The global e-waste problem is becoming increasingly serious. China, as one of the largest producers and consumers of electronic products, still has a low formal recycling rate. Consumers, as the owners of waste electronics, are the key to successful reverse logistics. However, many choose to store or dispose of e-waste at home rather than use official recycling channels. While many previous studies focus on factors that encourage recycling, fewer examine what stops people from taking part. This study applies Valence Theory to identify the factors that increase consumers’ psychological resistance to recycling small e-waste in China’s first-tier cities. It also examines how these factors influence social value and resistance behavior. The research model includes perceived price unfairness, perceived inconvenience, perceived benefits, and information publicity, with social value as a mediator. Data were collected through an online survey of 303 residents in Beijing, Shanghai, Guangzhou, and Shenzhen. Structural equation modeling (SEM) was used for analysis. The results show that perceived inconvenience and perceived benefits significantly influence social value. Perceived price unfairness, perceived inconvenience, and social value significantly affect consumer resistance. These findings expand the application of Valence Theory in e-waste research and address gaps in the Theory of Planned Behavior by considering both perceived risks and benefits. Practically, this study suggests that manufacturers, recyclers, and policymakers should improve recycling facilities, make the process more convenient, ensure fair and transparent pricing, and create targeted measures to reduce consumer resistance and encourage participation in formal recycling systems.

This qualitative study investigates how three experienced Native English-speaking teachers (NESTs) in Korea’s private education sector (hagwons) construct and negotiate their professional identities. Grounded in Wenger’s (1998) social theory of identity, the study explores (1) how NESTs navigate their roles across institutional, cultural, and global communities of practice, and (2) how their learning trajectories influence identity development over time. Data were collected through group discussions, reflective journals, and in-depth interviews. Findings reveal that NESTs’ identities are dynamic and continually reshaped through interactions with Korean English Teachers (KETs), students, and institutional contexts. While native-speaker status affords symbolic capital and instructional autonomy, it can also result in marginalization within Korea’s exam-oriented, hierarchical education system. The study highlights how sustained engagement, intercultural adaptation, and reflective practice foster more agentive and contextually grounded professional identities. It calls for institutional policies and teacher education programs that support long-term development, promote equitable team-teaching, and provide localized training responsive to the specific demands of Korean English classrooms.

In the production sites of small and medium sized manufacturing enterprises, the increasing proportion of foreign workers has led to frequent difficulties in responding promptly to process defects and equipment setting errors during night and weekend shifts due to the absence of Korean supervisors. If such issues are not addressed in a timely manner, they can lead to large scale defects and reduced production efficiency. In this study, we developed an AI-based defect prediction and prevention system for the bearing machining process to overcome these on site management limitations. Real time machining data, equipment information, and quality inspection results were collected from the production lines of the target company, and the prediction accuracy of three models, RNN(Recurrent Neural Network), LSTM(Long Short-Term Memory), and GRU(Gated Recurrent Unit), was compared. As a result, the LSTM model demonstrated the best performance. The developed system visualizes real time defect prediction results in the form of a dashboard, enabling workers to immediately detect anomalies and adjust the process accordingly. Particularly in bearing machining processes where mass production occurs in short periods, the risk of lot level defects is high, while this system can contribute to improved production quality and efficiency by enabling early defect prediction and immediate response.

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 investigates how Korean in-service English teachers’ awareness of World Englishes relates to their perceptions and practices in English listening instruction. A total of 200 secondary school teachers completed a questionnaire including Likert-scale and open-ended items. Results indicated that teachers aware of World Englishes were more critical of current listening instruction, which prioritizes American and British accents, and were more supportive of integrating diverse English accents into their teaching. A significant negative correlation was found between teaching experience and World Englishes awareness, suggesting that more experienced teachers may be less attuned to global perspectives on English. Qualitative responses supported these findings and revealed challenges such as curriculum constraints, limited teaching materials, and lack of training. Nonetheless, several teachers emphasized the importance of preparing learners for real-world communication by exposing them to diverse accents. The study highlights the need for curriculum reform and professional development aligned with the Teaching English as an International Language (TEIL) framework.

