A new centrality measure based on neighbor loop structure for network dismantling

Nearly all real-world networks are complex networks and usually are in danger of collapse.Therefore,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network functionalities.Network dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive size.To overcome the limitations and drawbacks of existing network dismantling methods,this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric,NL,which achieves a balance between computational efficiency and evaluation accuracy.In addition,we design a novel method combining NL-based nodes-removing,greedy tree-breaking and reinsertion.Moreover,we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erd€os-Renyi random networks,Watts-Strogatz smallworld networks and Barabasi-Albert scale-free networks with different network generation parameters.Experimental results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack nodes.Furthermore,the insights gained from this study may be of assistance to future practical research into real-world networks.

Comprehensive Measurement for Carrying Capacity of Resources and Environment of City Clusters in Central China

Studying the carrying capacity of resources and environment of city clusters in the central China has important practical guidance significance for promoting the healthy, sustainable and stable development of this region. According to their influencing factors and reciprocity mechanism, using system dynamics approaches, this paper built a SD model for measuring the carrying capacity of resources and environment of the city clusters in the central China, and through setting different development models, the comprehensive measurement analysis on the carrying capacity was carried out. The results show that the model of promoting socio-economic development under the protection of resources and environment is the optimal model for promoting the harmony development of resources, environment, society and economy in the city clusters. According to this model, the optimum population scale of the city clusters in 2020 is 42.80×106 persons, and the moderate economic development scale is 22.055×1012 yuan (RMB). In 1996-2020, the carrying capacity of resources and environment in the city clusters took on obvious phase-change characteristics. During the studied period, it is basically at the initial development stage, and will come through the development process from slow development to speedup development.

BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning

The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based features has been extensively studied in the literature.Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious hosts.Recently,Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network communications.The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML.We propose BotSward,a graph-based bot detection system that is based on ML.We apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the state-of-the-art.The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,etc.BotSward is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art techniques.The proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.

Central corneal thickness measurements in premature infants

AIM:To evaluate the central corneal thickness(CCT)measurements of premature infantsMETHODS:The premature infants who were born between 25-34wk of gestational age(GA)were retrieved for the study.All CCT measurements were performed within the first 2d of their life under topical anesthesia by using an ultrasonic pachymeter.RESULTS:The mean CCT of 200 eyes was 647.79±63.94μm.The mean CCT of the right and left eyes were similar(647.30±64.72μm and 648.29±63.47μm consecutively).The mean CCT of the girls was 642.08±54.89μm and the boys was 653.07±71.14μm,with no statistically significant difference(P】0.05).But there was a negative correlation of the CCT with GA and birth weight(BW)(P【0.01).CONCLUSION:The CCT values of premature babies were negatively correlated with GA and BW.

The dating and temperature measurement technologies for carbonate minerals and their application in hydrocarbon accumulation research in the paleouplift in central Sichuan Basin, SW China

A new method for reconstructing the geological history of hydrocarbon accumulation is developed, which are constrained by U-Pb isotope age and clumped isotope((35)47) temperature of host minerals of hydrocarbon-bearing inclusions. For constraining the time and depth of hydrocarbon accumulation by the laser in-situ U-Pb isotope age and clumped isotope temperature, there are two key steps:(1) Investigating feature, abundance and distribution patterns of liquid and gaseous hydrocarbon inclusions with optical microscopes.(2) Dating laser in-situ U-Pb isotope age and measuring clumped isotope temperature of the host minerals of hydrocarbon inclusions. These technologies have been applied for studying the stages of hydrocarbon accumulation in the Sinian Dengying gas reservoir in the paleo-uplift of the central Sichuan Basin. By dating the U-Pb isotope age and measuring the temperature of clumped isotope((35)47) of the host minerals of hydrocarbon inclusions in dolomite, three stages of hydrocarbon accumulation were identified:(1) Late Silurian: the first stage of oil accumulation at(416±23) Ma.(2) Late Permian to Early Triassic: the second stage of oil accumulation between(248±27) Ma and(246.3±1.5) Ma.(3) Yanshan to Himalayan period: gas accumulation between(115±69) Ma and(41±10) Ma. The reconstructed hydrocarbon accumulation history of the Dengying gas reservoir in the paleo-uplift of the central Sichuan Basin is highly consistent with the tectonic-burial history, basin thermal history and hydrocarbon generation history, indicating that the new method is a reliable way for reconstructing the hydrocarbon accumulation history.

