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.

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.

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.

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.

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.

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.

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.

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。

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.

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。

子宫动脉血流联合脑胎盘率预测胎儿生长受限的价值研究

目的分析子宫动脉血流与脑胎盘率(cerebral placental rate,CPR)对胎儿生长受限(fetal growth restriction,FGR)诊断的应用价值。方法选取2021年1月至2022年6月于江西省妇幼保健院建卡并住院分娩的临床诊断为晚发型FGR的114例孕妇纳入研究组,122名胎儿宫内发育正常的孕妇为对照组。超声测定两组孕妇的子宫动脉(uterine artery,UtA)、脐动脉(umbilical artery,UA)与大脑中动脉(middle cerebral artery,MCA)的血流参数,计算两组孕妇的CPR,比较两组孕妇的血流差异与妊娠结局,采用受试者操作特征曲线(receiver operating characteristic curve,ROC曲线)分析UtA及CPR单独及联合应用在FGR临床诊断中的应用价值。结果研究组孕妇UtA的阻力指数(resistance index,RI)高于对照组,胎儿UA的血流参数较对照组偏高,MCA的血流参数较对照组低,CPR值较对照组低,差异均有统计学意义(P<0.05);研究组新生儿出生体质量与1min Apgar评分较对照组低(P<0.001);研究组的急诊剖宫产手术率、早产及因各种并发症导致的新生儿转入新生儿重症监护病房(neonatal intensive care unit,NICU)的发生率高于对照组(P<0.05)。ROC曲线显示在预测FGR方面,UtA-RI的曲线下面积(area under the curve,AUC)为0.82(95%CI:0.77~0.88),CPR的预测效率AUC为0.75(95%CI:0.69~0.81)。UtA-RI和CPR参数组合在预测FGR方面具有较高的效率,AUC为0.92(95%CI:0.89~0.95)。结论CPR联合UtA-RI监测对临床早期发现FGR、指导干预、改善不良围生儿结局具有临床应用价值。

miR-263干扰对麦长管蚜生命表参数的影响

【目的】麦长管蚜Sitobion avenae是小麦生产的首要害虫。本研究旨在明确miR-263干扰对麦长管蚜室内种群的影响,为小麦蚜虫的绿色防控提供技术支持。【方法】以纳米材料为载体,利用400 nmol/L miR-263 agomir干扰麦长管蚜1龄若蚜,采用两性生命表评价miR-263干扰对其生长发育、繁殖和种群增长的影响,以清水阴性对照(water negative control, NCW)和纳米材料阴性对照(nanomaterial negative control, NCS)作为对照组。【结果】miR-263干扰后,与NCW和NCS相比,麦长管蚜1和2龄若蚜历期显著延长,3龄若蚜历期没有显著差异,4龄若蚜历期要显著短于NCW组,整个若蚜历期显著延长;全生育期(25.42 d)显著长于NCS的(23.73 d),与NCW (23.84 d)没有显著差异;成蚜寿命和繁殖历期有所延长,单雌产蚜量有所增加,但与NCW和NCS的比没有显著差异。miR-263干扰后麦长管蚜成蚜前存活率为43.33%,显著低于NCW和NCS的(分别为91.34%和91.32%)。种群生命表数据显示,miR-263干扰后内禀增长率(r)、周限增长率(λ)、净增殖率(R_0)均显著低于NCW和NCS的,平均世代周期(T)比NCW和NCS显著延长;种群预测结果显示,在miR-263干扰60 d时麦长管蚜种群约为8万头,显著低于NCS的454万头和NCW的394万头。【结论】miR-263干扰主要影响低龄若蚜的历期和存活率,并对麦长管蚜的种群增长具有抑制作用,研究结果为麦长管蚜RNAi生物农药的研发提供了候选基因。

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。

一株海洋杆菌属新菌种XAAS-72^(T)的植物促生功能分析及ACC脱氨酶蛋白结构预测

【目的】研究海洋杆菌属新菌种XAAS-72的植物促生功能,挖掘其潜在功能基因。【方法】通过对菌株全基因组测序,分析相关功能基因组成,挖掘ACC脱氨酶合成相关的候选基因,并进行功能预测。【结果】海洋杆菌属新种Pontibacter kalidii XAAS-72菌悬液处理可显著提高盆栽小麦麦苗生长,其基因组长度为5054860 bp,含1个环形质粒,总GC含量为54.52%,注释的基因数目为4391个,编码蛋白数4261个,具有多种抗逆和促生相关基因。其与Pontibacter sp.BAB1700的ACC脱氨酶(1-aminoeyclopropane-1-earboxylate-deaminase)相似度最高为72.48%。该蛋白属于不稳定亲水性蛋白,不具备跨膜结构,且无信号肽结构。【结论】海洋杆菌属新种XAAS-72蕴藏着丰富的抗逆和植物促生相关基因。