Coexistence of Dirac and Weyl points in non-centrosymmetric semimetal NbIrTe_(4)

Using angle-resolved photoemission spectroscopy and density functional theory calculations methods,we investigate the electronic structures and topological properties of ternary tellurides NbIrTe_(4),a candidate for type-II Weyl semimetal.We demonstrate the presence of several Fermi arcs connecting their corresponding Weyl points on both termination surfaces of the topological material.Our analysis reveals the existence of Dirac points,in addition to Weyl points,giving both theoretical and experimental evidences of the coexistence of Dirac and Weyl points in a single material.These findings not only confirm NbIrTe_(4) as a unique topological semimetal but also open avenues for exploring novel electronic devices based on its coexisting Dirac and Weyl fermions.

Exceptional points and quantum dynamics in a non-Hermitian two-qubit system

We study the exceptional-point(EP) structure and the associated quantum dynamics in a system consisting of a non-Hermitian qubit and a Hermitian qubit. We find that the system possesses two sets of EPs, which divide the systemparameter space into PT-symmetry unbroken, partially broken and fully broken regimes, each with distinct quantumdynamics characteristics. Particularly, in the partially broken regime, while the PT-symmetry is generally broken in the whole four-dimensional Hilbert space, it is preserved in a two-dimensional subspace such that the quantum dynamics in the subspace are similar to those in the PT-symmetry unbroken regime. In addition, we reveal that the competition between the inter-qubit coupling and the intra-qubit driving gives rise to a complex pattern in the EP variation with system parameters.

Prediction of collapse process and tipping points for mutualistic and competitive networks with k-core method

Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances,which is always called resilience.However,once the external disturbances exceed the tipping points of the system resilience,the consequences would be catastrophic,and eventually lead the ecosystem to complete collapse.We capture the collapse process of ecosystems represented by plant-pollinator networks with the k-core nested structural method,and find that a sufficiently weak interaction strength or a sufficiently large competition weight can cause the structure of the ecosystem to collapse from its smallest k-core towards its largest k-core.Then we give the tipping points of structure and dynamic collapse of the entire system from the one-dimensional dynamic function of the ecosystem.Our work provides an intuitive and precise description of the dynamic process of ecosystem collapse under multiple interactions,and provides theoretical insights into further avoiding the occurrence of ecosystem collapse.

Triple points and phase transitions of D-dimensional dyonic Ad S black holes with quasitopological electromagnetism in Einstein–Gauss–Bonnet gravity

By considering the negative cosmological constant Λ as a thermodynamic pressure, we study the thermodynamics and phase transitions of the D-dimensional dyonic Ad S black holes(BHs) with quasitopological electromagnetism in Einstein–Gauss–Bonnet(EGB) gravity. The results indicate that the small/large BH phase transition that is similar to the van der Waals(vdW) liquid/gas phase transition always exists for any spacetime dimensions. Interestingly, we then find that this BH system exhibits a more complex phase structure in 6-dimensional case that is missed in other dimensions.Specifically, it shows for D = 6 that we observed the small/intermediate/large BH phase transitions in a specific parameter region with the triple point naturally appeared. Moreover, when the magnetic charge turned off, we still observed the small/intermediate/large BH phase transitions and triple point only in 6-dimensional spacetime, which is consistent with the previous results. However, for the dyonic Ad S BHs with quasitopological electromagnetism in Einstein–Born–Infeld(EBI) gravity, the novel phase structure composed of two separate coexistence curves observed by Li et al. [Phys. Rev. D105 104048(2022)] disappeared in EGB gravity. This implies that this novel phase structure is closely related to gravity theories, and seems to have nothing to do with the effect of quasitopological electromagnetism. In addition, it is also true that the critical exponents calculated near the critical points possess identical values as mean field theory. Finally, we conclude that these findings shall provide some deep insights into the intriguing thermodynamic properties of the dyonic Ad S BHs with quasitopological electromagnetism in EGB gravity.

