Vector Of Characteristics Research Articles

한국의 소비불평등 요인분해에 관한 연구

Purpose: This study examines the determinants of household consumption expenditure and quantifies their contribution to within group inequality by gender of household head in South Korea.
 Research design, data, and methodology: This study first decomposes total inequality into within and between inequality by gender of household head. It also examines the association between a vector of household demographic and socioeconomic characteristics and household consumption expenditure in an attempt to identify how much inequality can be attributed to each of explanatory factors. This study empirically draws upon the recent national representative dataset, Survey of Household Finance and Living Conditions (SFLC) 2 021 c ollected b y Korea Statistical Office.
 Results: According to the present study, inequality in 2021 in South Korea was found to be mainly dominated by inequality within population sub groups. Examining the determinants of household consumption inequality, it shows that factors and their conributions are different between male and female headed households. For instance, whilst the type of job occupation of a head appears to have a large impact on inequality within male headed households, it is found to have marginal contribution compared to within female headed households (25.2% vs. 7.4%). In case of household demography, it is observed as the second largest factor increasing within inequality among female headed households (31.2%) whereas its contribution to inequality within inequality among male group was relatively small (18.6%).
 Implications: Our study suggests that target specific investigation and deep structural understanding need to be made on the different characteristics of within inequality as reflected by the different role of factors and their contributions. Amongst, the different inequality impact of higher education might reflect that the expansion of higher education had a relatively lower impact on the living stand of female group than that of its counterpart, male group. Finally, the discriminatory barriers (e.g. job occupation) against female may reduce within inequality, it could widen total inequality, worsening distribution between male and female groups.

Dynamic models for developing reference scenarios of energy system in the low-carbon transition

The proposed study is aimed at the development of dynamic input-output models in relation to energy development systems. The main task is to find optimal scenarios, defined as sequences of optimal balances between production and capital investment. These models are rooted in Leontief's conceptual concept of input-output, which emphasizes the timing of the introduction of new production systems. The article discusses the economic interpretation and mathematical conditions of intersectoral models, and examines their application to problems related to energy. The model uses the concept of balanced equilibrium growth, in which the ratio of consumption to net release is the same for all resources, and the reserve of at least one resource is fully utilized. The growth rate of the system is the most important parameter that determines the trajectory of balanced growth. Solving the system of inequalities yields balanced growth trajectories in which the dominant root and the associated vector of characteristics plays a key role. A balanced equilibrium growth rate has the properties of a minimax with the presence of a saddle point, which indicates its importance in achieving market equilibrium while preventing a reduction in inventories. The practical application of the model is described. The results of calculations of pessimistic, reference and optimistic scenarios of electricity production in the IPS of Ukraine are presented. Official statistical information and economic forecasts are used, and various restrictions are taken into account. The model aims to minimize the total cost of electricity generation while respecting environmental and operational constraints. The proposed model provides a comprehensive basis for understanding the dynamics of resource reserves, consumption and growth of electricity production of the IPS of Ukraine. It offers decision-makers a valuable tool to optimize electricity generation strategies, taking into account a range of scenarios and constraints, for the sustainable and cost-effective operation of the energy system. Keywords: Dynamic input-output models, optimal scenarios, balanced equilibrium growth, growth rate of the system.

The Impact of Mobile Phones on Change in Employment Status in South Africa

Abstract In this paper we analyse whether having a mobile phone impacts chances of getting employed. We use five waves of panel data from the National Income Dynamic Survey (NIDS), which was conducted in South Africa between years 2008 and 2017. In the estimation we include a vector of observable individual and household characteristics and account for unobserved heterogeneity amongst individuals. The estimation results suggest that mobile phone ownership has a positive impact on the change in employment status from unemployed to employed. On the other hand, ownership of a computer by a household and computer literacy do not increase the likelihood of getting employed. The average probability of becoming employed increases from 54.2 % when no one among unemployed adults has a mobile phone to 57.4 % when all of them have a mobile phone, which is an increase of 5.9 %.

The distribution of child physicians and early academic achievement.

