Practical Data Analysis Research Articles

Establishment of 3D Virtual Reality Model for Underground Pipeline Combining Hand Position and Shape Tracking Algorithm

With the development of the social economy, data in various fields are increasing exponentially but lacks practical data analysis and mining. In this paper, structural analysis and model simplification is performed on underground pipeline based on the hand position and shape tracking algorithm. The 3D modeling process of the pipeline using virtual reality (VR) is proposed, which has implemented the rapid modeling from 2D to 3D, and finally, rapid modeling and 3D visualization roaming of large-scene urban underground pipeline, providing support for the modeling of urban underground pipeline.

A new joint species distribution model for faster and more accurate inference of species associations from big community data

Abstract Joint species distribution models (JSDMs) explain spatial variation in community composition by contributions of the environment, biotic associations and possibly spatially structured residual covariance. They show great promise as a general analytical framework for community ecology and macroecology, but current JSDMs, even when approximated by latent variables, scale poorly on large datasets, limiting their usefulness for currently emerging big (e.g. metabarcoding and metagenomics) community datasets. Here, we present a novel, more scalable JSDM (sjSDM) that circumvents the need to use latent variables by using a Monte Carlo integration of the joint JSDM likelihood together with flexible elastic net regularization on all model components. We implemented sjSDM in PyTorch, a modern machine learning framework, which allows making use of both CPU and GPU calculations. Using simulated communities with known species–species associations and different number of species and sites, we compare sjSDM with state‐of‐the‐art JSDM implementations to determine computational runtimes and accuracy of the inferred species–species and species–environment associations. We find that sjSDM is orders of magnitude faster than existing JSDM algorithms (even when run on the CPU) and can be scaled to very large datasets. Despite the dramatically improved speed, sjSDM produces more accurate estimates of species association structures than alternative JSDM implementations. We demonstrate the applicability of sjSDM to big community data using eDNA case study with thousands of fungi operational taxonomic units (OTU). Our sjSDM approach makes the analysis of JSDMs to large community datasets with hundreds or thousands of species possible, substantially extending the applicability of JSDMs in ecology. We provide our method in an R package to facilitate its applicability for practical data analysis.

On the Measure-Theoretic Premises of Bayes Factor and Full Bayesian Significance Tests: a Critical Reevaluation

The Full Bayesian Significance Test (FBST) and the Bayesian evidence value recently have received increasing attention across a variety of sciences including psychology. Ly and Wagenmakers (2021) have provided a critical evaluation of the method and concluded that it suffers from four problems which are mostly attributed to the asymptotic relationship of the Bayesian evidence value to the frequentist p-value. While Ly and Wagenmakers (2021) tackle an important question about the best way of statistical hypothesis testing in the cognitive sciences, it is shown in this paper that their arguments are based on a specific measure-theoretic premise. The identified problems hold only under a specific class of prior distributions which are required only when adopting a Bayes factor test. However, the FBST explicitly avoids this premise, which resolves the problems in practical data analysis. In summary, the analysis leads to the more important question whether precise point null hypotheses are realistic for scientific research, and a shift towards the Hodges-Lehmann paradigm may be an appealing solution when there is doubt on the appropriateness of a precise hypothesis.

스포츠심리연구에서 일반선형모형의 이분산성 문제와 해결: SPSS를 중심으로

Purpose: This study quantitatively explains the characteristics of heteroscedasticity in the general linear model, and attempts to explain a series of procedures for confirmation and resolution of heteroscedasticity by comparing various statistical methods through empirical examples. Methods: Using the data of 185 male and female middle school students, a practical data analysis method for confirming and solving heteroscedasticity was specifically presented using an empirical example model. Conclusion: The problem of heteroscedasticity, its solution, and the need for explanation were discussed.

