Optimal Base Station Network Based on Topological Data Analysis

The decision of which base stations need to be removed due to the cost is always a difficult problem, because the influence on the cover rate of the network caused by the removal should be kept to a minimum. However, the common methods to solve this problem such as K-means Clustering show a low accuracy. Barcode, which belongs to TDA, has the possibility to show the result by identifying the Persistent Homology of base station network. This essay mainly illustrates the specific problem of optimal base station network, which applies the TDA(Topological Data Analysis) methods to find which base stations need removing due to the cost K-means Clustering and Topological Data Analysis methods were mainly used. With the simulated distribution of telecommunication users, K-means Clustering algorithm was used to locate 30 best base stations. By comparing the minimum distance between the results (K=25 and K=30), K-means Clustering was used again to decide base station points to be removed. Then TDA was used to select which 5 base stations should be removed through observing barcode. By repeating above steps five times, Finally the average and variance of cover area in original network, K-means Clustering and TDA were compared. The experiment showed that the average cover rate of original network was 81.20% while the result of TDA and K-means Clustering were 92.13% and 89.87%. It was proved by simulation that it is more efficient to use TDA methods to construct the optimal base station network.

Exploring dynamic metabolomics data with multiway data analysis: a simulation study

Background Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied. Results We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. Conclusion Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

Factors that Influence Purchase on Cinema Online Tickets Using Tix-Id Application, through Buying Interest

The purpose of this study was to determine the factors that affect directly and indirectly such as promotion, convenience, and security on the decision to purchase online cinema tickets with the TIX ID application through consumer buying interest. The data collection technique uses a questionnaire that is distributed online via the google application form. Data analysis methods include validity, reliability, classical assumptions, and SEM tests. The number of samples in this study were 200 respondents who had met the minimum requirements for using SEM (structural equation models). Data processing using SPSS and AMOS. The results showed that promotion and convenience factors had a significant effect on purchase intention, while security did not have a significant effect on purchase intention. The results of statistical tests show that there is no influence of the promotion, convenience, and security variables on purchasing decisions. Furthermore, the results show that promotion and convenience factors indirectly influence purchasing decisions through purchase intention.

Testing the EntreComp framework and its relation to start-up behaviour in seven European countries

PurposeThe European Commission has published a general framework of entrepreneurship competence (EC), EntreComp, to create a shared definition and support its development. This study proposes and tests a scale to measure EC based on the EntreComp framework and examines its relation to start-up behaviour using data from seven European countries.Design/methodology/approachThe data were gathered from seven European countries and consist of 1,128 answers from both secondary and higher education level students. The authors use explorative factor analysis (EFA), analysis of variance and logistic regression analysis as data analysis methods.FindingsThe results show that EC is related to start-up behaviour and sensitive to role models and prior experience of entrepreneurship but is not sensitive to gender or level of education. The results also show that although the framework proposes three distinct areas, EC is unidimensional.Originality/valueThe study tests the EntreComp framework and introduces a scale for measuring EC based on the framework. The results show that EC can be addressed as unidimensional construct and that it explains start-up behaviour and develops through enterprising activities. The study also shows the impact of role models on EC. No difference in EC between genders is observed, suggesting the gender differences in entrepreneurship arise from factors other than competence.

Big Data Visualization Tools and Techniques

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

ANALISIS SISTEM AKUNTANSI PENGGAJIAN DAN PENGUPAHAN PADA PT. X

The accounting information system for contracts and wages is very important for a company which can assist in making decisions and archieving the implementation of payroll and wages for employees. The goal is to be able to find out how the implementation of the accounting system for payroll and wages at PT. X. This type of research used in this research is descriptive research using data analysis methods, namely qualitative data analysis. This study aims to identify and analyze the payroll and wage accounting information system at PT. X. The results of this analysis indicate the payroll and wages accounting information system at PT. X which is still not supportive, as well as the lack of supervision on the attendance list of employees, this shows the weakness that accours in the company PT. X.

Pengaruh Promosi Dan Harga Terhadap Keputusan Pembelian Pada PT. Wahana Chemindo Jaya Medan

The studi aims to determine whether promotions and prices have a significant effect on purchasing decisions at PT. Wahana Chemindo Jaya Medan. Where the consumers from PT. Wahana Chemindo Jaya Medan. The population in this study were all customers of PT. Wahana Chemindo Jaya Medan. The sample taken was 65 respondent using a non probability sampling technique. The data analysis method used is quantitative metdhod and data analysis method consisting of normality test, and heteroscedasticity test, multiple linaer regression terst, hypothesis testing consisting of t test and f test and coefficient od determination test (R2). Working on data analysis methods using the help os SPSS 25 For windows.This study concludes that the promotion and price variabel have a positive and significant impact on purchasing decions at PT. Wahana Chemindo Jaya Medan. This result can be seen based on the coefficient of determination test which produces an R square value of 0,787. This shows that promotions and prices are able to influence the purchasing decisions of customers of PT. Wahana Chemindo Jay Medan by 78,7%.