Perceived Social Support as a Predictor of Teacher Candidates' Smartphone Cyberloafing

This study investigated whether teacher candidates' perceived social support (PSS) predicts smartphone use during lectures. To achieve the aim of the study, a relational survey model was used. The study sample composed of students studying at the College of Education in Elazığ Province, Turkey, during the 2019-2020 academic semester. The correlation analysis was conducted to determine the relationships among the variables, and hierarchical linear regression analysis was used to determine the predictive effects of demographic characteristics (gender and class) and PSS on smartphone cyberloafing. The results of the study revealed a significant, negative, and low relationship between PSS and smartphone cyberloafing. Social support from family negatively predicted smartphone cyberloafing during lectures, while social support from significant others positively predicted it. Finally, the family and significant other subscales significantly predicted interactive, browsing, and entertainment cyberloafing. In contrast, the friends subscale predicted only interactive cyberloafing. The belief that a sense of trust in family and needed family support in times of need could decrease smartphone cyberloafing and lead teacher candidates to focus more on teaching. Educational institutions should develop policies that involve families at every stage of education, and decisions should be made with the support of experts, institutions, or organizations on problematic issues.

long-read-tools.org: an interactive catalogue of analysis methods for long-read sequencing data

Background The data produced by long-read third-generation sequencers have unique characteristics compared to short-read sequencing data, often requiring tailored analysis tools for tasks ranging from quality control to downstream processing. The rapid growth in software that addresses these challenges for different genomics applications is difficult to keep track of, which makes it hard for users to choose the most appropriate tool for their analysis goal and for developers to identify areas of need and existing solutions to benchmark against. Findings We describe the implementation of long-read-tools.org, an open-source database that organizes the rapidly expanding collection of long-read data analysis tools and allows its exploration through interactive browsing and filtering. The current database release contains 478 tools across 32 categories. Most tools are developed in Python, and the most frequent analysis tasks include base calling, de novo assembly, error correction, quality checking/filtering, and isoform detection, while long-read single-cell data analysis and transcriptomics are areas with the fewest tools available. Conclusion Continued growth in the application of long-read sequencing in genomics research positions the long-read-tools.org database as an essential resource that allows researchers to keep abreast of both established and emerging software to help guide the selection of the most relevant tool for their analysis needs.

oposSOM-Browser: an interactive tool to explore omics data landscapes in health science

Background oposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization. Results These functionalities are now available as interactive web-browser application for a broader user audience interested in extracting detailed information from high-throughput omics data sets pre-processed by oposSOM. It enables interactive browsing of single-gene and gene set profiles, of molecular ‘portrait landscapes’, of associated phenotype diversity, and signalling pathway activation patterns. Conclusion The oposSOM-Browser makes available interactive data browsing for five transcriptome data sets of cancer (melanomas, B-cell lymphomas, gliomas) and of peripheral blood (sepsis and healthy individuals) at www.izbi.uni-leipzig.de/opossom-browser.

Lexical Explorer: extending access to the Database for Spoken German for user-specific purposes

This paper presents Lexical Explorer, 2 a tool that allows interactive browsing and filtering of quantitative corpus information. It further describes how this tool can be used to support linguistic work on corpora of spoken German. By using Lexical Explorer, users can analyse quantitative corpus data by interacting with frequency tables and obtaining customised word profiles of word distribution across word form variation, co-occurrences and metadata. Interaction with corpus examples of particular corpus counts is also enabled. Lexical Explorer was developed as a prototype for user-specific corpus access and is aimed at researchers of German lexicon in spoken interaction. Although Lexical Explorer was developed on the basis of two small speech corpora of the German language, the underlying principle of this tool can be easily adapted to other corpora and other user groups. Moreover, the tool can be used to gain insights into the corpus structure as well as to study and verify corpus content in a transparent and user-friendly way.

REPRESENTASI PENGETAHUAN PADA WEB SEMANTIK UNTUK MENINGKATKAN NILAI EFEKTIFITAS PENCARIAN DATA SURAT (STUDI KASUS: PT ANGKASA PURA I CABANG SEPINGGAN BALIKPAPAN )

PT. Angkasa Pura I branch Sepinggan, Balikpapan still uses the web with a little interactive (browsing or searching for certain information). The system still has some constraints between, the data searched only in accordance with the origin of the sender of the letter. Information gleaned from web searches will make it hard for users to find other broader content, consequently if the search keyword does not match the text contained in the information, the information will not appear. The method used is semantic ontology method, and use protege as ontology design, then use netbean and library Jena as interface design. The methodology used in this final project is the methodology methodology of Guidelines for Rapid APPlication Engineering (GRAPPLE). The GRAPPLE methodology is a simplified framework of the Rapid Application Development (RAD) system development method. GRAPPLE methodology is a system development methodology that each stage consists of several actions, and each action produces a UML diagram. UML is a good modeling language to apply in development. UML has the benefit of providing an overview of systems that are easily understood by developers. UML also makes it easy to translate diagram form into coding. The results obtained in making this application shows that the new application can cover the constraints existing in the previous application, so as to increase the effectiveness of searching a letter. Comparison of test results proved that new applications more minimize the constraints that occur. Keywords: Semantic Web, Ontology, Protege, Letter