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Medical images are increasingly popular; therefore, the analysis of these images based on deep learning helps diagnose diseases become more and more essential and necessary. Recently, the shoulder implant X-ray image classification (SIXIC) dataset that includes X-ray images of implanted shoulder prostheses produced by four manufacturers was released. The implant's model detection helps to select the correct equipment and procedures in the upcoming surgery.

This study proposes a robust model named X-Net to improve the predictability for shoulder implants X-ray image classification in the SIXIC dataset. The X-Net model utilizes the Squeeze and Excitation (SE) block integrated into Residual Network (ResNet) module. The SE module aims to weigh each feature map extracted from ResNet, which aids in improving the performance. The feature extraction process of X-Net model is performed by both modules: ResNet and SE modules. The final feature is obtained by incorporating the extracted features from the above steps, which brings more important characteristics of X-ray images in the input dataset. Next, X-Net uses this fine-grained feature to classify the input images into four classes (Cofield, Depuy, Zimmer and Tornier) in the SIXIC dataset.

Experiments are conducted to show the proposed approach's effectiveness compared with other state-of-the-art methods for SIXIC. The experimental results indicate that the approach outperforms the various experimental methods in terms of several performance metrics. In addition, the proposed approach provides the new state of the art results in all performance metrics, such as accuracy, precision, recall, F1-score and area under the curve (AUC), for the experimental dataset.

The proposed method with high predictive performance can be used to assist in the treatment of injured shoulder joints.

This study explored students' perspectives on designing and implementing the new clinical interprofessional education (IPE) module for chronic disease management at the primary care level in the Vietnamese context.

Students from seven different university-level healthcare programmes participated in a cross-sectional survey Course Experience Questionnaire-based study. Additionally, two open-ended questions were presented to gather qualitative data, mapping student perspectives. Statistical analyses and thematic analyses were performed.

The results show that students agree with quality statements about IPE design features, such as good teaching, clear goals and standards, appropriate workload, appropriate assessment and a blended learning approach. Answers to the open-ended comments pointed out the strengths of the IPE module in providing opportunities for interprofessional learning, improving interprofessional collaboration competencies, real-life interprofessional collaboration practice and continuous feedback from tutors. Students also reported weaknesses in the IPE module, including a lack of feedback on the care plan and logistics of the IPE module.

Students positively recognise key design features of the IPE module, combining classroom activities with standardised patient simulations, clinical practice and home visits. Chronic disease management at the primary care level is a suitable context for training students to work interprofessionally. The strengths and weaknesses identified by students could help in the redesign and future implementation of the IPE module in the Vietnamese context. They could inspire practices in Southeast Asian medical education and beyond.

Adopting digital transformation is changing the methods through which companies' function, generating novel possibilities and difficulties that force firms to adjust to remain competitive in the digital era. It is critical for firms to embrace this change and utilize technology to develop a more flexible, proactive and effective approach as digital transformation continues to advance at an accelerating pace. Vietnam has been placed at the forefront of these changes in attracting investments and becoming a hub of international trade. As a result, Vietnamese firms have been implementing restructuring and adopting digital transformation to remain competitive with the flow of foreign investment. This paper aims to examine the effects of digital transformation on corporate restructuring in Vietnam. The authors then investigate the moderating role of corporate governance in the digital transformation – corporate restructuring nexus.

The authors employ content analysis to extract information from the annual reports of 747 Vietnamese listed companies, where the authors focus on specific phrases, such as “digitalization”, “big data”, “cloud computing”, “blockchain” and “information technology” over a period of 11 years, from 2011 to 2021. The frequency count of these keywords is calculated to represent the level of digital transformation for the Vietnamese listed firms. A final sample of 118 Vietnamese listed firms with sufficient data is selected for the analysis using the generalized method of moments (GMM) approach.

The results indicate that digital transformation and corporate governance negatively impact corporate restructuring when their effect on corporate restructuring is examined independently. However, corporate governance strengthens the effect of digital transformation on corporate restructuring.

