Application of Grey System Theory to tree growth prediction

Based on Grey System theory, tree growth prediction models are developed by using 202 temporary plots and 206 stem analysis trees of Dahurian larch (Larix gemlinii Rupr) in 10 forestry bureaus of Yakeshi Forestry Administrative Bureau in Daxing’an Mountains of the Inner Mongolia Autonomous Region. By residual and posterior tests, their precisions are qualified. With several data, tree growth can be predicted using Grey System models. For DBH and volume, the fitting results of Grey System models are better than that of statistical models.

Research of Neuron Growth Prediction and Influence of Its Geometric Configuration

The neuron growth will bring series variation to the neuron characteristics of geometric configuration. Especially the growth of dendrite and axon can obviously change the space characteristic and geometric characteristic of neuron. This article is to build the prediction model of neuron growth through knowing the statistics rules of neuron geometric characteristics, better imitate the neuron growth, and clearly analyze the growth influence of geometric configuration.

Urban Growth Prediction Modelling Using Fractals and Theory of Chaos

Urban growth prediction has acquired an important consideration in urban sustainability. An effective approach of urban prediction can be a valuable tool in urban decision making and planning. A large urban development has been occurred during last decade in the touristic village of Pogonia Etoloakarnanias, Greece, where an urban growth of 57.5% has been recorded from 2003 to 2011. The prediction of new urban settlements was achieved using fractals and theory of chaos. More specifically, it was found that the urban growth is taken place within a Sierpinski carpet. Several shapes of Sierpinski carpets were tested in order to find the most appropriate, which produced an accuracy percentage of 70.6% for training set and 81.8% for validation set. This prediction method can be effectively applied in urban growth modelling, once cities are fractals and urban complexity can be successfully described through a Sierpinski tessellation.

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.

Simple Predicting Method for Fatigue Crack Growth Rate Based on Tensile Strength of Carbon Steel

Three types of fatigue tests for an annealed carbon steel containing carbon of 0.42%were carried out on smooth specimens and specimens with a small blind hole in order to investigate the fatigue crack growth law.A simple predicting method for crack growth rates has been proposed involving strengthσband the relation between cyclic stress and strain.The validity of proposed method has been confirmed by experiments on several carbon steels with different loadings.

Mesoscale Predictability of Moist Baroclinic Waves: Variable and Scale-dependent Error Growth

This study seeks to quantify the predictability of different forecast variables at various scales through spectral analysis of the difference between perturbed and unperturbed cloud-permitting simulations of idealized moist baroclinic waves amplify- ing in a conditionally unstable atmosphere. The error growth of a forecast variable is found to be strongly associated with its reference-state （unperturbed） power spectrum and slope, which differ significantly from variable to variable. The shallower the reference state spectrum, the more spectral energy resides at smaller scales, and thus the less predictable the variable since the error grows faster at smaller scales before it saturates. In general, the variables with more small-scale components （such as vertical velocity） are less predictable, and vice versa （such as pressure）. In higher-resolution simulations in which more rigorous small-scale instabilities become better resolved, the error grows faster at smaller scales and spreads to larger scales more quickly before the error saturates at those small scales during the first few hours of the forecast. Based on the reference power spectrum, an index on the degree of lack （or loss） of predictability （LPI） is further defined to quantify the predictive time scale of each forecast variable. Future studies are needed to investigate the scale- and variable-dependent predictability under different background reference flows, including real case studies through ensemble experiments.

The Predictive Value of Plasma Fibronectin Concentration on Fetal Growth Retardation at Earlier Stage of the Third Trimester