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.

Aluminum nitride (AlN) provides excellent thermal conductivity and electrical insulation, making it suitable for semiconductor heater applications. However, its low surface emissivity can lead to thermal energy loss, reducing heater efficiency. To address this issue, black AlN - obtained by doping with carbon and other impurities to enhance the surface emissivity - has recently been applied in various fields. In this study, black AlN was fabricated by adding TiO2 to AlN, and its densification behavior and electrical properties were evaluated to assess the feasibility of its use as a heater material for semiconductor photolithography. The sinterability of black AlN was improved by optimizing the granulation and forming conditions, with a particular focus on the heat treatment parameters that affect material properties such as color. Consequently, a black AlN heater material with a sintered density of 3.33 g/cm3, thermal conductivity of 162.7 W/m･K, and thermal diffusivity of 64.22 mm2/s was fabricated by optimizing the processing variables.

LGBTQ+ 권리와 종교의 자유의 조화를 위한 2015년 미국 유타주의 입법 타협 안은 양측의 권리를 둘러싼 갈등이 첨예하게 대립하는 상황 속에서 실질적이고 의미 있는 입법적 진전을 이룬 사례로 평가된다. 이 타협안이 마련되기 전까지 유타주는 성적 지향과 성 정체성에 기반한 차별로부터 개인을 보호하는 포괄적인 주(州) 차원의 법적 장치를 갖추지 못한 상태였다. 그러나 동성 결혼의 합법화와 같은 사법 판결, 시민 여론의 급격한 변화, 예수 그리스도 후기 성도 교회(LDS 교회)의 결정적인 지지 등이 계기가 되어, 성소수자 옹호 단체, 종교 단체, 입법 자, 기업인 등 다양한 이해관계자들이 협상 테이블에 함께하게 되었다. 그 결과 두 개의 상호보완적 법안이 제정되었는데, SB 296은 LGBTQ+ 개인을 위한 고용 및 주거 차별 금지를 주 전체로 확대하는 한편, 종교 기관에 대한 특정 예외 조 항을 포함하였고, SB 297은 결혼과 성과 관련된 종교적 표현의 자유와 양심적 거부권을 보호하는 내용을 담고 있다. 이 입법 타협은 시민적 다원주의와 실용적 정치 협상의 모범 사례로 많은 찬사를 받았지만, 동시에 보수와 진보 양측으로부 터 비판도 제기되었다. 보수 진영은 이 법안이 성소수자의 권리를 법적으로 인정 함으로써 종교의 자유를 훼손했다고 보았고, 진보 진영은 종교적 예외 조항이 평 등권의 실현을 지나치게 제한한다고 주장했다. 그럼에도 불구하고, 유타주의 경 험은 민주주의적 거버넌스, 상호 존중에 기반한 공존, 그리고 실용적 협상이라는 측면에서 중요한 통찰을 제공한다. 본 논문은 유타 절충안의 역사적 배경, 협상 과정, 입법 내용, 사회적 반응 및 정책적 함의를 분석하고, 특히 한국 사회에서 현재 논의되고 있는 LGBTQ+ 권리와 종교 자유의 균형 문제에 이 모델이 갖는 비교적 시사점을 제공한다.

In this paper, a water rescue mission system was developed for water safety management areas by utilizing unmanned mobility( drone systems) and AI-based visual recognition technology to enable automatic detection and localization of drowning persons, allowing timely response within the golden time. First, we detected suspected human subjects in daytime and nighttime videos, then estimated human skeleton-based poses to extract human features and patterns using LSTM models. After detecting the drowning person, we proposed an algorithm to obtain accurate GPS location information of the drowning person for rescue activities. In our experimental results, the accuracy of the Drown detection rate is 80.1% as F1-Score, and the average error of position estimation is about 0.29 meters.