An Energy-Saving Control Strategy on Central Air-Conditioning System

An energy-saving control strategy based on predictive control for central air-conditioning systems is proposed in this paper. The cold load model is developed to describe the dynamic characteristics of temperature control systems, and then parameters in the cold load model and in the central air-conditioning system model are estimated. Generalized predictive control (GPC) is used to establish an optimization model to minimize the consumption of energy and the control error of temperature. The simulated annealing (SA) algorithm, combined with quadratic programming, is adopted to solve the optimal problem. Contrasted with the simulation of traditional PID control, the results prove the effectiveness of this proposed strategy.

Energy performance assessment on central air-conditioning system of commercial building: A case study in China

Energy performance assessment on central air-conditioning system is essential to optimize operating, reduce operating costs, improve indoor environmental quality, and determine whether the retrofitting of the equipment is necessary. But it is difficult to evaluate it reasonably and comprehensively due to its complexity. A "holistic" approach was discussed to evaluate the energy performance of central air-conditioning system for an extra-large commercial building in a subtropical city. All procedures were described in detail, including field investigation method, field measurement instruments, data processing and data analyzing. The main factors affecting energy consumption of air-conditioning system were analyzed and the annual cooling-energy use intensity of this building was calculated and also compared with other shopping malls and other types of buildings in Guangzhou. And COP(coefficient of performance) of chiller, water transfer factor of chilled water system and cooling water system were taken into consideration. At last, the thermal comfort and indoor air quality issues were addressed. The results show that the chilled water pumps are over-sized and the indoor environmental quality should be improved. The purpose of this work is to provide reference for energy performance assessment method for air-conditioning system.

MEASUREMENT TECHNOLOGY OF HEAT AND COOL

The popularization of large scale central air conditioning system (CACS) has brought forward a new pro blem,i.e. how to measure the cooling and heating quantum accurately. This problem will directly affect the qua lity of modern property management. So the study on the technology of measuring heat and cool is very necessary. In this paper, the detailed thermal analysis and the mathematical model of relative parameters that affect the measurement of heat and cool are discussed. A sample instrument based on the model is made, After the trial use by several customers, the device is proved to be practical, worthwhile and of good capacity.

Centrality measure of complex networks based on resource flow

A new centrality measure for complex networks, called resource flow centrality, is pro- posed in this paper. This centrality measure is based on the concept of the resource flow in net- works. It not only can be applied to the connected networks, but also the disconnected networks. Moreover, it overcomes some disadvantages of several common centrality measures. The perform- ance of the proposed measure is compared with some standard centrality measures using a classic dataset and the results indicate the proposed measure performs more reasonably. The statistical dis- tribution of the proposed centrality is investigated by experiments on large scale computer generated graphs and two networks from the real world.

Centrality Measures Based on Matrix Functions

Network is considered naturally as a wide range of different contexts, such as biological systems, social relationships as well as various technological scenarios. Investigation of the dynamic phenomena taking place in the network, determination of the structure of the network and community and description of the interactions between various elements of the network are the key issues in network analysis. One of the huge network structure challenges is the identification of the node(s) with an outstanding structural position within the network. The popular method for doing this is to calculate a measure of centrality. We examine node centrality measures such as degree, closeness, eigenvector, Katz and subgraph centrality for undirected networks. We show how the Katz centrality can be turned into degree and eigenvector centrality by considering limiting cases. Some existing centrality measures are linked to matrix functions. We extend this idea and examine the centrality measures based on general matrix functions and in particular, the logarithmic, cosine, sine, and hyperbolic functions. We also explore the concept of generalised Katz centrality. Various experiments are conducted for different networks generated by using random graph models. The results show that the logarithmic function in particular has potential as a centrality measure. Similar results were obtained for real-world networks.

