Relationship Between Heterosis and Parental Genetic Distance Based on Molecular Markers for Functional Genes Related to Yield Traits in Rice

The genetic distances among 18 cytoplasmic male sterile lines and 11 restorer lines were analyzed with molecular markers derived from yield-related functional genes. The correlation between parental genetic distance and heterosis was investigated by analyzing the performance of 47 combinations. The results showed that the genetic distance was significantly correlated with yield heterosis （r=0.29^＊）, but not significantly correlated with heterosis for other traits, such as number of effective panicles per plant, seed setting rate, 1000-grain weight, number of grains per panicle and theoretical yield. However, the correlation coefficient was so small that the parental genetic distance could not to be used to predict heterosis.

Humbert＇s Self-defence for His Relation With Lolita

In Vladimir Nabokov＇s novel Lolita, we can find that Humbert tries to defend himself for his relation with Lolita. Humbert says Lolita has a precursor that is Annabel. That is pure love between children. Pure love is innocent, so Humbert wants to say he is not to blame. His relation with Lolita is pure love. In addition, Edgar Allan Poe wrote a poem about Annabel Lee. And Humbert wants to say that it is the poem that makes him love Lolita.

Reciprocity Relation between Alternative Gravity Formulas

We compare Newton’s force law of universal gravitation with a corrected simple approach based on Bhandari’s recently presented work, where the gravitation constant G is maintained. A reciprocity relation exists between both alternative gravity formulas with respect to the distances between mass centers. We conclude a one-to-one mapping of the two gravitational formulas. We don’t need Einstein’s construct of spacetime bending by matter.

DBLAR:A DISTANCE-BASED LOCATION-AIDED ROUTING FOR MANET

In location-aided routing of Mobile Ad hoc NETworks(MANET),nodes mobility and the inaccuracy of location information may result in constant flooding,which will reduce the network performance.In this paper,a Distance-Based Location-Aided Routing(DBLAR) for MANET has been proposed.By tracing the location information of destination nodes and referring to distance change between nodes to adjust route discovery dynamically,the proposed routing algorithm can avoid flooding in the whole networks.Besides,Distance Update Threshold(DUT) is set up to reach the balance between real-time ability and update overhead of location information of nodes,meanwhile,the detection of relative distance vector can achieve the goal of adjusting forwarding condition.Simulation results reveal that DBLAR performs better than LAR1 in terms of packet successful delivery ratio,average end-to-end delay and routing-load,and the set of DUT and relative distance vector has a significant impact on this algorithm.

Predicting the Relative Retention Time (RRT) of Polybrominated Diphenyl Ethers (PBDEs)

Using the molecular electronegativity distance vector descriptors derived directly from the molecular topological structures, the relative retention time （RRT） of polybrominated diphenyl ethers （PBDEs） were predicted. A four-variable regression model （M30） with the correlation coefficient of 0.9816 and the root mean square errors of 0.061 was developed using a training set including 30 PBDEs. The correlation coefficient of 0.9841 and the root mean square errors of 0.054 between the values of RRT predicted by M30 and the RRT observed for 16 external PBDEs show a good predictive potential of M30. The descriptors included in the M30 represent four interactions between four pairs of atom types, i.e., atom -C= and -C=, -C= and 〉C=, 〉C= and 〉C=, -C= and -Br.

Clustering Categorical Data Based on Within-Cluster Relative Mean Difference

The clustering on categorical variables has received intensive attention. In dataset with categorical features, some features show the superior performance on clustering procedure. In this paper, we propose a simple method to find such distinctive features by comparing pooled within-cluster mean relative difference and then partition the data upon such features and give subspace of the subgroups. The applications on zoo data and soybean data illustrate the performance of the proposed method.

