Lewin Behavior Model-Based Study on Influence Factors of Individuals to Mobile Service Demand

How to fmd main influence factors of individuals to mobile service demand is investiga- ted. The empirical research is conducted in the sample of high-value customers in China mobile market. Based on Lewin behavior model, this pa- per establishes factors-matrix from personal and environmental dimensions. Relationships among multiple factors are tested in the structural equa- tion model and their impacts on customers＇ de- mands are elaborated. Findings indicate that opera- tional convenience and business brand image have significant effects on sample users＇ demands. Fur- thermore, annual income, gender, occupation, the needs of access to information and the needs of enriching and improving social relationships are al- so important factors for high-value users. The re- suits may provide further insights into mobile service demand and the model can be popularized to other behavior researches.

A new approach for distinguishing different deformation trend blocks with displacement observations

The process for dividing the different deformation trend blocks with displacement observations includes three steps. They are datum detection, block scope told part and anomalous deformations detection in blocks. The three steps are implemented by Quasi-Accurate Detection(QUAD) in the paper. In the previous two steps, the prelimi-nary selection for Quasi-Accurate Observations (QAOs) is key. The preliminary selection is according to the size of deformation displacement for datum detection and according to the direction of deformation for block scope told part. At last through an example, each implementation process is introduced simply and the detection effect of QUAD is compared with that of the robust estimation (Huber) and the statistic test. The result indicates that the three steps can be implemented successfully with QUAD and that the anomalous deformations in blocks can be detected, but the steps of the datum detection and block scope told part are failed by robust estimation. The detec-tions of three steps are failure by the statistic test. The results show that the QUAD has the virtues that the location of gross errors is much accurate and the breakdown point is higher than the other two methods.

Neighbor Distinguishing Total Choice Number of Sparse Graphs via the Combinatorial Nullstellensatz

Let G =（V, E） be a graph and Ф ： V tA E → {1, 2,..., k） be a total-k-coloring of G. Let f（v）（S（v）） denote the sum（set） of the color of vertex v and the colors of the edges incident with v. The total coloring Ф is called neighbor sum distinguishing if （f（u） ≠ f（v）） for each edge uv∈ E（G）. We say that Фis neighbor set distinguishing or adjacent vertex distinguishing if S（u） ≠ S（v） for each edge uv ∈ E（G）. For both problems, we have conjectures that such colorings exist for any graph G if k 〉 △（G） ＋ 3. The maximum average degree of G is the maximum of the average degree of its non-empty subgraphs, which is denoted by mad （G）. In this paper, by using the Combinatorial Nullstellensatz and the discharging method, we prove that these two conjectures hold for sparse graphs in their list versions. More precisely, we prove that every graph G with maximum degree A（G） and maximum average degree mad（G） has ch＇＇∑（G） 〈 △（G） ＋ 3 （where ch＇＇∑（G） is the neighbor sum distinguishing total choice number of G） if there exists a pair （k, m） ∈ {（6, 4）, （5, 18/5）, （4, 16）} such that △（G） 〉 k and mad （G） 〈 m.