Rank correlation among different statistical models in ranking of winter wheat genotypes'

Several statistical methods have been developed for analyzing genotype×environment(GE)interactions in crop breeding programs to identify genotypes with high yield and stability performances.Four statistical methods,including joint regression analysis(JRA),additive mean effects and multiplicative interaction(AMMI)analysis,genotype plus GE interaction(GGE)biplot analysis,and yield–stability(YSi)statistic were used to evaluate GE interaction in20 winter wheat genotypes grown in 24 environments in Iran.The main objective was to evaluate the rank correlations among the four statistical methods in genotype rankings for yield,stability and yield–stability.Three kinds of genotypic ranks(yield ranks,stability ranks,and yield–stability ranks)were determined with each method.The results indicated the presence of GE interaction,suggesting the need for stability analysis.With respect to yield,the genotype rankings by the GGE biplot and AMMI analysis were significantly correlated(P<0.01).For stability ranking,the rank correlations ranged from 0.53(GGE–YSi;P<0.05)to0.97(JRA–YSi;P<0.01).AMMI distance(AMMID)was highly correlated(P<0.01)with variance of regression deviation(S2di)in JRA(r=0.83)and Shukla stability variance(σ2)in YSi(r=0.86),indicating that these stability indices can be used interchangeably.No correlation was found between yield ranks and stability ranks(AMMID,S2di,σ2,and GGE stability index),indicating that they measure static stability and accordingly could be used if selection is based primarily on stability.For yield–stability,rank correlation coefficients among the statistical methods varied from 0.64(JRA–YSi;P<0.01)to 0.89(AMMI–YSi;P<0.01),indicating that AMMI and YSi were closely associated in the genotype ranking for integrating yield with stability performance.Based on the results,it can be concluded that YSi was closely correlated with(i)JRA in ranking genotypes for stability and(ii)AMMI for integrating yield and stability.

Ranking environmental projects model based on multicriteria decision-making and the weight sensitivity analysis

With the fast growth of Chinese economic, more and more capital will be invested in environmental projects. How to select the environmental investment projects （alternatives） for obtaining the best environmental quality and economic benefits is an important problem for the decision makers. The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria. A model for ranking the projects of municipal sewage treatment plants is proposed by using exports＇ information and the data of the real projects. And, the ranking result is given based on the PROMETHEE method. Furthermore, by means of the concept of the weight stability intervals （WSI）, the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed. The result shows that some criteria, such as “proportion of benefit to project cost”, will influence the ranking result of alternatives very strong while others not. The influence are not only from the value of criterion but also from the changing the weight of criterion. So, some criteria such as “proportion of benefit to project cost” are key critera for ranking the projects. Decision makers must be cautious to them.

Rank-based deactivation model for networks with age

We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones. During evolution, each node experiences two stages： active and inactive. The transition from the active state to the inactive one is based on the rank of the node. In this paper, we adopt age as a criterion of ranking, and propose two deactivation models that generalize previous research. In model A, the older active node possesses the higher rank, whereas in model B, the younger active node takes the higher rank. We make a comparative study between the two models through the node-degree distribution.

Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.

Parametric estimation for the simple linear regression model under moving extremes ranked set sampling design

Cost effective sampling design is a major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming.Ranked set sampling(RSS)was first proposed by McIntyre[1952.A method for unbiased selective sampling,using ranked sets.Australian Journal of Agricultural Research 3,385-390]as an effective way to estimate the pasture mean.In the current paper,a modification of ranked set sampling called moving extremes ranked set sampling(MERSS)is considered for the best linear unbiased estimators(BLUEs)for the simple linear regression model.The BLUEs for this model under MERSS are derived.The BLUEs under MERSS are shown to be markedly more efficient for normal data when compared with the BLUEs under simple random sampling.