The Triangle Inequality and Its Applications in the Relative Metric Space

Let C be a plane convex body. For arbitrary points , a,b ∈E ndenote by │ab│?the Euclidean length of the line-segment ab. Let a1b1 be a longest chord of C parallel to the line-segment ab. The relative distance dc(a,b) between the points a and b is the ratio of the Euclidean distance between a?and b?to the half of the Euclidean distance between a1 and b1. In this note we prove the triangle inequality in E2 with the relative metric dc( .,.), and apply this inequality to show that 6≤l(P)≤8, where l(P) is the perimeter of the convex polygon P measured in the metric dp( .,.). In addition, we prove that every convex hexagon has two pairs of consecutive vertices with relative distances at least 1.

An Alternative Approach to AIC and Mallow’s Cp Statistic-Based Relative Influence Measures (RIMS) in Regression Variable Selection

Outlier detection is an important data screening type. RIM is a mechanism of outlier detection that identifies the contribution of data points in a regression model. A BIC-based RIM is essentially a technique developed in this work to simultaneously detect influential data points and select optimal predictor variables. It is an addition to the body of existing literature in this area of study to both having an alternative to the AIC and Mallow’s Cp Statistic-based RIM as well as conditions of no influence, some sort of influence and perfectly single outlier data point in an entire data set which are proposed in this work. The method is implemented in R by an algorithm that iterates over all data points;deleting data points one at a time while computing BICs and selecting optimal predictors alongside RIMs. From the analyses done using evaporation data to compare the proposed method and the existing methods, the results show that the same data cases selected as having high influences by the two existing methods are also selected by the proposed method. The three methods show same performance;hence the relevance of the BIC-based RIM cannot be undermined.

基于最优组合权重的GRA-TOPSIS地下采场爆破方案优选模型

从经济技术、人员设备安全及爆破质量参数等3个方面综合考虑,选取15项二级指标作为影响采场爆破效果的评判标准,将AHP法与熵权法相结合进行赋权以确定最优权重,并将优劣解距离法(TOPSIS)与灰色关联法(GRA)相结合,构建GRA-TOPSIS综合评价模型。以某铜矿为例,初选5种爆破方案,根据评价指标对各爆破方案进行定量分析,得出其综合优越度,进而确定最优爆破方案。研究结果表明,5种爆破方案综合优越度分别为58.5%、62.0%、35.0%、68.7%、32.8%,即最优爆破方案为倾斜掏槽、80°倾斜崩矿。经矿山实践验证,评价结果与实际爆破效果吻合,表明将该模型用于采场爆破方案优选是可行的。

基于贝叶斯网和RoBERTa的文本派生关系挖掘方法

对不实信息进行溯源分析是抑制社交网络中不实信息传播的重要手段,传统数据溯源方法主要针对结构化数据,难以准确判断文本之间的派生关系。针对这些问题,提出一种基于贝叶斯网和RoBERTa的文本派生关系挖掘方法,通过RoBERTa模型获得文本向量;通过RoBERTa模型初步预测文本间的派生关系,得到文本是否具有派生关系的分类标签;基于向量距离、文本距离、时间跨度和文本分类标签构建贝叶斯网,对文本派生关系进行判断。实验结果表明,所提方法查准率、查全率、F 1值均高于对比方法,验证了该方法的有效性。

Software Cost Estimation Using Social Group Optimization

This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often limit its adaptability,as they don’t account for variations across organizations.By fine-tuning these parameters with SGO,we aim to improve estimation accuracy.We train and validate our SGO-enhanced model using historical project data,evaluating its performance with metrics like the mean magnitude of relative error(MMRE)and Manhattan distance(MD).Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost models,offering valuable insights for project managers and practitioners in the field.However,the approach’s effectiveness may vary depending on the quality and quantity of available historical data,and its scalability across diverse project types and sizes remains a key consideration for future research.

