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With the revitalization of the online grocery trading market, many consumers are using mobile applications to purchase groceries. Although past studies were conducted on online grocery purchases, few measured mobile app users in a conceptual model that combines both motivational needs and behavioral components. Grounded in the uses and gratifications theory and the theory of planned behavior, this study investigated utilitarian motives, hedonic motives, experiential motives, attitudes, subjective norms, perceived behavioral control, purchase intention, and purchase behavior among mobile grocery app users in South Korea. As an additional analysis, a comparison between users and non-users of mobile grocery apps was implemented. The results showed that the utilitarian motives of grocery app users significantly influenced attitudes, attitudes and subjective norms influenced user intention, and user intention influenced grocery purchase behavior. Users showed statistically higher utilitarian motives, hedonic motives, and attitudes than non-users. The results suggest that South Korean consumers hold positive attitudes toward mobile grocery shopping and that the opinions of others may influence the decision to use the services. Mobile groceries in South Korea may have the potential for continued growth if individuals’ perceived control of the service improves. Implications and suggestions for future research are discussed.

Many businesses use chatbots in customer service, but the question remains whether these digital assistants can provide the same level of personalized and empathetic service as their human counterparts. This study examined the differences in how chatbot and human interactions influence consumer trust, involvement, and purchase intention. Employing a 2x2 between-subjects factorial design, the study compared groups experiencing human communication and chatbot communication, utilizing emotional and factual interaction modes. The results revealed no significant differences among the groups concerning credibility, benevolence, ability, enduring involvement, situational involvement, and purchase intention, with the exception of integrity. Specifically, participants in the human-emotional group perceived communications as more honest and trustworthy compared to those in the chatbot-factual group. There was no interaction effect between the groups and trust concerning involvement and intention. This study supports the media equivalence framework and extends our comprehension of chatbots' effectiveness in customer service roles. The results indicate that chatbots have the potential to emulate human roles in e-commerce customer service effectively.

As online grocery transactions become more common, many consumers are turning to mobile applications for buying groceries. While numerous studies have explored online grocery shopping, few have focused on the motivational factors and user satisfaction specifically related to grocery apps in the mobile sector. This study addresses this gap by examining how engagement and experiential factors influence consumers' intentions to continuously use grocery apps, form attachments to them, and have genuine experiences. We employed structural equation modeling to analyze data from users familiar with mobile grocery shopping apps. The findings revealed that innovation diffusion attributes significantly impacted user attachment to the apps. However, these attributes did not predict authentic user experiences. Uses and gratifications, on the other hand, significantly influenced both user attachment and authentic experiences. Additionally, the results of a MANOVA analysis indicated that users reported higher levels of response compared to non-users in terms of simplicity, benefit, compatibility, informativeness, playfulness, attachment, and intention to use. This study offers several new insights into research on mobile grocery transactions.

Conventional file management systems primarily rely on structured metadata such as filenames, file extensions, and creation dates to manage and organize files. However, such metadata alone fails to capture the actual content or semantic meaning of a file, often leading to results misaligned with user intent. To overcome these limitations, we developed the Content-based File Classification and Organization System (CFCOS), which integrates a Large Language Model (LLM) to perform content-aware file analysis. The LLM generates semantic summaries of file contents and classifies files into meaningful categories based on composition-derived criteria, enabling organization strategies that go beyond rigid, rule-based methods. Through a range of evaluations, we analyze how CFCOS addresses key limitations of conventional file management systems and characterize the properties of LLM-based approaches to content-aware file organization. Furthermore, these results suggest that our approach can be generalized beyond file systems, enabling the semantic and personalized transformation of existing services through prompt engineering.

Firm-level financial statement data form multivariate annual time series with strong cross-variable dependencies and temporal dynamics, yet publicly available panels are often short and incomplete, limiting the generalization of predictive models. We present Latent Financial Time-Series Diffusion (LFTD), a structure-aware augmentation framework that synthesizes realistic firm-level financial time series in a compact latent space. LFTD first learns information-preserving representations with a dual encoder: an FT-Transformer that captures within-year interactions across financial variables and a Time Series Transformer (TST) that models long-horizon evolution across years. On this latent sequence, we train a Transformer-based denoising diffusion model whose reverse process is FiLM-conditioned on the diffusion step as well as year, firm identity, and firm age, enabling controllable generation aligned with firm- and time-specific context. A TST-based Cross-Decoder then reconstructs continuous and binary financial variables for each year. Empirical evaluation on Korean listed-firm data from 2011 to 2023 shows that augmenting training sets with LFTD-generated samples consistently improves firm-value prediction for market-to-book and Tobin’s Q under both static (same-year) and dynamic (τ → τ+1) forecasting settings and outperforms conventional generative augmentation baselines and ablated variants. These results suggest that domain-conditioned latent diffusion is a practical route to reliable augmentation for firm-level financial time series.

