化疗患者复发转移的影响因素(P<0.05)。基于上述因素构建风险预测模型,结果显示该列线图模型的校准曲线和Y=X直线相近,模型校准度良好,C-index值为0.844,提示该模型具有良好区分度。绘制ROC曲线发现,列线图风险预测模型预测HER2阴性乳腺癌化疗患者复发转移的AUC为0.844。结论月经状态、临床分期、p53蛋白、Ki-67、VEGF是HER2阴性乳腺癌化疗患者复发转移的影响因素,基于上述影响因素构建的风险预测模型可有效评估患者复发转移。

双能量CT定量参数联合CT征象预测中晚期肺腺癌表皮生长因子受体基因突变

目的探究中晚期肺腺癌患者的双能量CT定量参数联合CT征象、临床特征与表皮生长因子受体(EGFR)基因突变的相关性,预测中晚期肺腺癌EGFR基因的突变情况。方法回顾性收集盐城市第一人民医院2022年1月~2023年6月经病理学(纤维支气管镜、淋巴结、经皮肺穿刺)活检确诊的172例中晚期肺腺癌(临床分期Ⅲ~Ⅳ期)。采集患者的一般临床特征、CT征象及双能量CT(DECT)参数。根据EGFR基因检测结果分为阳性组和阴性组,采用独立样本t检验或秩和检验分析比较组间的差异,对有统计学意义的参数,逐步建立基于临床特征、常规CT征象、DECT定量参数及联合的二元Logistic回归模型,评价联合模型预测效能。结果172例肺腺癌患者EGFR基因表达阳性者80例,阴性者92例。动脉期IC、NIC、斜率K_(40-100)keV及静脉期IC在两组之间的差异有统计学意义(P<0.001);空气支气管征及胸膜牵拉征在两组之间的差异有统计学意义(P<0.05);单因素Logistic回归显示动脉期IC、NIC、斜率K_(40-100)keV、静脉期IC、空气支气管征、胸膜牵拉征与EGFR基因突变相关;DECT联合参数模型Model1、DECT模型联合临床特征模型Model2、DECT模型联合临床特征及CT征象模型Model3的ROC曲线下面积分别为0.746(敏感度63.75%,特异度92.39%)、0.787(敏感度65.00%,特异度91.30%)、0.819(敏感度77.50%,特异度82.61%)。经DeLong检验,3个模型曲线下面积的差异无统计学意义(P>0.05)。结论联合临床特征、CT征象的DECT模型能有效预测中晚期肺腺癌患者EGFR突变,且优于单一模型。

Creep crack growth in a Cr-Mo-V type steel: experimental observation and prediction

In this study, the creep crack growth （CCG） properties and fracture mechanism of a Cr-Mo-V steel at 566 C in compact tension （CT） specimens were investigated, and the CCG rate was predicted by using the NSW model. The results show that the CCG rate measured by CT specimens is much lower than that predicted by the NSW model under plane-strain state. This means that the NSW model prediction for the CCG rate of the steel is over-conservative. In addition, the CCG rate da/dt versus C measured by the experiments shows the piecewise linear relation on log-log scale instead of a single linear relation predicted by the NSW model. The main reasons for these results are that the actual creep fracture mechanism of the steel and the actual creep crack tip stress field in the CT specimens have not been fully captured in the NSW model. The experimental observation shows that the creep crack propagates in a discontinuous way （step by step） at meso-scale, and the cracks at micro-scale are usually formed by the growth and coalescence of voids on grain boundaries. The NSW model based on the creep ductility exhaustion approach may not correctly describe this creep fracture process. In addition, the opening stress and triaxial stress ahead of crack tips calculated by three-dimensional finite element method is lower than those predicted by the HRR stress field which is used in the NSW model under plane-strain state. The use of the high HRR stress field will cause high CCG rates. The change in the creep fracture mechanism at micro-scale in different ranges of C may cause the piecewise linear relation between the da/dt and C . Therefore, it is necessary to study the actual CCG mechanism in a wide range of C and the actual creep crack tip stress field to establish accurate CCG prediction models.