Spurious Effects Research Articles

Comparative analysis of MEMS (Micro-Electro-Mechanical Sensor) and IEPE (Integrated Electronics Piezo-Electric) accelerometers for measurement of wide spectrum damping

Damping is an important material property for dynamically loaded structures, such as aircraft, vehicles, and rotary or reciprocating machinery. Low damping may increase vibration amplitudes which can cause excessive dynamic loading and can detrimentally affect a structures performance. Accurate prediction of the dynamic loads is necessary to avoid catastrophic failure especially during resonant vibrations. In this work, a MEMS (micro-electro-mechanical sensor) accelerometer system is constructed to measure vibration over a wide frequency range (from 0-3000 Hz). Measurements of the damping ratio taken with MEMS accelerometers were compared to similar measurements using IEPE (integrated electronics piezo-electric) accelerometers and the experimental results validated against a thermoelastic model and published studies. The damping measured using IEPE accelerometers was larger due to spurious damping effects attributed to the much larger cables. The low spurious damping of the MEMS accelerometers allows for the accurate determination of the damping of a material over a wide frequency range. The MEMS accelerometer setup used in this work offers a method to accurately measure the damping of dynamically loaded, lightweight structures, where attaching traditional accelerometers can cause significant error in the measured damping or where non-contact measurement may be unsuitable.

IllustrisTNG in the HSC-SSP: No Shortage of Thin Disk Galaxies in TNG50

We perform a thorough analysis of the projected shapes of nearby galaxies in both observations and cosmological simulations. We implement a forward-modeling approach to overcome the limitations in previous studies, which hinder accurate comparisons between observations and simulations. We measure axis ratios of z = 0 (snapshot 99) TNG50 galaxies from their synthetic Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) images and compare them with those obtained from real HSC-SSP images of a matched galaxy sample. Remarkably, the comparison shows excellent agreement between the observations and the TNG50 simulation, challenging previous claims that ΛCDM models underproduced the abundance of thin galaxies. Specifically, for galaxies with stellar masses 10≤log(M⋆/M☉)≤11.5 , we find ≲0.1σ tensions between the observations and the simulation, a stark contrast to the previously reported ≳10σ tensions. We reveal that low-mass galaxies (M⋆≲109.5M☉) in TNG50 are thicker than their observed counterparts in HSC-SSP and attribute this to the spurious dynamical heating effects that artificially puff up galaxies. We also find that, despite the overall broad agreement, TNG50 galaxies are more concentrated than the HSC-SSP ones at the low- and high-mass end of the stellar mass range of 9.0≤log(M⋆/M☉)≤11.2 and are less concentrated at intermediate stellar masses. But we argue that the higher concentrations of the low-mass TNG50 galaxies are not likely the cause of their thicker/rounder appearances. Our study underscores the critical importance of conducting mock observations of simulations and applying consistent measurement methodologies to facilitate proper comparison with observations.

On the spurious effect of intergroup friendship on outgroup attitudes in schools: The role of social influence and the positive impact of exposure to outgroup peers.

Promoting intergroup friendships in schools is regarded as a powerful strategy for improving outgroup attitudes. However, stochastic actor-oriented modelling (SAOM) studies have often revealed no association between intergroup friendship and outgroup attitudes. I investigated whether SAOM studies reported no effect of intergroup friendship on outgroup attitudes, what was responsible for this surprising finding and whether exposure to outgroup peers was positively related to outgroup attitudes. The meta-analysis of SAOM studies confirms no association between intergroup friendship and outgroup attitudes. Examining longitudinal social network data of 2700 German students, I found that a positive link between intergroup friendship and outgroup attitudes appeared only when I did not control for social influence. This indicates that intergroup friendship did not automatically improve outgroup attitudes. Instead, the development of outgroup attitudes among students depended on social influence and, thus, the quality of outgroup attitudes among their outgroup friends. Exposure to outgroup peers was, however, positively associated with outgroup attitudes. These findings reframe intergroup contact theory by suggesting that intergroup friendship is not essential for improving outgroup attitudes. Rather, social influence and exposure to outgroup peers could be key factors shaping outgroup attitudes.

