An Improved Graphic Representation for Structured Program Design

In this paper,an improved graphic representation for Structured Program Design——N-S-Z (Nassi-Shneiderman-Zhou Diagram)is proposed.It not only preserves the advantages of the conventional graphic and non-graphic representations,but also adds some new features which will enhance the representa- tive power of the original diagram.

Graphical representations and worm algorithms for the O(N) spin model

We present a family of graphical representations for the O(N)spin model,where N≥1 represents the spin dimension,and N=1,2,3 corresponds to the Ising,XY and Heisenberg models,respectively.With an integer parameter 0≤ℓ≤N/2,each configuration is the coupling of ℓ copies of subgraphs consisting of directed flows and N−2ℓ copies of subgraphs constructed by undirected loops,which we call the XY and Ising subgraphs,respectively.On each lattice site,the XY subgraphs satisfy the Kirchhoff flow-conservation law and the Ising subgraphs obey the Eulerian bond condition.Then,we formulate worm-type algorithms and simulate the O(N)model on the simple-cubic lattice for N from 2 to 6 at all possibleℓ.It is observed that the worm algorithm has much higher efficiency than the Metropolis method,and,for a given N,the efficiency is an increasing function ofℓ.Besides Monte Carlo simulations,we expect that these graphical representations would provide a convenient basis for the study of the O(N)spin model by other state-of-the-art methods like the tensor network renormalization.

Computational prediction of over-annotated protein-coding genes in the genome of Agrobacterium tumefaciens strain C58

Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants.Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as ＂hypothetical＂ were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58.

Examining data visualization pitfalls in scientific publications

Data visualization blends art and science to convey stories from data via graphical representations.Considering different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full force.While the art component involves creating visually appealing and easily interpreted graphics for users,the science component requires accurate representations of a large amount of input data.With a lack of the science component,visualization cannot serve its role of creating correct representations of the actual data,thus leading to wrong perception,interpretation,and decision.It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers.To address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical representations.We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color,shape,size,and spatial orientation.Moreover,a text mining technique was applied to extract practical insights from common visualization pitfalls.Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color,shape,size,and spatial orientation.The findings showed that the pie chart is the most misused graphical representation,and size is the most critical issue.It was also observed that there were statistically significant differences in the proportion of errors among color,shape,size,and spatial orientation.

The Role of Technology in the Process of Learning Mathematics

The research was achieved by applying several questionnaires and evaluation tests that have shown the efficacy of using the computer in building active thought and skills in the graphical representation of functions , within a Cartesian reference system xOy. Our research was based on the following hypothesis: if we systematically use, in lessons of algebra and mathematical analysis, the GeoGebra software for drawing the graph of a function, then we shall contribute to building attitudes and competences in using the computer for the graphical representation of functions , within a Cartesian reference system xOy. The purpose of our study is to present a practical computer use model for the students from the Pre- and In-Service Teacher Training Department, “Vasile Alecsandri” University of Bacau, during their pedagogical practice stage of continuous training, aimed at building practical skills in supporting the teaching-learning process with computers. To deal with these issues, we are trying to find the best teaching-learning strategies by using the calculus technique. The research was conducted at the “Stefan cel Mare” National Pedagogical College from Bacau, by assisting 20 lessons of mathematics and 20 lessons of Information and Communication Technology, involving a group of 104 students in the 11th grade. In order to verify the hypothesis, we have established several objectives that orient and guide our activity: knowing the initial level of mathematical training in terms of graphical representation of functions;identifying the frame and reference objectives of the curriculum for mathematical education regarding the graphical representation of functions;designing and conducting a teaching process focused on the use of the computer and the GeoGebra software in learning the graphical representation of functions;final evaluation of the students’ level of training regarding the graphical representation of functions in a plane.