In order to evaluate the predictive value of maternal plasma fibronectin (FN) concentration at 24-34 weeks on fetal intrauterine growth retardation (IUGR), a prospective double-blinded study was performed. The maternal plasma FN concentrations were measured by using a rate nephelometric procedure in the 130 initial normal nulliparous pregnant woman at 24-34 gestational weeks. The outcome of pregnancies and birth weight of their infants were followed up. IUGR was defined as that the birth weight was less than the 10th percentile for gestational age. The receiver operating characteristic curves and predictive values of FN predicting on outcome of pregnancy with IUGR were analyzed. The results showed that: (1) In a cohort of 130 initially normal nulliparous pregnant women, IUGR occurred in 14 cases during the follow-up; (2) The plasma FN levels in the women with IUGR (467.58±104.43 mg/L) were significantly higher than in the normal control group (299.44±105.55 mg/L, P<0.01). However, there was no significant difference in the mean maternal age, gravidity, sampling gestational ages, delivering gestational ages between the two groups (P>0.05); (3) The areas under ROC curve for predicting the outcome of pregnancy in IUGR was 0.893; (4) At the cut point of 475 mg/L FN level, the sensitivity, specificity, positive predictive value, negative predictive value and Kappa index for predicting the outcomes of pregnancy in IUGR were 57.14 %, 95.69 %, 61.54 %, 94.87 %, 0.5455 respectively. It was concluded that the maternal plasma FN might be used as an earlier predictor for screening of IUGR.

Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model

Based on the high-resolution Regional Ocean Modeling System(ROMS) and the conditional nonlinear optimal perturbation(CNOP) method, this study explored the effects of optimal initial errors on the prediction of the Kuroshio large meander(LM) path, and the growth mechanism of optimal initial errors was revealed. For each LM event, two types of initial error(denoted as CNOP1 and CNOP2) were obtained. Their large amplitudes were found located mainly in the upper 2500 m in the upstream region of the LM, i.e., southeast of Kyushu. Furthermore, we analyzed the patterns and nonlinear evolution of the two types of CNOP. We found CNOP1 tends to strengthen the LM path through southwestward extension. Conversely,CNOP2 has almost the opposite pattern to CNOP1, and it tends to weaken the LM path through northeastward contraction.The growth mechanism of optimal initial errors was clarified through eddy-energetics analysis. The results indicated that energy from the background field is transferred to the error field because of barotropic and baroclinic instabilities. Thus, it is inferred that both barotropic and baroclinic processes play important roles in the growth of CNOP-type optimal initial errors.

Predictors of Recurrence of Hepatocellular Carcinoma after Transarterial Chemoembolization: Role of Hepatocyte Growth Factor

Background: Hepatocellular carcinoma is the third leading cause of tumor related mortality and develops mostly in patients with chronic liver disease and liver cirrhosis. Human hepatocyte growth factor (HGF) is produced in various organs of the body and is characterized as a multifunctional factor with various biologic activities. Aim: Our aim was to investigate the predictive factors of recurrence specially the role of HGF in patients with HCC treated with TACE. Patients and Methods: one hundred HCC patients treated by TACE who achieved complete response were included and divided into two groups according to disease free survival (DFS) status at 1 year: the non-early recurrence (NER) group (1) and the early recurrence (ER) group (2). Univariate binary logistic regression analysis for the possible risk factors of recurrence showed that AFP, multinodularity and HGF level were significant. Conclusion: high AFP, multinodularity and high HGF were inter-related possible risk factors for 1-year recurrence of HCC in patients with initial remission following TACE.

Predictive value of serum levels of transforming growth factor beta 1 for the short-term effects of radiotherapy and chemotherapy in patients with esophageal cancer

Objective To investigate variation in levels of transforming growth factor beta 1(TGF-β1)before and after radiotherapy in patients with esophageal cancer in order to evaluate the predictive value of TGF-β1 for the effects of radiotherapy Methods A total of 140 patients with esophageal squamous carcinoma undergoing radical radiation therapy in the Department of Oncology from March 2015 to December 2017 were enrolled.The patients were divided into the effective(115 cases)and ineffective(25 cases)groups according to World Health Organization(WHO)criteria for the evaluation of solid tumors(2009 RECIST standard).TGF-β1 levels were measured in all patients by using enzyme-linked immunosorbent assay(ELISA).Multiple-factor analysis of the predictive value of the treatment efficacy was performed by Cox regression analysis.Results After radiotherapy,36,79,and 25 cases experienced complete response(CR),partial response(PR),and no response(NR),respectively,with a total effective rate of 82.14%.The TGF-β1 level was significantly lower in the effective group than that in the ineffective group(P<0.05)and covariance analysis revealed significantly reduced TGF-β1 level in esophageal cancer patients following radiotherapy.The multi-factor Cox regression model revealed that the predictive value of TGF-β1 for the effect of radiotherapy was largest,with a hazard ratio[HR]of 1.955(P=0.002),followed by exposure dose,with(HR=1.367;P=0.035).Conclusion Serum TGF-β1 level can serve as a predictor for the short-term effects of radiotherapy in patients with esophageal cancer.

