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This study aims to assess the impacts of land use change (LUC) on soil properties and soil organic carbon content (SOCC) in the mountainous regions of Nghe An province, Vietnam. Soil samples were surveyed from forest plant area (FPA), annual plant area (APA), crop plant area (CPA), and bare hill area (BHA) at depths ranging from 0-50 cm during the period 2021-2023. The findings indicated that the conversion from FPA to BHA resulted in a significant decline in SOCC. In comparison to the topsoil layer, reductions of approximately 18.6-22.1% for FPA, 26.5-36.0% for CPA and APA, and up to 37.5% for BHA were observed in the soil profiles. These results underscore the sensitivity of SOCC to LUC across the study area. Moreover, the study contributes to our understanding of the implications of LUC on soil health and offers valuable insights for effective land management and conservation strategies. The conversion of FPA into various types of land use (TLU) has a detrimental impact on soil properties, highlighting the urgent necessity for sustainable land use practices. [ABSTRACT FROM AUTHOR]

Accurate geoid modeling is significant in geodetic, geological, and environmental sciences. Owing to challenges in establishing reference stations, particularly in rugged terrains, such as in Northern Vietnam, leveraging global geopotential models (GGMs) is imperative. Herein, we proposed a superior method that integrates GGMs with advanced artificial intelligence (AI) algorithms to enhance the accuracy and spatial resolution of regional geoid models. A total of six contemporary GGMs (XGM2019e_2159, SGG-UGM-2, SGG-UGM-1, GECO, EIGEN-6C4, and EGM2008) were systematically evaluated to identify the optimal GGM that represents the Earth’s gravitational field in Northern Vietnam. Subsequently, sophisticated AI algorithms, including tree-based ensembles, support vector machines, Gaussian linear regression, regression trees, and linear regression models, were implemented. These AI algorithms were trained on the integrated global navigation satellite system (GNSS) leveling data and corresponding height anomalies to capture complex relationships in the geopotential field. Among the six investigated GGMs, XGM2019e_2159 shows optimal performance for Northern Vietnam, displaying a standard deviation of ±0.17 m. Rigorous assessment results from cross-validation and validation against independent datasets demonstrate satisfactory accuracy across all considered models. However, the Gaussian process regression model with an exponential kernel exhibits marginal superiority, boasting a standard deviation of approximately 0.07 m. This model is therefore chosen for the construction of the geoid model by integrating ground data with optimal GGMs, which shows superior performance, particularly in challenging topographic and geophysical conditions, thereby contributing to a marked improvement in the realized spatial resolution.

Abstract Background Participatory approaches have become a widely applied research approach. Despite their popularity, there are many challenges associated with the evaluation of participatory projects. Here we describe an evaluation of a community-based participatory research study of underserved communities in Ho Chi Minh City (HCMC), Vietnam at risk for hepatitis C virus. The goals of our evaluation were to explore the main benefits and challenges of implementing and participating in a participatory study and to describe study impacts. Methods We conducted two meetings with leaders and members of the participating groups followed by in-depth interviews with 10 participants. We then held a dissemination meeting with over 70 participants, including the representatives of each group, researchers from non-governmental organizations (community-based, national and international), and govenrment officials from the Vietnam Ministry of Health and the Department of Health of HCMC. Results Results include four categories where we describe first the participatory impacts, followed by the collaborative impacts. Then we describe the benefits and challenges of creating and belonging to one of the groups, from members’ and leaders’ points of view. Finally, we describe the key suggestions that participants provided for future research. Conclusion In conclusion, the evaluation approach led to both a research reflection on the ‘success’ of the project and enabled participants themselves to reflect on the outcomes and benefits of the study from their point of view.

Forests play a crucial role in preserving the living environment for humans, but their area is increasingly shrinking under the influence of socio-economic development. This article presents the results of applying remote sensing and GIS to assess forest resource changes in a rural district - Loc Ninh, Binh Phuoc Province, Vietnam. The study used Landsat 5 TM and Landsat 8 OLI images and processed them using ENVI 4.7 and ArcGIS Desktop 10.7 software to create forest distribution maps for the periods 2000, 2010, and 2020. The research results show that the forest area in Loc Ninh rural district experienced significant changes during the 2000-2020 period. The total forest area lost was over 13.4% (8824 ha) compared to 2000. The reasons are explained by the pressure of rapid population growth, remnants of shifting cultivation practices, and the consequences of land use conversion and expansion for production purposes. The research results provide valuable reference materials for forest management and protection in Loc Ninh rural district, Vietnam.

Rivers, such as the Dong Nai, Saigon, and Mekong, are subject to urban, industrial, and agricultural pollution, leading to a high risk of heavy metal accumulation in bottom sediments. With the aid of a boat and bucket as tools, a survey was conducted to collect sediment samples from the coastal areas (downstream of Dong Nai, Saigon, Mekong Rivers) and islands (Con Dao, Tho Chu) in the Southern region of Vietnam during April and May 2023. Out of a total of 35 samples collected, preserved according to the guidelines of the Ministry of Natural Resources and Environment, the heavy metal indicators were analyzed by atomic absorption spectroscopy, and the average heavy metal concentrations were in the following order: Zn>Cu>Cd>Pb>As. Compared with the Vietnamese Technical Regulation (QCVN 43:2017/BTNMT) on sediment quality and the sediment quality of the Canadian Environment Ministry, the heavy metal concentrations in the research area have not exceeded the specified standards. However, compared with standards from the USA, the Russian Federation, and other regions in Vietnam, the levels are quite high. Moreover, the Igeo index indicates that the concentration of Pb, As, and Cd at many coastal locations downstream of the rivers is heavily polluted. This evidences that in addition to pollution from upstream sources, the activities of the local population significantly contribute to the accumulation of pollution. The research results provide a scientific basis for limiting pollution sources to ensure sustainable development of the studied areas, which is necessary today.

Abstract Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively (p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998–0.999 vs. 0.982 95% CI 0.962–0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9–21.7) to 9.4 min (IQR 7.2–11.7) compared to when using the AI tool (p