The prediction of satisfactory orthodontic treatment outcomes can be very challenging owing to the subjectivity of orthodontists’ judgment, along with the inherent difficulties when considering numerous factors. Therefore, this study introduced a deep learning-based method for predicting orthodontic treatment outcomes based on the image-to-image translation of dental radiographs using the Pix2Pix model. This proposed method addresses the aforementioned issues using a Pix2Pix-based prediction model constructed from adversarial deep learning. Patient datasets and prediction models were separated and developed for extraction and non-extraction treatments, respectively. The patients’ radiographs were pre-processed and standardized for training, testing, and applying the Pix2Pix models by uniformly adjusting the degree of blackness for the region of interest. A comparison of actual with Pix2Pix-predicted images revealed high accuracy, with correlation coefficients of 0.8767 for extraction orthodontic treatments and 0.8686 for non-extraction treatments. The proposed method establishes a robust clinical and practical framework for digital dentistry, offering both quantitative and qualitative insights for orthodontists and patients.

This study explores strategies for companies to gain competitive advantage amid technological innovation and transformation. By applying the human performance technology model to a global multinational corporation, Company A, we analyzed performance issues and solutions. Key findings identified deficiencies in the compensation system and career development tools, with strategies proposed to address these issues. Success factors include executive support, clear goal setting, and alignment of strategy and execution. This research offers practical insights into building performance-oriented organizations and is expected to contribute to the field of performance consulting.

This study aimed to analyze elementary school teachers’ needs regarding the functions of AI-based digital English textbooks. Data were collected from a survey of 96 elementary school teachers. First, a paired sample t-test was first used to examine differences between perceived importance and current implementation. Borich’s needs assessment model was then applied to identify priority items, and the Locus for Focus model was followed visualize them. Finally, a comprehensive analysis was conducted to determine the most urgent needs. Results showed that among five categories—Diagnostic and Evaluation Functions, Support for Personalized Instructional Design, Adaptive Learning Support, AI Tutor and Assistant Teacher, and Dashboard and User Interface—the highest need was in Diagnostic and Evaluation Functions. Personalized Instructional Design and Adaptive Learning Support also showed high demand. These findings suggest that AI-based digital textbooks remain in a developmental phase, requiring further discussion on how to implement them effectively according to subject-specific needs.

The Public Procurement Service was established to ensure efficient supply of necessary goods for public institutions and their quality stability. The Public Procurement Service operates the Excellent Product Designation System for quality improvement. Due to the convenience of sole-source contracts and pricing advantages, suppliers prefer to obtain excellent product certification. However, despite these advantages, the designation does not guarantee customer satisfaction, as consumers pay higher prices without assured quality improvements. The goal of this study is to propose a new evaluation system better reflecting customer satisfaction and repurchase intention. Metal window products were selected as the study subject. Candidate factors were derived through literature review, and surveys were conducted to identify significant items for the new evaluation system. Item weights were then calculated using AHP analysis. The proposed system was validated through case analysis comparing two excellent products with two general products.

‘Efficiency,’ a key performance factor of an organization, is affected by various factors in addition to ‘cost-benefit,’ which can be measured. Data Envelopment Analysis (DEA) is a method to evaluate the relative efficiency of an organization by simultaneously considering various factors that are difficult to measure. The significance of this study is that it presents a ‘method for developing an efficiency performance indicator using DEA’ and provides a practical application plan for inefficient organizations (DMUs) to develop and manage appropriate performance indicators to improve efficiency. It presents a methodology for performing research procedures ranging from selection of input and output variables, correlation analysis, DEA execution, calculation of virtual efficiency units (VEUs) through the latent price of the reference group (DMU), and derivation of efficiency performance indicators of the organization.

The purpose of this study is to empirically analyze the causal relationship between the influence of the differentiation strategy on the management performance of small and medium-sized business successors, who are shaped by the characteristics of the company and its environment. A survey was conducted on 256 business successors in the metropolitan area, and SPSS 29.0 and AMOS 29.0 programs were used to test the hypotheses of the established research model. The results of the empirical analysis showed that environmental characteristics had a greater influence on business successors than the company's characteristics. Second, the influence of the business successors had a positive effect on the company's differentiation strategy. Third, the differentiation strategy was found to have a strong correlation with the company's financial performance, and it was found to have a positive (+) effect on non-financial performance. Fourth, the financial performance of family businesses was found to have a significant influence on their non-financial performance. This study aims to broaden the understanding of why business successors prefer and choose differentiation strategies by combining theories of strategic management and business succession. Existing research on business succession has focused primarily on succession and management performance, with relatively little empirical research on strategy selection. The novelty of this study lies in its unique focus on strategy selection, which will likely aid in designing customized consulting and support policies for future succession companies. This novel approach is sure to intrigue and interest the audience.

