Adaptation To Salinity Intrusion For Rice Farming Household In The Vietnamese Mekong Delta

Agriculture has considerably been affected due to the increased salinity in recent years in the Vietnamese Mekong Delta (VMD). The saline intrusion has increasingly caused problems to irrigation management, making it impossible to sustain the desired crop productivity. Rice farming are actually the most vulnerable as they have limited adaptive capacities and are more dependent on water for food production and other economic activities. This paper aims to understand how rice farming households responded to impacts of saline intrusion in the VMD, focusing the adaptive capacity and adaptation to saline intrusion at household level. The study showed that most rice farming households perceived the impacts of saline intrusion on their production activities, but only a few households prepared for adaptation options. Their decisions were not based on long-term saline intrusion impacts because households made decisions and changed farming practices due to economic factors and government policy support. The environment factors such as saline intrusion always came after economic and government policy factors. Government policy strongly affected production conditions of rice farming households through building irrigation, dyke and sluice gate systems. It means that change of production activities of rice farming households much more depended on government programs and development goals. Thus, households have fewer choices of production diversification away from rice farming

ANALYZE THE TREATMENT REGIMENS AND THROMBOSIS PROPHYLAXIS USED IN CORONARY ARTERY INTERVENTION AT INTERVENTIONAL CARDIOLOGY UNIT IN CAN THO CENTRAL GENERAL HOSPITAL

Objective: The study was conducted to analyze the rationality of treatment regimens and thrombosis prophylaxis used in coronary artery intervention to compare to guidelines for treatment according to VNHA and recommendation of ACC/AHA at Interventional cardiology in Can Tho Central General Hospital. Methods: The cross-sectional study was based on the data collected from entire medical records of patients at Interventional cardiology in Can Tho Central General Hospital from August 2017 to February 2018. The rationality of the antithrombotic regimen used at the Hospital is assessed through criteria such as medical combination, dosage, time to take medicine, clinical trials during the treatment. Results: The study found that 95.6% and 90.7% were suitable for medical combination before and after PCI; 100% fit for the use of medicine; and 100% was suitable for antithrombotic agents and clinical trials during treatment time; in terms of dosage, the result showed that entrance and maintenance were 84.9% and 100% for aspirin respectively; 71.7% and 100% for clopidogrel; 100% and 94.7% for ticagrelor; 90.2-92.8% and 98.1% for enoxaparin; especially, heparin-100% anticoagulant was appropriate to recommend. Conclusion: The study showed that treatment regimens and thrombosis prophylaxis in percutaneous coronary intervention at Interventional cardiology in Can Tho Central General Hospital were quite suitable compared to the recommendations of the Heart Association. The results from the study are a scientific basis for the Hospital to maintain or consider adjustments to improve the quality of treatment, ensure the effectiveness and safety of patients

Regional Integration Of Cham Muslims In The Mekong Delta

An Giang province is located in the Mekong Delta of Vietnam, recognized as Vietnam’s Mecca for large Muslim population in Vietnam. This paper introduces the root of the Cham (Sunni) Muslims living in this southwestern part of Vietnam. The historical research and documentary research (in which relevant historical documents and articles were selected to review and comment) were utilized in the study. The paper shows some differences between the Cham Muslims in this region and the Hindu Chams (Balamon). More importantly, this paper indicates that the Chams in the Mekong Delta have had more mobile ways of life and a more highly regional integration in the ASEAN Community and a larger Muslim world rather than other Cham groups in Vietnam

Effects of Web 2.0 experience on consumers’ online purchase intention: the Social Networking and Interaction Orientation Factors

This research examines the effects of Web 2.0 experience, especially the social networking and interaction orientation factors, which are likely to influence online consumers’ purchase intention and buying behaviour. Based on theoretical foundations of what has been identified as Web 2.0 experience, this study proposes a research model consisting of these two factors acting as the main parameters influencing online purchase intention. These antecedents were modelled as first-order constructs with reflective indicators. The proposed model has been developed with two major objectives. The first objective is to provide insight into online consumer behaviour within the Web 2.0 e-commerce context. The second objective is to investigate the relative importance role of social networking and interaction orientation on online purchase intention. Based on these objectives, the research first reviews the literature related to online buying behaviour, online experience and Web 2.0 experience. The review provides support for developing the research model and the hypotheses. Data collection was conducted in New Zealand through an anonymous survey of 173 students, who were asked to visit an existing Web 2.0 online store and initiate the purchase of a product, operation which was stopped before the transaction was completed. Statistical analyses using structural equation modelling (SEM) are used to validate the model and identify the relative importance of the key antecedents to online purchase intention. On the one hand, the results confirm the direct positive influence of the interaction orientation factor on purchase intention. On the other, they suggest that the relationship between the social networking factor and intention to buy is mediated by the interaction orientation factor