Resolution performance analysis of cumulants-based rank reduction estimator in presence of unexpected modeling errors

Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and mitigating the negative influences of the Gaussian colored noise. However, in the presence of unexpected modeling errors, the resolution behavior of the FOC-RARE also deteriorate significantly as SOS-RARE, even for a known array covariance matrix. For this reason, the angle resolution capability of the FOC-RARE was theoretically analyzed. Firstly, the explicit formula for the mathematical expectation of the FOC-RARE spatial spectrum was derived through the second-order perturbation analysis method. Then, with the assumption that the unexpected modeling errors were drawn from complex circular Gaussian distribution, the theoretical formulas for the angle resolution probability of the FOC-RARE were presented. Numerical experiments validate our analytical results and demonstrate that the FOC-RARE has higher robustness to the unexpected modeling en＇ors than that of the SOS-RARE from the resolution point of view.

Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

In the conventional technique,in the evaluation of the severity index,clustering and loading suffer from more iteration leading to more com-putational delay.Hence this research article identifies,a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects.The polynomial load modelling or ZIP(constant impedances(Z),Constant Current(I)and Constant active power(P))is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security.The process of finding the severity of the line using a Hybrid Line Stability Ranking Index(HLSRI)is used for assisting the concepts of machine learning with J48 algorithm,infers the superior affected lines by adopting the IEEE standards in concern to be compensated in maintaining the power system stability.The simulation is performed in the WEKA environment and deals with the supervisor learning in order based on severity to ensure the safety of power system.The Unified Power Flow Controller(UPFC),facts devices for the purpose of compensating the losses by maintaining the voltage characteristics.The finite element analysis findings are compared with the existing procedures and numerical equations for authentications.

Performance of cumulant-based rank reduction estimator in presence of unexpected modeling errors

Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.

Ranking-Based Recommendation System with Text Modeling

In the study of recommendation systems,many methods based on predicting ratings have been put forward.However,the rating-predicting methods have some shortages.It pays too much attention to predicting,instead of the nature of recommendation,which is predicting the order of ratings.Thus,we use a pairwise-based learning algorithm to learn our model and take the zero-sampling method to improve our model.In addition,we propose a text modeling method making the recommendations more explicable.It is proved that our system performs better than other state-of-art

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

RPL-Based IoT Networks under Decreased Rank Attack:Performance Analysis in Static and Mobile Environments

The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.Our systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and APC.These results underscore the urgent need for robust security measures to protect RPL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.

基于OPG/RANKL/RANK信号通路探讨傣药肾叶山蚂蝗的抗骨质疏松作用

目的探讨傣药肾叶山蚂蝗对去卵巢骨质疏松大鼠模型的作用及其机制。方法60只雌性SD大鼠随机分为假手术组、模型组、仙灵骨葆组(0.24 g·kg^(-1))、肾叶山蚂蝗低(1.35 g·kg^(-1))、中(2.70 g·kg^(-1))和高剂量组(5.40 g·kg^(-1))。采用双侧卵巢切除术建立骨质疏松模型,药物干预14周后,检测血清中骨钙素(Bone gamma-carboxyglutamic-acid-containing Proteins,BGP)、骨保护素(Ostoeprotegerin,OPG)和碱性磷酸酶(Alkaline phosphatase,ALP)的含量,苏木精-伊红(HE)染色法观察骨组织中骨小梁的变化;实时荧光定量PCR(qPCR)检测大鼠胫骨中OPG/RANKL/RANK信号通路相关基因的表达。另取破骨细胞前体细胞株RAW264.7,分为阴性对照组、核因子κB受体活化因子配体(RANKL)组、仙灵骨葆组、肾叶山蚂蝗低、中、高剂量组。阴性对照组不加RANKL,其余各组使用50 ng·mL^(-1) RANKL诱导,药物组同时分别加入不同浓度的仙灵骨葆和肾叶山蚂蝗含药血清进行干预。10天后进行抗酒石酸酸性磷酸酶(TRAP)染色,观察破骨细胞分化情况,qPCR测定OPG/RANKL/RANK信号通路相关基因的表达。结果与假手术组比较,模型组血清中OPG含量显著降低(P<0.01),ALP和BGP显著升高(P<0.01),骨小梁显著减少,断裂,排列稀疏,骨小梁之间间距大,胫骨组织OPG mRNA表达显著减少(P<0.01),RANKL、肿瘤坏死因子受体相关蛋白6(TRAF6)、活化T-细胞核因子1(NFATc1)、组织蛋白酶K(CTK)和降钙素受体(CalcR)mRNA水平的表达升高(P<0.01),肾叶山蚂蝗干预后能显著改善以上指标。RAW264.7培养10天后,与阴性对照组比较,RANKL组破骨细胞明显增多,肾叶山蚂蝗能显著降低破骨细胞的数量,OPG/RANKL/RANK通路相关基因表达趋势和动物实验一致。结论肾叶山蚂蝗能有效改善去卵巢大鼠模型的骨质疏松,其作用机制可能是通过抑制破骨细胞增殖分化,调节OPG/RANKL/RANK信号通路来实现。