Detection of facial landmarks and accurate tracking of their shape are essential in real-time applications such as virtual makeup, where users can see the makeup’s effect by moving their face in diverse directions. Typical face tracking techniques detect facial landmarks and track them using a point tracker such as the Kanade-Lucas-Tomasi (KLT) point tracker. Typically, 5 or 64 points are used for tracking a face. Even though these points are enough to track the approximate locations of facial landmarks, they are not sufficient to track the exact shape of facial landmarks. In this paper, we propose a method that can track the exact shape of facial landmarks in real-time by combining a deep learning technique and a point tracker. We detect facial landmarks accurately using SegNet, which performs semantic segmentation based on deep learning. Edge points of detected landmarks are tracked using the KLT point tracker. In spite of its popularity, the KLT point tracker suffers from the point loss problem. We solve this problem by executing SegNet periodically to recalculate the shape of facial landmarks. That is, by combining the two techniques, we can avoid the computational overhead of SegNet and the point loss problem of the KLT point tracker, which leads to accurate real-time shape tracking. We performed several experiments to evaluate the performance of our method and report some of the results herein.

This study introduces the Korean Tax Avoidance Panel (KoTaP), a long-term panel dataset of non-financial firms listed on KOSPI and KOSDAQ between 2011 and 2024. After excluding financial firms, firms with non-December fiscal year ends, capital impairment, and negative pre-tax income, the final dataset consists of 12,653 firm-year observations from 1,754 firms. KoTaP is designed to treat corporate tax avoidance as a predictor variable and link it to multiple domains, profitability, stability, growth, and governance. Tax avoidance itself is measured using complementary indicators—cash effective tax rate, GAAP effective tax rate, and book–tax difference measures—with adjustments to ensure interpretability. A key strength of KoTaP is its standardized firm-year panel structure with standardized variables and its consistency with international literature on the distribution and correlation of core indicators. At the same time, it reflects distinctive institutional features of Korean firms, such as concentrated ownership, high foreign shareholding, and elevated liquidity ratios, providing both international comparability and contextual uniqueness. KoTaP enables applications in econometric and machine-learning applications, including explainable methods.

A 7-year-old spayed female domestic shorthair cat presented with a swollen right forelimb and mild lameness. On physical examination, the mass was palpable in the right humeral region, and the cat exhibited pain on palpation. Radiography revealed an expansile osteolytic lesion at the proximal end of the right humerus. Computed tomography (CT) revealed an expansile bony mass on the proximal end of the right humerus and a mild periosteal reaction around the acromion of the scapula. Amputation of the right forelimb, including the scapula and removal of the axillary lymph node, were strongly recommended to the owner, but were declined. Four months after the initial presentation, the cat presented with a dramatically swollen right forelimb and progressive lameness. CT was performed again. In addition to osteolytic changes in the mass, vascular development had occurred at the cranioproximal region. The right forelimb, including the scapula and ipsilateral lymph nodes, was removed. The cat died during the postoperative recovery period. Based on clinical, diagnostic imaging, and histological findings, the final diagnosis was aneurysmal bone cyst. To the best of our knowledge, this is the first case of an aneurysmal bone cyst in the humerus of a cat.

Besides genetic characteristics, people also undergo a process of skin aging under the influence of diverse factors such as sun exposure, food intake, sleeping patterns, and drinking habits, which are closely related to their personal lifestyle. So far, many studies have been conducted to analyze skin conditions quantitatively. However, to describe the current skin condition or predict future skin aging effectively, we need to understand the correlation between skin aging and lifestyle. In this study, we first demonstrate how to trace people’s skin condition accurately using scale-invariant feature transform and the color histogram intersection method. Then, we show how to estimate skin texture aging depending on the lifestyle by considering various features from face, neck, and hand dermoscopy images. Lastly, we describe how to predict future skin conditions in terms of skin texture features. Based on the Pearson correlation, we describe the correlation between skin aging and lifestyle, and estimate skin aging according to lifestyle using the polynomial regression and support vector regression models. We evaluate the performance of our proposed scheme through various experiments.

Time series forecasting is a critical task that provides key information for decision-making across various fields, such as economic planning, supply chain management, and medical diagnosis. After the use of traditional statistical methodologies and machine learning in the past, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed and applied to solve time series forecasting problems. However, the structural limitations caused by the inductive biases of each deep learning architecture constrained their performance. Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting. However, recent research has shown that alternatives such as simple linear layers can outperform Transformers. These findings have opened up new possibilities for using diverse architectures, ranging from fundamental deep learning models to emerging architectures and hybrid approaches. In this context of exploration into various models, the architectural modeling of time series forecasting has now entered a renaissance. This survey not only provides a historical context for time series forecasting but also offers comprehensive and timely analysis of the movement toward architectural diversification. By comparing and re-examining various deep learning models, we uncover new perspectives and present the latest trends in time series forecasting, including the emergence of hybrid models, diffusion models, Mamba models, and foundation models. By focusing on the inherent characteristics of time series data, we also address open challenges that have gained attention in time series forecasting, such as channel dependency, distribution shift, causality, and feature extraction. This survey explores vital elements that can enhance forecasting performance through diverse approaches. These contributions help lower entry barriers for newcomers by providing a systematic understanding of the diverse research areas in time series forecasting (TSF), while offering seasoned researchers broader perspectives and new opportunities through in-depth exploration of TSF challenges.