Differences in Career Preparation Behavior According to Personal Variables of College Students Majoring in Hairdressing

This study was conducted for the purpose of providing realistic data necessary for successful career development and job guidance for college students majoring in hair design. For this study, 250 questionnaires were distributed to college students majoring in hairdressing in Daegu and Gyeongsangbuk-do from April 15 to May 30, 2021, and a total of 243 copies were used for analysis, excluding inaccurate responses. For the analysis, the SPSS 26.0K statistical program was used, and factor analysis, t-tset, and one-way batch variance analysis were performed. The analysis results are as follows. It was analyzed that female students had a higher level of preparation behavior than male students. The level of career preparation behavior was high for those with major certificates, those with experience in industrial sites, and those with more than one year of experience in player learning. In addition, the level of career preparation behavior of the group who chose the career path according to their aptitude and the group who thought positively about the job prospects was high.

Few-Shot Wideband Tympanometry Classification in Otosclerosis via Domain Adaptation with Gaussian Processes

Otosclerosis is a common middle ear disease that requires a combination of examinations for its diagnosis in routine. In a previous study, we showed that this disease could be potentially diagnosed by wideband tympanometry (WBT) coupled with a convolutional neural network (CNN) in a rapid and non-invasive manner. We showed that deep transfer learning with data augmentation could be applied successfully on such a task. However, the involved synthetic and realistic data have a significant discrepancy that impedes the performance of transfer learning. To address this issue, a Gaussian processes-guided domain adaptation (GPGDA) algorithm was developed. It leveraged both the loss about the distribution distance calculated by the Gaussian processes and the loss of conventional cross entropy during the transferring. On a WBT dataset including 80 otosclerosis and 55 control samples, it achieved an area-under-the-curve of 97.9±1.1 percent after receiver operating characteristic analysis and an F1-score of 95.7±0.9 percent that were superior to the baseline methods (r=10, p<0.05, ANOVA). To understand the algorithm’s behavior, the role of each component in the GPGDA was experimentally explored on the dataset. In conclusion, our GPGDA algorithm appears to be an effective tool to enhance CNN-based WBT classification in otosclerosis using just a limited number of realistic data samples.

ANALYSIS OF DELAY ENTRY AND PLANNING OF PARKING GATES QMALL BANJARBARU

Qmall Banjarbaru is located at Jalan Ahmad Yani KM. 37. The existence of Qmall Banjarbaru caused the impact of increased traffic density and decreased speed in the surrounding road network. With the increasing movements that occur from Qmall Banjarbaru, it will potentially be the cause of congestion between vehicles that will enter the Qmall Banjarbaru with vehicles moving straight on Jalan Ahmad Yani KM. 37. The purpose of this research is to know the influence of delay entrance parking Qmall Banjarbaru against the performance of Jalan Ahmad Yani KM. 37.This research conducted a field survey that aims to find volume data on the road, the time of parking door service, the number of vehicles that enter the parking, the time delay the parking door, and the length of the delay that occurs on the parking door. From the results of data analysis using the Calculation of field survey (realistic) data obtained the distance of the parking door previously 16.5 meters to be redated to 25 meters and the parking door that originally had two doors of parking service made into three doors parking service. This change was made to delay enter parking Qmall Banjarbaru does not reach Jalan Ahmad Yani Km. 37.

Hyperbolic compartmental models for epidemic spread on networks with uncertain data: Application to the emergence of COVID-19 in Italy

The importance of spatial networks in the spread of an epidemic is an essential aspect in modeling the dynamics of an infectious disease. Additionally, any realistic data-driven model must take into account the large uncertainty in the values reported by official sources such as the amount of infectious individuals. In this paper, we address the above aspects through a hyperbolic compartmental model on networks, in which nodes identify locations of interest such as cities or regions, and arcs represent the ensemble of main mobility paths. The model describes the spatial movement and interactions of a population partitioned, from an epidemiological point of view, on the basis of an extended compartmental structure and divided into commuters, moving on a suburban scale, and non-commuters, acting on an urban scale. Through a diffusive rescaling, the model allows us to recover classical diffusion equations related to commuting dynamics. The numerical solution of the resulting multiscale hyperbolic system with uncertainty is then tackled using a stochastic collocation approach in combination with a finite volume Implicit–Explicit (IMEX) method. The ability of the model to correctly describe the spatial heterogeneity underlying the spread of an epidemic in a realistic city network is confirmed with a study of the outbreak of COVID-19 in Italy and its spread in the Lombardy Region.

Emotions and Dog Bites: Could Predatory Attacks Be Triggered by Emotional States?

