Satellite remote sensing has rapidly evolved from an experimental support tool into a structural component of preventive archaeology and cultural heritage governance. Drawing on scientific publications and policy-oriented grey literature from 2010–2025, this study provides an integrated review of how optical, SAR, and multi-sensor satellite data are used to detect archaeological sites, monitor landscape and structural change, and support risk-informed planning across diverse legal and institutional contexts. A multi-platform workflow combines AI-assisted semantic querying (Consensus), bibliometric searches (Scopus), and the collaborative management and geospatial visualisation of references through Zotero, VOSviewer (1.6.19), and QGIS (3.44)-based literature mapping, thereby linking thematic trends, co-authorship networks, and geographical patterns of research and regulation. The results show non-linear but marked publication growth, a strongly interdisciplinary profile, and the consolidation of international hubs that drive advances in Sentinel-2-based prospection, Landsat and night-time lights urbanisation metrics, and SAR time series for deformation, looting, and conflict-damage mapping. Parallel analysis of grey literature and institutional initiatives (Copernicus Cultural Heritage Task Force, national “extraordinary plans”, regional declarations, and UNESCO guidelines) reveals the codification of satellite Earth observation within rescue archaeology protocols, emergency archaeology, and long-term conservation strategies. Overall, the evidence indicates a transition towards data-driven, multi-sensor, and multi-scalar research, underpinned by open satellite data, reproducible workflows, and AI-supported evidence synthesis.

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Purpose: The rapid development of artificial intelligence (AI) has changed many fields, including banking, where new technologies like chatbots have become important tools for improving customer interactions. This study focuses on the legal framework and investigates the impact of Chatbot usability and Customer empowerment on Customer satisfaction in Vietnamese commercial banks, aiming to improve service delivery in a competitive digital banking environment. Design/methodology/approach: Employing a quantitative methodology, the study tests hypotheses using Structural Equation Modeling (SEM) with SMART-PLS software. The research framework, built on a comprehensive literature review, highlights four chatbot usability dimensions - ease of use, responsiveness, reliability, and personalisation and customer empowerment as key determinants of satisfaction. Data were collected from 300 customers using chatbot services in Vietnamese commercial banks. Regression analysis, supported by validation metrics (outer loadings, HTMT, VIF), was used to assess the model’s robustness and the significance of relationships. Findings: The findings reveal a strong positive correlation between chatbot usability, customer empowerment, and customer satisfaction. All four usability dimensions - Ease of Use, Responsiveness, Reliability, and Personalisation significantly enhance satisfaction, with customer empowerment further amplifying these effects. These results indicate that user-friendly and empowering chatbots improve customer experiences, fostering loyalty in Vietnam’s banking sector. Research limitations/implications: Limitations include reliance on self-reported data, which may introduce biases, and a sample limited to Hanoi, potentially restricting generalizability. Future studies could adopt longitudinal designs or broader demographics to explore long-term effects. The findings offer valuable implications for bank managers and policymakers to optimize chatbot features and align with legal frameworks, enhancing customer satisfaction and competitiveness. Originality/value: This research contributes to understanding how chatbot usability and customer empowerment drive satisfaction in Vietnam’s banking sector, integrating legal considerations. It provides practical insights for banks to implement effective, compliant chatbot strategies, enhancing customer experiences and supporting national digital transformation goals

