Design and Application of an Intelligent Agent for Web Information Discovery

With the propagation of applications on the internet, the internet has become a great information source which supplies users with valuable information. But it is hard for users to quickly acquire the right information on the web. This paper an intelligent agent for internet applications to retrieve and extract web information under user's guidance. The intelligent agent is made up of a retrieval script to identify web sources, an extraction script based on the document object model to express extraction process, a data translator to export the extracted information into knowledge bases with frame structures, and a data reasoning to reply users' questions. A GUI tool named Script Writer helps to generate the extraction script visually, and knowledge rule databases help to extract wanted information and to generate the answer to questions.

A comparative study of seismic provisions between International Building Code 2003 and Uniform Building Code 1997

This study focuses on the comparison of the Uniform Building Code （UBC） 1997 and International Building Code （IBC） 2003 in relation to the seismic design and analysis of special steel moment resisting frame buildings （SMRF）. This paper formulates a numerical study of a steel SMRF building, studied in four different situations, namely： as an office building in San Francisco; as an office building in Sacramento; as an essential facility in San Francisco, and as an essential facility in Sacramento. The analytical results of the model buildings are then compared and analyzed taking note of any significant differences. This case study explores variations in the results obtained using the two codes, particularly the design base shear and drift ratios as they relate to different locations and occupancy use. This study also proves that IBC 2003 is more stringent for the redundancy factor under design category E for the SMRF building, and drift limits for essential facilities.

Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range.In most radiators that are used to cool an engine,water serves as a cooling fluid.The performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water.Concentration and uniformity of nanoparticle distribution are the two major factors for the practical use of nanofluids.The shape and size of nanoparticles also have a great impact on the performance of heat transfer.Many researchers are investigating the impact of nanoparticles on heat transfer.This study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion engine.The implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the nanofluid.Accuracies of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and temperatures.In artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training phase.The findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of accuracy.The best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.

AN APPROXIMATION THEOREM OF A M-P INVERSE BY OUTER INVERSES

Let H1 and H2 be separable Hilbert spaces, and B(H1, H2) all of bounded linear operators from H1 into H2. In this note, we prove the following theorem: for any positive integer N and T ∈ B(H1, H2) with a closed range, there exists an outer inverse T#N with finite rank N such that T+y = lira T#Ny for any y ∈ H2, where T+N →∞denotes the Moore-Penrose inverse of T. Thus computing T+ is reduced to computing outer inverses T#N with finite rank N. Moreover, because of the stability of bounded outer inverse of a T ∈ B(H1,H2), this is very useful.

一种基于Agent结构的地理信息系统(英文)

把智能体 ( Agent)技术引入地理信息系统中 ,用以增强地理信息系统的人机交互能力和多信息源的可扩展性 .用户的问题与用户的查询目标和领域知识相关 ,因此 ,交互界面智能体能通过匹配知识库的过程引导用户细化问题 ,最终提供给用户满意的答案 .而且 ,基于智能体体系结构的建立能够更好地集成各种异构信息源 ,由于各种异构信息源的集成 ,所以可以提供给用户数量更多和质量更高的信息 .此外还详细描述了一个基于智能体结构的原形系统

The impact of XBRL on information asymmetry:evidence from loan contracting

This paper examines whether and how the adoption of eXtensible Business Reporting Language(XBRL)reduces information asymmetry in loan contracting.We hypothesize that the adoption of XBRL can enhance information dissemination and mitigate the information asymmetry problem between borrowers and lenders.Using a sample of 554 US bank loan contracts,we find that borrowers who adopt XBRL enjoy more favourable price and nonprice terms of bank loan contracts.Additional analyses indicate that the relations among XBRL adoption and bank loan price vary with loan structure.Overall,this research provides evidence that technology advancements such as XBRL reduce cost of bank loans by decreasing information asymmetry between borrowers and lenders.

向量值一阶逻辑及其可靠性定理

讨论了向量值一阶逻辑形式系统。通过对于在智能系统多认知环境下事物逻辑性质的分析,把逻辑真值推广到一个n维向量。在对于向量值命题逻辑形式语言建立可靠性定理与完备性定理的讨论基础上,这里进一步建立了向量值一阶逻辑形式系统,证明了它的可靠性定理。

The Generalized Regular Points and Narrow Spectrum Points of Bounded Linear Operators on Hilbert Spaces

In this paper, we introduce the concepts of generalized regular points and narrow spectrum points of bounded linear operators on Hilbert spaces. The concept of generalized regular points is an extension of the concept regular points, and so, the set of all spectrum points is reduced to the narrow spectrum. We present not only the same and different properties of spectrum and of narrow spectrum but also show the relationship between them. Finally, the well known problem about the invariant subspaces of bounded linear operators on separable Hilbert spaces is simplified to the problem of the operator with narrow spectrum only.

