Approach to conceptual data integration for multidimensional data analysis in e-commerce

In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to multidimensional data model the UML galaxy diagram is presented in order to conduct multidimensional data analysis for multiple subjects. The approach is illuminated using a case of 2_roots UML galaxy diagram that takes marketing analysis of TV products involved one retailer and several suppliers into consideration.

Bibliometric analysis of researches of empowerment theory applied to nursing in China

Objective:To analysis the development trend and research focus of empowerment theory applied to Nursing in China.Methods:Literatures related to the objective were searched and collected from CNKI,WangFang,VIP and CBM,then Excel 2003 was used to setup the database and co-word matrix,SPSS 21.0 was utilized to make the visualized analysis by way of multivariate statistics analysis,cluster analysis and multidimensional scaling analysis.Results:Literatures with the number of 486 were selected out and 18 high frequency keywords were retrieved from 140 journals.Among the literatures,the first one was published in 2002,then a tremendous rising started since 2009,and reached the peak in 2017,mainly from the southern part of China,such as the province of Jiangsu,Guangdong,and Zhejiang.Regarding the content of the literatures,the research of intervention accounted for 61.32%,then the research of description came to the second at the ratio of 24.49%.What’s more,378(77.78%)were cited,154(31.69%)were funded.Conclusion:Nowadays,empowerment applied in the therapy of chronic disease is the focus and trend of the research of empowerment theory,and the psychological empowerment to nursing staff,as well as the constructed empowerment is going mature.In the future,more attention should be paid to the study and practice of empowerment theory,in order to vary the direction of research and enrich the theory.

A multidimensional examination of performances of HSR (High-Speed Rail) systems

This paper deals with a multidimensional examination of the infrastructural, technical/technological, operational, economic, social, and environmental performances of high-speed rail （HSR） systems, including their overview, analysis of some real-life cases, and limited （analytical） modeling. The infrastructural performances reflect design and geometrical characteristics of the HSR lines and stations. The technical/technological performances relate to the characteristics of rolling stock, i.e., high-speed trains, and supportive facilities and equipment, i.e., the power supply, signaling, and traffic control and management system（s）. The operational performances include the capacity and productivity of HSR lines and rolling stock, and quality of services. The economic per- formances refer to the HSR systems＇ costs, revenues, and their relationship. The social performances relate to the impacts of HSR systems on the society such as congestion, noise, and safety, and their externalities, and the effects in terms of contribution to the local and global/country social- economic development. Finally, the environmental performances of the HSR systems reflect their energy consumption and related emissions of green house gases, land use, and corresponding externalities.

Convolutional Neural Network and Bayesian Gaussian Process in Driving Anger Recognition

With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.

Traffic Characteristics Based Dynamic Radio Resource Management in Heterogeneous Wireless Networks

The traffic with tidal phenomenon in Heterogeneous Wireless Networks(HWNs)has radically increased the complexity of radio resource management and its performance analysis.In this paper,a Simplified Dynamic Hierarchy Resource Management(SDHRM)algorithm exploiting the resources dynamically and intelligently is proposed with the consideration of tidal traffic.In network-level resource allocation,the proposed algorithm first adopts wavelet neural network to forecast the traffic of each sub-area and then allocates the resources to those sub-areas to maximise the network utility.In connection-level network selection,based on the above resource allocation and the pre-defined QoS requirement,three typical network selection policies are provided to assign traffic flow to the most appropriate network.Furthermore,based on multidimensional Markov model,we analyse the performance of SDHRM in HWNs with heavy tailed traffic.Numerical results show that our theoretical values coincide with the simulation results and the SDHRM can improve the resource utilization.

Mass Spectrometry-Based Human Breath Analysis:Towards COVID-19 Diagnosis and Research

COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath.Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring breath biomarkers.Mass spectrometry(MS)-based approaches off er a promising analytical platform for human breath analysis due to their high speed,specificity,sensitivity,reproducibility,and broad coverage,as well as its versatile coupling methods with different chromatographic separation,and thus can lead to a better understanding of the clinical and biochemical processes of COVID-19.Herein,we try to review the developments and applications of MS-based approaches for multidimensional analysis of COVID-19 breath samples,including metabolites,proteins,microorganisms,and elements.New features of breath sampling and analysis are highlighted.Prospects and challenges on MS-based breath analysis related to COVID-19 diagnosis and study are discussed.