Dog biting events pose severe public health and animal welfare concerns. They result in several consequences for both humans (including physical and psychological trauma) and the dog involved in the biting episode (abandonment, relocation to shelter and euthanasia). Although numerous epidemiological studies have analyzed the different factors influencing the occurrence of such events, to date the role of emotions in the expression of predatory attacks toward humans has been scarcely investigated. This paper focuses on the influence of emotional states on triggering predatory attacks in dogs, particularly in some breeds whose aggression causes severe consequences to human victims. We suggest that a comprehensive analysis of the dog bite phenomenon should consider the emotional state of biting dogs in order to collect reliable and realistic data about bite episodes.

The Problem of Fairness in Synthetic Healthcare Data

Access to healthcare data such as electronic health records (EHR) is often restricted by laws established to protect patient privacy. These restrictions hinder the reproducibility of existing results based on private healthcare data and also limit new research. Synthetically-generated healthcare data solve this problem by preserving privacy and enabling researchers and policymakers to drive decisions and methods based on realistic data. Healthcare data can include information about multiple in- and out- patient visits of patients, making it a time-series dataset which is often influenced by protected attributes like age, gender, race etc. The COVID-19 pandemic has exacerbated health inequities, with certain subgroups experiencing poorer outcomes and less access to healthcare. To combat these inequities, synthetic data must “fairly” represent diverse minority subgroups such that the conclusions drawn on synthetic data are correct and the results can be generalized to real data. In this article, we develop two fairness metrics for synthetic data, and analyze all subgroups defined by protected attributes to analyze the bias in three published synthetic research datasets. These covariate-level disparity metrics revealed that synthetic data may not be representative at the univariate and multivariate subgroup-levels and thus, fairness should be addressed when developing data generation methods. We discuss the need for measuring fairness in synthetic healthcare data to enable the development of robust machine learning models to create more equitable synthetic healthcare datasets.

Node and edge nonlinear eigenvector centrality for hypergraphs

Network scientists have shown that there is great value in studying pairwise interactions between components in a system. From a linear algebra point of view, this involves defining and evaluating functions of the associated adjacency matrix. Recent work indicates that there are further benefits from accounting directly for higher order interactions, notably through a hypergraph representation where an edge may involve multiple nodes. Building on these ideas, we motivate, define and analyze a class of spectral centrality measures for identifying important nodes and hyperedges in hypergraphs, generalizing existing network science concepts. By exploiting the latest developments in nonlinear Perron−Frobenius theory, we show how the resulting constrained nonlinear eigenvalue problems have unique solutions that can be computed efficiently via a nonlinear power method iteration. We illustrate the measures on realistic data sets.

Bifuzzy-Bilevel Programming Model: Solution and Application

Bi-level programming is widely used in processing various questions, but it cannot deal with the complex and fuzzy information contained in problems. In order to solve such problems better with intricate and vague information that can be efficiently handled by bifuzzy theory, a bifuzzy–bilevel programming model that sets the parameters to bifuzzy variables is proposed in this paper, which can process complex realistic data more accurately and improve the feasibility and validity of bi-level programming models. To ensure the solvability of the model, the equivalent form of the bifuzzy–bilevel programming model is obtained by utilizing the expected value operator. According to the linear and nonlinear characteristics of the model, the Karush–Kuhn–Tucker condition and particle swarm optimization algorithm are employed to handle the problem, respectively. Finally, by taking the distribution center location problem of the supplier as an example, the bifuzzy–bilevel programming model is applied in practice to balance highly intricate customer demands and corporate cost minimization, obtaining the feasible solution of functions at the upper and lower levels, and the bifuzzy information in the problem can also be processed well, which proves the effectiveness of the proposed methodology.

Macroeconometric Input-Output Model For Transport Sector Analysis

Effective government transport policy can be based only on realistic data, sophisticated and detailed transport sector analysis, and productive modelling. The aim of the paper is to demonstrate the main elements used to develop a relatively small macro-economic input-output model with the emphasis on transport for one European Union (EU) country. Transport sector faces similar problems in various countries linked with emissions, transport flows, road accidents and other issues hence appropriate modelling tool should be selected. The model presented in this article consists of econometric and input-output relations. The research analyses and examines three scenarios and stresses the importance of the transport investment not only for development of the transport sector, but also for the economic development in general. The scenarios imply zero, 9 million and 6.7 million additional investment in transport sector eligible to the EU funding. As the result of additional investment, GDP recovers faster leading to 0.3-1.7%points faster growth rates as compared to the base scenario with no additional investment leading to faster cohesion with the average EU level, as well as higher number and turnover of passengers in the public and commercial transport, while the number of passenger cars is lower. The model can also be applied to study regional development, if it is possible to distinguish, which regions will benefit from the investment, as well as influence on fuel consumption and CO2 emissions, if the investments are targeted to specific means of transport.