Automatic Defect  in Nonwovens Using Images and Metadata Analysis—A Deep  Approach

2021 ◽  
pp. 115-126
Author(s):  
Marco Calderisi ◽  
Gabriele Galatolo ◽  
Ilaria Ceppa ◽  
Tommaso Motta ◽  
Francesco Vergentini
Keyword(s):  
Deep Approach ◽  
Metadata Analysis

‘Deep Approach’ in Translation Project Supervision

Perspectives ◽  
2007 ◽  
Vol 15 (3) ◽  
pp. 153 ◽  
Author(s):  
Clara Chan Ho Yan
Keyword(s):  
Deep Approach ◽  
Project Supervision

scholarly journals Evaluation of midwifery students’ learning approaches to the compulsory biochemistry course

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Funda Kosova ◽  
Nurcan Çelik ◽  
Hanife Nurseven Şimşek ◽  
Seval Cambaz Ulaş
Keyword(s):  
Cross Sectional Study ◽  
Health Science ◽  
Learning Approach ◽  
Learning Approaches ◽  
Deep Approach ◽  
Cross Sectional ◽  
Professional Career ◽  
Motivation For Learning ◽  
Package Program ◽  
Research Population

AbstractObjectivesLearning approach in a certain learning process is based on the student’s intentions, behaviors, and habits according to his/her perceptions of the task of learning and determines the amount and quality of learning. The objective of this study is to evaluate the learning approaches of 1st and 4th grade midwifery students to biochemistry course and the change, if there is any, through their education.MethodsThis is an observational, cross-sectional study. The research population consisted of the voluntary students (86.47%, n:147) of the 2017–2018 season of the 1st and 4th class of Manisa Celal Bayar University, Health Science Faculty, Midwifery Department (n:170). Data were collected by using the “Introductory Information Form” and the “Learning Approach Scale”, and evaluated in the SPSS package program by performing number, percentile, mean, standard deviation, independent t test.ResultsThe mean age of the students was 20.82 ± 1.81. Over 95% of the students stated that biochemistry lesson was necessary, while 59.9% reported that their biochemistry knowledge was insufficient. Over 87% of the student expressed their belief that the content of the biochemistry classes will help them in their professional career. Mean score of deep approach for Learning Approach Scale was 34.13 ± 6.07 (Min:19.00–Max:50.00), and mean score of superficial approach for Learning Approach Scale was 26.94 ± 6.37 (Min:15.00–Max:50.00). There was a significant relation between deep approach scale score and the perception of high importance of biochemistry in the professional life (p<0.05).ConclusionsMidwifery students, who believe that biochemistry is necessary for their professional career have a higher motivation for learning biochemistry, thus perform a deeper approach to learning. In general, creating effective and dynamic educational environments that support deep learning is necessary for enhancing the learning of biochemistry.

scholarly journals Ontology-based semantic similarity to metadata analysis in the information security domain

2021 ◽  
pp. 034-041
Author(s):  
A.Y. Gladun ◽  
◽  
K.A. Khala ◽  
Keyword(s):  
Big Data ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Network Attacks ◽  
Semantic Level ◽  
Operational Information ◽  
Metadata Analysis ◽  
Data Elements ◽  
Different Levels

It is becoming clear with growing complication of cybersecurity threats, that one of the most important resources to combat cyberattacks is the processing of large amounts of data in the cyber environment. In order to process a huge amount of data and to make decisions, there is a need to automate the tasks of searching, selecting and interpreting Big Data to solve operational information security problems. Big data analytics is complemented by semantic technology, can improve cybersecurity, and allows you to process and interpret large amounts of information in the cyber environment. Using of semantic modeling methods in Big Data analytics is necessary for the selection and combination of heterogeneous Big Data sources, recognition of the patterns of network attacks and other cyber threats, which must occur quickly to implement countermeasures. Therefore to analyze Big Data metadata, the authors propose pre-processing of metadata at the semantic level. As analysis tools, it is proposed to create a thesaurus of the problem based on the domain ontology, which should provide a terminological basis for the integration of ontologies of different levels. To build a thesaurus of the problem, it is proposed to use the standards of open information resources, dictionaries, encyclopedias. The development of an ontology hierarchy formalizes the relationships between data elements that will be used in future for machine learning and artificial intelligence algorithms to adapt to changes in the environment, which in turn will increase the efficiency of big data analytics for the cybersecurity domain.

scholarly journals Large-Scale Metadata Analysis of Ovary Based Multi-Omics Datasets for Understanding the Genes Regulating Litter Size

2020 ◽  
Author(s):  
Ayyappa Kumar Sista Kameshwar ◽  
Julang Li
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Litter Size ◽  
Large Scale ◽  
Expression Patterns ◽  
Genetic Selection ◽  
Follicular Development ◽  
Oocyte Quality ◽  
Differentially Expressed ◽  
Metadata Analysis

