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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

FIXED POINTS OF α-TYPE F-CONTRACTIVE MAPPINGS WITH AN APPLICATION TO NONLINEAR FRACTIONAL DIFFERENTIAL EQUATION

In this paper, we introduce new concepts of a-type F-contractive mappings which are essentially weaker than the class of F-contractive mappings given in [21, 22] and different from a-GF-contractions given in [8]. Then, sufficient conditions for the existence and uniqueness of fixed point are established for these new types of contractive mappings, in the setting of complete metric space. Consequently, the obtained results encompass various generalizations of the Banach contraction principle. Moreover, some examples and an application to nonlinear fractional differential equation are given to illustrate the usability of the new theory.

Second Hankel Determinants and Fekete-Szegö Inequalities for Some Sub-Classes of Bi-Univalent Functions with Respect to Symmetric and Conjugate Points Related to a Shell Shaped Region

In this paper, we have investigated second Hankel determinants and FeketeSzeg? inequalities for some subclasses of Bi-univalent functions with respect to symmetric and Conjugate points which are subordinate to a shell shaped region in the open unit disc ?.

用Stigmatic Points法设计高性能80通道阵列波导光栅器件

采用StigmaticPoints方法改进传统的罗兰圆结构设计方法 ,给出一个实用化、高性能 80通道阵列波导光栅器件设计方案 .对采用这两种设计方法得到的器件性能结果进行了比较分析 ,指出了传统设计方法在设计大通道数阵列波导光栅器件中存在的问题以及StigmaticPoints方法对器件性能的改进 .

基于knee points的改进多目标人工蜂群算法

传统的人工蜂群算法(Artificial Bee Colony algorithm,ABC)及其在多目标上的扩展(Multi Objective Artificial Bee Colony algorithm,MOABC)存在着在高维、多峰函数情况下收敛速度变慢、后期容易陷入局部最优以及寻优精度丢失等问题。基于knee points提高收敛性和分布性的特点,设计了一种快速识别knee point的算法并将其应用到多目标人工蜂群算法中,提出了一种基于knee points的改进多目标人工蜂群算法(Kn MOABC)。算法在迭代过程中考虑pareto支配关系的同时,优先选择knee point作为下一代个体,极大地增强了算法的收敛速度,同时,在knee point识别算法中加入自适应的策略以保持良好的分布性。实验结果表明,Kn MOABC的性能优于三个最新的多目标人工蜂群对比算法。

ON THE EXISTENCE OF INFINITELY MANY CRITICAL POINTS OF THE EVEN FUNCTIONAL integral from n=Q to (F(x,u,Du))+integral from n=■Q to (G(x,u))

In this paper we deal with the existence of infinitely many critical points of the even functional I(u)=integral from n=Q to (F(x,u,Du))+integral from n=(?)Q to (G(x,u)), u∈W^(1,p)(Ω),where G(x, u)=integral from n=o to u (g(x,t)dt), under the weak structure conditions on F(x, u, q) by the Mountain Pass Lemma.

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.