To describe the distribution of pediatricians and family physicians (child physicians) across school districts and examine the association between physician supply and third-grade test scores. Data come from the January 2020 American Medical Association Physician Masterfile, the 2009-2013 and 2014-2018 waves of American Community Survey 5-Year Data, and the Stanford Education Data Archive (SEDA), which uses test scores from all U.S. public schools. We use covariate data provided by SEDA to describe student populations. This descriptive analysis constructs a physician-to-child-population ratio for every school district in the country and describes the child population served by the current distribution of physicians. We fit a set of multivariable regression models to estimate the associations between district test score outcomes and district physician supply. Our model includes state fixed effects to control for unobservable state-level factors, as well as a covariate vector of sociodemographic characteristics. Public data from three sources were matched by district ID. Physicians are highly unequally distributed across districts: nearly 3640 (29.6%) of 12,297 districts have no child physician, which includes 49% of rural districts. Rural children of color in particular have very little access to pediatric care, and this inequality is more extreme when looking exclusively at pediatricians. Districts that have higher child physician supplies tend to have higher academic test scores in early education, independent of community socioeconomic status and racial/ethnic composition. While the national data show this positive relationship (0.012 SD, 95% CI, 0.0103-0.0127), it is most pronounced for districts in the bottom tertile of physician supply (0.163 SD, 95% CI, 0.108-0.219). Our study demonstrates a highly unequal distribution of child physicians in the U.S., and that children with less access to physicians have lower academic performance in early education.

한국 가계빈곤의 결정요인 분석

Purpose: As household characteristics are differently associated with poverty within population sub-groups, the present study examines the determinants of household poverty by gender and age of a household heads in Korea.
 Research design, data, and methodology: The present study examines the association between a vector of household demograpic and socioeconomic characteristics and poverty. Specifically, sample household survey is decomposed into male and female headed households, and elderly and non-elderly headed households to assess how attributes to poverty vary depending on population sub-goups. For empirical analysis, this study draws upon the recent national representative household data, survey of household finance and living conditions (SFLC) 2021 collected by Korea Statistical Office.
 Results: Three findings are highlighted; (1) household head’s age is prone to poverty for the non-elderly male-headed households but not for the non-elderly female headed households, partly reflecting relative weakness in earning power of female in labour market.; (2) the type of Job occupation of a household head has different effects on the elderly male and the elderly female households; (3) a hump-shape relationship between a household head’s age and poverty, partly capturing the life-cycle income hypothesis, is only observed from the non-elderly households whilst the relationship was linear for the elderly household. Finally we found that education played a role for the elderly household from anti-poverty perspective but not for the non-elderly household.
 Implications: Some findings suggests policy implication related to poverty relief. For example, the different age effect of poverty between the non-elderly male and the non-elderly female headed households for partly implies the weakness of earning power in income generating activities and suggests a government anti-poverty intervention should be taken into account wage differential stemming from gender discrimination in labour market.

On the Epidemiological Evolution of Colistin-ResistantAcinetobacter Baumanniiin the City of Valencia: An Agent-Based Modelling Approach

Antibiotic resistance is one of the greatest public health threats today, mainly due to the non-rational use of antibiotics.Acinetobacter baumanniiis an example of a microorganism with high antibiotic resistance that has developed rapidly in recent years. Consequently, only a few lastresort antibiotics, such as colistin, are currently effective against it. In this work, we propose a random agent-based computational model to describe the evolution of colistin-resistantA. baumanniiin the population of Valencia (Spain) and to predict its impact both on the whole population and by age groups. The agent- based model uses a synthetic population of individuals with a vector of characteristics or state variables. These variables change over time based on a series of random events with certain conditional probabilities. The synthetic population statistical features and the probabilities have been found in demographic and hospital databases. One of these probabilities, the probability of infection by a resistant strain, has been modeled using random differential equations. The model takes into account antibiotic consumption as the primary driving force of variation and assumes non-rewersibility of resistance as the worst-case scenario. The agent-based model calibration and the selection of a real-world representative set of solutions have been carried out using the Partide Swarm Optimization evolutionary algorithm. This approach takes into account the inherent stochasticity of the model and the uncertainty of the data. Finally, projections of the incidence and absolute cases of colistin-resistantA. baumanniihave been performed. Our results suggest that, if the same consumption pattern continues, the ervolution of the colistin-resistant strain proportion will be exponential, exceeding 50% in 2025. Additionally, the results reveal that, despite the low incidence in Valencian hospitals, the impact on people over 60 years old will be more significant in terms of the number of cases. Based on these findings, it can be deduced that colistin will cense to be an effective antibiotic in the coming years, negatively impacting the human population, especially the most advanced age groups.

