Vertex-Edge Degree Based Indices of Honey Comb Derived Network

Chemical graph theory is a branch of mathematics which combines graph theory and chemistry.Chemical reaction network theory is a territory of applied mathematics that endeavors to display the conduct of genuine compound frameworks.It pulled the research community due to its applications in theoretical and organic chemistry since 1960.Additionally,it also increases the interest the mathematicians due to the interesting mathematical structures and problems are involved.The structure of an interconnection network can be represented by a graph.In the network,vertices represent the processor nodes and edges represent the links between the processor nodes.Graph invariants play a vital feature in graph theory and distinguish the structural properties of graphs and networks.In this paper,we determined the newly introduced topological indices namely,first ve-degree Zagreb?index,first ve-degree Zagreb?index,second ve-degree Zagreb index,ve-degree Randic index,ve-degree atom-bond connectivity index,ve-degree geometric-arithmetic index,ve-degree harmonic index and ve-degree sum-connectivity index for honey comb derived network.In the analysis of the quantitative structure property relationships(QSPRs)and the quantitative structure-activity relationships(QSARs),graph invariants are important tools to approximate and predicate the properties of the biological and chemical compounds.Also,we give the numerical and graphical representation of our outcomes.

MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

Given a protein sequence, how can we identify whether it is a metalloprotein or not? If it is, which main functional class and subclasses it belongs to? This is an important biological question because they are closely related to the biological function of an uncharacterized protein. Particularly, with the avalanche of protein sequences generated in the post genomic era and since conventional techniques are time consuming and expensive, it is highly desirable to develop an automated method by which one can get a fast and accurate answer to these questions. Here, a top-down predictor, called MetalloPred, is developed which consists of 3 level of hierarchical classification using cascade of neural networks from sequence derived features. The 1st layer of the prediction engine is for identifying a query protein as metalloprotein or not;the 2nd layer for the main functional class;and the 3rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 60% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has 30% sequence identity with any other in the same class or subclass. MetalloPred achieved good prediction accuracies and could nicely complement experimental approaches for identification of metal binding proteins. MetalloPred is freely available to be used in-house as a standalone and is accessible at http://www.juit.ac.in/assets/Metallopred/.

Non-derivative solution to nonlinear dynamic optimal design of class two for deformation network monitoring

Based on the nonlinear error equation of deformation network monitoring, the mathematical model of nonlinear dynamic optimal design of class two was put forward for the deformation network monitoring, in which the target function is the accuracy criterion and the constraint conditions are the network’s sensitivity, reliability and observing cost. Meanwhile a new non derivative solution to the nonlinear dynamic optimal design of class two was also put forward. The solving model uses the difference to stand for the first derivative of functions and solves the revised feasible direction to get the optimal solution to unknown parameters. It can not only make the solution to converge on the minimum point of the constraint problem, but decrease the calculating load.

Local Gateway Assisted Handover Key Derivation in Enterprise Femtocell Network

With the dense deployment of femtocells in enterprise femtocell network and the small coverage of femtocells, handover in enterprise femtocell network will be frequent. The general handover key derivation method which is used in handover procedures in LTE is not suitable for handover in this scenario because of its long time cost and the weak security. To solve this problem, this paper has proposed a new local gateway assisted handover key derivation schema in enterprise femtocell network. It can meet the fast derivation and good forward/backward key secrecy requirement of handover key derivation in enterprise femtocell network. The simulation result has verified that the proposed handover key derivation schema works better than the existing method.

IMPULSIVE EXPONENTIAL SYNCHRONIZATION OF FRACTIONAL-ORDER COMPLEX DYNAMICAL NETWORKS WITH DERIVATIVE COUPLINGS VIA FEEDBACK CONTROL BASED ON DISCRETE TIME STATE OBSERVATIONS

This article aims to address the global exponential synchronization problem for fractional-order complex dynamical networks(FCDNs)with derivative couplings and impulse effects via designing an appropriate feedback control based on discrete time state observations.In contrast to the existing works on integer-order derivative couplings,fractional derivative couplings are introduced into FCDNs.First,a useful lemma with respect to the relationship between the discrete time observations term and a continuous term is developed.Second,by utilizing an inequality technique and auxiliary functions,the rigorous global exponential synchronization analysis is given and synchronization criterions are achieved in terms of linear matrix inequalities(LMIs).Finally,two examples are provided to illustrate the correctness of the obtained results.

