This study examines output divergence and club convergence among 34 newly established provinces in Vietnam from 2010 to 2023. Applying the Phillips and Sul (Econometrica 75:1771–1855, 2007; J Appl Econom 24:1153–1185, 2009)’s methodology to panel data at both aggregate and sectoral levels, the analysis reveals significant divergence in real GDP per capita across provinces. The results also identify multiple convergence clubs, with notable differences in club membership across economic sectors. At the aggregate level, four convergence clubs are observed. In a first-of-its-kind sectoral analysis for Vietnam, the study uncovers six convergence clubs in the Agriculture, Forestry, and Fishery sector, and four clubs each in the Industry and Construction, and Services sectors. Additionally, results from an ordered probit model show that initial GDP per capita, investment, labour force size, human capital, sectoral output shares, and population growth significantly influence club membership. Policy recommendations are derived from these findings.
To achieve agricultural automation, deep learning applications for early and accurate disease detection in tomato plants have been extensively developed. However, there is a fundamental trade-off between computational efficiency and diagnostic accuracy in resource-constrained agricultural edge environments. This paper proposes an evaluation framework for seven architectures that represent standard, efficient, and hybrid CNN structures to assess their implementation potential. Through evaluations of explainability, computational efficiency, and diagnostic performance, seven lightweight architectures (ShuffleNetV2, MobileNetV3-Small, SqueezeNet, MobilePlantViT, DenseNet121, ResNet50, and VGG16) are thoroughly examined. Three significant findings are derived from experiments conducted on a subset of tomato diseases in the PlantVillage dataset. First, the MobilePlantViT architecture accurately strikes the ideal balance between efficiency and performance. Second, in order to quantitatively assess the explainability of XAI models (Grad-CAM, SHAP, and LIME) and identify the best option for edge devices, we propose the perturbation stability score (PSS) metric. Third, we test CPU inference measurements to better reflect the actual scenario and find that the hybrid design effectively leverages parallel computing. According to these findings, MobilePlantViT is the ideal architecture for applications that require operation on edge devices with limited resources and achieve high diagnosis accuracy (above 99.5%).
Reversible data hiding (RDH) has garnered significant attention from researchers due to its ability to completely restore the original images. absolute moment block truncation coding (AMBTC)-compressed images are widely used in RDH schemes. However, to obtain a large embedding capacity, several AMBTC-based RDH schemes use the decompressed pixels to carry the message bits. Therefore, stego-images cannot be decoded by traditional AMBTC decoders. In this paper, the new AMBTC-based RDH scheme is introduced to address this problem. The multi-pair value shifting technique is applied to embed data into the AMBTC code without changing the coding structure. Unlike existing AMBTC-based RDH methods that employ a large location map (LM) to ensure the correctness of the data extraction, our scheme can minimize the LM or avoid using it entirely by applying the multi-pair value shifting technique. The experimental results indicate that the proposed scheme achieves an appropriate embedding capacity while maintaining good visual quality.
This study examines the socio-economic and behavioral factors influencing sustainable consumption through secondhand clothing purchases among young consumers in Hanoi, Vietnam. By addressing the changing consumption patterns, this research contributes to understanding how youth behavior supports the transition toward sustainability in emerging urban markets. This research integrates the Theory of Planned Behavior (TPB) with additional constructs such as perceived economic benefits, environmental concern, perceived risk, shopping experience, and gender differences to provide an integrated socio-economic framework. Data were collected through a structured questionnaire administered to university students and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that perceived economic benefits and subjective norms are the strongest predictors of purchase intention across both general and luxury secondhand fashion segments, emphasizing affordability and social acceptance. Environmental concern and attitude also positively influence general secondhand purchase intentions, while perceived behavioral control notably impacts luxury secondhand purchases. Contrary to prior studies, perceived risk was found to be insignificant, and male consumers exhibited a higher engagement rate than females in this context. These findings underscore the complex interplay of economic, social, and environmental dimensions shaping sustainable fashion consumption among youth. This study suggests targeted marketing and policy strategies to promote sustainable consumption and supports the expansion of circular economy practices in emerging urban markets. Limitations related to sample scope and self-reported data warrant further research to generalize the findings and explore additional moderating variables.
This article presents the criminalization process of asset appropriation through computer networks and electronic means in Vietnam by analyzing the theory of criminalization, the constituent elements of the offence, and the way in which the legal characteristics and criminal liability of this crime are reflected in criminal law, with particular emphasis on Article 290 of the 2015 Penal Code. Based on the classification of cybercrime and the theory of criminal elements, the article evaluates the strengths and limitations of Vietnam’s means-based model in comparison with the target-based model, and proposes adjustments to legislative techniques, the proportionality of penalties, and enforcement conditions in order to enhance the clarity, proportionality, and feasibility of criminal-law provisions. The article also offers experience and lessons for the domestic incorporation of international legal frameworks on combating cybercrime in general, and the recently adopted United Nations international convention against cybercrime, for a range of countries and territories in addressing cybercrime.