Simultaneous determination of manufacturer’s process mean and production run length,and retailer’s order quantity

In Chen and Liu’s optimum profit model with a traditional production system,they did not consider the effect of product quality on the customer’s demand order quantity,and also ignored the used cost of customers for product.In fact,the customer’s demand quantity is always seriously related to product quality.Hence,in the present paper,we modify Chen and Liu’s model to address the determination of the optimal process parameters by employing the idea of quality loss and single sampling rectifying inspection plan.Assuming that the quality characteristic of the product is normally distributed,Taguchi’s symmetric quadratic quality loss function is applied in evaluating the product quality.Three decision variables,i.e.,the mean of the process characteristic,the production run length of the product and the retailer’s order quantity,are jointly determined in our modified model to maximize the expected total profit of society,which includes both the manufacturer and the retailer.A heuristic solution procedure is developed for this optimization problem,and a numerical example is provided for illustration.From the numerical results,it can be seen that both the sale price per unit and the intercept of the mean demand of the customer are two major(or significant)parameters in the model and should be more accurately estimated in practice.Finally,the quality investment policy is provided to compare its effect on the optimum profit model with quality improvement.

Cluster Analysis in Data-Driven Management and Decisions

Clustering plays an important role in management and decision-making processes.This paper first discusses three types of cluster analysis methods-centroid-based,connectivity-based,and density-based.Then the challenges to traditional clustering in new business environments are highlighted,with algorithmic extensions and innovative efforts for coping with data that is dynamic,large-scale,representative,non-convex,and consensus in nature.In addition,three application cases are illustrated,where clustering is incorporated into the overall solution in the contexts of management support,business of sharing economy,and healthcare decision assistance.

Simultaneous settings of order quantity,wholesale price,production run length,process mean,and warranty period

In this article,the authors propose a modified version of S.L.Chen and Liu’s model with a two-stage production system.Assume that the retailer’s order quantity is concerned with the manufacturer’s selling price and the warranty period of product.The used cost of the customer is measured under the Taguchi’s quadratic quality loss function and concluded in the retailer’s profit function.The quality of the lot for the manufacturer is determined by adopting a two-stage single sampling rectifying inspection plan.The modified economic manufacturing quantity(EMQ)model is addressed in formulating the manufacturer’s expected profit.The retailer’s order quantity,manufacturer’s wholesale price,production run length,process mean,and warranty period of product will be jointly determined by maximizing the total expected profit of the supply chain system including the manufacturer and the retailer.Finally,the quality investment policy is introduced to illustrate the profit improvement for the supply chain system.

应急状态下新冠流行曲线预测的方法学研究——基于北京市百度搜索和传统流感样病例监测

Surveillance is an essential work on infectious diseases prevention and control.When the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore new surveillance methods.This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness(ILI)surveillance.A novel hybrid model(multiattention bidirectional gated recurrent unit(MABG)-susceptible-exposed-infected-removed(SEIR))was developed,which leveraged a deep learning algorithm(MABG)to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever,pyrexia,cough,sore throat,anti-fever medicine,and runny nose.By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019(COVID-19)cases,a transmission dynamics model(SEIR)was formulated to estimate the transmission dynamics and epidemic curve of SARS-Co V-2.During the COVID-19 pandemic,when conventional surveillance measures have been suspended temporarily,cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19.In the specific case of Beijing,it has been ascertained that cumulative infection attack rate surpass 80.25%(95%confidence interval(95%CI):77.51%-82.99%)since December 17,2022,with the apex of the outbreak projected to transpire on December 12.The culmination of existing patients is expected to occur three days subsequent to this peak.Effective reproduction number(Rt)represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic,remained below 1 since December 17,2022.The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support.Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks.Syndrome surveillance on COVID-19 should be established to following on the epidemic,clinical severity,and medical resource demand.

A new polynomial algorithm for a parallelidentical scheduling problem

A precedence order is defined based on the release dates of jobs＇ direct successors. Using the defined precedence order and Heap Sort, a new polynomial algorithm is provided which aims to solve the parallel scheduling problem P｜pj = 1, r j, outtree｜ ∑Cj Cj. The new algorithm is shown to be more compact and easier to implement.

Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness-China,2014-2019

Introduction:This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage(ILI%)across regions in China.The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics.Methods:In this study,data on ILI%and Baidu Index were collected from 30 provincial-level administrative divisions(PLADs)spanning April 2014 to March 2019.The Baidu Index was categorized into Overall Index,Ordinary Index,Prevention Index,Symptom Index,and Treatment Index based on search query themes.The lagged correlation between the Baidu Index and ILI%was examined through the cross-correlation function(CCF)method.Results:Correlating the Baidu Overall Index of 30 PLADs with ILI%revealed CCF values ranging from 0.46 to 0.86,with a median lag of 0.5 days.Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%,with median lags of−9 and−0.5 days,respectively,compared to 0 and 3 days for the Ordinary and Treatment Indexes.The median lag days between the Baidu Index and the ILI%were earlier in the northern PLADs compared to the southern PLADs.Discussion:The Prevention and Symptom Indexes show promising predictive capabilities for influenzalike illness epidemics.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019:A modelling and comparative study

Background Influenza is an acute respiratory infectious disease with a significant global disease burden.Additionally,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of influenza.However,comparative studies on the performance of innovative models and approaches used for influenza prediction are limited.Therefore,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.Methods The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to 2019.Three relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three models.Results Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China.Additionally,the models’predictive performance declined as the weeks ahead increased.Furthermore,blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.Conclusions Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model.Therefore,in the future,the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones,thereby contributing to influenza control and prevention efforts.