Identification of key genes regulating the synthesis of quercetin derivatives in Rosa roxburghii through integrated transcriptomics and metabolomics

Rosa roxburghii fruit is rich in flavonoids, but little is known about their biosynthetic pathways. In this study, we employed transcriptomics and metabolomics to study changes related to the flavonoids at five different stages of R. roxburghii fruit development. Flavonoids and the genes related to their biosynthesis were found to undergo significant changes in abundance across different developmental stages, and numerous quercetin derivatives were identified. We found three gene expression modules that were significantly associated with the abundances of the different flavonoids in R. roxburghii and identified three structural UDP-glycosyltransferase genes directly involved in the synthesis of quercetin derivatives within these modules. In addition, we found that RrBEH4, RrLBD1 and RrPIF8could significantly increase the expression of downstream quercetin derivative biosynthesis genes. Taken together,these results provide new insights into the metabolism of flavonoids and the accumulation of quercetin derivatives in R. roxburghii.

Decoupling Control Method Based on Neural Network for Missiles

In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.

FRACTIONAL HALANAY INEQUALITY AND APPLICATION IN NEURAL NETWORK THEORY

The (integer order) Halanay inequality with distributed delays is extended to the fractional order case. It is proved that solutions decay to zero as a Mittag-Leffler function as time goes to infinity provided that the delay feedback are bounded by similar functions.An application to a problem arising in neural network theory is provided showing that the equilibrium is Mittag-Leffler stable.

SDN-Based Data Offloading for 5G Mobile Networks

The rapid growth of 3G/4G enabled devices such as smartphones and tablets in large numbers has created increased demand for mobile data services. Wi-Fi offloading helps satisfy the requirements of data-rich applications and terminals with improved multi- media. Wi-Fi is an essential approach to alleviating mobile data traffic load on a cellular network because it provides extra capacity and improves overall performance. In this paper, we propose an integrated LTE/Wi-Fi architecture with software-defined networking （SDN） abstraction in mobile baekhaul and enhanced components that facilitate the move towards next-generation 5G mo- bile networks. Our proposed architecture enables programmable offloading policies that take into account real-time network conditions as well as the status of devices and applications. This mechanism improves overall network performance by deriving real- time policies and steering traffic between cellular and Wi-Fi networks more efficiently.

Geometric precision evaluation methodology of multiple reference station network algorithms

To evaluate the performance of real time kinematic (RTK) network algorithms without applying actual measurements, a new method called geometric precision evaluation methodology (GPEM) based on covariance analysis was presented. Three types of multiple reference station interpolation algorithms, including partial derivation algorithm (PDA), linear interpolation algorithms (LIA) and least squares condition (LSC) were discussed and analyzed. The geometric dilution of precision (GDOP) was defined to describe the influence of the network geometry on the interpolation precision, and the different GDOP expressions of above-mentioned algorithms were deduced. In order to compare geometric precision characteristics among different multiple reference station network algorithms, a simulation was conducted, and the GDOP contours of these algorithms were enumerated. Finally, to confirm the validation of GPEM, an experiment was conducted using data from Unite State Continuously Operating Reference Stations (US-CORS), and the precision performances were calculated according to the real test data and GPEM, respectively. The results show that GPEM generates very accurate estimation of the performance compared to the real data test.

ANN-Time Varying GARCH Model: Simulations and Application in Modelling Temperature for Weather Derivatives

In economics and finance, minimising errors while building an abstract representation of financial assets plays a critical role due to its application in areas such as risk management, decision making and option pricing. Despite the many methods developed to handle this problem, modelling processes with fixed and random periodicity still remains a major challenge. Such methods include Artificial Neural networks (ANN), Fuzzy Inference system (FIS), GARCH models and their hybrids. This study seeks to extend literature of hybrid ANN-Time Varying GARCH model through simulations and application in modelling weather derivatives. The study models daily temperature of Kenya using ANN-Time Varying GARCH (1, 1), Time Lagged Feedforward neural network (TLNN) and periodic GARCH family models. Mean square error (MSE) and coefficient of determination R2 were used to determine performance of the models under study. Results obtained show that the ANN-Time Varying GARCH model gives the best results.

