Risks, impacts and management of invasive plant species in Vietnam

In Southeast Asia, research on invasive plant species (IPS) is limited and biased by geography, research foci and approaches. This may hinder understanding of the extent of invasion problems and effective management to prevent and control IPS. Because biological invasions are a complicated issue involving multiple disciplines, this thesis utilized diverse approaches to evaluate risk, impacts, and management of IPS in Vietnam. Distribution models of 14 species predicted that large areas of Vietnam are susceptible to IPS, particularly in parts bordering China. Native IPS, which are often overlooked in assessment, posed similar risks as non-native IPS. From the model results, a native grass Microstegium ciliatum was selected to quantify its impacts on tree regeneration in secondary forests. A field experiment in Cuc Phuong National Park found that tree seedling abundance and richness increased within one year of grass removal; this effect strengthened in the second year. These results highlight the impacts of IPS on tree regeneration and the importance of IPS management to forest restoration projects. Given the risks and impacts of IPS, strategic management is needed to achieve conservation goals in national parks (NPs). However, interviews with both state and non-state entities revealed poor and reactive management of IPS in Vietnamese NPs from national to local levels. Institutional arrangements challenge IPS management in Vietnam. Involvement of multiple sectors with unclear mandates leads to overlaps in responsibilities and makes collaboration among sectors difficult. Lack of top-down support from the national level (legislation, guidance, resources) and limited power at the local level weakens implementation and ability of NPs to respond to IPS. The findings of this thesis provide important information for achieving effective management of IPS in Vietnam. Knowledge of vulnerable areas and species likely to invade and cause impacts can help Vietnam efficiently allocate management resources to prevent and control IPS, but adjustments to institutional arrangements and enhanced cooperation may be necessary to ensure management occurs

Building forecast maps of water quaůity for main rivers and canals in Tien Giang province, Vietnam

This study aims to enhance the mapping of forecast for water quality assessment in Mekong Delta provinces. The data from 32 sites from main rivers and canals in an area of around 2,482 km2 in Tien Giang Province, Vietnam, were used for calculation and mapping. The ArcGIS 9.3 software, Inverse Distance Weighting (IDW) interpolation method, hydrologic data, and water quality parameters in March (2010-2014) were applied to build the maps showing 2020 water quality predictions for main rivers and canals in Tien Giang Province. The estimation was based on the Water Quality Index (WQI) with 6 parameters such as pH, total suspended solid (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), total nitrogen (T_N), and coliform. The results showed that water quality in the studied area in dry season will not be improved by the year 2020. The finding could be a scientific reference for the selection of effective approaches to improve water quality in main rivers and canals in Tien Giang Province

Tension Gastrothorax in a Child Presenting with Abdominal Pain

A 4-year-old girl was brought to our hospital by her parents because of abdominal pain. She had suffered minor trauma after rolling from her standard-height bed 2 days prior. Vital signs were appropriate for age. Physical examination was remarkable for decreased breath sounds to the left side of the chest. A chest radiograph (Figure) demonstrated a large gas-filled structure in the left side of the chest with mediastinal shift

A real-time defect detection in printed circuit boards applying deep learning

Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due to the complexity of the PCB layouts and the shrinking down tendency of the electronic component size. In this paper, a real-time automated supervision algorithm is proposed to test the PCBs quality among different scenarios. The density of the PCBs layout and the complexity on the surface are analyzed based on deep learning and image feature extraction algorithms. To be more detailed, the ORB feature and the Brute-force matching method are utilized to match perfectly the input images with the PCB templates. After transferring images by aiding the RANSAC algorithm, a hybrid method using modern computer vision algorithms is developed to segment defective areas on the PCBs surface. Then, by applying the enhanced Residual Network –50, the proposed algorithm can classify the groove defects on the surface mount technology electronic components which minimum size up to 1x3 mm. After the training process, the proposed system is capable to categorize various types of overproduced, recycled, and cloned PCBs. The speed of the quality testing operation maintains at a high level with an average precision rate up to 96.29 % in case of good brightness conditions. Finally, the computational experiments demonstrate that the proposed system based on deep learning can obtain superior results and it outperforms several existing works in terms of speed, precision, and robustnes

Support Vector Machine for Regression of Ultimate Strength of Trusses: A Comparative Study

Thanks to the rapid development of computer science, direct analyses have been increasingly used in the design of structures in lieu of member-based design methods using the effective length factor. In a direct analysis, the ultimate strength of a whole structure can be sufficiently estimated, so that the need for member capacity checks is eliminated. However, in complicated structural design problems where many structural analyses are required, the use of direct analyses requires an excessive computation cost. In such cases, Machine Learning (ML) algorithms are used to build metamodels that can predict the structural responses without performing costly structural analysis. In this paper, the support vector machine (SVM) algorithm is employed for the first time to develop a metamodel for predicting the ultimate strength of trusses using direct analysis. Several kernel functions for the SVM model, including linear, sigmoid, polynomial, radial basis function (RBF), are considered. A planar 39-bar nonlinear inelastic steel truss is taken to study the performance of the kernel functions. The results confirm the applicability of the SVM-based metamodel for predicting the ultimate strength of trusses. In particular, the RBF appears to be the best kernel among others. This investigation also provides a deeper understanding of the effect of the parameters on the efficiency of the kernel functions