Bayesian methods for system reliability and community detection

Bayesian methods are valuable for their natural incorporation of prior information and their practical convenience for modeling and estimation. This dissertation develops flexible Bayesian parametric methods for system reliability and Bayesian nonparametric models for community detection. The Bayesian parametric models proposed allow the assessment of system reliability for multi-component systems simultaneously. We start with a model that considers lifetime data at every component. Then we generalize to a unified framework with heterogeneous information. We demonstrate this unified methodology with pass/fail, lifetime, and degradation data at both the system level and the component level. Further, we propose a Bayesian melding approach to combine prior information from multiple levels. For community detection, we propose a series of statistical models based on Bayesian nonparametric techniques. These statistical models provide a natural approach for identifying communities in networks using only data on edges. We take advantage of the Bayesian nonparametric approach to include an important feature in our models: the number of communities is an implied parameter of the model, which is therefore inferred during estimation. We also introduce an “Erdős Rényi” group for those nodes that do not belong to communities. Other important aspects of this series of models include increasing flexibility of modeling probabilities for edge presence, linking these probabilities to community sizes, and obtaining communities from posterior samples under a decision theory framework. When presenting our models, we discuss model selection and model checking, which are necessary considerations when applying statistical approaches to real problems

Bayesian Methods for Estimating the Reliability of Complex Systems Using Heterogeneous Multilevel Information

We propose a Bayesian approach for assessing the reliability of multicomponent systems. Our models allow us to evaluate system, subsystem, and component reliability using the available multilevel information. Data are collected over time, and include pass/fail, lifetime, censored, and degradation data. We illustrate the methodology through an example and discuss how to extend the approach to more complex systems

Rational design and directed evolution of a bacterial-type glutaminyl-tRNA synthetase precursor.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links field.Protein biosynthesis requires aminoacyl-transfer RNA (tRNA) synthetases to provide aminoacyl-tRNA substrates for the ribosome. Most bacteria and all archaea lack a glutaminyl-tRNA synthetase (GlnRS); instead, Gln-tRNA(Gln) is produced via an indirect pathway: a glutamyl-tRNA synthetase (GluRS) first attaches glutamate (Glu) to tRNA(Gln), and an amidotransferase converts Glu-tRNA(Gln) to Gln-tRNA(Gln). The human pathogen Helicobacter pylori encodes two GluRS enzymes, with GluRS2 specifically aminoacylating Glu onto tRNA(Gln). It was proposed that GluRS2 is evolving into a bacterial-type GlnRS. Herein, we have combined rational design and directed evolution approaches to test this hypothesis. We show that, in contrast to wild-type (WT) GlnRS2, an engineered enzyme variant (M110) with seven amino acid changes is able to rescue growth of the temperature-sensitive Escherichia coli glnS strain UT172 at its non-permissive temperature. In vitro kinetic analyses reveal that WT GluRS2 selectively acylates Glu over Gln, whereas M110 acylates Gln 4-fold more efficiently than Glu. In addition, M110 hydrolyzes adenosine triphosphate 2.5-fold faster in the presence of Glu than Gln, suggesting that an editing activity has evolved in this variant to discriminate against Glu. These data imply that GluRS2 is a few steps away from evolving into a GlnRS and provides a paradigm for studying aminoacyl-tRNA synthetase evolution using directed engineering approaches.National Institute of General Medical Sciences GM02285

Stan: A Probabilistic Programming Language

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting

Spatiotemporal variations and risk characteristics of potential non-point source pollution driven by LUCC in the Loess Plateau Region, China

With increasing human activities, regional substrate conditions have undergone significant changes. These changes have resulted in temporal and spatial variations of non-point source pollution sources, which has a significant impact on the quality of the regional soil, surface water, and groundwater environments. This study focused on the human-disturbed Loess Plateau region and used an enhanced potential non-point-source pollution index (PNPI) model to explore the dynamic changes of regional potential non-point-source pollution (PNP) and the associated risk due to land use and land cover change (LUCC) over the past 31 years. The Loess Plateau region is mainly composed of cultivated land, grassland and forest, which together account for 93.5% of the watershed area. From 1990 to 2020, extensive soil and water conservation measures were implemented throughout the Loess Plateau region, resulting in a significant reduction in the non-point source pollution risk. Using the quantile classification method, the study area’s PNP risk values were categorized into five distinct levels. The results revealed a polarization phenomenon of PNP risk in the region, with an increase in non-point source pollution risk in the human-influenced areas and a rapid expansion of the very high-risk area. However, the non-point source pollution risk in the upstream water source area of the watershed reduced over the study period. In recent years, the rapid urbanization of the Loess Plateau region has been the primary reason for the rapid expansion of the very high PNP risk area throughout the watershed. This study highlights the significant impact of LUCC on the dynamic changes in PNP risk within the Loess Plateau region, providing crucial insights into future conservation and urban planning policies aimed at enhancing the ecological health and environmental quality of the region

Bayesian methods for system reliability and community detection

Bayesian methods are valuable for their natural incorporation of prior information and their practical convenience for modeling and estimation. This dissertation develops flexible Bayesian parametric methods for system reliability and Bayesian nonparametric models for community detection. The Bayesian parametric models proposed allow the assessment of system reliability for multi-component systems simultaneously. We start with a model that considers lifetime data at every component. Then we generalize to a unified framework with heterogeneous information. We demonstrate this unified methodology with pass/fail, lifetime, and degradation data at both the system level and the component level. Further, we propose a Bayesian melding approach to combine prior information from multiple levels. For community detection, we propose a series of statistical models based on Bayesian nonparametric techniques. These statistical models provide a natural approach for identifying communities in networks using only data on edges. We take advantage of the Bayesian nonparametric approach to include an important feature in our models: the number of communities is an implied parameter of the model, which is therefore inferred during estimation. We also introduce an “Erdős Rényi” group for those nodes that do not belong to communities. Other important aspects of this series of models include increasing flexibility of modeling probabilities for edge presence, linking these probabilities to community sizes, and obtaining communities from posterior samples under a decision theory framework. When presenting our models, we discuss model selection and model checking, which are necessary considerations when applying statistical approaches to real problems.</p

Bayesian Methods for Estimating the Reliability of Complex Systems Using Heterogeneous Multilevel Information

We propose a Bayesian approach for assessing the reliability of multicomponent systems. Our models allow us to evaluate system, subsystem, and component reliability using the available multilevel information. Data are collected over time, and include pass/fail, lifetime, censored, and degradation data. We illustrate the methodology through an example and discuss how to extend the approach to more complex systems.This preprint was published as Jiqiang Guo & Alyson G. Wilson, "Bayesian Methods for Estimating System Reliability Using Heterogeneous Multilevel Information", Technometrics (2013): 461-472, doi: 10.1080/00401706.2013.804441.</p

Numerical Simulation of Steel-Reinforced Reactive Powder Concrete Beam Based on Bond-Slip

In this study, based on the concrete damaged plasticity (CDP) model in the ABAQUS software, various plastic damage factor calculation methods were introduced to obtain CDP parameters suitable for reactive powder concrete (RPC) materials. Combined with the existing tests for the bending performance of steel-reinforced RPC beams, the CDP parameters of the RPC material were input into ABAQUS to establish a finite element model considering the bond and slip between the steel and RPC for numerical simulation. The load-deflection curve obtained by the simulation was compared with the measured curve in the experiment. The results indicated that on the basis of the experimentally measured RPC material eigenvalue parameters, combined with the appropriate RPC constitutive relationship and the calculation method of the plastic damage factor, the numerical simulation results considering the bond-slip were in good agreement with the experimental results with a deviation of less than 10%. Thus, it is recommended to select a gentle compressive stress-strain curve in the descending section, an approximate strengthening model of the tensile stress-strain curve, and to use the energy loss method and Sidoroff’s energy equivalence principle to calculate the RPC plastic damage parameters

The X-Ray Transform Projection of 3D Mother Wavelet Function

As we all know, any practical computed tomography (CT) projection data more or less contains noises. Hence, it will be inconvenient for the postprocessing of a reconstructed 3D image even when the noise in the projection data is white. The reason is that the noise in the reconstructed image may be nonwhite. X-ray transform can be applied to the three dimensional (3D) CT, depicting the relationship between material density and ray projection. In this paper, nontensor product relationship between the two dimensional (2D) mother wavelet and 3D mother wavelet is obtained by taking X-ray transform projection of 3D mother wavelet. We proved that the projection of the 3D mother wavelet is a 2D mother wavelet if the 3D mother wavelet satisfies certain conditions. So, the 3D wavelet transform of a 3D image can be implemented by the 2D wavelet transform of its X-ray transform projection and it will contribute to the reduction complexity and computation time during image processing. What is more, it can also avoid noise transfer and amplification during the processing of CT image reconstruction