Petrophysical Evaluation of Cape Three Points Reservoirs

The findings of a study to ascertain and assess the petrophysical characteristics of Cape Three Points reservoirs in the Western basin with a view to describe the reservoir quantitatively using Well Logs, Petrel and Techlog. The investigated characteristics, which were all deduced from geophysical wire-line logs, include lithology, porosity, permeability, fluid saturation, and net to gross thickness. To characterise the reservoir on the field, a suite of wire-line logs including gamma ray, resistivity, spontaneous potential, and density logs for three wells (WELL_1X, WELL_2X, and WELL_3X) from the Tano Cape Three Point basin were studied. The analyses that were done included lithology delineation, reservoir identification, and petrophysical parameter determination for the identified reservoirs. The tops and bases of the three wells analysed were marked at a depth of 1203.06 - 2015.64 m, 3863.03 - 4253.85 m and 2497.38 - 2560.32 m respectively. There were no hydrocarbons in the reservoirs from the studies. The petrophysical parameters computed for each reservoir provided porosities of 13%, 3% and 11% respectively. The water saturation also determined for these three wells (WELL_1X, WELL_2X and WELL_3X) were 94%, 95% and 89% respectively. These results together with the behaviour of the density and neutron logs suggested that these wells are wildcat wells.

Quantitative determination of the critical points of Mott metal–insulator transition in strongly correlated systems

Mottness is at the heart of the essential physics in a strongly correlated system as many novel quantum phenomena occur in the metallic phase near the Mott metal–insulator transition. We investigate the Mott transition in a Hubbard model by using the dynamical mean-field theory and introduce the local quantum state fidelity to depict the Mott metal–insulator transition. The local quantum state fidelity provides a convenient approach to determining the critical point of the Mott transition. Additionally, it presents a consistent description of the two distinct forms of the Mott transition points.

Analysis of the Significance of Key Points of Patient Interventional Psychological Care and Pain Care

Interventional therapy has become increasingly popular in clinical practice due to advancements in medical technology.However,patients often experience psychological and physiological pressure due to its invasive nature.The management of patient discomfort and tension is crucial to ensure effective treatment.Psychological and pain management are essential components of interventional therapy,as they significantly impact patient recovery and prognosis.This article discussed the importance of interventional psychological and pain care for patients,starting with the development and spread of interventional therapy.The significance of providing high-quality nursing services to patients and improving their quality of life was also discussed.

Effects of sowing date and ecological points on yield and the temperature and radiation resources of semi-winter wheat

Exploring the effects of sowing date and ecological points on the yield of semi-winter wheat is of great significance.This study aims to reveal the effects of sowing date and ecological points on the climate resources associated with wheat yield in the Rice–Wheat Rotation System.With six sowing dates,the experiments were carried out in Donghai and Jianhu counties,Jiangsu Province,China using two semi-winter wheat varieties as the objects of this study.The basic seedlings of the first sowing date (S1) were planted at 300×10^(4)plants ha^(-1),which was increased by 10%for each of the delayed sowing dates (S2–S6).The results showed that the delay of sowing date decreased the number of days,the effective accumulated temperature and the cumulative solar radiation in the whole growth period.The yields of S1 were higher than those of S2 to S6 by 0.22–0.31,0.5–0.78,0.86–0.98,1.14–1.38,and 1.36–1.59 t ha^(–1),respectively.For a given sowing date,the growth days increased as the ecological point was moved north,while both mean daily temperature and effective accumulative temperature decreased,but the cumulative radiation increased.As a result,the yields at Donghai County were 0.01–0.39 t ha–1lower than those of Jianhu County for the six sowing dates.The effective accumulative temperature and cumulative radiation both had significant positive correlations with yield.The average temperature was significantly negatively correlated with the yield.The decrease in grain yield was mainly due to the declines in grains per spike and 1 000-grain weight caused by the increase in the daily temperature and the decrease in the effective accumulative temperature.

