This review examined existing evidence to investigate the link between tobacco and poverty in Vietnam, to assess the impact of tobacco control policies on employment related to tobacco consumption and to identify information gaps that require further research for the purposes of advocating stronger tobacco control policies. A Medline, PubMed and Google Scholar search identified studies addressing the tobacco and poverty association in Vietnam using extensive criteria. In all, 22 articles related either to tobacco and health or economics, or to the potential impact of tobacco control policies, were identified from titles, abstracts or the full text. 28 additional publications were identified by other means. PHA, LTT and LTTH reviewed the publications and prepared the initial literature review. There is extensive evidence that tobacco use contributes to poverty and inequality in Vietnam and that tobacco control policies would not have a negative impact on overall employment. Tobacco use wastes household and national financial resources and widens social inequality. The implementation and enforcement of a range of tobacco control measures could prove beneficial not only to improve public health but also to alleviate poverty.
Timely monitoring the large-scale civil structure is a tedious task demanding expert experience and significant economic resources. Towards a smart monitoring system, this study proposes a hybrid deep learning algorithm aiming for structural damage detection tasks, which not only reduces required resources, including computational complexity, data storage but also has the capability to deal with different damage levels. The technique combines the ability to capture local connectivity of Convolution Neural Network and the well-known performance in accounting for long-term dependencies of Long-Short Term Memory network, into a single end-to-end architecture using directly raw acceleration time-series without requiring any signal preprocessing step. The proposed approach is applied to a series of experimentally measured vibration data from a three-story frame and successful in providing accurate damage identification results. Furthermore, parametric studies are carried out to demonstrate the robustness of this hybrid deep learning method when facing data corrupted by random noises, which is unavoidable in reality.
Keywords:
structural damage detection; deep learning algorithm; vibration; sensor; signal processing.
Abstract. This paper introduces an improved response surface-based fuzzy finite element analysis of structural dynamics. The free vibration of structure is established using superposition method, so that fuzzy displacement responses can be presented as functions of fuzzy mode shapes and fuzzy natural frequencies. Instead of direct determination of these fuzzy quantities by modal analysis which will involve the calculation of the whole finite element model, the paper proposes a felicitous approach to design the response surface as surrogate model for the problem. In the design of the surrogate model, complete quadratic polynomials are selected with all fuzzy variables are transformed to standardized fuzzy variables. This methodology allows accurate determination of the fuzzy dynamic outputs, which is the major issue in response surface based techniques. The effectiveness of the proposed fuzzy finite element algorithm is illustrated through a numerical analysis of a linear two-storey shear frame structure.
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