Purpose: In recent years, robo-advisory services have gradually emerged in Vietnam's financial market, yet individual investors remain hesitant to adopt these services. Although previous studies have explored this topic, few have focused on the role of trust and technology in influencing investor intention. This study was conducted to identify and analyze the key factors that affect Vietnamese individual investors’ intention to use robo-advisors, especially emphasizing the role of trust in technology and service quality. Design/methodology/approach: This research developed a questionnaire using validated measurement scales from various previous empirical studies to construct a model assessing the factors influencing the intention to use Roboadvisors. A five-point Likert scale (from 1 = totally disagree to 5 = totally agree) was applied. The questionnaire was initially written in English, then translated into Vietnamese for distribution. Data were collected from 385 individual investors in Vietnam via an online survey (Google Forms) conducted between October 2024 and December 2024. The study employed both qualitative and quantitative methods, with data analyzed using Microsoft Excel and SmartPLS version 4. The model included six dependent variables (Reputation, Information Quality, Service Quality, Attitude toward Artificial Intelligence, Service Commitment, and Government Regulation), three mediator variables (Trust in Robo-advisor Vendors, Trust in Technology, and Trust in Robo-advisors), one moderator (Supervisory Control), and the main dependent variable (Intention to Use Robo-advisors). Findings: The analysis revealed that Information Quality, Service Quality, Attitude toward Artificial Intelligence, Service Commitment, Government Regulation, and Trust in Technology have a significant positive influence on the intention to use Robo-advisors. Trust in Robo-advisors was identified as a critical mediating factor. The findings also show that the majority of investors are willing to adopt Robo-advisors if supported by regulatory credibility, service transparency, and user control. In contrast, Reputation and Trust in Robo-advisor Vendors did not show significant influence. These findings highlight the importance of technological trust and regulatory confidence in promoting adoption. Research limitations/implications: This study acknowledges limitations in sample size and data quality due to the self-reported online survey method. Additionally, the Robo-advisor market in Vietnam remains at an early stage, and many investors have not yet experienced such services in practice, potentially influencing perception-based responses. Furthermore, as Robo-advisors are still developing in Vietnam, the market context may limit the generalizability of findings. Nevertheless, the study has strong implications: firms should focus on enhancing trust in technology and service reliability, while regulators can support trust-building through clear policies and education initiatives. Originality/value: This study contributes to the emerging literature on Robo-advisory services in Vietnam by highlighting the role of technological trust as a key factor influencing investor intention. From an objective perspective, it provides new insights into how individual investors perceive and adopt AI-based financial services, helping vendors and regulators design more effective trust-building strategies

The study evaluates the influence of internal factors within banks (bank size, financial leverage, credit quality, cost management efficiency, and digital transformation) and external exogenous factors (GDP growth, inflation, and the development of Fintech companies) on the profitability of commercial banks listed on the Vietnamese stock market. The study uses the Random Forest and XGBoost regression models to estimate the contribution of bank-specific and macroeconomic variables to ROA and ROE. According to the feature importance values, the dominant factor in both models is CR, with values of about 0.55 for ROA and 0.48 for ROE, indicating that nearly half of the predictive ability is attributable to credit risk. CDS and SIZE have an importance of 0.15-0.18, and CIR and DFL have an importance of 0.05-0.08 each. FIN, INF, and GDP all register below 0.05, suggesting that there is little incremental information when the core internal factors are included. These numerical patterns support the econometric results and give a non-parametric ranking of the profitability determinants. Overall, the ML evidence indicates that the intensity of both credit risk control and digital transformation jointly dominates in predicting ROA and ROE, underscoring the need to combine technology investments with conservative risk management and efficiency improvements in Vietnamese commercial banks. The research findings provide empirical evidence to support bank managers and policymakers in improving operational efficiency amid digital transformation

Bài báo trình bày kết quả phân tích nghề Công nghệ Bán dẫn theo phương pháp DACUM cho ba cấp trình độ đào tạo: sơ cấp, trung cấp và cao đẳng, được triển khai và ứng dụng tại Trường Cao đẳng Công nghiệp Thanh Hóa. Nghiên cứu tập trung xác định hệ thống nhiệm vụ và công việc đặc trưng của nghề ở từng cấp trình độ, làm rõ sự khác biệt về mức độ phức tạp, yêu cầu kiến thức, kỹ năng, cũng như mức độ tự chủ và trách nhiệm nghề nghiệp. Trên cơ sở đó, bài báo đề xuất lộ trình phát triển nghề và định hướng xây dựng chương trình đào tạo theo tiếp cận năng lực, bảo đảm tính liên thông giữa các trình độ, đáp ứng yêu cầu của doanh nghiệp và phù hợp với xu hướng phát triển của ngành công nghiệp bán dẫn tại Việt Nam.

Artificial intelligence (AI) and machine learning (ML) have been increasingly adopted in the banking sector due to their ability to analyze large-scale datasets, process complex variables, and uncover hidden patterns, especially in the context of liquidity risk, posing a significant challenge for commercial banks. This study contributes to the field by conducting a comprehensive evaluation of several widely used early warning models, such as least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and extreme gradient boosting (XGBoost), to identify the most suitable approach for forecasting liquidity risk in Vietnamese commercial banks (VCBs) based on VCBs data over the period of 2014–2023. By pinpointing key indicators associated with liquidity crises, these models can assist banks and regulatory authorities in implementing timely preventive measures and enhancing risk management strategies. As a result, the RF model outperforms other methods in identifying possible liquidity crises, according to the empirical results, with an accuracy rate of 99.8 percent. These findings provide bank managers and policymakers with a powerful tool for timely preventive measures, thereby enhancing the resilience and stability of the financial system.