This paper is one of the first to investigate the moderating role of corporate governance on the effect of digital transformation on corporate restructuring in Vietnam. The findings inspire listed firms in Vietnam to implement digital transformation during their corporate restructuring to enhance performance.

The purpose of this paper is to examine the degree of the exchange rate pass-through (ERPT) to the consumer price index (CPI) at both aggregated and disaggregated levels in Vietnam. Updated data of the nominal effective exchange rate (NEER) and bilateral exchange rate (BiER) have been utilized in this study for the comparison purposes.

Advanced time-series approaches such as a structural vector autoregressive framework, structural impulse response functions (SIRFs), and structural forecast-error variance decomposition (SFEVD) are utilized in this paper.

Empirical findings from this paper present an incomplete degree of the ERPT to the aggregated CPI. The ERPT based on the BiER is observed to have substantially larger magnitude than the NEER-based pass-through. For the disaggregated level, the degree of the ERPT varies considerably across sub-components of the CPI, with a higher magnitude of the ERPT elasticity being found from the BiER estimations. The index of housing and construction materials has the largest ERPT based on the BiER, followed by the food and foodstuffs (1.00 and 0.56, respectively). The macroeconomic and financial environments as well as an economic integration into the global market may be the main causes of a higher ERPT in Vietnam in comparison with other ASEAN countries.

The significant and incomplete pass-through of the exchange rate in Vietnam can affect firms’ and households’ budget planning, savings and profits. This finding generally implies that the cost of devaluation of the domestic currency affects the society as the whole in terms of welfare. The State Bank of Vietnam should carefully consider the overall effect of welfares when formulating and implementing strategies of currency devaluation. In addition, the Vietnamese economy becomes more sensitive to external vulnerabilities via changes of the exchange rate during an increasingly economic integration into the global market. In order to maintain inflation stability, it is vitally important to reduce the impact of exchange rate movements on the domestic prices, both aggregated and disaggregated levels, by pursuing either monetary policy credibility or inflation targeting.

Previous studies on the ERPT literature in the Asia region or for emerging countries focus mainly on the aggregated data of the CPI. Previous studies were conducted before the global financial crisis in 2008/2009. The current paper is the first of its kind to examine the pass-through from exchange rates to consumer prices in Vietnam using both aggregated and disaggregated data.

This paper presents concepts important for understanding urban poor housing in Vietnam, with a focus on key environmental, socio-economic, and cultural dimensions that bear on the housing sector. The paper draws on extensive field studies and presents a diagnosis of the context of and prospects for housing of the urban poor in Vietnam's two main cities: Hanoi and Ho Chi Minh City. While the literature on this theme is scanty, it points to the market-orientated economic reforms initiated in the 1980s as a key factor in creating imbalance in the housing supply. Recognising the current challenges in balancing affordability and sustainability, the study explores Vietnam's lack of adequate and affordable housing and the problems faced by the urban poor in accessing adequate housing.

The priority of this study is to contribute to the literature by examining herding behavior at different periods of the COVID-19 pandemic. Furthermore, this study aims to investigate the herding behavior conditioned on market liquidity and information demand.

This study investigates herding behavior in Vietnam's stock exchanges (Ha Noi Stock Exchange and Ho Chi Minh Stock Exchange) on a sample of daily stock closing prices of 425 firms from 2018 to the first half of 2022.

The research confirms the existence of herding behavior not only for the whole but also during and post-COVID periods. These results are robust in both bull and bear markets, further confirming the influence of COVID-19 on herding in Vietnamese background. Moreover, when the authors condition exogenous factors for each period, the herding tendency is more evident at the medium market liquidity level than at high and low levels. Besides, the pandemic causes herding behavior of investors with low and medium information demand.

These findings imply some recommendations that facilitate investors, policymakers and researchers in the context of the COVID-19 crisis.