Observation points classifier ensemble for high-dimensional imbalanced classification

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction technique.First,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible.Second,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data space.Third,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples.Exhaustive experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets.Experimental results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms.This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

Traffic prediction enabled dynamic access points switching for energy saving in dense networks

To meet the ever-increasing traffic demand and enhance the coverage of cellular networks,network densification is one of the crucial paradigms of 5G and beyond mobile networks,which can improve system capacity by deploying a large number of Access Points(APs)in the service area.However,since the energy consumption of APs generally accounts for a substantial part of the communication system,how to deal with the consequent energy issue is a challenging task for a mobile network with densely deployed APs.In this paper,we propose an intelligent AP switching on/off scheme to reduce the system energy consumption with the prerequisite of guaranteeing the quality of service,where the signaling overhead is also taken into consideration to ensure the stability of the network.First,based on historical traffic data,a long short-term memory method is introduced to predict the future traffic distribution,by which we can roughly determine when the AP switching operation should be triggered;second,we present an efficient three-step AP selection strategy to determine which of the APs would be switched on or off;third,an AP switching scheme with a threshold is proposed to adjust the switching frequency so as to improve the stability of the system.Experiment results indicate that our proposed traffic forecasting method performs well in practical scenarios,where the normalized root mean square error is within 10%.Furthermore,the achieved energy-saving is more than 28% on average with a reasonable outage probability and switching frequency for an area served by 40 APs in a commercial mobile network.

Approach for Predicting the Knowledge Points of Math Questions

In order to provide high-quality learning services,various online systems should possess the fundamental ability to predict the knowledge points and units to which a given test question belongs.The existing methods typically rely on manual labeling or traditional machine learning methods.Manual labeling methods have high time costs and high demands for human resources,while traditional machine learning methods only focus on the shallow features of the topics,ignoring the deep semantic relationship between the topic text and the knowledge point units.These two methods have relatively large limitations in practical applications.This paper proposes a convolutional neural network method combined with multiple features to predict the knowledge point units.We construct a binary classification dataset in the three grades of primary mathematics.Considering the supplementary role of Pinyin to Chinese text and the unique identification characteristics of Unicode encoding for characters,we obtain the Pinyin representation and the Unicode encoding representation of the original Chinese text.Then,we put the three representation methods into the convolutional neural network for training,obtain three kinds of semantic vectors,fuse them,and finally obtain higher-dimensional fusion features.Our experimental results demonstrate that our approach achieves good performance in predicting the knowledge units of test questions.

Slope deformation partitioning and monitoring points optimization based on cluster analysis

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

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.

Solving Fractional Differential Equations via Fixed Points of Chatterjea Maps

In this paper,we present the existence and uniqueness of fixed points and common fixed points for Reich and Chatterjea pairs of self-maps in complete metric spaces.Furthermore,we study fixed point theorems for Reich and Chatterjea nonexpansive mappings in a Banach space using the Krasnoselskii-Ishikawa iteration method associated withSλand consider some applications of our results to prove the existence of solutions for nonlinear integral and nonlinear fractional differential equations.We also establish certain interesting examples to illustrate the usability of our results.

Natural forest ALS-TLS point cloud data registration without control points

Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.

基于多线激光雷达的井下斜坡道无人矿卡定位与建图方法

井下斜坡道的定位与建图是实现井下斜坡道无人驾驶的关键技术之一,矿山井下斜坡道区域为典型非结构化环境特征,且道路具有一定倾斜角度,采用传统SLAM算法无法获得精确里程计信息,导致定位与建图精度难以满足无人矿卡行驶需求。针对上述问题,通过研究激光SLAM(Simultaneous Localization And Mapping)算法LeGO-LOAM,笔者提出一种适用于矿山井下斜坡道环境的定位与建图方法。首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了基于人工路标的辅助增强定位方法,有效增加点云特征数量,从而优化位姿估计结果,避免建图漂移现象;然后在特征预处理阶段,提出了一种基于激光点云高度差与坡度信息融合的提取地面点高效算法,通过改善地面地点的选取策略,针对倾斜坑洼路面仍能有效识别地面点,解决了井下斜坡道定位与建图倾斜角度大、误差大等问题;其次,基于CVC(Curved-Voxel Clustering)聚类算法设计了一种斜坡道点云曲率体素聚类算法,采用曲率体素和基于哈希的数据结构对点云进行分割,大幅提高在井下稀疏、噪声环境下点云聚类的鲁棒性;最后,运用Scan-To-Map进行点云匹配,同时兼顾点云配准的性能与速度。在中钢集团山东某井下斜坡道的现场实验证明:与原算法相比精度提升13.15%,Z轴误差降低22.3%,地图质量明显提升,能有效解决井下无人驾驶建图及定位的难题。