Development of a Novel Dynamic Modeling Approach for a Three-Axis Machine Tool in Mechatronic Integration

This paper proposes a novel, fast, and automatic modeling method to build a virtual model with minimum degrees of freedom (DOFs) without the need for FE models or human judgment. The proposed program uses the iterative closest point (ICP) algorithm to analyze the mode shape vector of structural dynamic characteristics to define the position and DOFs of the joints between structural components. After the multi-body dynamics model was developed in software, it was converted into an SSM to connect the servo loop model. Then, the mechatronic integration analysis was performed to verify the dynamic characteristics of the tool center point (TCP) and the workbench in the experiment and simulation. The model created by the proposed identification process has a small DOF and can accurately simulate the dynamic characteristics of a machine. This model can be used for dynamic testing and control strategy development in mechatronic integration.

An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis.

Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person’s gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory’s physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper’s primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher’s uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher’s dataset.

Social Determinants of Health and Health Inequality in South Korea

Purpose: Recognizing the social factors behind population health and health equity, the present study examines the role of locality in regional health and assesses the its contribution to local health inequality at municipality level in South Korea. Research design, data, and methodology: The present study investigates the association between a vector of municipal socioeconomic characteristics and population health, taking into account possible unobservable geographical fixed effects at metropolitan/province level, and quantifies the contribution of sources of inter-municipality health inequality by decomposing the disparity in life expectancy across municipalities. For empirical analysis, it draws upon life expectancy data at municipal level in South Korea estimated by The Korean Society for Equity in Health in 2018 and data obtained from various souces such as National Health Insurance Service (NHIS), Annual Local Tax Statistical Report of Ministry of the Interior and Safety (MOIS), National Election Commission (NEC) and Korea Statistical Information Service (KOSIS). Results: This study provides the empirical evidence to the importance of social determinants on local population health and health inequality within and between municipalities. Taking life expectancy as a proxy of municipal population health, we finds the different role of social determinants on local health and health inequality between the highest and the lowest income group within a municipality. While some variables (eg. income inequality among municipal population), for instance, are associated with both life expectancy and its gap, many of other factors such as municipal income level and the degree of financial self-reliance are not prone to life expectancy but the disparity of life expectancy in the context of population health. Implications: The study highlights three findings; (1) level of local autonomy contributed in reducing life expectancy gap across municipality; (2) Geography is closely associated with health inequality across municipalities. Approximately 40 % of inequality stem from non-residence in Seoul metropolitan area; (3) Given the universal population coverage of the national health insurance service, a positive association between the local health related resources and health disparity among municipal population might imply a discrepancy in the level of treatment of diseases. This may result from different household financial capacity and an unequal access to private health insurance.

A macro-level analysis of language learning and migration

Abstract This article investigates the macro-level drivers of adult-age language learning with a focus on migration based on a new dataset on German language learning in 77 countries (including Germany) for 1992–2006. Fixed-effects regressions show that language learning abroad is strongly associated with immigration from countries of the European Union and the Schengen Area whose citizens enjoy free access to Germany, while language learning in Germany is strongly associated with immigration from countries with restricted access. The different degrees of uncertainty about access to Germany seem to be of importance for preparatory language learning. To shed light on country heterogeneities, we substitute the location fixed effects with a vector of country characteristics, which include several distance measures among others, and we estimate a random-effects model. Last, we provide some tentative arguments in favour of a causal interpretation. The main results related to the role of uncertainty are mostly unaffected. The Skilled Immigration Act from 2020 removes part of this uncertainty with potential positive effects on preparatory language learning and economic and social integration.

Cross-Cultural Awareness of College Students Based on Cross-Cultural Communication

Objectives: Cross-cultural communication lays the foundation for sustainable development education. In order to further improve the level of cross-cultural awareness of college students in China, a recommendation algorithm based on cross-cultural communication comparison model is proposed. Methods: The algorithm uses the keywords with high weight in the recommended items as the attribute characteristics of the recommendation items, and then recommends the items with the closest vectors (the highest score of the vector calculation with user characteristics) to the users. Results: TF-IDF method is used to calculate and calculate the keywords with high weight in intercultural communication mode as describing user characteristics, and these keywords are used as describing vector of user characteristics, so as to construct the intercultural awareness model of College students. Finally, APRIORI ALL algorithm is used to train and test the model. Conclusion: The test results show that the error is less than 5%. The experimental results show that the proposed algorithm is effective. The intercultural awareness model can improve the intercultural communicative competence of college students and provide a reference for English intercultural teaching in basic education stage.