Sensitivity Analysis of Radial Basis Function Networks for River Stage Forecasting

Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.

盾构掘进姿态控制技术研究现状与未来展望

为系统地分析我国盾构掘进姿态控制技术的研究进展,基于知网检索到的32篇相关文献,总结盾构掘进姿态的主要表征参数和影响因素,并以盾构液压推进系统为例论述其控制原理。同时,结合盾构姿态智能控制的部分案例,总结PID控制、自适应控制、模糊控制、基于神经网络的控制和基于智能算法的控制等技术的优劣势及应用场景。基于以上分析,对盾构姿态控制技术的发展方向进行展望。研究发现:1)盾构掘进姿态的影响因素主要包括几何参数、地层参数和盾构掘进参数。2)由于盾构推进系统需要同时完成盾构向前推进和姿态调整等复杂任务,因此该系统的参数对盾构姿态有着很大的影响,是姿态控制的关键因素之一。3)相较于传统PID控制方法,智能控制方法与PID控制的结合可以提高系统的响应速度、精度、适应能力和鲁棒性。4)未来研究可以围绕基于多源数据融合的控制算法、构建数据-机制混合驱动的控制技术以及加强控制技术在实际工程中的实用性等方面展开,实现更精准、更高效的盾构掘进姿态控制。

基于BERTopic模型的网络暴力事件衍生舆情探测

[研究目的]在海量用户生成内容中及时探测和剖析网络暴力事件的衍生舆情能够为舆情事件链的演化分析、同类舆情的研判介入、衍生事件的监测预警提供理论支持。[研究方法]使用BERTopic模型对短文本内容主题建模并采用聚类的方式展示主题的潜在层次结构。根据词向量余弦相似度设计主题衍生度的计量算法,同时融合词共现网络在文档-词语层面信息捕捉的优势以及桑基图直观演示舆情演化过程的特点,衡量主题间的影响力与衍生关系。[研究结论]在开源数据集下多组主题模型的对照实验中,BERTopic模型在短文本建模以及下游任务的平均得分提高2.13%。在网络暴力热点事件的应用实例中,多维细粒度分析与交互式可视化方法可达到直观展示暴力事件的主题聚类、词义关联与演化态势的效果,实现网络暴力事件衍生舆情的探测与分析。

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。

基于贝叶斯衍生分类器的社交网络用户影响力评价模型

为了防止社交网络中的负面信息快速传播,则需要通过评价社交网络中用户的影响力来找出影响力大的社交网络节点。针对传统算法在社交网络领域中交叉特性缺失的问题,结合高斯贝叶斯衍生分类器,提出一种网络用户影响力评价模型。该模型结合用户活跃度、用户联系度、用户覆盖度等维度,建立社交网络用户影响力刻画指标,同时考虑社交网络用户之间的关系特征和用户自身的行为特征,降低僵尸粉和垃圾社交网络对网络评价结果的影响,通过建立连续属性朴素贝叶斯分类器方法,提出基于高斯贝叶斯衍生分类器的模型求解方法。使用新浪微博中152059423条媒体报纸用户评论作为实验数据,分析影响该评价模型的关键因素,利用仿真软件完成和HRank等传统模型对比实验,结果表明,该模型体现了社交网络用户的交叉特性,提升了模型的实用性,相比于其他传统算法,该模型分类误差更趋于稳定,分类结果的误差率更低,适应性更好。