Climate Change Impacts on Central China and Adaptation Measures

In Central China, the obvious climate change has happened along with global warming. Based on the observational analysis, the climate change has significant effects, both positive and negative, in every field within the study area, and with the harmful effects far more prevalent. Under the A1B scenario, it is reported that temperature, precipitation, days of heat waves, and extreme precipitation intensity will increase at respective rates of 0.38℃ per decade, 12.6 mm per decade, 6.4 d and 47 mm per decade in the 21st century. It is widely believed that these climate changes in the future will result in some apparent impacts on agro-ecosystems, water resources, wetland ecosystem, forest ecosystem, human health, energy sectors and other sensitive fields in Central China. Due to the limited scientific knowledge and researches, there are still some shortages in the climate change assessment methodologies and many uncertainties in the climate prediction results. Therefore, it is urgent and essential to increase the studies of the regional climate change adaptation, extend the research fields, and enhance the studies in the extreme weather and climate events to reduce the uncertainties of the climate change assessments.

Satellite-measured water vapor isotopologues across the Tianshan Mountains,central Asia

The satellite-based water vapor stable isotope measurements have been widely used in modern hydrological and atmospheric studies.Their use is important for arid areas where the precipitation events are limited,and below-cloud evaporation is strong.This study presents the spatial and temporal characteristics of water vapor isotopologue across the Tianshan Mountains in arid central Asia using the NASAAura Tropospheric Emission Spectrometer(TES).The near-surface water vapor stable isotopes are enriched in summer and depleted in winter,consistent with the seasonality of precipitation isotopes.From the surface to 200 hPa,the isotope values in water vapor show a decreasing trend as the atmospheric pressure decreases and elevation rises.The vapor isotope values in the lower atmosphere in the southern basin of the Tianshan Mountains are usually higher than that in the northern basin,and the seasonal difference in vapor isotopes is slightly more significant in the southern basin.In addition,bottom vapor isotopologue in summer shows a depletion trend from west to east,consistent with the rainout effect of the westerly moisture path in central Asia.The isotopic signature provided by the TES is helpful to understand the moisture transport and below-cloud processes influencing stable water isotopes in meteoric water.

Performance of Risk Measures in Portfolio Construction on Central and South-East European Emerging Markets

Aim of this paper is to characterize different risk measures in portfolio construction on seven Central and South-East European stock markets;Slovenia, Croatia, Hungary, Poland, Chez Republic, Bulgaria and Romania. Selected countries are members of EU, except Croatia and Turkey which have candidate status. Empirical part of this paper consists of three stages;at first descriptive statistic on stock returns was performed, afterwards different risk measures were employed in portfolio construction and in the last part, portfolios were tested in the out-of-sample period. Results indicate presence of extreme kurtosis and skewness in stock return series. Resulting portfolios incorporate stocks with extremely high kurtosis and stocks with negative skewness. Portfolio construction based only on risk and return results in major exposure to extreme returns and unsatisfactory portfolio out of sample results.

10 Finance Measures Constituted by the Party's Central Committee to Support Enterprise Reform

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利用伯努利滤波的多目标机动雷达误差配准方法

传统的组网雷达多目标误差配准方法通常假设数据关联关系已知,但在平台机动的情况下,系统同时存在雷达测量偏差和平台姿态角偏差,且雷达观测过程中会受到杂波干扰,导致数据关联尤为困难。针对这一问题,该文提出一种基于伯努利滤波的多目标机动雷达误差配准方法。首先建立系统偏差的量测与状态方程,然后将系统偏差建模成伯努利随机有限集,利用公共坐标系下的原始量测可实现系统偏差在伯努利滤波框架下的递推估计,有效避免了数据关联问题。同时,为了充分利用多目标量测信息,提出一种修正的贪婪量测划分方法,在每个滤波时刻挑选出系统偏差对应的最优量测子集,利用量测子集中的多量测信息实现系统偏差的集中式融合估计,提高了系统偏差的估计精度和收敛速度。仿真实验表明,所提方法能够在数据关联未知的多目标多杂波场景下对雷达测量偏差和平台姿态角偏差进行有效估计,在平台姿态角变化率较低时,所提方法具有较强的适应性。