Assessing the Spurious Impacts of Ice-Constraining Methods on the Climate Response to Sea-Ice Loss using an Idealised Aquaplanet GCM

Abstract Coupled climate model simulations designed to isolate the effects of Arctic sea-ice loss often apply artificial heating, either directly to the ice or through modification of the surface albedo, to constrain sea ice in the absence of other forcings. Recent work has shown that this approach may lead to an overestimation of the climate response to sea-ice loss. In this study, we assess the spurious impacts of ice-constraining methods on the climate of an idealised aquaplanet general circulation model (GCM) with thermodynamic sea ice. The true effect of sea-ice loss in this model is isolated by inducing ice loss through reduction of the freezing point of water, which does not require additional energy input. We compare results from freezing point modification experiments with experiments where sea-ice loss is induced using traditional ice-constraining methods, and confirm the result of previous work that traditional methods induce spurious additional warming. Furthermore, additional warming leads to an overestimation of the circulation response to sea-ice loss, which involves a weakening of the zonal wind and storm track activity in midlatitudes. Our results suggest that coupled model simulations with constrained sea ice should be treated with caution, especially in boreal summer, where the true effect of sea-ice loss is weakest but we find the largest spurious response. Given that our results may be sensitive to the simplicity of the model we use, we suggest that devising methods to quantify the spurious effects of ice-constraining methods in more sophisticated models should be an urgent priority for future work.

A statistical analysis of the first stages of freezing and melting of Lennard-Jones particles: Number and size distributions of transient nuclei.

The freezing/melting transition is at the heart of many natural and industrial processes. In the classical picture, the transition proceeds via the nucleation of the new phase, which has to overcome a barrier associated with the free energy cost of the growing nucleus. The total nucleation rate is also influenced by a kinetic factor, which somehow depends on the number of attempts to create a nucleus, that translates into a significant density of proto-nuclei in the system. These transient tiny nuclei are not accessible to experiments, but they can be observed in molecular simulations, and their number and size distributions can be acquired and analyzed. The number distributions are carefully characterized as a function of the system size, showing the expected behavior, with limited spurious effects due to the finite simulation box. It is also shown that the proto-nuclei do exist even in the stable phase, in agreement with the fact that the (unfavorable) volume contribution to their free energy is negligible in the first stages of nucleation. Moreover, the number and size distributions evolve continuously between the stable and the metastable phases, in particular when crossing the coexistence temperature. The size distributions associated with any nucleus and with the largest one have also been calculated, and their relationship recently established for bubbles in a liquid [Puibasset, J. Chem. Phys. 157, 191102 (2022)] has been shown to apply here. This is an important relation for free energy barrier calculations with biased molecular simulations.

Dynamic exchange correlation effects in the strongly coupled electron liquid

The first quasiexact path integral Monte Carlo (PIMC) investigation of the dynamic local field correction G̃(q,zl;rs,Θ) in the imaginary Matsubara frequency domain is reported, focusing on the unpolarized uniform electron gas in the warm dense matter and strongly coupled regimes. The impact of dynamic exchange–correlation effects on the static structure factor is investigated. A straightforward explanation is provided for previously reported spurious effects in the so-called [Dornheim , ], where the frequency dependence of the local field correction is neglected. The comprehensive analysis of the asymptotic Matsubara order behavior, the density dependence, and the temperature dependence hints at the intriguing possibility of constructing an analytical four-parameter representation of G̃(q,zl;rs,Θ) valid for a substantial part of the uniform electron gas phase diagram, which would constitute key input for thermal density functional theory simulations. Published by the American Physical Society 2024

Could Regression of Stationary Series Be Spurious?

Spurious regression, commonly associated with independent and (nearly) non-stationary time series, has been extensively studied. However, the potential for spurious outcomes in regression involving stationary time series remains largely unexplored, representing a gap in the literature. To address this gap, we propose that regression of stationary time series may yield spurious outcomes and conduct a comprehensive investigation to verify this conjecture. Additionally, we present a remedy algorithm to mitigate spurious effects and improve model interpretability. Through extensive simulations, we validate our conjecture and demonstrate the efficacy of the proposed remedy. A numerical analysis further illustrates the practical utility of our approach. This study offers a fresh perspective on spurious regression and provides a practical solution to enhance the reliability of regression analyses involving stationary time series data.

