Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Aggregation operators and CRITIC-VIKOR method for confidence complex q-rung orthopair normal fuzzy information and their applications

Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.

Theory and Method of Mineral Resource Prediction Based on Synthetic Information

Metallogenic prognosis of synthetic information uses the geological body and the mineral resource body as a statistical unit to interpret synthetically the information of geology, geophysics, geochemistry and remote sensing from the evolution of geology and puts all the information into one entire system by drawing up digitalized interpretation maps of the synthetic information. On such basis, different grades and types of mineral resource prospecting models and predictive models of synthetic information can be established. Hence, a new integrated prediction system will be formed of metallogenic prognosis (qualitative prediction), mineral resources statistic prediction (determining targets) and mineral resources prediction (determining resources amount).

Hazard Assessment of Co-seismic Landslides Based on Information Value Method:A Case in 2018 MW6.6 Hokkaido Earthquake,Japan

An MW6.6 earthquake occurred in eastern Hokkaido,Japan on September 6th,2018.Based on the pre-earthquake image from Google Earth and the post-earthquake image from high resolution(3 m)planet satellite,we manually interpret 9293 coseismic landslides and select 7 influencing factors of seismic landslide,such as elevation,slope,slope direction,road distance,flow distance,peak ground acceleration(PGA)and lithology.Then,9293 landslide points are randomly divided into training samples and validation samples with a proportion of 7:3.In detail,the training sample has 6505 landslide points and the validation sample has 2788 landslide points.The hazard risk assessment of seismic landslide is conducted by using the information value method and the study area is further divided into five risk grades,including very low risk area,low risk area,moderate risk area high risk area and very high risk area.The results show that there are 7576 landslides in high risk area and very high risk area,accounting for81.52%of the total landslide number,and the landslide area is 22.93 km^2,accounting for 74.35%of the total area.The hazard zoning is in high accordance with the actual situation.The evaluation results are tested by using the curve of cumulative percentage of hazardous area and cumulative percentage of landslides number.The results show that the success rate of the information value method is 78.50%and the prediction rate is 78.43%.The evaluation results are satisfactory,indicating that the hazard risk assessment results based on information value method may provide scientific reference for landslide hazard risk assessment as well as the disaster prevention and mitigation in the study area.

Non-intrusive hybrid interval method for uncertain nonlinear systems using derivative information

This paper proposes a new non-intrusive hybrid interval method using derivative information for the dynamic response analysis of nonlinear systems with uncertain-but- bounded parameters and/or initial conditions. This method provides tighter solution ranges compared to the existing polynomial approximation interval methods. Interval arith- metic using the Chebyshev basis and interval arithmetic using the general form modified affine basis for polynomials are developed to obtain tighter bounds for interval computation. To further reduce the overestimation caused by the ＂wrap- ping effect＂ of interval arithmetic, the derivative information of dynamic responses is used to achieve exact solutions when the dynamic responses are monotonic with respect to all the uncertain variables. Finally, two typical numerical examples with nonlinearity are applied to demonstrate the effective- ness of the proposed hybrid interval method, in particular, its ability to effectively control the overestimation for specific timepoints.

A Hybrid Method of Coreference Resolution in Information Security

In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists of two parts:the first extracts all candidates(including noun phrases,pronouns,entities,and nested phrases)from a given document and classifies them;the second is coreference resolution of the selected candidates.In the first part,a method combining rules with a deep learning model(Dictionary BiLSTM-Attention-CRF,or DBAC)is proposed to extract all candidates in the text and classify them.In the DBAC model,the domain dictionary matching mechanism is introduced,and new features of words and their contexts are obtained according to the domain dictionary.In this way,full use can be made of the entities and entity-type information contained in the domain dictionary,which can help solve the recognition problem of both rare and long entities.In the second part,candidates are divided into pronoun candidates and noun phrase candidates according to the part of speech,and the coreference resolution of pronoun candidates is solved by making rules and coreference resolution of noun phrase candidates by machine learning.Finally,a dataset is created with which to evaluate our methods using information security data.The experimental results show that the proposed model exhibits better performance than the other baseline models.

Exploring the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method

Objective:Explore the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method.Methods:The complex network and point wise mutual information system-developed by Chinese academy of Chinese medical sciences wereused to analyze the specific acupoints,compatibility,frequency etc.Results:174 acumoxibustion prescriptions were collected,including 163 acupoints.among them eighteen acupoints were used more than 30 times such as Hegu(LI4),Zusanli(ST36),Quchi(LI11)and Fengshi(GB31).The combinations of 31 acupoints were used more than 15 times,such as the combination of Quchi(LI11)and Zusanli(ST36),the combination of acupoint Quchi(LI11)and Jianyu(LI15),Hegu and Quchi(LI11).The most commonly used treatment method for stroke treatment is to dredge the Yangming meridian and Shaoyang meridian through acupuncture the multiple acupoints located on these two meridians..The commonly used acupoints are mainly distributed in the limbs,head and face.The most commonly used specific acupoint is intersection acupoint.The usage frequency of specific acupoints are higher than that of non-specific acupoints.Conclusion:Dredging the collaterals,dispelling wind-evil and restoring consciousness are the main principle for the treatment of stroke.Specific acupoints in head,face and climbs maybe the main targeted acupoints.Combination of Yang meridians with other meridians is needed to improve the effects.The Yangming meridian and Shaoyang meridian are most used meridians and Hegu(LI4),Quchi(LI11)and Zusanli(ST36)are the most used acupionts.