Purification of alumina-containing sweepings by dry air classification methods

The article deals with the formation and processing of waste at aluminum production plants. Based on the analysis of literary sources and practical data, the reasons for the formation of metallurgical waste in the territory of the Russian Federation are established. The analysis of research conducted in scientific organizations is presented. It was found that one of the most promising for waste processing is alumina-containing sweepings formed during the production of liquid aluminum in Soderberg cells during various process operations. Alumina-containing sweepings are waste of complex variable composition swept away in electrolysis shops. It was found that alumina-containing sweepings consist of cryolite (Na3AlF6), chiolite (Al3F14Na5), corundum (Al2O3), siderite (FeCo3), pyrite (FeS2), quartz (SiO2), feldspar ((Ca, Na)(Al, Si) AlSi2O8), carbonaceous matter and (NaF)•1,5CaF2•AlF5 technogenic phase. Their processing is of interest due to the fact that they contain a significant amount of valuable components (Na3AlF6, Al2O3, AlF3) that can be extracted and reused in aluminum production to reduce the cost per unit. The problem is that sweepings contain components (SiO2, Fe2O3), which have a negative effect on the electrolysis process when in contact with electrolyte. The data obtained when studying the chemical composition of monofractions made it possible to conclude that the exclusion of the dark (grayish-black mass) with the maximum content of impurities (SiO2, Fe2O3) will substantially solve the stated problem of the study. Based on the hypothesis put forward, the paper presents the results of 0—10 mm and 0—5 mm sweepings air classification in cascade-gravity and centrifugal classifiers. Based on the studies conducted, we recommend using 0—10 mm sweepings air classification in the cascade gravity classifier in the processing flow of alumina-containing sweepings of aluminum production.

The role of aesthetics in learning science in an art-science lesson

ABSTRACT In this paper, we analyse results from one classroom session within an 8-week program in which Year 10 students constructed ‘trash’ puppets of endangered Australian animals. In making the puppets and using them as part of a ‘theatre in a suitcase’ performance at Melbourne Zoo, students were expected to integrate both scientific and artistic goals to demonstrate knowledge of specific endangered species. In this process, students needed to learn how their more immediate, everyday positive and negative aesthetic responses could be made continuous with a scientific aesthetic to produce both a coherent puppet and an advocacy performance. Through micro-ethnographic practical epistemology analysis of video data of this session, we demonstrate how this mix of everyday and subject-specific aesthetic responses, judgements and intentions interacted to shape and promote students’ learning in science. In addressing this multimodal and multi-purpose task, students learnt and applied science knowledge to a real-world issue of species endangerment.

Research on blasting vibration effect and time-frequency characteristics of vibration signals in a road corridor at xianning nuclear power station

Drill and blast is a major method for foundation excavation in nuclear power plant engineering. For transporting heavy components to engineering construction, the blast-induced vibration of foundation excavation in a road corridor may has a great influence on new pouring mass concrete and surrounding bedrock in Xianning Nuclear Power Station, which will affect the safety of the engineering construction. So it is necessary to monitor the process of blasting excavation and put forward effective control measures on blasting vibration effect, in which the peak particle velocity (PPV) attenuation law are investigated through regression analysis of practical field data by Duvall empirical formula and the time-frequency characteristics was analyzed by wavelet transform and wavelet package decomposition. By analyzing the velocity amplitude and power spectrum density amplitude of wavelet components, it shows the blast energy of original vibration signal is mainly within the frequency below 250 Hz and has three sub-band which varies from 10 Hz to 30 Hz, from 40 Hz to70 Hz and from 75 Hz to 125 Hz, respectively. Wavelet packet analysis indicates the energy of the signal in 0-125 Hz accounts for 94.25 % of the total signal. At the same time, for the sake of ensuring the safety of road corridor, Chinese admissibility standards (GB6722-2014) of blasting-induced vibration and the constants obtained from the regression were used to establish the maximum explosive charge per delay for an acceptable ground vibration level that would not cause damage for new pouring mass concrete and bedrock in road corridor. The safety criteria of particle vibration velocity for new pouring mass concrete and bedrock in road corridor could be both set as 5 cm/s, which show the remarkable effect on blasting damage control of the new pouring mass concrete and surrounding bedrock, the results demonstrate that controlling maximum explosive charge per delay methodology can be commendably applied to road corridor to control blasting-induced vibration effects.