CEO age, shareholder monitoring, and the organic growth of European firms

The question of why some firms grow faster than others is of high theoretical and practical importance. Beyond a wealth of studies based on stochastic models, firm growth has mostly been explained by looking at the structural characteristics of firms, sectors, and countries. The role of managers’ characteristics in fostering firms’ growth has been explored much less. In this study, we adopt one key characteristic of managers, the age of the chief executive officer (CEO) and examine its relationship with the firm’s organic growth. Using data from a large sample of European manufacturing firms, we find that firms managed by young CEOs grow faster in terms of sales and assets, but not in terms of profitability. These results hold with the inclusion of a large vector of firm and CEO characteristics, and a battery of robustness checks, including issues related to the time horizon and appointment of CEOs, the educational attainment of younger cohorts of managers, and endogeneity. We hypothesize that young CEOs are incentivized to boost firm growth to signal their talent in the managerial market and to secure a longer stream of future compensation benefits. To the extent that firm growth does not translate into higher profitability, this may create an agency problem, due to the divergence of this corporate strategy from shareholders’ targets. In line with this hypothesis, we find that a more concentrated ownership that allows for more effective monitoring moderates the relationship between CEO age and firm growth.

Topological Data Analysis for Eye Fundus Image Quality Assessment.

The objective of this work is to perform image quality assessment (IQA) of eye fundus images in the context of digital fundoscopy with topological data analysis (TDA) and machine learning methods. Eye health remains inaccessible for a large amount of the global population. Digital tools that automize the eye exam could be used to address this issue. IQA is a fundamental step in digital fundoscopy for clinical applications; it is one of the first steps in the preprocessing stages of computer-aided diagnosis (CAD) systems using eye fundus images. Images from the EyePACS dataset were used, and quality labels from previous works in the literature were selected. Cubical complexes were used to represent the images; the grayscale version was, then, used to calculate a persistent homology on the simplex and represented with persistence diagrams. Then, 30 vectorized topological descriptors were calculated from each image and used as input to a classification algorithm. Six different algorithms were tested for this study (SVM, decision tree, k-NN, random forest, logistic regression (LoGit), MLP). LoGit was selected and used for the classification of all images, given the low computational cost it carries. Performance results on the validation subset showed a global accuracy of 0.932, precision of 0.912 for label “quality” and 0.952 for label “no quality”, recall of 0.932 for label “quality” and 0.912 for label “no quality”, AUC of 0.980, F1 score of 0.932, and a Matthews correlation coefficient of 0.864. This work offers evidence for the use of topological methods for the process of quality assessment of eye fundus images, where a relatively small vector of characteristics (30 in this case) can enclose enough information for an algorithm to yield classification results useful in the clinical settings of a digital fundoscopy pipeline for CAD.

Directionally sensitive weighted adaptive multivariate CUSUM mean charts

AbstractIn many service and manufacturing industries, process monitoring involves multivariate data, instead of univariate data. In these situations, multivariate charts are employed for process monitoring. Very often when the mean vector shifts to an out‐of‐control situation, the exact shift size is unknown; hence, multivariate charts for monitoring a range of the mean shift sizes in the mean vector are adopted. In this paper, directionally sensitive weighted adaptive multivariate CUSUM charts are developed for monitoring a range of the mean shift sizes. Directionally sensitive charts are useful in situations where the aim lies in monitoring either an increasing or a decreasing shift in the mean vector of the quality characteristics of interest. The Monte Carlo simulation is used to compute the run length characteristics in comparing the sensitivities of the proposed and existing multivariate CUSUM charts. In general, the directionally sensitive and weighted adaptive features enhance the sensitivities of the proposed multivariate CUSUM charts in comparison with the existing multivariate CUSUM charts without the adaptive feature or those that are directionally invariant. It is also found that the variable sampling interval feature enhances the sensitivities of the proposed and existing charts as compared to their fixed sampling interval counterparts. The implementation of the proposed charts in detecting upward and downward shifts in the in‐control process mean vector is demonstrated using two different datasets.