Abstract Background : Litter size is a very important production index in the livestock industry, which is controlled by various complex physiological processes. To understand and reveal the common gene expression patterns involved in controlling prolificacy, we have performed a large-scale metadata analysis of five genome-wide transcriptome datasets of pig and sheep ovary samples obtained from high and low litter groups, respectively. We analyzed separately each transcriptome dataset using GeneSpring v14.8 software by implementing standard, generic analysis pipelines and further compared the list of most significant and differentially expressed genes obtained from each dataset to identify genes that are found to be common and significant across all the studies. Results : We have observed a total of 62 differentially expressed genes common among more than two gene expression datasets. The KEGG pathway analysis of most significant genes has shown that they are involved in metabolism, the biosynthesis of lipids, cholesterol and steroid hormones, immune system, cell growth and death, cancer-related pathways and signal transduction pathways. Of these 62 genes, we further narrowed the list to the 25 most significant genes by focusing on the ones with fold change >1.5 and p<0.05. These genes are CYP11A1, HSD17B2, STAR, SCARB1, IGSF8, MSMB, SERPINA1 , FAM46C, HEXA, PTTG1, TIMP1, FAM167B, CCNG1, FAXDC2, HMGCS1, L2HGDH, Lipin1, MME, MSMO1, PARM1, PTGFR, SLC22A4, SLC35F5, CCNA2, CENPU, CEP55, RASSF2, and SLC16A3 . Conclusions : Interestingly, comparing the list of genes with the list of genes obtained from our literature search analysis, we found only three genes in common. These genes are HEXA, PTTG1, and TIMP1. Our finding points to the potential of a few genes that may be important for ovarian follicular development and oocyte quality. Future studies revealing the function of these genes will further our understanding of how litter size is controlled in the ovary while also providing insight on genetic selection of high litter gilts.

Improving simulation-based learning of a practical skill in nursing education

2020 ◽  
Vol 17 (1) ◽  
pp. 36-50
Author(s):  
Monika Ravik
Keyword(s):  
Nursing Education ◽  
Nursing Students ◽  
Ways Of Knowing ◽  
Skill Learning ◽  
Practical Nursing ◽  
Deep Approach ◽  
Peripheral Vein ◽  
Approach To Learning ◽  
Vein Cannulation ◽  
Deep Approach To Learning

ABSTRACTBackground: Many newly qualified nurses lack competence in practical nursing skills. Peripheral vein cannulation is particularly challenging to learn and perform. Skill learning is often developed from a reproduction and memorizing of knowledge and guidelines. Learning peripheral vein cannulation associated with successful placement require a more thorough and deeper approach to learning.Framework: Marton and Saljö’s ways of knowing, a surface and a deep approach to learning can be used during peripheral vein cannulation learning to guide development and competence in this practical nursing skill.Aim: The aim of this theoretical article was to provide knowledge and understanding about two approaches to skill learning, a surface and a deep, and how they can contribute to learning of peripheral vein cannulation.Conclusion: Nursing students learning of peripheral vein cannulation influence pedagogy choice by supervisors. Contextual factors, such as supervisors, influences learning opportunities and development of PVC competence.Key words: vein cannulation, nursing education, learning, surface approach to learning, deep approach to learning.

A Context and Content Reflection on Business-IT Alignment Research

2012 ◽  
Vol 3 (2) ◽  
pp. 21-37 ◽  
Author(s):  
John Joe Parappallil ◽  
Novica Zarvic ◽  
Oliver Thomas
Keyword(s):  
Point Of View ◽  
Future Research ◽  
Research Directions ◽  
It Alignment ◽  
Systems Research ◽  
Information Systems Research ◽  
Metadata Analysis ◽  
Future Research Directions ◽  
Business It Alignment ◽  
Research Domain

In this paper, the authors present the results of a recently performed literature analysis on the topic of Business-IT Alignment. They have thereby investigated 270 articles from the period 1993-2011 in a structured way. The articles were selected on the basis of three well-known ranking lists of publications in the Information Systems research domain. In the authors’ analysis they distinguish a context and a content point of view. The former one focuses on metadata analysis of the articles under consideration whereas the latter one uses text mining techniques to dive into the articles´ body of content. Finally, they discuss their results and present conceivable future research directions that should be tackled by alignment researchers and conclude their paper.

scholarly journals Calculus Self-Efficacy Inventory: Its Development and Relationship with Approaches to learning

2019 ◽  
Vol 9 (3) ◽  
pp. 170 ◽  
Author(s):  
Yusuf F. Zakariya ◽  
Simon Goodchild ◽  
Kirsten Bjørkestøl ◽  
Hans K. Nilsen
Keyword(s):  
Self Efficacy ◽  
Quantitative Research ◽  
Approaches To Learning ◽  
Deep Approach ◽  
Surface Approach ◽  
Approach To Learning ◽  
Economics Students ◽  
Exploratory Factor Analyses ◽  
Deep Approach To Learning ◽  
Surface Approach To Learning

This study was framed within a quantitative research methodology to develop a concise measure of calculus self-efficacy with high psychometric properties. A survey research design was adopted in which 234 engineering and economics students rated their confidence in solving year-one calculus tasks on a 15-item inventory. The results of a series of exploratory factor analyses using minimum rank factor analysis for factor extraction, oblique promin rotation, and parallel analysis for retaining extracted factors revealed a one-factor solution of the model. The final 13-item inventory was unidimensional with all eigenvalues greater than 0.42, an average communality of 0.74, and a 62.55% variance of the items being accounted for by the latent factor, i.e., calculus self-efficacy. The inventory was found to be reliable with an ordinal coefficient alpha of 0.90. Using Spearman’ rank coefficient, a significant positive correlation ρ ( 95 ) =   0.27 ,   p <   0.05 (2-tailed) was found between the deep approach to learning and calculus self-efficacy, and a negative correlation ρ ( 95 ) =   − 0.26 ,   p <   0.05 (2-tailed) was found between the surface approach to learning and calculus self-efficacy. These suggest that students who adopt the deep approach to learning are confident in dealing with calculus exam problems while those who adopt the surface approach to learning are less confident in solving calculus exam problems.

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