The U.S.-Japan alliance and ASEAN-centric security institutions : Vietnam's perspective

For more about the East-West Center, see http://www.eastwestcenter.org/Huy Pham Quang, Visiting Fellow at the East-West Center in Washington, explains that "Beijing under Xi Jinping's administration may benefit the most from a divided ASEAN. In such an environment, the increased presence of the United States and Japan in the region should be seen first and foremost as a strategic move in the balance against China.

Regional rivalry in the Indo-Pacific : Vietnam's role as the 2020 Chair of ASEAN

For more about the East-West Center, see http://www.eastwestcenter.org/With rivalry escalating between the US and China, the stability of the Indo-Pacific region is under threat. As a newly elected non-permanent member of the UN Security Council and the 2020 chair of ASEAN--the Association of Southeast Asian Nations--Vietnam will have an opportunity to help maintain peace and stability. At the same time, as one of the smaller countries, Vietnam will look for ways to use regional rivalries to promote its own national interest. Vietnam's perception of the balance of power between the US and China determines its foreign policy toward these two countries and toward ASEAN. In response to the China-US rivalry, Hanoi supports further US engagement in the region, not only to offset Beijing's influence but also to leverage the role of ASEAN and avoid any extreme outcomes

Machine Learning Approaches for Breast Cancer Survivability Prediction

Breast cancer is one of the leading causes of cancer death in women. If not diagnosed early, the 5-year survival rate of patients is just about 26\%. Furthermore, patients with similar phenotypes can respond differently to the same therapies, which means the therapies might not work well for some of them. Identifying biomarkers that can help predict a cancer class with high accuracy is at the heart of breast cancer studies because they are targets of the treatments and drug development. Genomics data have been shown to carry useful information for breast cancer diagnosis and prognosis, as well as uncovering the disease’s mechanism. Machine learning methods are powerful tools to find such information. Feature selection methods are often utilized in supervised learning and unsupervised learning tasks to deal with data containing a large number of features in which only a small portion of them are useful to the classification task. On the other hand, analyzing only one type of data, without reference to the existing knowledge about the disease and the therapies, might mislead the findings. Effective data integration approaches are necessary to uncover this complex disease. In this thesis, we apply and develop machine learning methods to identify meaningful biomarkers for breast cancer survivability prediction after a certain treatment. They include applying feature selection methods on gene-expression data to derived gene-signatures, where the initial genes are collected concerning the mechanism of some drugs used breast cancer therapies. We also propose a new feature selection method, named PAFS, and apply it to discover accurate biomarkers. In addition, it has been increasingly supported that, sub-network biomarkers are more robust and accurate than gene biomarkers. We proposed two network-based approaches to identify sub-network biomarkers for breast cancer survivability prediction after a treatment. They integrate gene-expression data with protein-protein interactions during the optimal sub-network searching process and use cancer-related genes and pathways to prioritize the extracted sub-networks. The sub-network search space is usually huge and many proteins interact with thousands of other proteins. Thus, we apply some heuristics to avoid generating and evaluating redundant sub-networks

Sustainable Decision Making in The Time of Uncertainty: Does Moral Intelligence Make It Different?

Background: The cybersecurity has been pondered as a great concern for professionals, legislators as well as all decision-makers and effectiveness of accounting information system (EAIS) has long been well-acknowledged as the prerequisite organizational management. Against this backdrop, big data analytics capabilities (BDAC) will become a must-have element of any fruitful cybersecurity resolution and organizational EAIS to enable public sector to achieve sustainable decision-making (SDM) in operation within the time of uncertainty. This research aims at investigating the interconnection between BDAC and SDM. It also delves into the mediation mechanism of EAIS and cybersecurity risk management (CRM) in the linkage between BDAC and SDM. Outstandingly, it examines whether the interconnections between these aforementioned components varies resting on specific degree of moral intelligence (MI). Method: The structural equation modeling is employed to investigate the statistical data captured from paper-and-pencil survey circulated to a convenience and snowball sample of 683 respondents in the Southern areas of Vietnam. Additionally, the multi-group analysis is applied to examine the moderating impact of MI. Results: The results analysis substantiates the markedly positive interconnection between BDAC and SDM. Simultaneously, this interconnection is partially mediated by CRM and EAIS. One of the most noteworthy observations is the moderating role of MI as a catalyst in enabling public sector to achieve SDM. Conclusion: The study\u27s findings provide important, realistic, and useful theoretical contributions to the current literature on the issue, as well as beneficial inputs for practitioners. Accordingly, these findings recommend that practitioners and policy-makers can benefit from enhancing BDAC and EAIS as well as implementing CRM, which are proactive measures to achieve SDM. Also increasing MI of accountants as an effective solution to foster the advantages of big data analytics, accounting information system and CRM to succeed in SDM

Study on the Use of Construction and Demolition Waste for Road Base or Subbase Pavement Construction in Hanoi

Reuse or recycling of construction and demolition waste (CDW) has become an inevitable trend in the world. Currently, the amount of CDW generated in Hanoi is estimated at more than 4,000 tons per day, of which only about 30% has been controlled and recycled. The CDW comes from many different sources such as construction, repair, renovation, demolition of houses, residential buildings, public buildings, transportation infrastructure works, etc... The CDW commonly comprises soil, bricks, mortar and concrete, and has been reused in many applications around the world. In Vietnam, there are also some research programs set up for reutilizing the material, however, has not been concretely applied in practice. In order to consider the applicability of CDW in road construction, an experimental program was conducted using CDW as the aggregate for the cement treated grain material base layer in road pavement structure. The weight ratios of cement used in the mixture were 4%, 5%, 6%, 7% and 8%. The test results showed that the main mechanical properties of compressive strength, split tensile strength and elastic modulus of the mixture, increased proportionally with the cement content in the mixture

Degradation of 2,3,7,8-TCDD by a consortium of bacterial strains isolated from heavil herbicide/dioxin contaminated soil in Bienhoa airbase

From two different soil sources in Bienhoa airbase (heavy herbicide/dioxin contaminated West-South region and bioremediated cell), five microbial strains were isolated and their 2,3,7,8-TCDD biodegrability in consortium was investigated. Based on the colony and cell morphological characteristics as well as 16S rRNA gene sequences, these strains were classified into 5 genera, including Methylobacterium (strain BHBi1), Hydrocarboniphaga (strain BHBi4), Agrobacterium (strain BHBi5), Bosea (strain BHBi7) and Microbacterium (strain BH09). Two strains BHBi7 and BHBi4 were the first representatives of the genera Bosea and Hydrocarboniphaga that were isolated from heavyly herbicide/dioxin contaminated soil. All five strains were able to grow well in mineral salt medium (MSM) supplemented with soil extract (SE) containing 2,3,7,8-TCDD (this congener is the main soil total compound toxicity) and other congeners, including PCDDs, PCDFs, 2,4,5-T, 2,4-D, PAHs and their intermediates. This microbial consortium degraded 2,537.34 ngTEQ/kg of 2,3,7,8-TCDD congener in soil, equivalent to 59.1% lost of total toxicity in comparison to the control without bacterial seeding (4,294.12 ng TEQ/kg). Such a high ratio of dioxin degradation by a bacterial consortium was reported here for the first time, contributing more evidences for convincing the successful dioxin bioremediation of “Active Landfill” technology at large scale in Z1 area at Bienhoa airbase, Dongnai, Vietnam