嘌呤霉素氨基核苷肾病肾脏中RANK-RANKL的表达

目的探讨RANK-RANKL在嘌呤霉素氨基核苷肾病(PAN)大鼠动物模型肾脏中的表达。方法 36只SD大鼠被分配为PAN组及对照组,单次静脉注射嘌呤霉素氨基核苷(PA,100 mg/kg)制作PAN动物模型。大鼠于第3、7、14天检测蛋白尿及血肌酐。检测肾脏病理,并分析RANK和RANKL变化。结果(1)在PAN SD大鼠动物模型中,第3、7、14天时蛋白尿与对照组比较有显著差别,第7天达到高峰;(2)用Western blotting及RT-PCR定量检测发现RANK-RANKL蛋白及mRNA在PAN模型组高于对照组;(3)用激光共聚焦技术显示足细胞标记蛋白Synaptopodin与RANK完全重合,提示RANK主要在足细胞表达;(4)免疫电镜发现RANK在PAN模型组明显增多,且主要定位于足突的顶部胞膜和胞质内。结论在PAN SD大鼠动物模型足细胞异常表达RANK-RANKL,提示RANK及RANKL在足细胞损伤中可能发挥重要作用。

基于Rank Logit模型对城际交通分担率的计算方法研究

城际交通对城市经济发展有显著作用,同样也是交通规划领域一个重要课题.随着高铁的快速发展,城际交通分担率也随之有相应变化.计算分担率的方法很多,其中Rank Logit模型能够充分利用SP调查中乘客对交通工具选择的排序数据,可以获得比其他非集计模型更高精度的特点.本文采集891份有效SP数据并建立Rank Logit模型,对广州—武汉的城际交通分担率展开研究以验证该模型的优点.模型结果从评价指标、参数实际意义和乘客时间价值等方面对比Rank Logit模型和Multinomial Logit模型,证明RL模型拥有更高的模型精度和可靠性.在此基础上本文进一步对武广通道上政策性调整进行了6组敏感性分析,为政府进行交通规划、交通运输政策的制订及修改等提供决策依据,也为铁道部、航空公司等运营部门制定策略提供了理论参考.

新的PageRank优化算法

为了克服PageRank在搜索过程中重复性地把当前受欢迎的网页放在搜索结果的首要位置,而不受欢迎的网页被大多数用户忽略的问题,采用了一种改进的评估函数及有效的用户模型,获得了一个新的PageRank优化算法。实验结果表明,该算法达到了较好的公平性。

基于病例组合指数校正抗菌药物使用强度的模型拟合研究

目的:探究病例组合指数与抗菌药物使用强度之间的关联关系,并对抗菌药物使用的评价进行调整,对其综合考量。方法:收集安徽省某三甲医院2021-2023年病例组合指数与抗菌药物相关指标,首先通过线性模型、二次曲线等8种模型拟合探索病例组合指数与抗菌药物相关指标之间的关系,随后通过秩和比综合评价法对不同科室间抗菌药物的使用情况重新评价。结果:病例组合指数与抗菌药物使用强度之间关联性较弱,但病例组合指数与同期出院人数的乘积与抗菌药物使用累积数存在较强关联,以此为基础进行的秩和比综合评价结果前后出现较大波动。结论:病例组合指数与抗菌药物使用强度之间存在间接关联,在后续抗菌药物使用的模型运算时应考虑校正,以增强评价结果的真实性。

