The effectiveness of group guidance services using the Gestalt Counseling Approach with projection techniques in preventing bullying among students

Bullying behavior is an act that violates the strength and power that is hurting someone who is weaker verbally, physically, or psychologically so that the victim feels under pressure and tends to be helpless. Factors that influence the occurrence of bullying behavior are the influence of the school environment, peer groups, social environmental conditions and television shows and social media. So that efforts are needed to prevent bullying behavior among students. One way that can be done to prevent bullying behavior among students is to provide group guidance services using the Gestalt approach with projection techniques. This study aims to test the effectiveness of group guidance services using the Gestalt approach with projection techniques in preventing bullying among students. The findings of this study are: (1) there is a significant difference in bullying behavior in the experimental group before and after attending group guidance services using the Gestalt approach with projection techniques (2) there is a significant difference in bullying behavior in the control group before and after being given group guidance services on bullying behavior without special treatment, (3) There are differences in the bullying behavior of experimental group students who are treated with group guidance services using the Gestalt approach with projection techniques with the control group who are given group guidance services about bullying behavior without special treatment, this can be seen in the posttest average score of the experimental group dropped higher than the mean posttest score of the control group.

Qualitative methods

The main goal of the chapter is to present how to use qualitative methods in sustainability research. First, the theoretical basis of the methods is presented, i.e., the essence of qualitative methods, what differs them from quantitative methods, and their types. The second part of this chapter covers designing and conducting a focus group interview (FGI): its essence and main stages, sampling, projection techniques and the script, as well as it contains the case study of ecological culture of Bulgarians. The third part presents considerations necessary to conduct a qualitative research, i.e., the organizational aspects of FGI and the guidelines for the work of the moderator. Finally, the last part shows considerations concerning data analysis—using CAQDAS software.

Quantitative and Qualitative Comparison of 2D and 3D Projection Techniques for High-Dimensional Data

Projections are well-known techniques that help the visual exploration of high-dimensional data by creating depictions thereof in a low-dimensional space. While projections that target the 2D space have been studied in detail both quantitatively and qualitatively, 3D projections are far less well understood, with authors arguing both for and against the added-value of a third visual dimension. We fill this gap by first presenting a quantitative study that compares 2D and 3D projections along a rich selection of datasets, projection techniques, and quality metrics. To refine these insights, we conduct a qualitative study that compares the preference of users in exploring high-dimensional data using 2D vs. 3D projections, both without and with visual explanations. Our quantitative and qualitative findings indicate that, in general, 3D projections bring only limited added-value atop of the one provided by their 2D counterparts. However, certain 3D projection techniques can show more structure than their 2D counterparts, and can stimulate users to further exploration. All our datasets, source code, and measurements are made public for ease of replication and extension.

Matrix Equation Techniques for Certain Evolutionary Partial Differential Equations

We show that the discrete operator stemming from time-space discretization of evolutionary partial differential equations can be represented in terms of a single Sylvester matrix equation. A novel solution strategy that combines projection techniques with the full exploitation of the entry-wise structure of the involved coefficient matrices is proposed. The resulting scheme is able to efficiently solve problems with a tremendous number of degrees of freedom while maintaining a low storage demand as illustrated in several numerical examples.

Knowledge discovery using clustering analysis of rainfall timeseries

<p>Rainfall time series analysis using clustering involves the identification of temporal patterns, with each data item representing an individual storm. This analysis results in clusters of data items that trend in a common way and can be utilized in stochastic simulation, water resources planning and the identification of future directions due to climate change. A comparative analysis is carried out of several methods that use intra versus inter-cluster distances, for the estimation of the relevant number of clusters using a big dataset of the described rainfall time series. Visualization using topographic maps that are produced via nonlinear projection techniques is applied, to validate the presence of both distance and density structures and to assist in the final determination of the numbers of clusters. This stands in contrast to empirical and not completely data-driven approaches of the literature, in which constrained clustering methods are employed with assumptions on the presence of four classes.</p>

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

Prediction of miRNA-disease associations based on Weighted K-Nearest known neighbors and network consistency projection

MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since the experimental techniques used to find that new miRNA-disease relationships are time-consuming and expensive, many computational techniques have been developed. In this study, Weighted [Formula: see text]-Nearest Known Neighbors and Network Consistency Projection techniques were suggested to predict new miRNA-disease relationships using various types of knowledge such as known miRNA-disease relationships, functional similarity of miRNA, and disease semantic similarity. An average AUC of 0.9037 and 0.9168 were calculated in our method by 5-fold and leave-one-out cross validation, respectively. Case studies of breast, lung, and colon neoplasms were applied to prove the performance of our proposed technique, and the results confirmed the predictive reliability of this method. Therefore, reported experimental results have shown that our proposed method can be used as a reliable computational model to reveal potential relationships between miRNAs and diseases.