Effective scheduling algorithm for on-demand XML data broadcasts in wireless environments

The organization of data on wireless channels, which aims to reduce the access time of mobile clients, is a key problem in data broadcasts. Many scheduling algorithms have been designed to organize flat data on air. However, how to effectively schedule semi-structured information such as XML data on wireless channels is still a challenge. In this paper, we firstly propose a novel method to greatly reduce the tuning time by splitting query results into XML snippets and to achieve better access efficiency by combining similar ones. Then we analyze the data broadcast scheduling problem of on-demand XML data broadcasts and define the efficiency of a data item. Based on the definition, a Least Efficient Last (LEL) scheduling algorithm is also devised to effectively organize XML data on wireless channels. Finally, we study the performance of our algorithms through extensive experiments. The results show that our scheduling algorithms can reduce both access time and tuning time signifcantly when compared with existing work

Di-μ-acetato-κ4 O:O-bis­({2-[(piperidin-2-ylmeth­yl)imino­meth­yl]phenolato-κ3 N,N′,O}copper(II)) monohydrate

In the binuclear centrosymmetric title compound, [Cu2(C13H17N2O)2(C2H3O2)2]·H2O, the CuII atom is coordin­ated by two N atoms and one O atom from the Schiff base ligand and an acetate O atom in a distorted suare-planar geometry. The water O atom is invoved in three different hydrogen-bonding interactions, as donor to the acetate O atom and to the the ligand O atom and as acceptor to a ligand N atom

Epidemic modelling by ripple-spreading network and genetic algorithm

Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results

Looking Beyond Content: Modeling and Detection of Fake News from a Social Context Perspective

The widespread fake news on social media has boosted the demand for reliable fake news detection techniques. Such dissemination of fake news can influence public opinions and society. More recently, a growing number of methods for detecting fake news have been proposed. However, most of these approaches have significant limitations in timely detection of fake news. To facilitate early detection of fake news, we propose a unique framework FNEPP (Fake News Engagement and Propagation Path) from a social context perspective, which explicitly combines news contents, user engagements, user characteristics, and the news propagation path as composite features of two collaborative modules. The engagement module captures news contents and user engagements, while the propagation path module learns global and local patterns of user characteristics and news dissemination patterns. Experimental results on two real-world datasets demonstrate the effectiveness and efficiency of the proposed FNEPP framework