Fractional quantum Hall states with variational projected entangled-pair states: A study of the bosonic Harper-Hofstadter model

An important class of model Hamiltonians for investigation of topological phases of matter consists of mobile, interacting particles on a lattice subject to a semiclassical gauge field, as exemplified by the bosonic Harper-Hofstadter model. A unique method for investigations of two-dimensional quantum systems are the infinite projected-entangled pair states, as they avoid spurious finite-size effects that can alter the phase structure. However, due to no-go theorems in related cases, this was often conjectured to be impossible in the past. In this Letter, we show that upon variational optimization, the infinite projected-entangled pair states can be used to this end by identifying fractional Hall states in the bosonic Harper-Hofstadter model. The obtained states are characterized by showing exponential decay of bulk correlations, as dictated by a bulk gap, as well as chiral edge modes via the entanglement spectrum. Published by the American Physical Society 2024

Does self-care improve coping or does coping improve self-care? A structural equation modeling study

BackgroundSupport interventions often address both self-care and coping. Different approaches are used to promote self-care and coping so clarifying the intervention effect can guide clinicians and researchers to provide interventions that achieve benefit. PurposeTo compare two models to determine whether self-care improves coping or coping improves self-care. MethodsWe used cross-sectional data from 248 caregivers obtained at enrollment into a randomized controlled trial testing the efficacy of a support intervention. Factor scores for scales measuring caregiver demand, self-care, coping, stress appraisal, and mental health were derived from exploratory factor analysis. Structural equation models were analyzed using the factor scores as estimates of each construct. To control possible spurious effects caregiver age, gender, relationship with the patient, and income adequacy were included. ResultsBoth models were compatible with the data, but the self-care model was stronger than the coping model. That model had a non-significant chi square and an excellent fit to the data, χ2(4, N = 248) = 2.64, p = .62. The percentage of variance explained by the self-care model was 54 % for mental health, 42 % for stress appraisal, 10 % for avoidance coping, and 6 % for active coping. In the coping model the explained variance of stress appraisal dropped to 33 %, avoidance coping dropped to 0 %, and active coping dropped to 3 %. ConclusionsThe self-care model was strongest, illustrating that self-care decreases stress, promotes coping, and improves mental health. These results suggest that promoting self-care may be more effective in improving mental health than interventions aimed at improving coping.

Unidimensional and Multidimensional Reliability Coefficient of Karmapatha Philosophy-based Student Behavior Questionnaire

Guidance and Counseling teachers often highlight student behavior, especially in Junior High Schools. This is because many junior high school students' behavior contains anomalies compared to the school rules in the student handbook. The purpose of this research is to create a student behavior questionnaire based on the teachings of karmapatha that has a standard when assessed based on the reliability of the questionnaire. This research is a type of instrument calibration research. The population of this study was all students of grade VIII junior high school, with the sample being the answers of 100 students selected by purposive sampling. Data collection methods with tests. The results of data analysis obtained unidimensional reliability, student behavior questionnaire based on karmapatha teachings before being corrected by spurious overlap effect obtained rxx' coefficient of 0.850 meets the feasibility to be used as a research instrument; and when examined from unidimensional reliability, student behavior questionnaire based on karmapatha teachings after being corrected by spurious overlap effect obtained rxx' coefficient of 0.853. Multidimensional reliability, the karmapatha teaching-based student behavior questionnaire, before being corrected by the spurious overlap effect, obtained an αs coefficient of 0.945. and when examined from multidimensional reliability, the karmapatha teaching-based student behavior questionnaire, after being corrected by the spurious overlap effect, obtained an αs coefficient of 0.942, overall meeting the feasibility to be used as a research instrument.