Key Review Points on Cosmetic Labeling and Relevant Cases

Interpreted the latest cosmetic labeling requirements one by one from the basic principles on labels,content, positions, preceded terms and so on;analyzed the relevant cases, so as to help cosmetic manufacturers and operation enterprises to deeply understand the latest requirements on cosmetics labeling after the promulgation of the new regulations.

ARP协议仿真实验设计与实现

ARP协议是TCP/IP协议栈中重要的协议之一,掌握其工作原理对于理解局域网底层通信逻辑至关重要。文章不仅分析了同网段通信时ARP协议的工作原理,而且还分析了跨网段通信时ARP协议的工作原理,并给出了相关仿真实验过程。研究对于更好地理解和掌握ARP协议,解决在现实中遇到的网络问题具有实践指导意义。

吴茱萸碱对H_(2)O_(2)诱导ARPE-19细胞的炎症反应、细胞凋亡和SIRT1表达的影响

目的 探究吴茱萸碱对H_(2)O_(2)刺激下人视网膜色素上皮细胞ARPE-19的炎症反应和细胞凋亡的影响,阐明去乙酰化酶1(SIRT1)在其中的作用和相关机制。方法 分别采用不同浓度的H_(2)O_(2)(0,25,50,100,200,400μmol/L)和不同浓度的吴茱萸碱(0,2.5,5.0,10.0,20.0,40.0μmol/L)处理ARPE-19细胞,CCK-8法筛选H_(2)O_(2)和吴茱萸碱的最佳作用浓度。使用H_(2)O_(2)(200μmol/L)与不同浓度的吴茱萸碱(2.5,5,10,20μmol/L)联合处理ARPE-19细胞,Western blot法检测细胞中SIRT1蛋白表达情况。按处理方式的不同将ARPE-19细胞分为二甲基亚砜处理的对照组、200μmol/L H_(2)O_(2)处理组(H_(2)O_(2)组)、200μmol/L H_(2)O_(2)与不同浓度吴茱萸碱处理组(H_(2)O_(2)+吴茱萸碱10μmol/L组、H_(2)O_(2)+吴茱萸碱20μmol/L组)及100μmol/L的SIRT1抑制剂Sirtinol拮抗组(H_(2)O_(2)+吴茱萸碱20μmol/L+Sirtinol组),处理24 h后,ELISA法检测各组ARPE-19细胞上清液中炎症因子肿瘤坏死因子-α(TNF-α)、白细胞介素-1β(IL-1β)和白细胞介素-6(IL-6)水平,Annexin V-FITC/PI染色检测各组ARPE-19细胞的凋亡率,Western blot法检测各组ARPE-19细胞中核因子-κB p65(NF-κB p65)、环氧化酶-2(COX-2)、B细胞淋巴瘤/白血病-2(Bcl-2)、Bcl-2相关X蛋白(Bax)、裂解型半胱氨酸天冬氨酸蛋白酶3(Cleaved Caspase-3)、Caspase-3、磷脂酰肌醇3-激酶(PI3K)、磷酸化PI3K(p-PI3K)、蛋白激酶B(Akt)、磷酸化Akt(p-Akt)蛋白表达情况。结果 200μmol/L的H_(2)O_(2)对ARPE-19细胞的生长抑制相对稳定。