Obsolescence determination of network information based on “double-proportion” method

Purpose:It is necessary to determine whether the value of network information exists or not from users’perspective.This study is to develop a new approach,called'double-proportion'method(DPM),to determine the point of information obsolescence.Design/methodology/approach:Firstly,DPM is produced and introduced with statistics in great detail.Secondly,two data sets from Qiangguo Forum and NetEase News are used to validate DPM.Finally,the distributions of lifetime of the two sets are compared and reasons for the differences are revealed.Findings:In Qiangguo Forum,posts with no comments within 17 h can be identified as obsolescence,which indicates that approximately 83%of posts information has died within24 h,92%died within 36 h and almost all died within 54 h;While in NetEase News,news without comments within 28 h can be identified as obsolescence.Research limitations:This study adopted comments as the only behavior indicator,for further study other indicators like page views,republished times need to be used,to exploit better solutions for information obsolescence.Practical implications:According to obsolescence determination,network managers can optimize webpages more rationally,delete aged information in time,preserve valuable information,help search engines ignore aged webpages,optimize reptiles’mechanisms,and help advertisers choose webpages rationally to advertise online.Originality/value:Firstly,DPM is designed from users’perspective to describe the lifetime of information;Secondly,DPM is easy to use.DPM merges and improves former methods by taking the long-tail theory and Pareto principle into consideration.

A method to calculate effective Hamiltonians in quantum information

Effective Hamiltonian method is widely used in quantum information. We introduce a method to calculate effective Hamiltonians and give two examples in quantum information to demonstrate the method. We also give a relation between the effective Hamiltonian in the Shrodinger picture and the corresponding effective Hamiltonian in the interaction picture.Finally, we present a relation between our effective Hamiltonian method and the James–Jerke method which is currently used by many authors to calculate effective Hamiltonians in quantum information science.

Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

Plotting the Sequential Rock Remote Sensing Information by Optimization Dichotomy Method

The sequential rock remote sensing information is a group of rocks that are correlative in space or in space and time. For the sake of plottiug them, someone had brought forward the optimization segn.entotion metkod. We have ased this method to plot the sequential rock remote sensing information at tbe remote sensing hyperspetral test field of Daqing mountain, Inner Mongolia Autonomous Region, China, and found some disadvantages of this method. Therefore, we put forward the optimization dichotomy to plot them, and get better results. Finally we make a conclusion.

An Entropy Measurement Method of Quantum Information System under Uncertain Environment

Under uncertain environment, it is very difficult to measure the entropy of quantum information system, because there is no effective method to model the randomness. First, different from the traditional classic uncertainty, a quantum uncertain model is proposed to simulate a quantum information system under uncertain environment, and to simplify the entropy measurement of quantum information system. Second, different from the classic random seed under uncertain environment which is often called as pseudo-random number, here the quantum random is employed to provide us a true random model for the entropy of quantum information system. Third, the complex interaction and entangling activity of uncertain factors of quantum information is modeled as quantum binary instead of classic binary, which can help us to evaluate the entropy of uncertain environment, to analyze the entropy divergence in quantum information system. This work presents a non-classic risk factor measurement for quantum information system and a helpful entropy measurement.

Stepwise Method Based on Confidence Bound and Information Incorporation for Identifying the Maximum Tolerable Dose

The primary goal of a phase I clinical trial is to find the maximum tolerable dose of a treatment. In this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine the maximum tolerable dose among given dose levels. On the one hand, in order to avoid severe even fatal toxicity to occur and reduce the experimental subjects, the new method is executed from the lowest dose level, and then goes on in a stepwise fashion. On the other hand, in order to improve the accuracy of the recommendation, the final recommendation of the maximum tolerable dose is accomplished through the information incorporation of an additional experimental cohort at the same dose level. Furthermore, empirical simulation results show that the new method has some real advantages in comparison with the modified continual reassessment method.

Strategic information perception methods and practices in the open source intelligence

Open source intelligence is one of the most important public data sources for strategic information analysis. One of the primary and core issues of strategic information research is information perception,so this paper mainly expounds the perception method for strategic information perception in the open source intelligence environment as well as the framework and basic process of information perception. This paper argues that in order to match the information perception result with the information depiction result,it conducts practical exploration for the results of information acquisition,perception,depiction and analysis. This paper introduces and develops a monitoring platform for information perception. The results show that the method proposed in this paper is feasible.

