Combination Of Data Analysis Techniques Research Articles

Dental Sleep Medicine Education Amongst Accredited Orthodontic Programmes in Thailand

Dental sleep medicine education (DSME) should be emphasised in postgraduate orthodontic training; however, there appears to be no clear guideline for its implementation into the curriculum. The aim was to investigate the current status of DSME as well as its feasibility and implementation in postgraduate orthodontic programmes. A structured interview with predetermined response options was chosen as a data collection method to gather relevant information from representatives of all accredited postgraduate orthodontic programmes in Thailand. These interviews were conducted online via the Cisco Webex Meeting platform. A combination of data analysis techniques was employed to achieve a thorough comprehension of the research findings, including descriptive statistics, quantitative content analysis, thematic analysis, and alignment analysis. All participating programmes reported the inclusion of DSME in their curricula. A didactic approach was adopted by all programmes. However, only 2 out of 7 programmes offered clinical sessions for their students. Several challenges in implementing DSME within orthodontic programmes were identified, including a shortage of expertise and limited patient access. The participants also suggested that knowledge and resource sharing amongst institutions could serve as a potential solution to enhance the feasibility of DSME. This research highlighted the significant disparities and inadequacy of DSME within postgraduate orthodontic programmes in Thailand due to various challenges. Consequently, there is a compelling need to place greater emphasis on DSME and establish a national-level standardisation within orthodontic programmes. This effort is essential for enhancing the awareness and competency of orthodontists in the field of DSME.

An international study of early-stage entrepreneurship using global entrepreneurship monitor data

PurposeThe goal of this study is to identify and validate some selected determinants of early-stage entrepreneurial activity (ESEA) by assessing the impact of entrepreneurial knowledge and skills (EK&S), fear of failure (FoF), the social status of entrepreneurs (SSE) and entrepreneurial intentions (EI) on ESEA.Design/methodology/approachThe study utilised cross-sectional data gathered by the Global Entrepreneurship Monitor (GEM) team from 49 countries, with a total of 162,077 respondents. The data analyses involved correlation, simple regression and path analyses, with a specific focus on testing for mediated and moderated effects. To complement the statistical analyses, fuzzy-set qualitative comparative analysis was also employed.FindingsThe path analysis revealed EK&S as primary drivers of EI and ESEA. Also, EK&S moderated the effects of FoF on EI, and the inclusion of EI improved the model significantly. The fuzzy-set qualitative comparative analysis result showed that the presence of EI, EK&S, FoF and SSE were sufficient but not necessary conditions for ESEA.Practical implicationsThe tested model demonstrates the importance of EK&S and EI, as well as the need to mitigate the effects of the fear factor in promoting entrepreneurial activity. As such, the support of EK&S programmes seems justifiable.Originality/valueThe findings of this study provide a deeper insight into the intricate relationships that underlie entrepreneurial activity by utilising a combination of data analysis techniques.

ANALISIS MINAT SISWA SISWI SMP/MTS TERHADAP EKSTRAKURIKULER MA MUHAMMADIYAH 1 PONOROGO MENGGUNAKAN METODE ASSOCIATION RULES BERBASIS WEB

Currently, the world of education must be managed professionally, because of the increasingly fierce competition in education today. Educational institutions will be abandoned by the community if they are managed poorly. MA Muhammadiyah 1 Ponorogo School is one of the private schools owned by the Muhammadiyah business charity in Ponorogo district. MA Muhammadiyah 1 Ponorogo has a small number of students, due to the lack of student interest in entering the school. To find out that students are interested in entering MA Muhammadyah 1 Ponorogo, the school made a new breakthrough that could attract students to register there. To find out, the researchers used data mining techniques with the Apriori Algorithm method, and supported by the Apriori web browser system by examining student extracurricular interest data with 177 respondents with 47 extracurriculars. Data Mining is a combination of data analysis techniques, while Apriori Algorithm is a method which is most often used because it is very simple, easy and the most widely proposed by several researchers, because there are two parameters, namely Support and Confidence. So the results of the” research predictions obtained are that the most popular extracurriculars for junior high school/MTs students are futsal and volleyball with a minimum value of support = 11% and a minimum value of confidence = 52%.

SHARING KNOWLEDGE OF CREATIVITY OF SMALL AND MEDIUM BUSINESSES FACING THE CHALLENGES OF THE CONDITION OF COVID-19

The main objective of this study is to find out and resolve the issues of Small and Medium-Sized Enterprises (SMEs) in Indonesia, particularly those located in Central Java's industrial sector, the northern sub-section. Primary and secondary data are used, and thereby a combination of data analysis techniques is used. The SMEs criteria are based on the Republic of Indonesia's Law No. 20 of 2008 on Micro, Small, and Medium-Sized Enterprises. According to the study's findings, SME owners were able to thrive by repackaging products, redesigning packaging, and establishing new marketing relations. This study developed the concepts of the creative knowledge sharing model, which is a synthesis of two theories, namely knowledge sharing and social identity.
 