Data Analysis Outside of Clinical Practice: An Innovative Application of the Nutrition Care Process and Model

The use of data analysis in nutrition practice is essential. The specific goal of analysis dictates what type of data should be collected, how it should be analyzed, and how results should be implemented. This paper explores different ways that practitioners collect, analyze, and use data, for the benefit of the client, customer, and overall organization in nonclinical settings. Using the Nutrition Care Process and Model (NCPM) as a template, a practitioner can Assess the situation, Diagnose the problem, create Interventions, and Monitor and Evaluate the results regardless of their setting, using a range of data. The collection of data, whether through organizational surveys, comment cards, or focus groups, or extrapolated from national registries, is beneficial to the profession because the data allow for measurable evaluation of interventions implemented through evidence-based analysis. The experiences of different industry professionals are documented herein, providing examples of ways they add value to their organizations and how they use data to drive overall improvement.

Pengembangan modul pembelajaran atletik berbantuan QR code

This study aims to develop an athletic learning module assisted by QR code for grade X students of MA Mutiara Al-Qur'an. The research method used was the research and development model of Dick and Carey with nine steps. Data collection techniques in research using interviews, observation, questionnaires. The data analysis technique used the steps of analyzing the validity of the media expert, getting a value of 87.03%, the linguist with a value of 82.14%, and the material expert getting a value of 95.83% so that from these results the validity of the module was categorized as very valid, then the practical questionnaire data analysis One to one and small group test students got a score of 87.22% so that the practicality of the athletic learning module assisted by the QR code for the tenth grade students of MA Mutiara Al-Qur'an was categorized as having very strong practicality.

Marginal false discovery rate for a penalized transformation survival model

Survival analysis that involves moderate/high dimensional covariates has become common. Most of the existing analyses have been focused on estimation and variable selection, using penalization and other regularization techniques. To draw more definitive conclusions, a handful of studies have also conducted inference. The recently developed mFDR (marginal false discovery rate) technique provides an alternative inference perspective and can be advantageous in multiple aspects. The existing inference studies for regularized estimation of survival data with moderate/high dimensional covariates assume the Cox and other specific models, which may not be sufficiently flexible. To tackle this problem, the analysis scope is expanded to the transformation model, which is robust and has been shown to be desirable for practical data analysis. Statistical validity is rigorously established. Two data analyses are conducted. Overall, an alternative inference approach has been developed for survival analysis with moderate/high dimensional data.

Estimation and optimal structure selection of high-dimensional Toeplitz covariance matrix

The estimation of structured covariance matrix arises in many fields. An appropriate covariance structure not only improves the accuracy of covariance estimation but also increases the efficiency of mean parameter estimators in statistical models. In this paper, a novel statistical method is proposed, which selects the optimal Toeplitz covariance structure and estimates the covariance matrix, simultaneously. An entropy loss function with nonconvex penalty is employed as a matrix-discrepancy measure, under which the optimal selection of sparse or nearly sparse Toeplitz structure and the parameter estimators of covariance matrix are made, simultaneously, through its minimization. The cases of both low-dimensional (p≤n) and high-dimensional (p>n) covariance matrix estimation are considered. The resulting Toeplitz structured covariance estimators are guaranteed to be positive definite and consistent. Asymptotic properties are investigated and simulation studies are conducted, showing that very high accurate Toeplitz covariance structure estimation is made. The proposed method is then applied to practical data analysis, which demonstrates its good performance in covariance estimation in practice.

Optimization and Augmentation for Data Parallel Contour Trees.

