Diplomatic Academy of Vietnam

This study examines the localization of the Korean music reality television format Sea of Hope in Vietnam from an intercultural communication perspective through a qualitative case study of Biển của Hy Vọng. While previous studies have primarily explained television format localization from industrial or cultural adaptation perspectives, relatively little research has explored localization as an intercultural communicative process. To address this gap, the study proposes an Integrated Analytical Framework that combines television format studies, localization theory, and intercultural communication across four dimensions: intercultural context and actors, interaction and negotiation, format adaptation, and cultural outcomes. The analysis draws upon the complete Korean and Vietnamese versions of the program, together with official production documents and publicly available secondary materials. The findings indicate that the Vietnamese adaptation successfully preserves the core identity and healing philosophy of the original format while selectively modifying its structure, narrative organization, musical repertoire, production practices, and cultural representations to suit the Vietnamese cultural and media environment. These adaptations emerge through continuous interaction and negotiation among institutional stakeholders, production teams, participating artists, and the local sociocultural context, resulting in new hybrid cultural meanings. The study argues that television format localization should be understood not merely as an industrial adaptation strategy but as a dynamic process of intercultural communication. By integrating three major strands of scholarship into a unified analytical framework, this study contributes to the theoretical understanding of television format localization and provides practical implications for future transnational television production and cultural cooperation between South Korea and Vietnam.

This study utilizes the Panel Vector Autoregressive (PVAR) and dynamic panel threshold models, using data from 32 countries from 2013 to 2022. E-government produces a negative response in the first two periods, followed by a transition to a positive response from approximately the third period onward, before stabilizing and converging to zero after about the fifth period. This pattern indicates a delayed positive adjustment in the long run, consistent with the time required for digital governance improvements to reduce informational frictions and enhance investment conditions. Furthermore, the quantity of e-government threshold is 0.105. Below the threshold, e-government has a positive impact on green FDI, while above the threshold, this effect becomes less positive. Policymakers should prioritize the gradual development of e-government systems to reach and maintain levels below the identified threshold, where digital governance most effectively supports green FDI, while ensuring that further advancements are accompanied by balanced regulatory frameworks to avoid diminishing marginal benefits.

Generational labels are often attached to younger people, shaping how they are perceived and how they navigate the world around them. In today’s global culture, mass media plays a major role in forming and solidifying these labels. One such emerging label is the “Snowflake Generation,” often used to portray Generation Z (Gen Z) as emotionally fragile or overly sensitive. This study explores how that label has gradually appeared in Vietnamese mass media between 2020 and 2024. It raises concerns about age-based stereotypes, adopting an exploratory approach to analyze media portrayal of Gen Z. The “Snowflake Generation” narrative may limit professional and academic opportunities for Gen Z by fostering negative perceptions of their resilience and competence. Drawing on content analysis of news and television coverage (2020–2024) and in-depth interviews with experts, journalists, and selected Gen Z individuals, this study investigates how the mass media constructs the “Snowflake Generation” label and examines views on its relevance to Gen Z. This study adds to existing theory by exploring the label’s use in non-Western contexts and offers practical insights for media professionals.

This study explores linguistic legitimation strategies in Dan Brown’s The Da Vinci Code using Critical Discourse Analysis (CDA). Applying Theo van Leeuwen’s (2007) taxonomy – Authorization, Moral Evaluation, Rationalization, and Mythopoesis - and Fairclough’s (1995a) three-dimensional model, the research analyzes how ideologies are constructed and legitimized in fiction. A series of 59 selected excerpts is analyzed through a mixed-method approach combining qualitative and quantitative tools. Findings show that a great number of authorizations, particularly through frequent use of epistemic modality and declaratives, support ideological claims. The novel serves a dual ideological stance: it delegitimizes institutional religious authority, especially the Catholic Church, and legitimizes alternative spiritualities like goddess worship and paganism. Through narrative structure and linguistic strategies such as metaphor, metonymy, intertextuality, and interdiscursivity, the novel demonstrates how fiction can subtly transmit ideology and critique dominant power structures. The study contributes to extending CDA into literary analysis and values popular fiction’s role in shaping public discourse and ideological perspectives.

The 275 nm ultraviolet light transmittance is a core quality index of coal-to-ethylene glycol, and its accurate prediction is crucial for process optimization and quality control. Aiming to address the issues of insufficient domain knowledge integration, weak model generalization ability, and poor interpretability of prediction results in existing data-driven methods, this paper proposes an intelligent prediction framework that integrates mechanism-guided feature engineering with a two-layer regularized Blending ensemble. Specifically, domain knowledge is injected into the feature space through periodic encoding and the construction of process mechanism-based interactive features, thereby enhancing the model’s robustness for small-sample industrial data through the combination of Recursive Feature Elimination (RFE) and a Blending ensemble strategy that strictly separates training data streams. Experiments on actual industrial production data show that the framework achieves a coefficient of determination ($\mathbf{R}^{\mathbf{2}}$) of 0.8867 and a Mean Absolute Error (MAE) of 0.4380 on the independent test set with a low degree of overfitting. Meanwhile, key process coupling mechanisms are identified via feature importance analysis, forming a cognitive closed loop of data-driven prediction and mechanism discovery and verification. This study provides a practical tool for the process optimization of coal-to-ethylene glycol production, and also offers a feasible methodology for constructing interpretable and highly robust intelligent prediction models in the process industry.