自由场听觉距离感知因素的动态虚拟实验研究

已有的物理分析和基于真实声源的实验研究表明,响度和近场头相关传输函数(HRTF,包括双耳声级差ILD和耳的声压谱)是自由场下听觉距离感知因素。但近年基于虚拟听觉重放(VAD)平台的相关实验结论却与此不一致,特别是在近场HRTF中的ILD是否为自由场距离感知的重要因素问题上存在争议。为解决这些争议并进一步研究不同因素的贡献及其协同作用,利用动态VAD平台和准确的近场HRTF合成的双耳声信号并使用耳机重放,采用心理声学实验研究了距离感知因素。结果表明,首先不同距离感知因素的贡献与声源的方向有关:(1)响度因素在所有方向都有重要贡献;(2) ILD主要在侧向有重要贡献,其贡献随着声源向前、后或高仰角偏离侧向而减少;(3)谱是较弱的因素,只有在中垂面ILD为零时才有可能检测到其贡献。其次,不同因素间存在协同交互作用和一定冗余:(1)响度与ILD的交互作用明显增强侧向距离感知;(2)响度与谱因素交互作用一定程度上增强后方的距离感知;(3)单一的ILD或响度可在一定程度上产生侧向距离感知。近年距离感知研究中的争议主要是因为这些实验是在稳态VAD平台上进行的,稳态VAD容易引起头中定位,影响距离感知;同时不准确的近场HRTF数据、不合适的实验方法与数据统计也会导致不一致的实验结论。

Deep learning method for testing the cosmic distance duality relation

The cosmic distance duality relation(DDR)is constrained by a combination of type-Ⅰa supernovae(SNe la)and strong gravitational lensing(SGL)systems using the deep learning method.To make use of the full SGL data,we reconstruct the luminosity distance from SNeⅠa up to the highest redshift of SGL using deep learning,and then,this luminosity distance is compared with the angular diameter distance obtained from SGL.Considering the influence of the lens mass profile,we constrain the possible violation of the DDR in three lens mass models.The results show that.in the singular isothermal sphere and extended power-law models,the DDR is violated at a high confidence level,with the violation parameterη0=-0.193-0.019+0.021andη0=-0.247-0.013+0.014,respectively.In the power-law model,however,the DDR is verified within a 1σconfidence level,with the violation parameterη0=-0.014-0.045+0.053.Our results demonstrate that the constraints on the DDR strongly depend on the lens mass models.Given a specific lens mass model,the DDR can be constrained at a precision of O(10-2)using deep learning.

一种铝电解槽极距的测算方法

从电解铝基础理论出发,研究铝电解槽极距与相关技术条件参数之间的数学关系,利用电解常规测量和化学分析数据快速测算极距,通过实践对该测算方法的可行性进行了有效验证,列举了测算极距的注意事项,并对影响铝电解槽极距的现场因素进行了分析。

The Four Intersection-and-Difference Model for Line-Line Topological Relations

The description of line-line topological relations is still an unsolved issue although much effort has been done. The problem is involved in many practical applications such as spatial query, spatial analysis and cartographic generalization. To develop a sound and effective approach to describe line-line relations, it is first necessary to define the topology of an individual line, i.e., local topology. The concept of connective degree is used for the identification of topological differences in the geometric structure of a line. The general topological definition of a line is given, i.e., endpoints set and interior point set. This definition can be applied to the embedded spaces of different dimensions, whether co-dimension is equal to or larger than zero. On this basis, a generic model called the 4 intersection-and-difference is set up for the description of basic line-line topological relations, upon which a conceptual neighborhood graph is built with consideration of topological distance, it is concluded that the proposed model can represent the property of topological changes, and basic relations between line segments in IR^1 and IR^2.

Evaluating Relational Ranking Queries Involving both Text Attributes and Numeric Attributes

In many database applications, ranking queries may reference both text and numeric attributes, where the ranking functions are based on both semantic distances/similarities for text attributes and numeric distances for numeric attributes. In this paper, we propose a new method for evaluating such type of ranking queries over a relational database. By statistics and training, this method builds a mechanism that combines the semantic and numeric distances, and the mechanism can be used to balance the effects of text attributes and numeric attributes on matching a given query and tuples in database search. The basic idea of the method is to create an index based on WordNet to expand the tuple words semantically for text attributes and on the information of numeric attributes. The candidate results for a query are retrieved by the index and a simple SQL selection statement, and then top-N answers are obtained. The results of extensive experiments indicate that the performance of this new strategy is efficient and effective.