The expansion of online retail markets has driven the development of personalized product recommendation services leveraging platform-based product and customer data. Large retailers have implemented seller-oriented recommendation systems, where AI analyzes POS sales data to identify similar stores and recommend products not yet introduced but successful elsewhere. However, small and medium-sized retailers face challenges in adapting to rapidly evolving online market trends due to limited resources. This study proposes a recommendation algorithm tailored for small-scale retailers using sales data from an online shopping mall. We analyzed 600,000 transaction records from 13,607 sellers and 95,938 products, focusing on Beauty Supplies, Kitchenware, and Cleaning Supplies categories. Three algorithms—Attentional Factorization Machines (AFM), Deep Factorization Machines (DeepFM), and Neural Collaborative Filtering (NCF)—were applied to recommend top 10% weekly sales items, with an ensemble model integrating their strengths. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was employed, and performance was evaluated using AUC, Accuracy, Precision, and Recall metrics on separate training and test datasets. The ensemble model outperformed individual models across all metrics, while DeepFM excelled in Precision. These findings demonstrate that ensemble-based recommendation algorithms enhance recommendation accuracy for suppliers in large-scale online retail environments, offering practical implications for small-scale retailers.

Ensuring operational safety and reliability in Unmanned Aerial Vehicles (UAVs) necessitates advanced onboard fault detection. This paper presents a novel, mobility-aware multi-sensor health monitoring framework, uniquely fusing visual (camera) and vibration (IMU) data for enhanced near real-time inference of rotor and structural faults. Our approach is tailored for resource-constrained flight controllers (e.g., Pixhawk) without auxiliary hardware, utilizing standard flight logs. Validated on a 40 kg-class UAV with induced rotor damage (10% blade loss) over 100+ minutes of flight, the system demonstrated strong performance: a Multi-Layer Perceptron (MLP) achieved an RMSE of 0.1414 and R² of 0.92 for rotor imbalance, while a Convolutional Neural Network (CNN) detected visual anomalies. Significantly, incorporating UAV mobility context reduced false positives by over 30%. This work demonstrates a practical pathway to deploying sophisticated, lightweight diagnostic models on standard UAV hardware, supporting real-time onboard fault inference and paving the way for more autonomous and resilient health-aware aerial systems.

This study proposes a weighted ensemble deep learning framework for accurately predicting the State of Health (SOH) of lithium-ion batteries. Three distinct model architectures—CNN-LSTM, Transformer-LSTM, and CEEMDAN-BiGRU—are combined using a normalized inverse RMSE-based weighting scheme to enhance predictive performance. Unlike conventional approaches using fixed hyperparameter settings, this study employs Bayesian Optimization via Optuna to automatically tune key hyperparameters such as time steps (range: 10-35) and hidden units (range: 32-128). To ensure robustness and reproducibility, ten independent runs were conducted with different random seeds. Experimental evaluations were performed using the NASA Ames B0047 cell discharge dataset. The ensemble model achieved an average RMSE of 0.01381 with a standard deviation of ±0.00190, outperforming the best single model (CEEMDAN-BiGRU, average RMSE: 0.01487) in both accuracy and stability. Additionally, the ensemble's average inference time of 3.83 seconds demonstrates its practical feasibility for real-time Battery Management System (BMS) integration. The proposed framework effectively leverages complementary model characteristics and automated optimization strategies to provide accurate and stable SOH predictions for lithium-ion batteries.

This integrative review examined 38 published articles on the topic of formative assessment conducted in South Korean EFL settings, from 2014 to 2023. Despite strong interests in using quality formative assessments that are aimed at improving English learners’ performance, no prior systematic analyses have been performed to date. To that end, this study draws on Wiliam and Thompson’s (2008) conception of the formative assessment five-strategy model, and Yan and Pastore’s (2022) Teacher Formative Assessment Practice Scale (TFAPS), as a guiding framework to examine the extent to which formative assessment research conducted in South Korean EFL classroom settings has enacted the five key strategies and thereby promoted learning and improved teaching. For all five key strategies, more studies showcased weak evidence of implementing each strategy, and that positive student learning was more likely to be seen in studies that fully, or nearly fully, implemented the key strategies. Recommendations for enhancing formative assessment teaching and research practice are provided at the end.