Definition,measuring methods and status of carbon information disclosure in Chinese enterprises

From the perspective of carbon disclosure goal,the paper defined the detail types of contents about carbon information disclosure,then constructed the carbon information disclosure index,by using the indices to 2011 annual report of A-share listed companies in Shanghai and Shenzhen Security Exchange,we found that Chinese enterprises' carbon disclosure scattered,industry structure quite different characteristics,quantity information and quality information asymmetry.

Quantum-Mechanical Information Content of Multiples Hartree-Fock Solutions. The Multi-Reference Hartree-Fock Configuration Interaction Method

The Hartree-Fock equation is non-linear and has, in principle, multiple solutions. The ωth HF extreme and its associated virtual spin-orbitals furnish an orthogonal base Bω of the full configuration interaction space. Although all Bω bases generate the same CI space, the corresponding configurations of each Bω base have distinct quantum-mechanical information contents. In previous works, we have introduced a multi-reference configuration interaction method, based on the multiple extremes of the Hartree-Fock problem. This method was applied to calculate the permanent electrical dipole and quadrupole moments of some small molecules using minimal and double, triple and polarized double-zeta bases. In all cases were possible, using a reduced number of configurations, to obtain dipole and quadrupole moments in close agreement with the experimental values and energies without compromising the energy of the state function. These results show the positive effect of the use of the multi-reference Hartree-Fock bases that allowed a better extraction of quantum mechanical information from the several Bω bases. But to extend these ideas for larger systems and atomic bases, it is necessary to develop criteria to build the multireference Hartree-Fock bases. In this project, we are beginning a study of the non-uniform distribution of quantum-mechanical information content of the Bω bases, searching identify the factors that allowed obtain the good results cited

Research on the Processing Methods of the Computer Format Information Flow in the Network English Translation System

The development o f the network technology, and especially the web search engine, has brought great changes to the field of the English translation. Translators can acquire the background information of the translated texts by using the web search engine correctly, inquire about the correct translation methods of the rare professional terms, apply the fixed sentence patterns, and check the correctness of the translation, so as to improve the translation speed and quality.

复杂环境下基于改进Informed RRT*的无人机路径规划算法

针对无人机在复杂环境中进行路径规划时,快速搜索随机树(RRT)算法易出现规划时间长、路径冗余、狭窄空间中易陷入局部约束导致规划失败的问题,提出一种改进的Informed RRT*算法.首先,引入人工势场法使采样点按照势场下降的方式向目标点移动,以提高RRT树扩展的目的性和方向性.然后,考虑随机树在扩展过程中全局环境的复杂度,引入自适应步长调整策略以增加随机树在无障碍环境下的扩展速度,并在随机树扩展的过程中加入相关约束条件以确保生成路径的可行性.在找到第一条可达路径后,采用变化的椭圆或椭球采样域限制采样点选取和自适应步长的扩展范围,加快算法收敛到渐进最优的速度.最后,在复杂二维和三维环境下进行传统算法和改进算法的对比实验,仿真分析表明:改进算法可以在很少的迭代次数下找到更优的初始路径,更快地锁定椭圆或椭球采样域,从而给路径优化留出更多时间,算法规划效果更好.

Comprehensive assessment method for environmental impact of railway based on geographic information system

By integrating the merits of the map overlay method and the geographic information system (GIS), a GIS based map overlay method was developed to analyze comprehensively the environmental vulnerability around railway and its impact on the environment, which is adapted for the comprehensive assessment of railway environmental impact and the optimization of railway alignments. The assessment process of the GIS based map overlay method was presented, which includes deciding the system structure and weights of assessment factors, making environmental vulnerability grade maps, and evaluating the alternative alignments comprehensively to obtain the best one. With the GIS functions of spatial analysis, such as overlay analysis and buffer analysis, and functions of handling attribute data, the GIS based map overlay method overcomes the shortcomings of the existing map overlay method and the conclusion is more reasonable. In the end, a detailed case study was illustrated to verify the efficiency of the method.

基于聚合二次模态分解及Informer的短期负荷预测

针对区域级负荷的非平稳性及长序列预测精度低的问题,该文提出了一种基于聚合二次模态分解及Informer的短期负荷预测方法。首先,运用改进完全集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)对负荷序列进行初步分解,削弱原始序列的随机性与波动性;其次,根据子序列的样本熵计算结果进行聚合,并通过比较不同的聚合方式选出最优重构方案;然后,利用变分模态分解对高复杂度的合作模态函数进行二次分解;充分考虑到电价、气象等因素对负荷的影响,采用随机森林(random forest,RF)算法进行相关性分析,从而为每个子序列构建不同的高耦合度特征矩阵并输入Informer进行建模,并通过其多层次编码及稀疏多头自注意力机制等方式提高对负荷序列的预测效率;最后采用巴塞罗那区域级负荷数据集进行实例验证,结果显示所提框架有效解决了模态分解过程中的模态混叠以及高频分量问题,并且其长序列预测均方根误差相比其他经典深度学习模型最高降低了65.28%。