基于Low-rank一步法波场延拓的黏声各向异性介质纯qP波正演模拟

各向异性介质纯qP波正演模拟及逆时偏移近年受到广泛关注,但它虽考虑了地下介质的各向异性特征,却忽略了黏滞性特征,使得最终偏移结果中噪声增加、分辨率降低。常规拟声波方程存在伪横波干扰、受模型参数限制(ε≥δ)、传播不稳定等因素影响,极大地限制了其应用。为此,引入一步法波场延拓方法,推导了黏声介质方程在空间—波数域的表达形式;结合空间—波数域各向异性介质延拓算子,构建一种适用于黏声各向异性介质的空间—波数域纯qP波波场延拓算子;引入Low-rank分解算法,实现基于Low-rank一步法波场延拓的黏声各向异性介质纯qP波正演模拟。数值模拟结果表明:①地震波场能同时表现出各向异性特征和黏滞性特征,更符合实际地下介质情况;②该方法克服了拟声波方程的局限性,消除了伪横波干扰,不受模型参数限制且地震波场能稳定传播;③在适当增大时间步长情形下无数值频散现象,所提算法能同时兼顾计算效率和计算精度,是一种稳定、高效的正演模拟方法,为基于Q补偿的各向异性介质逆时偏移提供了理论依据。

基于页面链接结构Page Rank算法的改进——有向访问模型

互联网上的信息每天都以指数量级的速度爆炸性增长,面对如此浩瀚的资源,从web中的大量信息中准确并且有效的提取用户所需要的信息成为了Internet的用户的迫切需要。web信息检索系统可以利用web页面的这种特殊的链接结构关系来改进检索的算法,以提高检索的精度。链接结构分析显著地提高了检索结果的相关性。在充分分析基于链接结构的算法的基础上,本文提出了一个更接近真实情形的模型——有向访问模型,它假定访问者将根据与查询相关的概率模型来指导下一步的访问,它能够真实地描述用户在浏览网页时的行为。

基于Low-rank分解的复杂TI介质纯qP波正演模拟与逆时偏移

近年来,面向实际应用的TI介质准P波正演模拟与逆时偏移成像技术受到空前的关注.基于常规耦合型传播方程的正演模拟方法不仅存在伪横波及频散假象干扰,而且还遭受模型参数限制(η>0)和不稳定影响;而纯qP波方程的推导繁琐,且由于方程中包含拟微分算子造成求解难度大且精度有限.为此,本文首先构建了一种适用于任意TI介质的纯qP波传播算子,然后借助Low-rank分解求取该算子中的空间-波数域矩阵,同时引入Cerjan衰减边界条件来压制边界反射干扰,最终实现了一种间接的纯qP波波场外推方案,并将其成功应用于复杂TI介质正演模拟与逆时偏移成像中.通过开展数值模拟,并与其他方法对比表明:①该方法既避免了纯qP波方程的繁琐推导,又克服了耦合型方程对模型参数的限制;②还彻底消除了残余伪横波噪音及数值频散;③且能适应较大时间或空间步长及高频震源,是一种相对准确且稳定的各向异性纵波正演与成像策略.

基于PageRank的在线社交网络消息传播模型

经典的消息传播模型没有充分考虑在线社交网络的复杂性以及网络节点间的拓扑结构差异。针对这种情况,提出一种基于Page Rank的在线社交网络的消息传播模型P-SIR。该模型利用节点的Page Rank值作为节点权威度并考虑在线社交网络传播机理,刻画不同类型节点随着时间变化的状态演化关系,反映消息传播过程受到网络拓扑结构和传播机理的影响。该模型还考虑在线社交网络中影响消息传播过程中的一些实际因素,动态指定节点的权威度以适应非均质网络,并考虑外部社会加强效应。采用3种不同类型的网络模拟消息传播过程,通过仿真实验验证P-SIR模型可以有效反映在线社交网络中的消息传播过程。