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.

基于原位EIS参数与改进BiGRU算法的侧柏苗木生长预测研究

侧柏苗木的生长预测对于苗木未来的出圃时间、调整现有苗木的种植方案等方面意义重大。本文运用原位电阻抗图谱技术发现4 kHz下的阻抗相位角、400 kHz下阻抗正切值可以表示侧柏根系的健康程度,与茎粗、株高、分枝数等指标共同构成代表侧柏苗木质量的特征向量;同时,设计了基于多维双向的时空融合模型(CNN-DBiGRU-WBLS),该模型可以有效解决多因素输入下未来4周、8周、16周的侧柏生长数据预测问题,Willmott一致性指数(WI)值达到0.993、0.975和0.948,比CNN-BiGRU、CNN-LSTM、CNN-BiLSTM和CNN-GRU等现有模型WI最优值提升了0.6%、1.1%和1.5%以上。试验结果表明,本文算法对侧柏苗木生长指标具有较好的预测性能。

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.

三维能量多普勒超声参数联合胎盘生长因子对早发型胎儿生长受限的预测价值

目的探讨三维能量多普勒超声参数联合胎盘生长因子(PLGF)对早发型胎儿生长受限(FGR)的预测价值。方法选取早发型FGR孕妇80例为FGR组,另选取同期产检健康孕妇50例为对照组。在孕11~13周+6对所有研究对象进行三维能量多普勒超声检查,收集胎盘容积(PV)、血管化指数(VI)、血流指数(FI)、血管化-血流指数(VFI)等指标。在孕14~16周+6检测所有研究对象血清PLGF水平。结果FGR组的PV、VI、FI、VFI以及血清PLGF水平均低于对照组,差异有统计学意义(P<0.05)。多元Logistic回归方程分析显示,PV、VI、FI、VFI以及血清PLGF水平过低是早发型FGR的危险因素(P<0.05)。受试者工作特征(ROC)曲线分析显示,PV、VI、VFI以及血清PLGF均对早发型FGR有一定的预测价值,曲线下面积分别为0.723(95%CI:0.629~0.817)、0.776(95%CI:0.693~0.860)、0746(95%CI:0.653~0.839)、0.799(95%CI:0.713~0.884),FI对早发型FGR的预测价值一般,曲线下面积为0.625(95%CI:0.524~0.725)。经分析显示,PLGF联合VI以及PLGF联合VFI对早发型FGR的预测价值较好,PLGF联合VI的敏感度、特异度和约登指数分别为86.25%、76.00%、0.623,PLGF联合VFI的敏感度、特异度和约登指数分别为81.25%、80.00%、0.613。结论三维能量多普勒超声参数联合PLGF对早发型FGR有一定的预测价值,可用于临床筛查早发型FGR高风险人群。