Inference for possibly misspecified generalized linear models with nonpolynomial-dimensional nuisance parameters

Summary It is routine practice in statistical modelling to first select variables and then make inference for the selected model as in stepwise regression. Such inference is made upon the assumption that the selected model is true. However, without this assumption, one would not know the validity of the inference. Similar problems also exist in high-dimensional regression with regularization. To address these problems, we propose a dimension-reduced generalized likelihood ratio test for generalized linear models with nonpolynomial dimensionality, based on quasilikelihood estimation that allows for misspecification of the conditional variance. The test has nearly oracle performance when using the correct amount of shrinkage and has robust performance against the choice of regularization parameter across a large range. We further develop an adaptive data-driven dimension-reduced generalized likelihood ratio test and prove that, with probability going to one, it is an oracle generalized likelihood ratio test. However, in ultrahigh-dimensional models the penalized estimation may produce spuriously important variables that deteriorate the performance of the test. To tackle this problem, we introduce a cross-fitted dimension-reduced generalized likelihood ratio test, which is not only free of spurious effects, but robust against the choice of regularization parameter. We establish limiting distributions of the proposed tests. Their advantages are highlighted via theoretical and empirical comparisons to some competitive tests. An application to breast cancer data illustrates the use of our proposed methodology.

Benchmarking Bayesian quantum estimation

The quest for precision in parameter estimation is a fundamental task in different scientific areas. The relevance of this problem thus provided the motivation to develop methods for the application of quantum resources to estimation protocols. Within this context, Bayesian estimation offers a complete framework for optimal quantum metrology techniques, such as adaptive protocols. However, the use of the Bayesian approach requires extensive computational resources, especially in the multiparameter estimations that represent the typical operational scenario for quantum sensors. Hence, the requirement to characterize protocols implementing Bayesian estimations can become a significant challenge. This work focuses on the crucial task of robustly benchmarking the performances of these protocols in both single and multiple-parameter scenarios. By comparing different figures of merits, evidence is provided in favor of using the median of the quadratic error in the estimations in order to mitigate spurious effects due to the numerical discretization of the parameter space, the presence of limited data, and numerical instabilities. These results, providing a robust and reliable characterization of Bayesian protocols, find natural applications to practical problems within the quantum estimation framework.

The effect of head motion on brain age prediction using deep convolutional neural networks

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age—referred to as brain-predicted age difference (brain-PAD)—is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

Towards in-situ water quantification via neutron imaging: insights from NeXT-Grenoble

Neutron imaging has gained increasing attention in recent years. A notable domain is the in-situ study of flow and concentration of hydrogen-rich materials. This demands precise quantification of the evolving concentrations. Several implementations deviate from the ideal conditions that allow the direct applicability of the Beer–Lambert law to assess this concentration. The objective of this work is to address these deviations by applying both calibration and correction procedures to ensure and validate accurate quantitative measurements during 2D and 3D neutron imaging conducted at the cold neutron source at the NeXT instrument of the Institute Laue–Langevin, Grenoble, France. Linear attenuation coefficients and non-linear correlations have been proposed to measure the water concentration based on the sample-to-detector distance. Furthermore, the effectiveness of the black body grid correction method, introduced by Boillat et al (2018 Opt. Express 26 15769), is evaluated which accounts for spurious deviations arising from the scattering of neutrons from the sample and the surrounding environment. The applicability of the Beer–Lambert law without any data correction is found to be reasonable within limited equivalent thickness (e.g. below 4 mm of water) beyond which the correction algorithm proves highly effective in eliminating spurious effects. Notably, this correction method maintains its effectiveness even with transmissions below 1%. We examine here the impact of grid location and resolution with respect to sample heterogeneity.