MARKETING OBJECTIVES OF RETAILERS WITH DIFFERENTIATED GOODS:AN EVOLUTIONARY PERSPECTIVE

In the real world, revenue maximization behavior may prevail in various markets. To understand this phenomenon, we develop a two-population model with two-vertically integrated channels. Every channel consists of one manufacturer and many （a sufficiently large number of） retailers that sell products in different markets by adopting pure marketing objective strategies： profit maximization and revenue maximization. We study the marketing objective behaviors in the quantity-setting duopoly and the price-setting duopoly situations respectively from an indirect evolutionary point of view. In the quantity-setting duopoly situation, we find that whether the equilibrium is an evolutionarily stable strategy depends on the type of strategic iateraction （substitutes or complements）, relative unit cost, market scale, etc. We extend it to the case with continuous preferences. We argue that revenue maximization may be an evolutionarily stable strategy and profit maximization strategy may be unstable. Under proper conditions, revenue maximization behavior can coexist with profit maximization behavior. In the price-setting duopoly situation with linear demand functions, we find that profit maximization is always an evolutionarily stable strategy and revenue maximization behavior will gradually become extinct. The extended model has a similar result but the retailers may compromise the two pure strategies.

基于因素空间的决策方法

本文以因素空间为知识表示的框架,提出反馈外延的概念;基于反馈外延,建立了一种直接面向目标的决策方法,它既适用于模糊的决策系统,亦可用于普通的决策过程。

The smooth Banach submanifold B*(E,F) in B(E,F)

Let E, F be two Banach spaces, B(E, F),B +(E, F), Φ(E, F), SΦ(E, F) and R(E, F) be bounded linear, double splitting, Fredholm, semi-Frdholm and finite rank operators from E into F, respectively. Let Σ be any one of the following sets: {T ∈ Φ(E, F): Index T = constant and dim N(T) = constant}, {T ∈ SΦ(E, F): either dim N(T) =constant< ∞ or codim R(T) =constant< ∞} and {T ∈ R(E, F): Rank T =constant< ∞}. Then it is known that gS is a smooth submanifold of B(E, F) with the tangent space T A Σ = {B ∈ B(E, F): BN(A) ? R(A)} for any A ∈ Σ. However, for B*(E, F) = {T ∈ B +(E, F): dimN(T) = codimR(T) = ∞} without the characteristic numbers, dimN(A), codimR(A), index(A) and Rank(A) of the equivalent classes above, it is very difficult to find which class of operators in B*(E, E) forms a smooth submanifold of B(E, F). Fortunately, we find that B*(E, F) is just a smooth submanifold of B(E, F) with the tangent space T A B*(E, F) = {T ∈ B(E, F): TN(A) ? R(A)} for each A ∈ B*(E, F). Thus the geometric construction of B +(E, F) is obtained, i.e., B +(E, F) is a smooth Banach submanifold of B(E, F), which is the union of the previous smooth submanifolds disjoint from each other. Meanwhile we give a smooth submanifold S(A) of B(E, F), modeled on a fixed Banach space and containing A for any A ∈ B +(E, F). To end these, results on the generalized inverse perturbation analysis are generalized. Specially, in the case E = F = ? n , it is obtained that the set Σ r of all n × n matrices A with Rank(A) = r < n is an arcwise connected and smooth hypersurface (submanifold) in B(? n ) with dimΣ r = 2nr × r 2. Then a new geometrical construction of B(? n ), analogous to B +(E, F), is given besides its analysis and algebra constructions known well.

Big data analytics:a literature review

AbstractWith more and more data generated,it has become a big challenge for traditional architectures and infrastructures to process large amounts of data within an acceptable time and resources.In order to efficiently extract value from these data,organizations need to find new tools and methods specialized for big data processing.For this reason,big data analytics has become a key factor for companies to reveal hidden information and achieve competitive advantages in the market.Currently,enormous publications of big data analytics make it difficult for practitioners and researchers to find topics they are interested in and track up to date.This paper aims to present an overview of big data analytics’content,scope and findings as well as opportunities provided by the application of big data analytics.

Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

Transfer learning has attracted a large amount of interest and research in last decades, and some effort has been made to build more precise recommendation systems. Most previous transfer recommendation systems assume that the target domain shares the same/similar rating patterns with the auxiliary source domain, which is used to improve the recommendation performance. However, almost all existing transfer learning work does not consider the characteristics of sequential data. In this paper, we study the new cross-domain recommendation scenario by mining novelty-seeking trait. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. Previous work performed on only one single target domain may not fully characterize users' novelty-seeking trait well due to the data scarcity and sparsity, leading to the poor recommendation performance. Along this line, we propose a new cross-domain novelty-seeking trait mining model (CDNST for short) to improve the sequential recommendation performance by transferring the knowledge from auxiliary source domain. We conduct systematic experiments on three domain datasets crawled from Douban to demonstrate the effectiveness of our proposed model. Moreover, we analyze the directed influence of the temporal property at the source and target domains in detail.