 Keywords: Knowledge Sharing, Creativity, Social Identity, SMEs.

Antibody-functionalized aluminum oxide-coated particles targeting neutrophil receptors in a multifrequency microfluidic impedance cytometer.

Personalized diagnostics of infectious diseases require monitoring disease progression due to their ever-changing physiological conditions and the multi-faceted organ system mechanisms involved in disease pathogenesis. In such instances, the recommended clinical strategies involve multiplexing data collection from critical biomarkers related to a patient's conditions along with longitudinal frequent patient monitoring. Numerous detection technologies exist both in research and commercial settings to monitor these conditions, however, they fail to provide biomarker multiplexing ability with design and data processing simplicity. For a recently conceived multiplexing biomarker modality, this work demonstrates the use of electrically sensitive microparticles targeting and identifying membrane receptors on leukocytes using a single detection source, with a high potential for multiplexing greater than any existing impedance-based single-detection scheme. Here, polystyrene microparticles are coated with varying thicknesses of metal oxides, which generate quantifiable impedance shifts when exposed to multifrequency electric fields depending on the metal oxide thickness. Using multifrequency impedance cytometry, these particles can be measured and differentiated rapidly across one coplanar electrode scheme. After surface-functionalizing particles with antibodies targeting CD11b and CD66b receptors, the particles are combined with isolated neutrophils to measure receptor expression. A combination of data analysis techniques including multivariate analysis, supervised machine learning, and unsupervised machine learning was able to accurately differentiate samples with up to 91% accuracy. This proof-of-concept study demonstrates the potential for these oxide-coated particles for enumerating specific leukocytes enabling multiplexing. Further, additional coating thicknesses or different metal oxide materials can enable a compendium of multiplexing targeting resource to be used to develop a high-multiplexing sensor for targeting membrane receptor expression.

Four almost unexplored species of Brazilian Connarus (Connaraceae): Chemical composition by ESI-QTof-MS/MS-GNPS and a pharmacologic potential.

Species of Connaraceae are globally used in traditional medicines. However, several of these have not been studied regarding their chemical composition, and some are even at risk of extinction without proper studies. Therefore, the chemical composition and pharmacological potential of Connarus blanchetii Planch., Connarus nodosus Baker, Connarus regnellii G. Schellenb., and Connarus suberosus Planch., which were previously unknown, were analyzed. This work aims to investigate the pharmacological potential of these four Connarus species. The chemical composition of different extracts was determined by high-resolution mass spectrometry (HRMS), with subsequent analysis by the GNPS platform and competitive fragmentation modeling (CFM). Leaf extracts (C. blanchetii, C. nodosus, C. regnellii, and C. suberosus) and bark extracts (C. regnellii and C. suberosus) were obtained by decoction, infusion, and maceration. LC/HRMS data were submitted to the GNPS platform and evaluated using CFM in order to confirm the structures. The HRMS-GNPS/CFM analysis indicated the presence of 23 compounds that were mainly identified as phenolic derivatives from quercetin and myricetin, of which 21 are unedited in the Connarus genus. Thus, from the analyses performed, we can identify different compounds with pharmacological potential, as well as the most suitable forms of extraction. Using HRMS-GNPS/CFM, 21 unpublished compounds were identified in the studied species. Therefore, our combination of data analysis techniques can be used to determine their chemical composition.

Another Approach to Track Reconstruction: Cluster Analysis

A novel combination of data analysis techniques is introduced for the reconstruction of primary charged particles and of daughters of photon conversions, created in high energy collisions. Instead of performing a classical trajectory building or an image transformation, efficient use of both local and global information is undertaken while keeping competing choices open. The measured hits in silicon-based tracking detectors are clustered with the help of a k-medians clustering. It proceeds by alternating between the hit-to-track assignment and the track-fit update steps, until convergence. The clustering is complemented with the possibility of adding new track hypotheses or removing unnecessary ones. A simplified model of a silicon tracker is employed to test the performance of the proposed method, showing good efficiency and purity characteristics.

A data-driven analysis of building energy use with emphasis on operation and maintenance: A case study from the UAE

Abstract A pre-requisite to reducing the energy intensity of the building sector is to assess and understand current drivers of energy consumption. Drivers include building design characteristics, outdoor environmental conditions, as well as the operation patterns of building systems by facility managers and occupants. In the United Arab Emirates (UAE), where buildings consume more than 70% of the total electricity generated, a comprehensive analysis of the drivers of building energy performance has not yet been conducted. This paper proposes an empirical top-down statistical framework to comprehensively evaluate drivers of energy use for a stock of buildings. The approach is unique in its ability to capture the combined effects of technical, as well as, operation-related building parameters. A combination of data analysis techniques, including CHAID decision trees and regression models, is proposed and applied to data gathered from 713 mixed-use buildings in downtown Abu Dhabi. Results indicate that in addition to physical building parameters such as window tinting, operational parameters, including AC cleanliness, chiller condition, and thermostat temperature setting have shown significant impacts on energy consumption levels. The findings shed light on the crucial – yet often overlooked – role of facility managers and occupants in ensuring efficient energy performance in the building sector.