妊娠期糖尿病孕妇血清微小RNA-29a-3p、胰岛素样生长因子-1表达水平及其对胎儿生长受限的预测价值

目的探究妊娠期糖尿病(GDM)孕妇血清微小核糖核苷酸(micro RNA,miR)-29a-3p、胰岛素样生长因子-1(IGF-1)表达水平及其对胎儿生长受限的预测价值。方法选择2019年8月—2022年12月在本院202例就诊分娩的GDM患者(132例)及同期体检健康孕妇(70名)作为研究对象,就诊分娩的GDM患者为患病组,同期体检健康孕妇为对照组。根据GDM患者是否伴有胎儿生长受限分为GDM组和胎儿生长受限组,GDM组88例,胎儿生长受限组44例。qRT-PCR法检测血清miR-29a-3p水平,放射免疫法检测血清IGF-1表达。TargetScanHuman网站预测miR-29a-3p与IGF-1靶向关系。分析miR-29a-3p、IGF-1及其与胰岛素水平、新生儿体重、新生儿身长的相关性;Logistic回归分析GDM孕妇发生胎儿生长受限的影响因素;ROC曲线分析血清miR-29a-3p,IGF-1水平评估GDM孕妇发生胎儿生长受限的预测价值。结果患病组孕妇血清miR-29a-3p水平高于对照组,IGF-1水平低于对照组,差异有统计学意义(P<0.05);GDM组患者血清miR-29a-3p水平低于胎儿生长受限组,IGF-1水平高于胎儿生长受限组,差异有统计学意义(P<0.05);miR-29a-3p直接靶向作用于IGF-1表达;GDM组和胎儿生长受限组孕妇胰岛素、新生儿体重、新生儿身长比较,差异具有统计学意义(P<0.05)。相关性分析表明,血清miR-29a-3p与IGF-1水平呈负相关(r=-0.402,P<0.05);血清miR-29a-3p水平与新生儿体重、新生儿身长呈负相关,IGF-1与新生儿体重、新生儿身长呈正相关(P<0.05)。Logistic回归分析表明,miR-29a-3p,IGF-1是GDM孕妇发生胎儿生长受限的影响因素(P<0.05)。血清miR-29a-3p,IGF-1水平联合评估GDM孕妇发生胎儿生长受限的曲线下面积(AUC)为0.877(95%CI:0.808-0.928),显著高于两指标单独检测(Z_(miR-29a-3p-联合)=2.893,P=0.004;Z_(IGF-1-联合)=2.810,P=0.005)。结论GDM孕妇血清miR-29a-3p水平上调,IGF-1水平下调,与胎儿生长受限密切相关,二者联合对GDM孕妇发生胎儿生长受限有较好预测价值。

基于运营数据的发动机压气机叶片裂纹扩展规律研究

为研究航空发动机实际服役中转子叶片在复杂载荷作用下的裂纹损伤扩展规律,基于航空发动机运营数据构建叶片的裂纹扩展寿命曲线,同时采用断裂力学理论建立裂纹扩展速率公式。以高压压气机叶片为实例,通过流固耦合方法构建压气机叶片结构的载荷模型,分别采用Paris、Walker、Newman裂纹扩展速率公式对压气机叶尖区域进行三维裂纹扩展分析。仿真结果表明,三种裂纹扩展速率公式在裂纹扩展前期预测结果与发动机运营数据裂纹扩展曲线具有一致性,可较好地描述叶片裂纹扩展规律;在裂纹稳定扩展阶段,Walker和Newman公式预测结果相对危险,Paris公式预测结果吻合度较高,并且能与厂家技术手册中关于其损伤发展控制要求相互印证。

时间序列下双流增强的肺癌生长演变预测模型

【目的】提出一种时间序列下双流增强的肺癌生长演变预测模型(DSGNet)。【方法】DSGNet充分利用CNN以及Transformer的优势,通过基于CNN的分支来提取肿瘤的静态特征,并用多尺度的方式加强所提取到的特征表示。用基于Transformer的分支来获取肿瘤序列图像间的顺序依赖关系,该分支将病灶序列图像映射成一个特征映射序列,然后将特征映射序列输入包含有多头自注意力的深度网络中,并从多期病灶序列图像的特征映射中,提取完整的肿瘤间生长关系。【结果】在肺癌NLST以及合作医院的数据集上评估该算法,实验结果表明,DSGNet对肿瘤生长预测的Precision达到了92.45%、Dice系数为82.78%,相比于其他的肿瘤预测算法,本文所提DSGNet在各方面都有一定程度提升,且在多方面被证明能够应用于临床研究。