Small and medium-sized manufacturing enterprises(SMEs) have traditionally relied on skilled labor to support multi-variety, small-batch production. However, demographic changes such as low birth rates and aging populations have led to severe labor shortages, prompting increased interest in collaborative robots(cobots) as a viable alternative. Despite this necessity, many SMEs continue to face significant challenges in implementing such technologies due to technical, organizational, and environmental(TOE) constraints. While prior research has mainly focused on technology adoption from the perspective of user organizations, this study adopts a differentiated approach by analyzing adoption factors from the perspective of smart factory experts—specifically, evaluators/mentors and solution providers—who play a critical role in Korea’s policy-driven smart manufacturing environment. Using the Analytic Hierarchy Process(AHP), the study evaluates the relative importance and prioritization of adoption factors across three dimensions: technology, organization, and environment. Survey data collected from 20 smart factory experts indicate that top management support, relative advantage, and safety are key determinants in cobot adoption. Furthermore, the findings reveal that organizational readiness and technical effectiveness have greater influence on implementation decisions than external pressures such as partner pressure. This study provides new insights by incorporating expert perspectives into the adoption framework and offers practical policy and managerial implications to support cobots implementation in the SMEs.

Business model(BM) innovation is widely known as a differentiated strategy and strategic framework for companies to secure a sustainable competitive advantage in an uncertain environment. While prior research has studied new business models in accordance with changes in manufacturing trends such as digitalization and servitization, empirical understanding of the dynamic processes of BM innovation is still lacking. This study addresses this gap by proposing an analytical framework of the BM innovation matrix that classifies companies' BM innovation cases into four types according to the degree of BM change and the influential level of the industry/market outcome through a critical literature review on business models and dynamics. Drawing on this framework, we conduct longitudinal case studies of leading global 3D printing firms to examine the dynamic processes and external environmental factors that shape the evolution of BM innovation. Our findings reveal previously underexplored patterns of co-evolution between firms’ business models and their broader industrial and market environments. This study has the significance of constructing a framework for dynamically analyzing BM innovation based on longitudinal case studies of emerging 3D printing companies. We presented implications for companies seeking successful commercialization of emerging technologies, such as the strategic usefulness of the BM innovation framework and the importance of co-evolution with industrial structure and environmental factors in the process of change.

The semi-direct speaking test format has limitations, particularly due to its lack of situational authenticity and contextualized input. To address this issue, virtual reality (VR) can be integrated into speaking proficiency tests to enhance authenticity. In this study, a newly designed VR speaking test was administered, and test-takers’ performances were compared with those on a conventional computer-delivered speaking test. Additionally, test-takers’ perceptions of the VR-based speaking test were examined through a post-test questionnaire. The results revealed a statistically significant difference in mean scores between the two test formats, indicating that the VR-based test enhanced test-takers’ speaking performance. More specifically, a one-way MANOVA showed that test-takers performed better on nearly all scoring criteria in the VR mode compared to the computer-delivered mode, except for completion and fluency. Furthermore, the 32 test-takers who participated in the VR test highlighted the highly contextualized settings and immersive experience as the most distinctive and positive aspects of using VR in speaking assessments.

The casting manufacturing process of aluminum automotive wheels often involves processing various wheel models during stages such as flow forming, machining, packaging, and delivery. Traditionally, separate equipment or production lines were required for each model, which led to higher facility investment costs and increased labor costs for classification. However, the implementation of machine learning-based model classification technology has made it possible to automatically and accurately distinguish between different wheel models, resulting in significant cost savings and enhanced production efficiency. Additionally, this approach helps prevent product mix-ups during the final inspection process and allows for the quick and precise identification of wheel models during packaging and delivery, reducing shipping errors and improving customer satisfaction. Despite these benefits, the high cost of machine learning equipment presents a challenge for small and medium-sized enterprises(SMEs) to adopt such technologies. Therefore, this paper analyzes the characteristics of existing machine learning architectures applicable to the automotive wheel manufacturing process and proposes a custom CNN(Convolutional Neural Network) that can be used efficiently and cost-effectively.