Categorization of continuous covariates and complex regression models—friends or foes in intersectionality research

ObjectivesTo reduce health inequities, it is important to identify intersections in characteristics of individuals subject to privilege or disadvantage. Different proposals for that have recently been published. One approach (1) considers models specified with first- and all second-order effects and another (2) the stratification based on multiple covariates; both categorize continuous covariates. A simulation study was conducted in order to review both methods with regard to identification of intersections showing true differences, rate of false-positive results, and generalizability to independent data compared to an established approach (3) of backward variable elimination according to Bayesian information criterion (BE-BIC) combined with splines. Study Design and SettingR software has been used to simulate the covariates age, sex, body mass index, education, and diabetes to examine their association with a continuous frailty score for osteoporosis using multiple linear regression. In setting 1, none of the covariates was associated with the frailty score, that is, only noise is present in the data. In setting 2, the covariates age, sex, and their interaction were associated with the frailty score, such that only females above 55 years formed an intersection associated with an increased frailty score. All approaches were compared under varying sample sizes (N = 200–3000) and signal-to-noise ratios (SNRs, 0.5–4) in 1000 replications. For model evaluation, bootstrap resampling was used. The models were fitted in internal learning data and then used to predict outcomes in the internal validation data. The mean squared error (MSE) was used for comparison and the frequency of false-positive findings calculated. ResultsIn setting 1, approaches 1 and 2 generated spurious effects in more than 90% of simulations across all sample sizes. In a smaller sample size, approach 3 (BE-BIC) selected 36.5% of the correct model, in larger sample size in 89.8% and always had a lower number of spurious effects. MSE in independent data was generally higher for approaches 1 and 2 when compared to 3. In setting 2, approach 1 selected most frequently the correct interaction but frequently showed spurious effects (>75%). Across all sample sizes and SNR, approach 3 generated least often spurious results and had lowest MSE in independent data. ConclusionCategorization of continuous covariates is detrimental to studies on intersectionality. Due to high and unrestricted model complexity, such approaches are prone to spurious effects and often lack interpretability. Approach 3 (BE-BIC) is considerably more robust against spurious findings, showed better generalizability to independent data, and can be used with most statistical software. For intersectionality research, we consider it most important to describe relevant differences between intersections and to avoid nonreproducible and spurious findings.

Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding.

This article deals with the discovery of causal relations from a combination of observational data and qualitative assumptions about the nature of causality in the presence of unmeasured confounding. We focus on applications where unobserved variables are known to have a widespread effect on many of the observed ones, which makes the problem particularly difficult for constraint-based methods, because most pairs of variables are conditionally dependent given any other subset, rendering the causal effect unidentifiable. In this article, we show that under the principle of independent mechanisms, unobserved confounding in this setting leaves a statistical footprint in the observed data distribution that allows for disentangling spurious and causal effects. Using this insight, we demonstrate that a sparse linear Gaussian directed acyclic graph (DAG) among observed variables may be recovered approximately and propose a simple adjusted score-based causal discovery algorithm that may be implemented with general-purpose solvers and scales to high-dimensional problems. We find, in addition, that despite the conditions we pose to guarantee causal recovery, performance in practice is robust to large deviations in model assumptions, and extensions to nonlinear structural models are possible.

On inductive biases for the robust and interpretable prediction of drug concentrations using deep compartment models

Conventional pharmacokinetic (PK) models contain several useful inductive biases guiding model convergence to more realistic predictions of drug concentrations. Implementing similar biases in standard neural networks can be challenging, but might be fundamental for model robustness and predictive performance. In this study, we build on the deep compartment model (DCM) architecture by introducing constraints that guide the model to explore more physiologically realistic solutions. Using a simulation study, we show that constraints improve robustness in sparse data settings. Additionally, predicted concentration–time curves took on more realistic shapes compared to unconstrained models. Next, we propose the use of multi-branch networks, where each covariate can be connected to specific PK parameters, to reduce the propensity of models to learn spurious effects. Another benefit of this architecture is that covariate effects are isolated, enabling model interpretability through the visualization of learned functions. We show that all models were sensitive to learning false effects when trained in the presence of unimportant covariates, indicating the importance of selecting an appropriate set of covariates to link to the PK parameters. Finally, we compared the predictive performance of the constrained models to previous relevant population PK models on a real-world data set of 69 haemophilia A patients. Here, constrained models obtained higher accuracy compared to the standard DCM, with the multi-branch network outperforming previous PK models. We conclude that physiological-based constraints can improve model robustness. We describe an interpretable architecture which aids model trust, which will be key for the adoption of machine learning-based models in clinical practice.

Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach

Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels deconfounded from the environments, advancing the technical roadmap of out-of-distribution (OOD) generalization. Despite spotlights around, recent theoretical result verified that some causal features recovered by IRLs merely pretend domain-invariantly in the training environments but fail in unseen domains. The fake invariance severely endangers OOD generalization since the trustful objective can not be diagnosed and existing causal remedies are invalid to rectify. In this paper, we review a IRL family (InvRat) under the Partially and Fully Informative Invariant Feature Structural Causal Models (PIIF SCM /FIIF SCM) respectively, to certify their weaknesses in representing fake invariant features, then, unify their causal diagrams to propose ReStructured SCM (RS-SCM). RS-SCM can ideally rebuild the spurious and the fake invariant features simultaneously. Given this, we further develop an approach based on conditional mutual information with respect to RS-SCM, then rigorously rectify the spurious and fake invariant effects. It can be easily implemented by a small feature selection subnet introduced in the IRL family, which is alternatively optimized to achieve our goal. Experiments verified the superiority of our approach to fight against the fake invariant issue across a variety of OOD generalization benchmarks.

Sex matters: The MouseX DW-ALLEN Atlas for mice diffusion-weighted MR imaging

Overcoming sex bias in preclinical research requires not only including animals of both sexes in the experiments, but also developing proper tools to handle such data. Recent work revealed sensitivity of diffusion-weighted MRI to glia morphological changes in response to inflammatory stimuli, opening up exciting possibilities to characterize inflammation in a variety of preclinical models of pathologies, the great majority of them available in mice. However, there are limited resources dedicated to mouse imaging, like those required for the data processing and analysis. To fill this gap, we build a mouse MRI template of both structural and diffusion contrasts, with anatomical annotation according to the Allen Mouse Brain Atlas, the most detailed public resource for mouse brain investigation. To achieve a standardized resource, we use a large cohort of animals in vivo, and include animals of both sexes. To prove the utility of this resource to integrate imaging and molecular data, we demonstrate significant association between the mean diffusivity from MRI and gene expression-based glia density. To demonstrate the need of equitable sex representation, we compared across sexes the warp fields needed to match a male-based template, and our template built with both sexes. Then, we use both templates for analysing mice imaging data obtained in animals of different ages, demonstrating that using a male-based template creates spurious significant sex effects, not present otherwise. All in all, our MouseX DW-ALLEN Atlas will be a widely useful resource getting us one step closer to equitable healthcare.

미용인의 NCS화장품 능력단위인지와 현장활용성 및 화장품학 교과만족도 관계 연구

Objectives The purspose of this study is to analyze the effect of cosmetics National Competency Standards recognition on cosmetics subject necessity and subject satisfaction through field usability.
 Methods A survey was conducted on 304 cosmetologists who had experience in cosmetic course. To verify the research hypothesis, the structural equation model was establised and analyzed using AMOS, and bootstrapping was used to verify the mediation effect. The significance of the mediation effect was verified again through the Sobel Test.
 Results Recognition of cosmetics NCS had a positive (+) effect on field usability. Field usability had a positive (+) significant effect on the cosmetics subject necessity and had a positive (+) effect on cosmetics subject satisfaction. It was confirmed that the recognition of cosmetics NCS did not have a direct effect on the cosmetics subject necessity, and only the indirect path was significant, confirming that field usability had a complete mediation effect. In the relationship between whether it is an cosmetics NCS recognition, field usability, and cosmetics sbject satisfaction, the indirect path generates a standardized path coefficient that greatly exceeds 1.0, and the direct path was an inconsistent mediation effect with opposite sign values, confirming that the direct path had a spurious effect. This means that in the relationship between cosmetics NCS recognition and cosmetics subject satisfaction, field usability has a complete mediation effect.
 Conclusions Cosmetics NCS recognition does not have a direct effect on the necessity of cosmetics subject and satisfaction of cosmetics subject, but field usability plays a complete mediation role and has an effect. This can be said to be a result that corresponds to the characteristics of the practical-oriented beauty occupation. The cosmetics NCS should be reorganized according to the beauty occupations so that cosmetics can be used more safely and appropriately in the field of beauty work by major that utilizes cosmetics.