Anomaly detection is a key technique for ensuring the reliability and stability of systems across various industrial domains. Autoencoder-based reconstruction models are particularly effective in learning normal patterns and detecting deviations. However, conventional loss functions such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) are limited in capturing anomalies that follow heavy-tailed or asymmetric distributions, which are commonly observed in real-world industrial settings. To address this limitation, we propose a Mixture Negative Log-Likelihood (Mixture NLL) loss function based on a combination of Gaussian, Laplace, and Student-t distributions. The loss is constructed using the probability density functions of each distribution, with key parameters such as standard deviation, scale, and degrees of freedom learned during training. The mixture weights representing the contribution of each distribution are also jointly optimized. Experimental results on real-world time-series anomaly detection datasets demonstrate that the proposed MixtureLoss consistently outperforms conventional loss-based Autoencoder models, particularly in detecting tail-end anomalies.

자기공명영상(MRI)은 고해상도 연조직 진단을 위한 필수 영상기술로 자리 잡았으나, 다양한 비전리방사선 요소인 정자기장, 고주파 에너지, 경사 자기장 등으로 인해 의료종사자의 직업적 노출과 환자의 안전의 체계적 관리가 요구 된다. 본 연구는 국내 MRI 환경의 안전관리 현황을 검토하고, 국제 가이드라인(미국 FDA, ICNIRP, ACR, IEC, EU 등) 간의 비교를 통해 국내 제도적 기준의 한계와 개선 방향을 도출하고자 하였다. 직업적 노출 증상, 소음 스트 레스, 임플란트 관련 기기 적합성 문제, 교육 및 사고 예방 프로그램의 필요성을 중점적으로 고찰하였다. 특히, 방사 선사의 작업환경에서 발생할 수 있는 생리학적 반응과 기기 상호작용과 관련해 선행 연구 결과를 통합하여 임상 현장 에서 적용할 수 있는 안전관리체계 수립의 근거를 제시한다. 본 연구는 향후 국내 MRI 안전 가이드라인 및 직업적 노출 표준화 기준 수립을 위한 기초자료로 활용될 수 있다.

본 연구는 동기부여 이론의 관점에서 인적자원관리 강도 인식(perceived HRM system strength)과 임 금수준이 이직 의도에 영향을 미치는 심리적 기제를 탐구하였다. 직무 만족도와 조직 동일시를 매개로 인사관리강도 인식과 임금수준의 영향을 확인함과 동시에, 고임금 집단과 저임금 집단으로 구분하여 임 금 수준이 양 집단간에 서로 다른 심리적 과정을 이끌어낼 수 있음을 주장한다. 연구 가설 검증을 위해 인적자본기업패널 2차 자료를 활용하였으며, 한국 기업 500개에 속한 2,566명의 표본을 대상으로 구조방 정식 모형 분석을 수행하였다. 분석 결과, 인사관리강도 인식과 실제 급여는 모두 직무 만족도 및 조직 동일시를 통해 이직 의도를 낮추는 것으로 나타났다. 또한, 고임금 집단과 저임금 집단 간에는 경로의 패 턴에서 차이가 발견되었다. 고임금 집단에서는 급여가 직무 만족도에 영향을 미치지 않으면서도 이직 의 도를 직접적으로 낮추는 반면, 저임금 집단에서는 급여가 직무 만족도를 통해서만 이직 의도에 간접적인 영향을 미치는 것으로 확인되었다. 우리의 분석 결과는 임금이 외적 동기 요소로서 위생 요인이며, 인사관 리강도 인식은 내적 동기를 자극할 수 있는 관리적 전략임을 강력하게 시사하며 전통적 동기부여 이론의 시각과 맥락을 같이한다. 이 연구는 실무적 차원에서 인사관리강도 인식 관리가 중요하다는 점을 강조함 과 동시에 인사관리 정책 변화 관리에 중요한 실질적인 시사점을 제공한다.