Contour trees are used for topological data analysis in scientific visualization. While originally computed with serial algorithms, recent work has introduced a vector-parallel algorithm. However, this algorithm is relatively slow for fully augmented contour trees which are needed for many practical data analysis tasks. We therefore introduce a representation called the hyperstructure that enables efficient searches through the contour tree and use it to construct a fully augmented contour tree in data parallel, with performance on average 6 times faster than the state-of-the-art parallel algorithm in the TTK topological toolkit.

Assisted estimation of gene expression graphical models.

In the study of gene expression data, network analysis has played a uniquely important role. To accommodate the high dimensionality and low sample size and generate interpretable results, regularized estimation is usually conducted in the construction of gene expression Gaussian Graphical Models (GGM). Here we use GeO-GGM to represent gene-expression-only GGM. Gene expressions are regulated by regulators. gene-expression-regulator GGMs (GeR-GGMs), which accommodate gene expressions as well as their regulators, have been constructed accordingly. In practical data analysis, with a "lack of information" caused by the large number of model parameters, limited sample size, and weak signals, the construction of both GeO-GGMs and GeR-GGMs is often unsatisfactory. In this article, we recognize that with the regulation between gene expressions and regulators, the sparsity structures of a GeO-GGM and its GeR-GGM counterpart can satisfy a hierarchy. Accordingly, we propose a joint estimation which reinforces the hierarchical structure and use the construction of a GeO-GGM to assist that of its GeR-GGM counterpart and vice versa. Consistency properties are rigorously established, and an effective computational algorithm is developed. In simulation, the assisted construction outperforms the separation construction of GeO-GGM and GeR-GGM. Two The Cancer Genome Atlas data sets are analyzed, leading to findings different from the direct competitors.

Analysis of SEC-SAXS data via EFA deconvolution and Scatter.

BioSAXS is a popular technique used in molecular and structural biology to determine the solution structure, particle size and shape, surface-to-volume ratio and conformational changes of macromolecules and macromolecular complexes. A high quality SAXS dataset for structural modeling must be from monodisperse, homogeneous samples and this is often only reached by a combination of inline chromatography and immediate SAXS measurement. Most commonly, size-exclusion chromatography is used to separate samples and exclude contaminants and aggregations from the particle of interest allowing SAXS measurements to be made from a well-resolved chromatographic peak of a single protein species. Still, in some cases, even inline purification is not a guarantee of monodisperse samples, either because multiple components are too close to each other in size or changes in shape induced through binding alter perceived elution time. In these cases, it may be possible to deconvolute the SAXS data of a mixture to obtain the idealized SAXS curves of individual components. Here, we show how this is achieved and the practical analysis of SEC-SAXS data is performed on ideal and difficult samples. Specifically, we show the SEC-SAXS analysis of the vaccinia E9 DNA polymerase exonuclease minus mutant.

The role of aesthetics in the teaching and learning of data modelling

ABSTRACT Reporting on a Grade 4 teaching and learning sequence, we highlight foundational constructs of measurement and data modelling which are fundamental to competence development in both science and mathematics. The sequence involved students generating and representing measures of their teacher’s arm-span, with a focus on the invention and refinement of data representations characteristic of STEM epistemic practices. We undertook a micro-ethnographic practical epistemology analysis of video data of classroom and group discussions and student work, to explore the teaching and learning processes driving students’ understandings of data variation and measures of central tendency. Through this analysis we appreciated aesthetic experiences as fundamental for students’ growing epistemic and conceptual understandings and the teacher’s pedagogical practices. We draw on the pragmatist aesthetic perspectives of Dewey and Peirce to show how students’ sign-making shifted from an everyday interest in the arm span measuring process to an interest in the potentialities of the data set as an exploratory field. We consider this a shift from an everyday ‘art’ aesthetic to a ‘mathematics’ disciplinary aesthetic. We argue that such an aesthetic perspective, in which feeling and meaning are intertwined, provides a powerful way to understand the role of affect in learning.