The study contributes to the herding literature by examining herd behavior during the post-COVID period, suggesting the long-term impact of the health crisis. Furthermore, the research provides new evidence of herding behavior conditioned on market liquidity and information demand during different COVID sub-periods.

This paper aims to identify and assess global risks in the supply chain performance.

First, global risks are identified and classified according to three criteria: content, probability and context. A set of supply chain performance indicators are then defined by the theory of resource-based view and balanced scorecard. Structural equation modeling is adopted to access risks in the global supply chain.

This article contributes to the supply chain risk management literature by providing a detailed operationalization of global supply chain risk constructs, e.g. natural disasters, war and terrorism, fire accidents, economic and political instability, social and cultural grievances, decease. Empirical results reveal that the supply chain is predominantly regarded as being vulnerable as the proposed model of risks can explain up to 12.6% variance of supplier performance, 25.2% innovation and learning, 23% internal business, 40.6% customer service and 32.4% finance.

These risks are relevant contextual variables in strategic supply chain decisions. Supply chain managers should keep in mind acceptable cost/benefit tradeoffs in their firms' mitigation efforts associated with major contingency risks. This research advocates the allocation of scarce resources to adopt the supply chain strategies of avoidance, speculative and postponement.

The application of the strategic content/process/context to explain global supply chain performance is an interesting approach. Moreover, globalization trends and the COVID-19 perspectives are considered to be the main reasons for increasing such complex factors. Data on validating research models collected during the COVID-19 pandemic reflect the topicality of this study.

Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.

In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.

The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.

Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.

This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.

The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.

Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

This study aims to estimate the pass-through rate of the increases in the excise tax and TCF tax on tobacco in Vietnam. This study seeks to shed light on how the tax burden is split between consumers and producers and inform policy discussions in the country. Using panel micro-level data collected from three waves of a nationwide retailer's survey, this study provides an evidence-based pass-through estimation for tobacco tax in Vietnam and contributes to the understanding of tax policy on smoking and smoking-related issues.

Following increases in the excise tax and TCF tax on tobacco in 2019, the differential effect of the tax hike on the “treatment group” (domestic cigarettes) versus the “control group” (illicit cigarettes) using a difference-in-difference (DID) analysis has been studied. The study utilized unique longitudinal retailers’ data on cigarettes prices in Vietnam from 2018 to 2019 to estimate the tax pass-through rate for some of the most popular factory-made cigarette brands.

This study found evidence of an over-shifting of cigarette taxes on smokers. Specifically, it discovered that the tax increase is absorbed more by low-priced brand smokers compared to premium brand users due to (1) the limited increase in prices under a pure ad valorem system and (2) the way the Vietnamese currency is denominated. Additionally, there is evidence of cushioning to mitigate price shock on consumers as the real prices increase gradually over the period of one year after the tax change.

To the best of the authors’ knowledge, this study is the first to collect and analyze a unique panel micro-level data from three waves of a nationwide retailers’ survey, which captures the changes in marketing and pricing strategies of the tobacco industry in Vietnam before and after an increase in excise tax in 2019. The results of this study could be used as a reference for future policymakers in considering increasing taxes on tobacco.

This study aims to investigate the impact of ownership structure on firm adjustment speed toward the target leverage.

The study employs a partial adjustment model on a sample of 694 publicly listed firms from 2007 to 2021 in Vietnam.

The study finds that state and foreign ownerships inhibit the speed at which firms approach their optimal level of debt. Interestingly, this negative association is less pronounced in firms managed by powerful CEOs (duality CEOs; highly experienced CEOs; CEOs who they own or their families possess a large number of shares). Further, state and foreign ownership are found to be positively associated with firm performance. This beneficial impact weakens, however, when firms use more debts.

The outcome reveals that having a powerful CEO on board can help businesses in this emerging market counteract the detrimental effects that foreign and state ownership leave on the process of adjusting leverage.