This study applies machine learning classification models to predict bankruptcy risk among real estate firms in Vietnam, particularly in the context of heightened market volatility and elevated credit risk during the period 2019–2023. Using firm-level financial data and macroeconomic variables obtained from the National Statistics Office, the authors develop and compare the predictive performance of several algorithms, including LightGBM, XGBoost, Random Forest, and Decision Tree, against the traditional Logistic Regression model. The empirical results indicate that boosting techniques, specifically LightGBM and XGBoost, outperform other approaches in terms of predictive accuracy and overall performance metrics. SHAP analysis reveals that the ratio of shareholders’ equity to total liabilities is the most influential predictor of bankruptcy risk. In addition, asset utilization efficiency and capital structure exert significant effects on financial distress probability. Profitability indicators and cash flow generation capacity further confirm their essential role in maintaining financial stability. While institutional and macroeconomic factors exhibit certain effects, their influence is not dominant relative to firm-specific financial characteristics. Overall, the findings suggest that bankruptcy risk in the real estate sector primarily stems from firms’ internal financial structure and operational efficiency rather than external macroeconomic conditions.

Nghiên cứu cắt ngang mô tả đầu tiên tại Việt Nam ứng dụng giải trình tự toàn bộ exome (WES) trên 150 trẻ em được sống sơ sinh không có hội chứng (NSHL) từ ba miền Bắc–Trung–Nam. Kết quả xác định được 7,33% có khả năng xác định phân tử dự kiến, với các gen chính bao gồm GJB2, COCH và MYO6. Biến GJB2 :c .109G>A (p.V37I) phổ biến nhất (19,33%), trong khi 66% trường hợp nhất vẫn chưa được xác định rõ ràng cho nhân nguyên. Nghiên cứu nhấn mạnh tính đặc thù quần nghiên cứu phổ biến của người Việt và đề xuất sản phẩm sẵn sàng lọc để truyền phù hợp cho trẻ em Việt Nam và khu vực Đông Nam Á.

Wetlands are globally recognized as important ecosystems, maintaining biodiversity and providing economic value. In Vietnam, Xuan Thuy National Park, the first Ramsar wetland site in
Southeast Asia and a typical coastal estuarine ecosystem in the northern region, is facing economic development pressures, leading to ecological imbalance and impacting its service provision capacity.
This study assesses ecosystem service values to identify the economic contributions of wetland resources. By combining multiple methods such as economic valuation, interviews, remote sensing, and Geographic Information System (GIS), the values of eleven types of ecosystem services of the wetland area in Xuan Thuy National Park are assessed and calculated, including use value and non-used value (conservation). The research results show that the total economic value of the provided xemptyzservices is 2,072.0 billion VND/year, equivalent to 138.1 million VND/ha/year. Indirect use values account for 50%, prominently featuring carbon sequestration value (the highest among the total economic value). Although the conservation and transmission value had insignificant economic value, 100% of the votes showed the awareness, attitude, and feelings of local people about the functions of ecosystem services in wetlands. The study provides a scientific basis for policymakers and authorities in planning and promoting sustainable socio-economic development, contributing to the protection and long-term balance of ecosystem services in wetland areas in the future.

This study investigates the current status of domestic water usage and assesses the willingness to pay (WTP) for clean water among residents in Binh Duong and Cao Duc communes, Gia Binh district, Bac Ninh1
province. Based on compiled data and interviews with 125 households,
the research applies the contingent valuation method to determine WTP, using linear regression analysis in Stata 17, combined with Pearson correlation analysis and multicollinearity tests to develop the WTP model. The results show that 100% of households have access to piped water; however, over one-third still use rainwater for drinking purposes. The average WTP values for three water-use models are 213,250 VND/m3 (for drinking water), 9,770 VND/m3 (for other domestic uses), and 14,340 VND/m3 (for overall domestic water). The average domestic water cost of households in Binh Duong and Cao Duc communes is 193,590 VND per household per month, accounting for 1.3% of total household income. Four factors significantly influencing WTP include household income, education level, gender, and age of the household head. By 2050, the local population is projected to reach approximately 17,000, increasing water demand by about 630 m3/day compared to 2024. Based on these findings, the study proposes integrated solutions in infrastructure, communication, and policy to ensure sustainable domestic water supply in the future.