Detection of Road Accident Accumulation Zones with a Visual Analytics Approach

Nowadays, road accidents are a major public health problem, which increase is forecasted if road safety is not treated properly, dying about 1.2 million people every year around the globe. In 2012, Portugal recorded 573 fatalities in road accidents, on site, revealing the largest decreasing of the European Union for 2011, along with Denmark. Beyond the impact caused by fatalities, it was calculated that the economic and social costs of road accidents weighted about 1.17% of the Portuguese gross domestic product in 2010.Visual Analytics allows the combination of data analysis techniques with interactive visualizations, which facilitates the process of knowledge discovery in sets of large and complex data, while the Geovisual Analytics facilitates the exploration of space-time data through maps with different variables and parameters that are under analysis. In Portugal, the identification of road accident accumulation zones, in this work named black spots, has been restricted to annual fixed windows. In this work, it is presented a dynamic approach based on Visual Analytics techniques that is able to identify the displacement of black spots on sliding windows of 12 months. Moreover, with the use of different parameterizations in the formula usually used to detect black spots, it is possible to identify zones that are almost becoming black spots. Through the proposed visualizations, the study and identification of countermeasures to this social and economic problem can gain new grounds and thus the decision-making process is supported and improved.

A multivariate statistical analysis approach to highlight molecular processes in plant cell walls through ATR FT-IR microspectroscopy: The role of the α-expansin PhEXPA1 in Petunia hybrida

Mid-infrared ATR FT-IR microspectroscopy is applied in conjunction with multivariate statistical analysis on petal samples of Petunia hybrida from wild-type and from two transgenic lines in which the PhEXPA1 expansin gene expression was down-regulated and up-regulated, respectively. New insights are given on the role of expansin in the rearrangement of the cell wall polymer network. Measurements are done without any previous sample treatment to preserve the native cell wall structure and natural biological variability. An original combination of data analysis techniques is proposed to highlight molecular processes in plant cells, based on an automated spectra selection procedure, Principal Component Analysis, Wilcoxon rank sum test and heat map data representation.

Listening to the wire: criteria and techniques for the quantitative analysis of phone intercepts

This paper focuses on phone conversations wiretapped by the police. It discusses issues of validity and reliability of this type of data and it proposes the use of a combination of data analysis techniques. In order to utilize wiretapped conversations in a valid manner, individuals under surveillance must talk freely on the phone, the coverage of the group must be reasonably wide, and a large enough sample of conversations must be available. As for the analysis, we propose the use of a set of techniques: content analysis, correspondence analysis, descriptive network analysis and longitudinal stochastic actor-oriented models. Each technique highlights a different aspect of the criminal network. Systematic analysis of phone conversations can yield valid inferences on the nature and activities of criminal groups and enrich the understanding of the ties within a criminal network. If followed, the procedures discussed here should facilitate comparisons across groups.

Analysis of the beverage data using cluster analysis, rotated principal components analysis and LOESS curves

In an attempt to determine what influences liking of the 28 beverages in the study, a combination of data analysis techniques was employed. While statistical rigor is an essential component of all sensory and consumer data analyses, these analyses would be remiss without the partnership of the expertise of both a sensory and statistical professional. This paper will discuss how specific statistical methodologies have been guided by a combination of solutions that provide the most acceptable statistical approach and the most interpretable sensory solution. Cluster analysis was used to determine the existence of consumer subgroups or clusters based on the liking patterns across the product set. Two consumer studies were conducted and for each, two distinct subgroups were identified. Cross tabulation of consumer subgroups with that of respondent demographics was employed to identify differences relevant to the subgroups. Rotated principal component analysis was used to create the sensory dimensions that describe/differentiate the product set. As a result six sensory dimensions were identified. Using the principal component scores, the products were grouped into nine flavor profile groups. LOESS curve fitting in a multi-panel display was used to relate the mean product liking scores of each consumer subgroup to the sensory component scores. This format allows for: (1) the determination of the nature of each sensory dimension's effect on liking for the subgroups in each of the two studies, and (2) an understanding of why the liking pattern for the subgroups differ. Through this it will be shown that the sensory dimensions effect on product liking differs for each of the subgroups in both of the studies. Finally, consumer attribute ratings were correlated with the sensory component scores to gain an understanding of the consumers' description of the product set.