Effects of steel corrosion to BFRP Strengthened columns under eccentric loading

The experiment consists of twenty-four mid-scale rectangular RC columns (200x200x800mm) strengthening by BFRP sheets and research variables include: BFRP layer (0, 1, and 3 layers); eccentricity (25mm and 75mm); and 4 levels of steel corrosion. The results reveal that SEL (ratio of ultimate load of strengthened member to that of corresponding controlled member) is direct proportion with steel corrosion while SEV (ratio of ultimate vertical displacement of strengthened member to that of corresponding controlled member) is inverse proportion with steel corrosion; SEL slightly increases with the increase of BFRP layer and eccentricity; but SEV decreases noticeably with the increase of BFRP layer and eccentricity. In addition,the interaction between FRP sheets, stirrups, and longitudinal reinforcement in steel degraded BFRP strengthened columns is very strong.However, column design basing on current design manuals and codes as ACI 440.2R and CNR DT 200R1 has not mentioned this affect. Thus, the load capacity prediction of column being strengthened by BFRP sheets should include levels of steel corrosion for reality, reasonable, and integral of the design

Effects of steel corrosion to BFRP Strengthened columns under eccentric loading

The experiment consists of twenty-four mid-scale rectangular RC columns (200x200x800mm) strengthening by BFRP sheets and research variables include: BFRP layer (0, 1, and 3 layers); eccentricity (25mm and 75mm); and 4 levels of steel corrosion. The results reveal that SEL (ratio of ultimate load of strengthened member to that of corresponding controlled member) is direct proportion with steel corrosion while SEV (ratio of ultimate vertical displacement of strengthened member to that of corresponding controlled member) is inverse proportion with steel corrosion; SEL slightly increases with the increase of BFRP layer and eccentricity; but SEV decreases noticeably with the increase of BFRP layer and eccentricity. In addition,the interaction between FRP sheets, stirrups, and longitudinal reinforcement in steel degraded BFRP strengthened columns is very strong.However, column design basing on current design manuals and codes as ACI 440.2R and CNR DT 200R1 has not mentioned this affect. Thus, the load capacity prediction of column being strengthened by BFRP sheets should include levels of steel corrosion for reality, reasonable, and integral of the design

Machine Learning Models for Inferring the Axial Strength in Short Concrete-Filled Steel Tube Columns Infilled with Various Strength Concrete

Concrete-filled steel tube (CFST) columns are used in the construction industry because of their high strength, ductility, stiffness, and fire resistance. This paper developed machine learning techniques for inferring the axial strength in short CFST columns infilled with various strength concrete. Additive Random Forests (ARF) and Artificial Neural Networks (ANNs) models were developed and tested using large experimental data. These data-driven models enable us to infer the axial strength in CFST columns based on the diameter, the tube thickness, the steel yield stress, concrete strength, column length, and diameter/tube thickness. The analytical results showed that the ARF obtained high accuracy with the 6.39% in mean absolute percentage error (MAPE) and 211.31 kN in mean absolute error (MAE). The ARF outperformed significantly the ANNs with an improvement rate at 84.1% in MAPE and 65.4% in MAE. In comparison with the design codes such as EC4 and AISC, the ARF improved the predictive accuracy with 36.9% in MAPE and 22.3% in MAE. The comparison results confirmed that the ARF was the most effective machine learning model among the investigated approaches. As a contribution, this study proposed a machine learning model for accurately inferring the axial strength in short CFST columns

CHARACTERIZATION OF CARBONATED STEELMAKING SLAG AND ITS POTENTIAL APPLICATION IN CONSTRUCTION

In the current context of Vietnam, the solid waste of steel slag occupy ground for dumping and lead to severe environmental issue due to their high content of heavy metal and fine dust. For the purpose of large-scale recycling steel slag, up to now one of the most relevant solutions is to use as aggregate for asphaltic and/or cement concrete. In this paper, we aim to analyze the influence of the accelerated carbonation condition in the laboratory on the physio-chemical properties of carbonated steel slag. Materials composition were characterized by using different analysis techniques of XRD, SEM, TG and others measurement of the physio-properties (density, L.O.I..) were also realized with regards to the requirement of the national standard for concrete aggregate. In conclusion, we will discuss the effect of reaction condition and on the feasibility of implementing this specific treatment method on a larger scale.Keywords: steelmaking slag, solid waste, CO2 sequestration, accelerated carbonation, concrete aggregate

Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods