Analysis of environmental selection pressure of superoxide dismutase in deep-sea sea cucumber

Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is essential for the survival of sea cucumbers.Six MnSODs were identified from the transcriptomes of deep and shallow-sea sea cucumbers.To explore their environmental adaptation mechanism,we conducted environmental selection pressure analysis through the branching site model of PAML software.We obtained night positive selection sites,and two of them were significant(97F→H,134K→V):97F→H located in a highly conservative characteristic sequence,and its polarity c hange might have a great impact on the function of MnSOD;134K→V had a change in piezophilic a bility,which might help MnSOD adapt to the environment of high hydrostatic pressure in the deepsea.To further study the effect of these two positive selection sites on MnSOD,we predicted the point mutations of F97H and K134V on shallow-sea sea cucumber by using MAESTROweb and PyMOL.Results show that 97F→H,134K→V might improve MnSOD’s efficiency of scavenging superoxide a nion and its ability to resist high hydrostatic pressure by moderately reducing its stability.The above results indicated that MnSODs of deep-sea sea cucumber adapted to deep-sea environments through their amino acid changes in polarity,piezophilic behavior,and local stability.This study revealed the correlation between MnSOD and extreme environment,and will help improve our understanding of the organism’s adaptation mechanisms in deep sea.

Physical and numerical investigations of target stratum selection for ground hydraulic fracturing of multiple hard roofs

Ground hydraulic fracturing plays a crucial role in controlling the far-field hard roof,making it imperative to identify the most suitable target stratum for effective control.Physical experiments are conducted based on engineering properties to simulate the gradual collapse of the roof during longwall top coal caving(LTCC).A numerical model is established using the material point method(MPM)and the strain-softening damage constitutive model according to the structure of the physical model.Numerical simulations are conducted to analyze the LTCC process under different hard roofs for ground hydraulic fracturing.The results show that ground hydraulic fracturing releases the energy and stress of the target stratum,resulting in a substantial lag in the fracturing of the overburden before collapse occurs in the hydraulic fracturing stratum.Ground hydraulic fracturing of a low hard roof reduces the lag effect of hydraulic fractures,dissipates the energy consumed by the fracture of the hard roof,and reduces the abutment stress.Therefore,it is advisable to prioritize the selection of the lower hard roof as the target stratum.

Application of Rollout Strategy to Test Points Selection for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depth-first graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rollout-based test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integer-coded fault wise tables. Computa- tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.

Selection of Minimal Test Points Set for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. On one hand, traditional exhaustive search method is computationally expensive. On the other hand, the space complexity of traditional exhaustive is low. A tradeoff method between the high time complexity and low space complexity is proposed. At first, a new fault-pair table is constructed based on the integer-coded fault wise table. The fault-pair table consists of two columns： one column represents fault pair and the other represents test points set that can distinguish the corresponding faults. Then, the rows are arranged in ascending order according to the cardinality of corresponding test points set. Thirdly, test points in the top rows are selected one by one until all fault pair are isolated. During the test points selection process, the rows that contain selected test points are deleted and then the dimension of fault-pair table decreases gradually. The proposed test points selection algorithm is illustrated and tested using an integercoded fault wise table derived from a real analog circuit. Computational results suggest show policies are better than the exhaustive strategy.

Continuous Selections and Fixed Points forφ-maps with Their Applications to Section Problems

The concept of finitely continuous topological space is introduced and the basic properties of the space are given. Several continuous selection theorems and fixed point theorems for Ф-maps are established, and as applications of the above fixed point theorems, some section problems are discussed. The results generalize and improve many corresponding conclusions.

Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm

A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems.

Data point selection for weighted least square fitting of cavity decay time constant

For the accurate extraction of cavity decay time, a selection of data points is supplemented to the weighted least square method. We derive the expected precision, accuracy and computation cost of this improved method, and examine these performances by simulation. By comparing this method with the nonlinear least square fitting （NLSF） method and the linear regression of the sum （LRS） method in derivations and simulations, we find that this method can achieve the same or even better precision, comparable accuracy, and lower computation cost. We test this method by experimental decay signals. The results are in agreement with the ones obtained from the nonlinear least square fitting method.

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.Distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.

Genes for the high life： New genetic variants point to positive selection for high altitude hypoxia in Tibetans

People living on the high plateaus of the world have long fascinated biological anthropologists and geneticists because they live in ＂thin air＂ and epitomize an extreme of human biological adaptation.

Use of Rough Sets Theory in Point Cluster and River Network Selection

In this paper, we applied the rough sets to the point cluster and river network selection. In order to meet the requirements of rough sets, first, we structuralize and quantify the spatial information of objects by convex hull, triangulated irregular network (TIN), Voronoi diagram, etc.;second, we manually assign decisional attributes to the information table according to conditional attributes. In doing so, the spatial information and attribute information are integrated together to evaluate the importance of points and rivers by rough sets theory. Finally, we select the point cluster and the river network in a progressive manner. The experimental results show that our method is valid and effective. In comparison with previous work, our method has the advantage to adaptively consider the spatial and attribute information at the same time without any a priori knowledge.

Comparison of clinical efficacy of neddle knife at acupoints selected from recent regions along meridians and according to anatomical structures in treating early and middle stage knee osteoarthritis

Objective:To stimulate tenderness points around knee joint in two metholds-corresponding acupoints selected from recent regions along meridians and anatomical structures,to compare the clinical efficacy of neddle knife of to cure early and middle stage knee osteoarthritis.Method:70 patients were randomly(Random Number Tables)divided into test group(acupoints selected from recent regions along meridians,n=35)and control group(anatomical structures,n=35),who were diagnosed as knee osteoarthritis.Observe the VAS(visual analogue scale),Lysholm,WOMAC(the Western Ontario and McMaster Unive rsities Osteoarthritis Index)and ROM(rang of motion)between two groups in first week,first month,third month after treatment.Recording the degree of improvement of knee joint’s pain,dysfunction and symptoms of osteoarthritis and rang of motion.Results:Within groups,the VAS,Lysholm,WOMAC and ROM were obviously different from pre-therapy scores in the third,sixth and twelfth week post-therapy(P<0.05).Between groups no significant difference were observed in the third week post-therapy about VAS,Lysholm,WOMAC scores(P>0.05).However,there were differences in the sixth,twelfth weeks post-therapy(P<0.05);compared with control group,the ROM of test group were difference in the third,sixth,twelfth weeks post-therapy(P<0.05).Conclusion:The clinical efficacy of stimulating corresponding acupoints tenderness points selected from recent regions along meridians to treat early and middle stage knee osteoarthritis was superior to anatomical structures,which can effectively relieve pain,dysfunction,symptoms of osteoarthritis of knee joint and rang of motion.

Study on the Rule of Acupoint Selection in Acupuncture and Moxibustion Treatment of Stomachache

A systematic collection of relevant literature on acupuncture and moxibustion treatment of stomach pain,and a summary of the rule of acupoints selection for acupuncture treatment of stomach pain.In conclusion,Zusanli,Zhongwan,Neiguan,and Liang Qiu are selected as the main acupoints.At the same time,according to the different syndrome types,dyspepsia hurt a stomach syndrome is compatible with Burong,Chengman,Tianshu and Shangjuxu acupoints.Cold evil invading the stomach syndrome is compatible with Shenque and Qihai acupoints.Liver-Qi attacking stomach syndrome is compatible with Danzhong,Zhangmen,and Qimen acupoints;for blood stasis to stop the stomach,it is compatible with Xuehai,Geshu,and Pishu acupoints.Adopting this rule of acupoints selection has a significant effect on acupuncture treatment of stomach pain,and it also provides a reference for other doctors to treat stomach pain.

Selection rules for electric multipole transition of triatomic molecule in scattering experiments

In the electron or x-ray scattering experiment,the measured spectra at larger momentum transfer are dominated by the electric dipole-forbidden transitions,while the corresponding selection rules for triatomic molecules have not been clearly elucidated.In this work,based on the molecular point group,the selection rules for the electric multipolarities of the electronic transitions of triatomic molecules are derived and summarized into several tables with the variation of molecular geometry in the transition process being considered.Based on the summarized selection rules,the electron energy loss spectra of H2O,CO2,and N2O are identified,and the momentum transfer dependence behaviors of their valence-shell excitations are explained.

Estimating time-based instantaneous total mortality rate based on the age-structured abundance index

The instantaneous total mortality rate(Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis,abundance and catch forecast,and fisheries management. A catch curve-based method for estimating time-based Z and its change trend from catch per unit effort(CPUE) data of multiple cohorts is developed. Unlike the traditional catch-curve method,the method developed here does not need the assumption of constant Z throughout the time,but the Z values in n continuous years are assumed constant,and then the Z values in different n continuous years are estimated using the age-based CPUE data within these years. The results of the simulation analyses show that the trends of the estimated time-based Z are consistent with the trends of the true Z,and the estimated rates of change from this approach are close to the true change rates(the relative differences between the change rates of the estimated Z and the true Z are smaller than 10%). Variations of both Z and recruitment can affect the estimates of Z value and the trend of Z. The most appropriate value of n can be different given the effects of different factors. Therefore,the appropriate value of n for different fisheries should be determined through a simulation analysis as we demonstrated in this study. Further analyses suggested that selectivity and age estimation are also two factors that can affect the estimated Z values if there is error in either of them,but the estimated change rates of Z are still close to the true change rates. We also applied this approach to the Atlantic cod(G adus morhua) fishery of eastern Newfoundland and Labrador from 1983 to 1997,and obtained reasonable estimates of time-based Z.