基于中心对称特征耦合仿射度量模型的图像匹配方法

针对当下较多图像匹配算法利用距离度量法来实现特征匹配,没考虑图像仿射变换的影响,使得匹配结果准确率不高的问题,提出了中心对称特征耦合仿射度量模型的图像匹配算法。首先,引入Forstner算子,利用像素点的GS梯度特征提取特征点;然后,利用图像的Haar小波信息与中心对称像素点的灰度值,求取特征向量;接着,利用特征点之间的旋转、平移以及缩放的仿射特征,构造仿射度量模型,利用其计算出匹配的特征点对;最后,采用结构相似度(SSIM)函数,计算匹配点对的结构相似性,对匹配点对去伪求真,以求取最优匹配效果。实验结果表明,与当下匹配方法相比,所提算法不仅能更准确地实现图像匹配,而且还能够更好地适应具有仿射变换关系图像之间的匹配。

基于节点影响值的社区网络稳定标签传播算法

针对社区网络标签传播准确性差的问题,提出基于节点影响值的社区网络稳定标签传播算法。该算法重设社区网络相邻节点相似度,结合链路加权思想将其变换为链路无向带权图;利用节点K-shell分解值判断节点影响力,计算种子节点的中心性度量值,升序排列节点更新顺序;根据越重要节点标签影响越大的规则,修正高频数标签,获取最佳社区网络划分结果。结果表明:该算法模块度在0.320以上,准确率达到99%,因此,经该方法划分后的网络结构清晰、准确,稳定性高。

快堆堆芯三操作头模型出口冷却剂温度振荡流固传递特性实验研究

来自堆芯不同通道的冷却剂存在温度差异,当其在堆芯上腔室混合后将产生流体温度振荡,从而会引发周围固体结构的温度振荡。长时间的温度振荡会导致结构的高周疲劳破坏,影响快堆安全运行。针对温度振荡的研究,多为流体的温度振荡,对于流固热耦合的研究相对较少。本文通过数值模拟计算,确定了三组操作头组合三叶梅花导流筒的实验装置,以水代替钠作为工质开展了堆芯出口温度振荡实验研究,并获得了温度振荡从流体到固体的传递特性。结果表明:流体在冷热操作头交界区域温度振荡比较强烈,在近操作头处冷热流体产生剧烈温度振荡,向中心测量柱底壁面逐渐衰减;固体温度振荡主要发生在壁面附近比较薄的区域,在40 K温差和0.3 m/s流速工况下,温度振荡传递到固体内2.5 mm处几乎消失;基于实验结果,修正了已有的温度振荡衰减经验公式,预测精度在±5.7%以内。

考虑相量量测单元数据不同步的双端输电线路正序以及零序参数辨识方法

针对相量量测单元(phasormeasurementunit,PMU)数据不同步导致参数辨识结果误差大的问题,提出了考虑PMU数据不同步的输电线路正序以及零序参数辨识方法。首先,分析线路集中参数模型和分布参数模型在一定电压等级下的长度适用范围。基于线路的集中参数模型,利用正常运行情况下PMU装置获取的线路两端功率以及电压信息,构建以正序参数和不同步角度为未知量的非线性方程,并运用Levenberg-Marquardt(L-M)算法求解正序参数以及不同步角度。其次,在辨识出正序参数以及不同步角度的基础上,分析单相接地故障情况下的正序、负序以及零序等效电路图,通过正序和负序网络求解出故障距离。在故障距离已知的情况下,基于零序网络构建以零序参数为未知量的方程,并利用L-M算法求解方程。最后,利用MATLAB软件搭建单回双端输电线路系统,并对所提正序以及零序参数辨识方法进行仿真,验证了所提参数辨识方法的有效性和精确性。

用磁阵列传感器去偏心误差的非接触式电流测量方法

磁性传感器圆形阵列是电流非接触式测量的有效方法,因为它成本低、重量轻、线性范围大、带宽宽和噪声低。针对现有阵列式电流传感器测量偏心时存在较大电流测量误差的问题,采用一种基于导线偏心位置的区域层次分析方法。通过对载流导线不同偏心位置,赋予磁传感器不同的权重进行输出叠加,与导线中心位置输出进行对比,求得最大相对误差为1.28%,传感器线性误差为1.04%,克服了导线偏心对电流测量误差的影响,证明了该方法的可行性。