0,2.5,5.0,10.0,20.0μmol/L的吴茱萸碱对ARPE-19细胞活力无明显影响(P均>0.05),40μmol/L吴茱萸碱可明显降低ARPE-19细胞活力(P均<0.05)。H_(2)O_(2)组和H_(2)O_(2)+吴茱萸碱各组ARPE-19细胞中SIRT1蛋白相对表达量均明显低于对照组(P均<0.05);H_(2)O_(2)+吴茱萸碱5μmol/L组、H_(2)O_(2)+吴茱萸碱10μmol/L组和H_(2)O_(2)+吴茱萸碱20μmol/L组中SIRT1蛋白相对表达量均明显高于H_(2)O_(2)组(P均<0.05),且H_(2)O_(2)+吴茱萸碱10μmol/L组和H_(2)O_(2)+吴茱萸碱20μmol/L组升高更明显。与对照组比较,H_(2)O_(2)组、H_(2)O_(2)+吴茱萸碱10μmol/L组、H_(2)O_(2)+吴茱萸碱20μmol/L组和H_(2)O_(2)+吴茱萸碱20μmol/L+Sirtinol组细胞中TNF-α、IL-1β、IL-6水平和COX-2、NF-κB p65、Bax蛋白相对表达量及Cleaved Caspase-3/Caspase-3、p-PI3K/PI3K、p-Akt/Akt比值均明显升高(P均<0.05), Bcl-2蛋白相对表达量均明显降低(P均<0.05);与H_(2)O_(2)组比较,H_(2)O_(2)+吴茱萸碱10μmol/L组、H_(2)O_(2)+吴茱萸碱20μmol/L组中TNF-α、IL-1β、IL-6水平和COX-2、NF-κB p65、Bax蛋白相对表达量及Cleaved Caspase-3/Caspase-3、p-PI3K/PI3K、p-Akt/Akt比值均明显降低(P均<0.05),Bcl-2蛋白相对表达量均明显升高(P均<0.05);H_(2)O_(2)+吴茱萸碱20μmol/L+Sirtinol组中TNF-α、IL-1β、IL-6水平和COX-2、NF-κB p65、Bax蛋白相对表达量及Cleaved Caspase-3/Caspase-3、p-PI3K/PI3K、p-Akt/Akt比值均明显高于H_(2)O_(2)+吴茱萸碱20μmol/L组(P均<0.05), Bcl-2蛋白相对表达量明显低于H_(2)O_(2)+吴茱萸碱20μmol/L组(P<0.05),各指标与H_(2)O_(2)组、H_(2)O_(2)+吴茱萸碱10μmol/L组比较差异均无统计学意义(P均>0.05)。结论 吴茱萸碱可在体外通过上调SIRT1表达来抑制NF-κB通路、线粒体介导的凋亡通路和PI3K/Akt通路,从而减轻H_(2)O_(2)刺激下ARPE-19细胞的炎症反应,减少细胞凋亡。

图像引导和点云空间约束的公路洒落物检测定位方法

公路洒落物是影响交通安全的重要因素之一,为了解决中小尺度公路洒落物检测中的漏检、误检以及难以定位等问题,本文提出了一种图像引导和点云空间约束的公路洒落物检测定位方法。该方法使用改进的YOLOv7-OD网络处理图像数据获取二维目标预测框信息,将目标预测框投影到激光雷达坐标系下得到锥形感兴趣区域(region of interest,ROI)。在ROI区域内的点云空间约束下,联合点云聚类和点云生成算法获得不同尺度的洒落物在三维空间中的检测定位结果。实验表明:改进的YOLOv7-OD网络在中尺度目标上的召回率和平均精度分别为85.4%和82.0%,相比YOLOv7网络分别提升6.6和8.0个百分点;在小尺度目标上的召回率和平均精度分别为66.8%和57.3%,均提升5.3个百分点;洒落物定位方面,对于距离检测车辆30~40 m处的目标,深度定位误差为0.19 m,角度定位误差为0.082°,实现了多尺度公路洒落物的检测和定位。