CONTINUOUS SELECTION THEOREMS FOR FAN-BROWDER MAPPINGS IN TOPOLOGICAL SPACES AND THEIR APPLICATIONS

The concept of Fan-Browder mappings was first introduced in topological spaces without any convex structure. Then a new continuous selection theorem was obtained for the Fan-Browder mapping with range in a topological space without any convex structure and noncompact domain. As applications, some fixed point theorems, coincidence theorems and a nonempty intersection theorem were given. Both the new concepts and results unify and extend many known results in recent literature.

Selection rules for electric multipole transition of diatomic molecule in scattering experiments

The knowledge of the energy level structures of atoms and molecules is mainly obtained by spectroscopic experiments. Both photoabsorption and photoemission spectra are subject to the electric dipole selection rules （also known as optical selection rules）. However, the selection rules for atoms and molecules in the scattering experiments are not identical to those in the optical experiments. In this paper, based on the theory of the molecular point group, the selection rules are derived and summarized for the electric monopole, electric dipole, electric quadrupole, and electric octupole transitions of diatomic molecules under the first Born approximation in scattering experiments. Then based on the derived selection rules, the electron scattering spectra and x-ray scattering spectra of H2, N2, and CO at different momentum transfers are explained, and the discrepancies between the previous experimental results measured by different groups are elucidated.

无蜂窝大规模MIMO系统接入点动态选择算法

在无蜂窝大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统中,所有接入点(Access Point,AP)为用户提供服务的方式会造成较大的功率消耗和增加前传链路压力。以“用户为中心”的AP选择方案为每个用户选择最佳的AP集合,使系统频谱效率和能量效率得以提升。为了给每个用户选择最佳的AP集合,提出了一种基于反向传播(Back Propagation,BP)神经网络的接入点动态选择算法。将AP与用户之间的大尺度衰落向量作为输入,AP与用户之间的连接关系作为BP神经网络输出的预测向量,同时,针对BP神经网络初始化权值,提出了一种改进的自适应遗传算法以提高算法的收敛速度和收敛性能。仿真结果表明,改进后的BP神经网络预测结果更接近于实际值,并且比全连接和启发式的AP选择方案具有更高的系统和速率。

基于混合算法的多峰值MPPT控制

针对光照不均匀时光伏阵列输出呈现非线性、多峰值的问题,提出一种基于自然选择粒子群(SelPSO)算法结合电导增量法(INC)的多峰值最大功率点追踪(MPPT)算法。SelPSO算法通过淘汰和替换迭代过程中适应度低的粒子,更快、更准确地完成对全局最大值点的搜索;使用INC进行局部跟踪,有效减少寻优过程中的功率振荡,达到最大功率点跟踪的目的。利用MATLAB/Simulink进行仿真试验。结果表明:与INC相比,SelPSO算法的功率振荡由12 W降至0.05 W;与PSO算法相比,跟踪时间由0.15 s缩短至0.07 s。该混合算法在复杂环境下具有较优的跟踪速度、收敛精度以及较低的功率波动。

非结构化网格下海洋流场的特征提取与种子点选取算法

对于非结构化网格流场数据,现有研究存在不能定位流场中临界点的具体位置、无法对临界点精细分类以及缺乏适用于非结构化网格的种子点选取算法等问题。针对上述问题,基于非结构化网格流场数据,分别在临界点提取和种子点选取方法上进行改进创新。提出非结构化网格中临界点的定位及分类方法,通过庞加莱指数法判断存在临界点的三角网格。构造质心迭代法定位临界点在网格中的准确位置,并设计三角网格内雅克比矩阵的构造方法,将临界点精细分类。基于非结构化网格提出基于最大得分和网格密度的种子点选取算法,先比较相邻格点的标量值大小来计算每个格点的得分,形成“最大得分”标量场,再按照网格密度动态设置阈值,将得分大于阈值的格点选为种子点,接着以种子点为起始点生成流线,生成的流线可以表达出流场的关键特征与全局信息。基于多个海域流场数据的实验结果表明,临界点分类的准确率可达99%以上,证明了临界点提取算法的准确性以及种子点选取算法对提升流场可视化效果的有效性。

基于LabVIEW的继电保护测试仪自动检测系统设计

针对现有的继电保护测试仪检测程序繁琐,自动化程度低,点对点调试效率低下等问题,提出基于LabVIEW的继电保护测试仪自动检测系统;系统硬件以STM32F407ZGT6为主控芯片研制了继电保护测试仪输出转换装置,自动控制测试仪与标准器连成试验回路,代替人工拆接线行为;系统软件以LabVIEW为开发平台,设计了仪器通信、自动检测、生成报告、强制选择测点等模块,实现了继电保护测试仪的全自动检测;通过对某型继电保护测试仪进行测试,结果表明该系统工作状态稳定,能够实现一键式全自动检测,用户界面友好,可以替代繁琐的人工检测方法,具有一定的工程应用前景。