Despite standardization since 3GPP Release 9, multicast and broadcast services (MBS) remain largely undeployed in commercial networks. Artificial intelligence may catalyze adoption by addressing the optimization complexity hindering practical deployment, particularly as 6G targets demanding applications requiring efficient group-oriented transmission. Following PRISMA guidelines, this survey reviews the 5G MBS architecture and analyzes 23 studies applying AI to multicast and broadcast optimization. Surveyed works demonstrate computational complexity reductions from O(N3) to O(N2), throughput gains of 18–50%, and resource savings up to 33%. Deep reinforcement learning variants dominate resource allocation and scheduling, while unsupervised clustering methods address multicast group formation and federated learning enables privacypreserving optimization across distributed deployments. We organize findings across six areas: physical layer intelligence, RAN slicing and scheduling, multicast group formation and routing, RIS-assisted transmission, D2D-assisted multicast, and end-to-end optimization. We identify underexplored areas, non-terrestrial networks, ISAC integration, graph neural networks, and foundation models, and provide a research roadmap addressing standardization gaps and deployment barriers.

Tương lai nghề nghiệp ngành dữ liệu lớn là một nội dung quan trọng thuộc lĩnh vực dữ liệu lớn. Chủ đề này giúp người học hiểu rõ hơn về xu hướng công nghệ hiện đại, vai trò của dữ liệu và hệ thống số trong cuộc sống, doanh nghiệp và xã hội. Việc nghiên cứu nội dung này góp phần nâng cao kiến thức công nghệ, kỹ năng phân tích và khả năng thích nghi với cuộc cách mạng công nghiệp 4.0.

Big Data trong ngành bảo hiểm là một nội dung quan trọng thuộc lĩnh vực dữ liệu lớn. Chủ đề này giúp người học hiểu rõ hơn về xu hướng công nghệ hiện đại, vai trò của dữ liệu và hệ thống số trong cuộc sống, doanh nghiệp và xã hội. Việc nghiên cứu nội dung này góp phần nâng cao kiến thức công nghệ, kỹ năng phân tích và khả năng thích nghi với cuộc cách mạng công nghiệp 4.0.

Dữ liệu lớn và phát triển bền vững là một nội dung quan trọng thuộc lĩnh vực dữ liệu lớn. Chủ đề này giúp người học hiểu rõ hơn về xu hướng công nghệ hiện đại, vai trò của dữ liệu và hệ thống số trong cuộc sống, doanh nghiệp và xã hội. Việc nghiên cứu nội dung này góp phần nâng cao kiến thức công nghệ, kỹ năng phân tích và khả năng thích nghi với cuộc cách mạng công nghiệp 4.0.

Vai trò của dữ liệu lớn trong logistics là một nội dung quan trọng thuộc lĩnh vực dữ liệu lớn. Chủ đề này giúp người học hiểu rõ hơn về xu hướng công nghệ hiện đại, vai trò của dữ liệu và hệ thống số trong cuộc sống, doanh nghiệp và xã hội. Việc nghiên cứu nội dung này góp phần nâng cao kiến thức công nghệ, kỹ năng phân tích và khả năng thích nghi với cuộc cách mạng công nghiệp 4.0.

Big Data và xu hướng tuyển dụng là một nội dung quan trọng thuộc lĩnh vực dữ liệu lớn. Chủ đề này giúp người học hiểu rõ hơn về xu hướng công nghệ hiện đại, vai trò của dữ liệu và hệ thống số trong cuộc sống, doanh nghiệp và xã hội. Việc nghiên cứu nội dung này góp phần nâng cao kiến thức công nghệ, kỹ năng phân tích và khả năng thích nghi với cuộc cách mạng công nghiệp 4.0.

Dữ liệu lớn trong nghiên cứu khoa học là một nội dung quan trọng thuộc lĩnh vực dữ liệu lớn. Chủ đề này giúp người học hiểu rõ hơn về xu hướng công nghệ hiện đại, vai trò của dữ liệu và hệ thống số trong cuộc sống, doanh nghiệp và xã hội. Việc nghiên cứu nội dung này góp phần nâng cao kiến thức công nghệ, kỹ năng phân tích và khả năng thích nghi với cuộc cách mạng công nghiệp 4.0.

Ứng dụng dữ liệu lớn trong ngành du lịch là một nội dung quan trọng thuộc lĩnh vực dữ liệu lớn. Chủ đề này giúp người học hiểu rõ hơn về xu hướng công nghệ hiện đại, vai trò của dữ liệu và hệ thống số trong cuộc sống, doanh nghiệp và xã hội. Việc nghiên cứu nội dung này góp phần nâng cao kiến thức công nghệ, kỹ năng phân tích và khả năng thích nghi với cuộc cách mạng công nghiệp 4.0.