Preface to the Special Topic on Practical Data Analysis: Data Analysis Competition in 2018

Preface to the Special Topic on Practical Data Analysis: Data Analysis Competition in 2018

Migraine in the real-life clinical practice: results of the observational study of diagnosis and treatment patterns in the urban population of the Russian Federation

Analysis of real-life clinical practice data regarding the patterns of diagnosis and treatment of migraine in the Russian Federation. An observational real-world cohort study was conducted from January 2013 to December 2018 (over a 5-year period) to analyze the electronic database of 5 large private clinic chains in Moscow and St. Petersburg. Among 1.15 million people who sought medical care, «G43 Migraine» diagnosis was established in 6.650 people, which accounted for 0.6%. A total of 54% of patients were diagnosed by a neurologist, while 35% by a GP. In roughly half the cases, to diagnose migraine, the doctors had to resort to additional methods of examination. While 53% of patients had indications for preventive treatment, it was prescribed only to 12% of those. Of these, the treatment strategy chosen for 29% of patients was changed: either the intragroup/intergroup switching of the medicinal product took place, or the combined treatment was prescribed. In the real-life outpatient clinical practice, the level of diagnosis of migraine is extremely low. Additional methods of examination are excessively used for the diagnosis of migraine. The insufficient use of preventive medication and common use of therapies with unsubstantiated efficacy in migraine result in frequent changes in the treatment strategy.

QuickIsoSeq for Isoform Quantification in Large-Scale RNA Sequencing.

RNA-sequencing (RNA-seq) is a powerful technology for transcriptome profiling. While most RNA-seq projects focus on gene-level quantification and analysis, there is growing evidence that most mammalian genes are alternatively spliced to generate different isoforms that can be subsequently translated to protein molecules with diverse or even opposing biological functions. Quantifying the expression levels of these isoforms is key to understanding the genes biological functions in healthy tissues and the progression of diseases. Among open source tools developed for isoform quantification, Salmon, Kallisto, and RSEM are recommended based upon previous systematic evaluation of these tools using both experimental and simulated RNA-seq datasets. However, isoform quantification in practical RNA-seq data analysis needs to deal with many QC issues, such as the abundance of rRNAs in mRNA-seq, the efficiency of globin RNA depletion in whole blood samples, and potential sample swapping. To overcome these practical challenges, QuickIsoSeq was developed for large-scale RNA-seq isoform quantification along with QC. In this chapter, we describe the pipeline and detailed the steps required to deploy and use it to analyze RNA-seq datasets in practice. The QuickIsoSeq package can be downloaded from https://github.com/shanrongzhao/QuickIsoSeq.

Обеспечение принципа справедливости в контексте защиты прав сотрудников органов внутренних дел

The relevance of the problems of ensuring justice
 is indicated by the results of the latest sociological research, which indicate a sharp increase in the demand
 for justice and the emerging tendency for citizens to understand justice in the context of the protection of human
 rights. On the example of the activities of the High Commissioner for Human Rights in the Russian Federation to
 protect the rights of employees of internal affairs bodies,
 approaches to understanding the categories of justice in
 practice are revealed. Based on the results of the analysis of law enforcement practice and empirical data, proposals are justified on the need to improve legislation in
 terms of protecting the rights of employees of internal
 affairs bodies.

COMPARING TOOLS PROVIDED BY PYTHON AND R FOR EXPLORATORY DATA ANALYSIS

To uncover the insight behind the data, a comprehensive analysis is needed. Exploratory Data Analysis (EDA) is one of practical data analysis that will guide how to reveal any hidden information in the data. By doing EDA, any pattern and issue in the data will be seen and eventually will lead the hypothesis. To do EDA, besides any basic statistic is needed, a good tool to simplify the analysis is also a consideration. Python and R as a famous programming language in the data science world provide methods to implement that analysis. This paper will show how to perform EDA by utilizing Python and R programming.