Machine learning-based real-time visible fatigue crack growth detection

Many large-scale and complex structural components are applied in the aeronautics and automobile industries.However,the repeated alternating or cyclic loads in service tend to cause unexpected fatigue fractures.Therefore,developing real-time and visible monitoring methods for fatigue crack initiation and propagation is critically important for structural safety.This paper proposes a machine learning-based fatigue crack growth detection method that combines computer vision and machine learning.In our model,computer vision is used for data creation,and the machine learning model is used for crack detection.Then computer vision is used for marking and analyzing the crack growth path and length.We apply seven models for the crack classification and find that the decision tree is the best model in this research.The experimental results prove the effectiveness of our method,and the crack length measurement accuracy achieved is 0.6 mm.Furthermore,the slight machine learning models help us realize real-time and visible fatigue crack detection.

外周血Lp-PLA2和FGF23水平变化与脑梗死后认知功能障碍的相关性

目的分析外周血脂蛋白相关磷脂酶A2(Lp-PLA2)、成纤维细胞生长因子23(FGF23)水平变化与脑梗死后患者认知功能障碍的相关性。方法选取2019-04—2022-12邯郸市中心医院收治的160例脑梗死患者为研究对象,根据患者是否发生认知功能障碍分为认知障碍组和非认知障碍组,对比2组基线资料及外周血Lp-PLA2、FGF23水平,并采用Logistic回归分析患者发生认知功能障碍的影响因素,采用Pearson相关性分析外周血Lp-PLA2、FGF23与简易智力状态评价量表(MMSE)评分的关系,采用ROC曲线评估外周血Lp-PLA2、FGF23对脑梗死后患者认知功能障碍的预测价值。结果160例脑梗死患者中,48例(30.00%)发生认知功能障碍。认知障碍组患者的平均年龄、高血压、糖尿病、吸烟、文化程度、MMSE评分及血清Lp-PLA2、FGF23水平等方面与非认知障碍组相比,差异有统计学意义(P<0.05)。Logistic回归分析显示,年龄、高血压、糖尿病、吸烟、文化程度低及血清Lp-PLA2、FGF23水平升高是影响脑梗死后认知功能障碍发生的独立危险因素(P<0.05)。Pearson相关性分析显示,脑梗死患者血清Lp-PLA2、FGF23水平与MMSE评分呈负相关(P<0.05)。ROC曲线显示,Lp-PLA2的曲线下面积为0.770,FGF23的曲线下面积为0.779,联合检测的曲线下面积为0.873(P<0.05),表示两者联合检测可作为评价脑梗死后认知功能障碍的有效指标。结论Lp-PLA2、FGF23在脑梗死后认知功能障碍患者血清中均呈高表达,二者联合检测有助于提高对脑梗死后认知功能障碍的预测价值。

基于改进Pix2Pix-HD网络的多品种水稻生长可视化预测方法

植物生长建模与预测能模拟植物的生长过程,有助于生理学家和植物学家分析植物未来的生长模式,缩短试验周期、降低试验成本,受时间和条件限制的植物试验与研究指导。生长可视化预测能提供未来生长时间点的植物图像,能更逼真、直观地描述植物的生长过程。水稻作为重要的粮食作物,实现水稻的生长可视化预测,对水稻生长发育分析具有十分重要的意义。针对传统作物生长预测方法存在的视觉真实度和可视化效果较差等问题,本文提出了一种基于改进Pix2Pix-HD模型的多品种水稻生长可视化预测方法,利用数据驱动的方式,实现了对水稻抽穗期到灌浆期的高分辨率生长可视化预测,通过水稻抽穗期的图像预测灌浆期水稻生长图像。方法评估中,本文从视觉相似性、表型准确性和不同尺度评估模型预测性能,通过消融实验评估改进方法的有效性,并与现有研究进行比较。结果表明,测试集预测的灌浆期水稻图像与真实灌浆期水稻图像之间的FID、PSNR和SSIM值分别达到24.75、13.58和0.78,预测表型和真实表型相关系数的平均值为0.762,在不同尺度上都能保持较好的准确性。本文提出的基于数据驱动的水稻生长预测方法能够实现高分辨率和高视觉真实性的水稻生长可视化预测,为水稻生长预测提供了新思路。