Android Collusion Detection by means of Audio Signal Analysis with Machine Learning techniques

Smartphones, tablets and other mobile devices have become objects that we can no longer do without, as a matter of fact for us they are like an extension of our body and many people are addicted to them; this behavior is a consequence of the use we make of it, since these devices allow us to manage sensitive data (i.e., financial ones) and access information of different types (i.e., photos, messages or health data). For this reason it is essential to detect the harmful behaviors present within our smartphones, taking into account the weaknesses of the current anti-malware mechanisms. In this article we propose an approach capable of discriminating trusted applications from those that instead have malicious behavior, since they are involved in a colluding attack. We resort to the processing of the audio signal extracted from the conversion of an application into an audio file. The processing allows to generate a vector of characteristics to be analyzed with different classifiers. The experimental analysis is performed on a set of Android applications consisting of 359 trusted and (colluding) untrusted applications, showing the effectiveness of our method in detecting colluding applications.

Applied Aspects of Optimization of Orbital Structures of Satellite Systems by Specifying Parameters of Orbital Motion

The paper deals with issues related to optimizing the ballistic structure of a satellite system for remote sensing of the Earth. Approaches to the ballistic design of the satellite system, previously developed by specialists from various scientific schools, were focused on maintaining the structural stability of the system by deploying groupings with the same geometry and with the same inclinations, which ensured the same age-old departures of elements from all the orbits. At the same time, there is a whole range of tasks that require the formation of a satellite system in different orbits. To achieve the required level of stability of a new cluster of orbital structures we provide an approach, including: heuristic formation of many target different height orbits; identifying some basic near-circular orbit; selection of possible variants of iterative quasi-synchronous orbits; coordination of the composition of the vector of characteristics of traffic conditions and final calculation of an acceptable option that provides the specified accuracy of the route closure cycle.
 Testing of the proposed approach is carried out on the example of determining the parameters of orbits that ensures equality of effective days in a given range of heights. The method of selecting the degree of consideration of various physical factors of the space environment, which ensures the achievement of identical deviations of the forecast trajectory from the reference one, is presented. The characteristics of the mathematical model of quasi-synchronous orbit motion used in forecasting are calculated from the condition of stability at a given time interval. To get the appropriate estimates, we use corrections to the orbit parameters given from the Greenwich coordinate system.
 A detailed algorithm is described that provides the possibility of unambiguously determining the characteristics of a stable structure, in the implementation of which the transition from the solution of a normal system of equations to the solution of two triangular systems is performed.
 The analysis of the subject area has shown that the proposed approach is new, and the solved scientific problem belongs to the class of inverse problems of space cybernetics.

Methods of fuzzy sets theory for statistical analysis of mass service systems.

Approaches to the processing of fuzzy information are proposed in the conditions of incomplete determination of the vector of input characteristics, which are based on the theory of fuzzy sets and fuzzy measures. Their analysis have been carried out, the limits of their use, and areas of effective application, in particular, regarding mass service systems.

Mornitoring, Forecasting and Optimization Technique When Multi-Variables Shift With Time

In this paper, the quality control technique and the optimization of adjustment interval of one dimensional quality characteristic are extended to multi-dimensional case in which the vector of the quality characteristics or important financial indices of firms shifts with time. A special multivariate triangle control chart is proposed to control such kind of process. An application is illustrated to monitor and predict the soundness of insurers of U.S. Finally, 7 important issues for further study in future are presented.

Posture Recognition Technology Based on Kinect

Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and three-dimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.

Methodology for ensuring sustainable automatic control of the manufacturing process of manufacturing electronics using the fuzzy logic apparatus

The production equipment on which the electronics products are manufactured has stringent requirements for controlling the conformity of physical characteristics, such as thickness, depth, width, and others, to standard values. It is necessary to formalize the task of evaluating the current state vector of product characteristics to ensure real adequacy in the generation of control action and the implementation of a process control strategy close to optimal. The article discusses the process control process for manufacturing electronics by introducing means of inter-machine intellectual interaction and a self-learning multi-parameter fuzzy classifier. Based on the developed procedure, a real assessment of the current state of the controlled characteristics of the object and adequate control of disturbing influences will be provided. The formulated technology is designed to provide an improvement in the quality of transients and to preventively eliminate the risk of defective products, and, consequently, to positively affect the effectiveness of the manufacturing process of electronics manufacturing.