结合特征增强和多尺度感受野的低照度目标检测

低照度图像普遍存在噪声、颜色失真和低对比度等图像退化问题,不仅影响视觉体验,而且严重影响低照度目标检测精度.为了更好地完成低照度目标检测任务,提出一种结合特征增强和多尺度感受野(feature enhancement and multi-scale receptive field,FEMR)的低照度目标检测算法.首先,像素级高阶映射(pixel-level high-order mapping,PHM)模块学习低照度到正常照度的高阶映射关系,进而提高低照度目标特征显著性,从而获得初步增强的特征信息.然后,关键信息增强(key information enhancement,KIE)模块结合多种注意力机制,突出重要特征并过滤噪声信息,获得进一步增强的特征信息.此外,长距离特征捕获(long distance feature capture,LFC)模块引入多种尺度的条状感受野,捕获低照度场景中孤立区域的长距离关系.实验表明,所提算法在低照度目标检测精度方面具有较好的表现,同时能直接输出正常照度风格图像下的检测结果,实现端到端的低照度目标检测,便于人眼直接评估检测结果的精度.

动作自主性与社会距离对主动控制感的影响:来自行为与ERPs的证据

本文通过2个实验,考察动作自主性与社会距离对主动控制感的影响,以厘清不道德事件下主动控制感降低的认知神经机制。实验1采用时间估计法,在行为层面上考察动作自主性对主动控制感的影响以及社会距离的调节作用,实验2采用事件相关电位技术进一步探究动作自主性与社会距离如何交互影响主动控制感,并试图检验外显主动控制感的结果稳健性。结果发现,服从命令时,相较于电击陌生人,电击朋友的决策诱发了更大的P3波幅,且音调反馈时诱发了更负的N1波幅,内隐主动控制感更强;在外显主动控制感上,仅发现自由选择时控制感和责任感评分比服从命令时更高。结果表明社会距离自下而上地调节了自主性对内隐主动控制感的影响,而外显主动控制感上个体有意识地产生了道德推脱。

特早熟甘蓝型春油菜产量相关性状的杂种优势分析

油菜是我国重要的油料作物,其中白菜型油菜在我国高海拔、高纬度春油菜区大面积种植,但其产量较低、品质差、育种难度大。为实现春油菜区的最重要育种目标,即培育出特早熟高产优质甘蓝型春油菜替代白菜型油菜,本研究对特早熟甘蓝型春油菜恢复系与不育系进行特异酶切位点扩增片段测序(SLAF-seq),分析遗传族群关系,在每类按比例选取部分恢复系与不育系配制组合,分析杂种表现、杂种优势及杂优模式。结果表明,98份甘蓝型油菜被分为五类。其中,第四类中包含不育系,恢复系与不育系的遗传距离介于0.01~0.52,平均遗传距离为0.35;第五类群恢复系与不育系遗传距离最大,介于0.45~0.52之间;在小区产量前二十的组合中,不育系×第五类恢复系组合10个,产量最高组合为(64-1)×16D3(第五类)。结合杂种表现和杂种优势结果,说明以特早熟不育系×第五类恢复系组配杂交组合,后代中产生强优势杂交种的潜力较大。本研究为后续特早熟甘蓝型油菜杂交种选育提供了亲本选择的分子依据,可促进春油菜区特早熟杂交育种进程。

基于改进灰色关联分析的航天产品性能样机仿真结果一致性验证方法

灰色关联分析法因其计算简单、不要求大样本容量等诸多优点被广泛应用于仿真模型验证领域,但邓氏关联度模型存在无法判断序列距离的弊端,容易造成仿真验证结果的误判。为了提高使用灰色关联分析法验证仿真结果可靠性的准确度,提出一种用以判断数据序列之间距离的距离分析方法。在验证航天产品性能样机仿真结果一致性时,将距离分析方法与灰色关联分析法相结合,形成考虑序列距离的灰色关联分析法,从曲线的形状与位置两个方面同时判断两组序列的一致性,从而提高了验证结果的准确度与可靠性。