A characterization of metacirculants

AbstractMetacirculants were introduced by Alspach and Parsons in 1982 and have been a rich source of various topics since then, including the Hamiltonian path problem in metacirculants. A metacirculant has a vertex-transitive metacyclic subgroup of automorphisms, and a long-standing interesting question in the area is if the converse statement is true, namely, whether a graph with a vertex-transitive metacyclic automorphism group is a metacirculant. We shall answer this question in the negative, and then present a classification of cubic metacirculants

Probabilistic Analysis of Ground Deformation Induced by Excavation based on Hypoplastic Constitutive Models

Empirical model and finite element method are two commonly-used methods for prediction of ground deformation induced by excavation. Compared with the former, the finite element method can not only predict the deformation of different modes, but also predict the distributed deformation of the whole site. However, results of finite element analysis depends on the constitutive model used in the analysis. This paper uses an advanced hypoplastic constitutive model and its improved edition, which considers the small-strain effect of soil, to represent the soil behavior. Uncertainties are unavoidable in excavation engineering, such as those in soil parameters, loads, and models, etc. These uncertainties have profound effects on the prediction of deformation induced by excavation obtained from the finite element analysis. In order to consider the effect of parameter uncertainty on the prediction results, random variables are used to characterize the parameter uncertainty. Direct Monte Carlo simulation (MCS) method was used to incorporate the parameter uncertainty into reliability analysis of the deformation induced by excavation. The computational costs and convergence issues of finite element method in together with advanced constitutive model result in significant computational challenges in MCS-based reliability analysis. In order to improve the computing efficiency and robustness, artificial neural network (ANN) is adopted as a surrogate model of the finite element method to compute the soil deformation for a given set of uncertain parameters. Results show that responses predicted by the improved hypoplastic model fit the real response better.This work was supported by the National Key R&D Program of China (Project No. 2016YFC0800200), and the National Natural Science Foundation of China (Project Nos. 51579190, 51528901, 51679174), and Young Elite Scientists Sponsorship Program by CAST (Project Nos. 2017QNRC001). The financial support is gratefully acknowledged

Extracellular Matrix Peptides of Artemia Cyst Shell Participate in Protecting Encysted Embryos from Extreme Environments

BACKGROUND: Many species of the brine shrimp Artemia are found in various severe environments in many parts of the world where extreme salinity, high UV radiation levels, high pH, anoxia, large temperature fluctuations, and intermittent dry conditions are often recorded. To withstand adverse environments, Artemia undergoes an oviparous developmental pathway to release cysts whereas, under favorable conditions, swimming nauplius larvae are formed directly via an ovoviviparous pathway. In the former case these cysts have an extraordinary ability to keep the embryos protected from the harsh environment for long periods. This is achieved through the protection by a complex out-wrapping cyst shell. However, the formation and function of the cyst shell is complex; the details remain largely unclear. PRINCIPAL FINDING: A shell gland-specific gene (SGEG2) was cloned and identified from a suppression subtractive hybridization library. Western blot analysis showed that SGEG2 presumably requires post-translational proteolysis in order to be processed into two mature peptides (SGEG2a and 2b). The three matrix peptides (SGEG1 reported previously, 2a, and 2b) were found to distribute throughout the cyst shell. The results of gene knockdown by RNAi and subsequent resistance to environmental stresses assays indicated that these matrix peptides are required for cyst shell formation and are involved in protecting the encysted embryos from environmental stress. CONCLUSIONS/SIGNIFICANCE: This study revealed that extracellular matrix peptides participate in protecting embryos from extreme salinity, UV radiation, large temperature fluctuations and dry environments, thereby facilitating their survival. The cyst shell provides an excellent opportunity to link the ecological setting of an organism to the underlying physiological and biochemical processes enabling its survival. The cyst shell material has also a high potential to become an excellent new biomaterial with a high number of prospective uses due, specifically, to such biological characteristics

Revisiting the Relationship between Scale of Fluctuation and Mean Cross Distance

Estimating scale of fluctuation is an intriguing issue, for which several methods have been developed, such as simple estimators (e.g., 0.8d¯-estimator) based on the mean cross distance d¯ of a soil property profile, sample autocorrelation function method, maximum likelihood method, Bayesian method, etc. Among these methods, the 0.8d¯-estimator is the simplest one and can be readily used by geotechnical practitioners whose training in probability theory and statistics is usually limited. It, however, shall be noted that the 0.8d¯-estimator was derived from the normal random field with squared exponential correlation function, which is largely ignored in its practical applications. Effects of the distribution type (e.g., normal or lognormal) and correlation function on the performance of the 0.8d¯-estimator remain unexplored and, hence, unknown to geotechnical practitioners, which potentially leads to misuse of the simple relationship. This paper aims to highlight the theoretical assumptions underlying the 0.8d¯-estimator and to, systematically, explore the effects of these theoretical assumptions on its performance (i.e., unbiasedness and variability). It is found that the 0.8d¯-estimator provides reasonably unbiased estimation of scale of fluctuation for the normal random field with squared exponential correlation function when there are, at least, two sampling data within a distance of scale of fluctuation. Whereas, results from the 0.8d¯-estimator for other cases violating the assumptions are biased, and may lead to a significant underestimation of scale of fluctuation. It is also found that the variability of the 0.8d¯-estimator increases as the sampling length decreases.The work described in this paper was supported by grants from National Key R&D Program of China (Project No. 2017YFC1501300), and the National Natural Science Foundation of China (Project Nos. 51528901, 51679174, 51779189), and an open fund from State Key Laboratory Hydraulics and Mountain River Engineering, Sichuan University (Project No. SKHL1619). The financial support is gratefully acknowledged

Bayesian Updating of Embankment Settlement on Soft Soils with Finite Element Method

Prediction of responses (e.g., embankment settlement) of geotechnical structures on soft soils is a challenging task due to their complex mechanical behaviors. In face with such complexity, the finite element method (FEM) combined with advanced soil constitutive models (e.g., soft soil creep (SSC) model) is frequently used to predict the short-term and long-term responses of geotechnical structures on soft soils, which involves a number of model parameters. Determination of these model parameters depends on knowledge obtained from site investigation data and/or monitoring information. This paper develops a Bayesian sequential updating (BSU) framework that incorporates monitoring information obtained at different construction stages to update FEM model parameters and their corresponding stochastic responses. To address the computational issues in Bayesian analysis, No-UTurn Sampler (NUTS) Markov chain Monte Carlo (MCMC) algorithm is introduced to populate posterior samples, and multiple Hermite response surfaces are constructed for different monitoring phases to reduce the computational efforts costed by evaluating the likelihood function. The proposed method is illustrated by a settlement prediction example of Ballina trial embankment, New South Wales, Australia. Effects of different likelihood functions (namely with and without model bias factor (MBF)) on Bayesian updating of settlement predictions are investigated. Results showed that the proposed BSU framework improves the prediction accuracy of soft soil settlement compared with prior predictions. NUTS is much more efficient in generating posterior samples compared with Metropolis-Hastings (MH) algorithm as the number of model parameters is relatively large. When considering short-term settlement behaviors of soft soils, the likelihood function without MBF is preferred because the adopted SSC can properly characterize short-term behaviors of soft soils. On the other hand, the likelihood function with MBF is recommended because SSC is hard to represent long-term behaviors of soft soils.This work was supported by the National Key R&D Program of China (Project No. 2016YFC0800200), the National Natural Science Foundation of China (Project Nos. 51679174, 51579190, 51528901), and Young Elite Scientists Sponsorship Program by CAST (Project No. 2017QNRC001). The financial support is gratefully acknowledged