二苯甲酮LED光引发剂的合成及性能研究

为了延长二苯甲酮光引发剂的吸收波长,使其与LED光源更好的匹配,本研究将苄氨基基团引入二苯甲酮结构,合成了3种4-双苄胺基二苯甲酮衍生物,其吸收波长延长至400 nm,与不同波长(365~405 nm)的LED光源能较好的匹配。利用实时红外对其光聚合动力学进行测试,通过电化学测试以及光降解测试等对其作用机理进行研究。结果表明:双苄胺基二苯甲酮衍生物是一类高效的LED光引发剂,其可作为单组分光引发剂引发1,6-己二醇二丙烯酸酯发生自由基聚合,双键转化率可达80%~83%。与碘鎓盐复配,引发阳离子聚合时环氧单体转化率可达45%,引发自由基-阳离子混杂光固化时双键转化率为69%~71%,环氧单体转化率为58%~61%,在自由基聚合及互穿网络聚合物结构光固化材料制备方面存在潜在应用价值。

丛枝菌根途径的土壤有机碳固存机制研究进展

菌根真菌已被认为是土壤碳库的重要部分,陆地植物中至少78%与丛枝菌根真菌(AMF)形成共生关系,故研究AMF途径的土壤有机碳(SOC)固存机投对提高生态系统碳汇具有重要意义,但目前缺乏系统探讨AMF途径的SOC固碳机制。AMF具有显著的生态特性,包括较根系更高的周转速度、广泛的菌丝扩展范围以及将代谢产物转化为土壤中的稳定碳源等,这些特征共同构成了AMF在固碳机制中的作用路径。AMF从植物根系获得碳源,经过菌丝生长、代谢产物(尤其球囊霉素相关蛋白)和残体形成,将其转化为AMF源碳。AMF的根外菌丝还能与其他微生物共存并协同作用,通过分解凋落物、促进微生物的合成代谢及其物质周转,不仅增加植物源碳输入和微生物源碳积累,还促进团聚体形成,有效保护土壤中的碳不被分解,从而实现AMF途径的土壤碳固存。AMF途径的土壤固碳能力在森林、草地和农田依次减弱,这与气候变化、土壤的生物与非生物因素、地下的共生菌根网络及人类活动紧密相关。还探讨了这些因素对AMF途径SOC固存的影响,并针对现有研究的不足提出了未来的研究展望。本综述以期更深入地理解AMF途径的SOC固存机制,为菌根途径提升生态系统碳汇能力的研究提供理论支持。

导数推广的拉格朗日插值公式及其在密文训练神经网络中的应用

拉格朗日多项式插值公式被用于经过若干点的未知函数的多项式刻画,也被用于对已有的非多项式函数进行多项式函数逼近,在许多领域有着广泛的应用。本文探索了拉格朗日多项式插值公式在导数情况下的一般性推广。推广的多项式插值公式不仅可以在插值点的函数值逼近目标函数,同时也能在插值点的变化趋势上对目标函数进行逼近。在已知插值点上给定某个阶数以内导数的情况下,能够使用推广的拉格朗日多项式插值公式获得对目标函数的深度多项式逼近。实验结果表明,用导数推广拉格朗日多项式代替逻辑回归函数重新构建的密文神经网络的训练准确率更高,均方误差更小。扩展后的拉格朗日多项式插值公式能够适用于更一般的场景。

基于Cornell的自适应电离层闪烁强度的模型研究

针对Cornell模型在同一闪烁条件下,数据更新周期越长,估计和模型输入的幅度闪烁指数S4的偏差越大,并随着电离层闪烁的增强而增强的问题,提出基于Cornell模型的自适应S4的电离层闪烁模型即AS4-Cornell模型。模型以S4的偏差作为BP(back propagation)神经网络比例积分微分(proportional integral derivative,PID)算法的反馈,自动调整输入的复高斯白噪声的区段权值,使最终产生的闪烁信号满足模型输入的闪烁指数S4的指标。结果表明:仿真时,AS4-Cornell模型的幅度和相位闪烁序列概率分布均符合电离层闪烁理论,估算得到的电离层幅度指数S4与模型输入S4指数的最大偏差为0.001;全球定位系统(global positioning system,GPS)电离层闪烁模拟器测试时,AS4-Cornell模型估算得到的电离层幅度指数S4与模型输入S4指数的最大偏差为0.09;相比Cornell模型,AS4-Cornell模型产生的电离层闪烁信号更能够很好地反映模型输入的电离层闪烁指数S4的强度。