Dataset Information

Mechanical behaviors of hydrate-bearing sediment with different cementation spatial distributions at microscales.

ABSTRACT: Unlike the conceptual models, the natural hydrate spatial distribution in sediments is multitype and presents different coalescence degrees. In this study, we present pore-scale triaxial test results for hydrate-bearing sediments with different hydrate spatial distributions for the first time. It shows that the specimen with a more dispersed hydrate distribution yields later and exhibits larger peak strength. Correspondingly, the localized deformation develops more slowly, and the shear band is steeper and thinner. The cementation failure in the specimen with a more dispersed hydrate distribution develops more slowly. However, the changing rate of the pore space characteristic does not seem to be affected by the hydrate spatial distribution. Moreover, the specimen with a more dispersed hydrate distribution has a larger hydrate-sand interfacial area, and further axial loading would increase it rapidly firstly, and then the increasing rate would be slowed down since the cementation structure failure.

SUBMITTER: Li Y

PROVIDER: S-EPMC8169796 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Mechanical behaviors of hydrate-bearing sediment with different cementation spatial distributions at microscales.

Li Yanghui Y Wu Peng P Sun Xiang X Liu Weiguo W Song Yongchen Y

iScience 20210416 5

Unlike the conceptual models, the natural hydrate spatial distribution in sediments is multitype and presents different coalescence degrees. In this study, we present pore-scale triaxial test results for hydrate-bearing sediments with different hydrate spatial distributions for the first time. It shows that the specimen with a more dispersed hydrate distribution yields later and exhibits larger peak strength. Correspondingly, the localized deformation develops more slowly, and the shear band is ...[more]

PMID: 34113811

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Project description:BackgroundEpidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010.ResultsPCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165(th) PCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r (2) = 0.579).ConclusionsPCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information.

Dataset Information

Mechanical behaviors of hydrate-bearing sediment with different cementation spatial distributions at microscales.

Publications

Mechanical behaviors of hydrate-bearing sediment with different cementation spatial distributions at microscales.

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