The computational efficiency of random field representations with the Karhunen–Loève (KL) expansion relies on the solution of a Fredholm integral eigenvalue problem. This contribution compares ...different methods that solve this problem. Focus is put on methods that apply to arbitrary shaped domains and arbitrary autocovariance functions. These include the Nyström method as well as collocation and Galerkin projection methods. Among the Galerkin methods, we investigate the finite element method (FEM) and propose the application of the finite cell method (FCM). This method is based on an extension to the FEM but avoids mesh generation on domains of complex geometric shape. The FCM was originally presented in Parvizian et al. (2007) 17 for the solution of elliptic boundary value problems. As an alternative to the L2-projection of the covariance function used in the Galerkin method, H1/2-projection and discrete projection are investigated. It is shown that the expansion optimal linear estimation (EOLE) method proposed in Li and Der Kiureghian (1993) 18 constitutes a special case of the Nyström method. It is found that the EOLE method is most efficient for the numerical solution of the KL expansion. The FEM and the FCM are more efficient than the EOLE method in evaluating a realization of the random field and, therefore, are suitable for problems in which the time spent in the evaluation of random field realizations has a major contribution to the overall runtime – e.g., in finite element reliability analysis.
One of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is high-throughput DNA- or ...RNA-based 16S rRNA (gene) amplicon sequencing in combination with bioinformatics analyses. However, focusing on environmental samples from contrasting habitats, it was not systematically evaluated (i) which analysis methods provide results that reflect reality most accurately, (ii) how the interpretations of microbial community studies are biased by different analysis methods and (iii) if the most optimal analysis workflow can be implemented in an easy-to-use pipeline. Here, we compared the performance of 16S rRNA (gene) amplicon sequencing analysis tools (i.e., Mothur, QIIME1, QIIME2, and MEGAN) using three mock datasets with known microbial community composition that differed in sequencing quality, species number and abundance distribution (i.e., even or uneven), and phylogenetic diversity (i.e., closely related or well-separated amplicon sequences). Our results showed that QIIME2 outcompeted all other investigated tools in sequence recovery (>10 times fewer false positives), taxonomic assignments (>22% better F-score) and diversity estimates (>5% better assessment), suggesting that this approach is able to reflect the in situ microbial community most accurately. Further analysis of 24 environmental datasets obtained from four contrasting terrestrial and freshwater sites revealed dramatic differences in the resulting microbial community composition for all pipelines at genus level. For instance, at the investigated river water sites Sphaerotilus was only reported when using QIIME1 (8% abundance) and Agitococcus with QIIME1 or QIIME2 (2 or 3% abundance, respectively), but both genera remained undetected when analyzed with Mothur or MEGAN. Since these abundant taxa probably have implications for important biogeochemical cycles (e.g., nitrate and sulfate reduction) at these sites, their detection and semi-quantitative enumeration is crucial for valid interpretations. A high-performance computing conformant workflow was constructed to allow FAIR (Findable, Accessible, Interoperable, and Re-usable) 16S rRNA (gene) amplicon sequence analysis starting from raw sequence files, using the most optimal methods identified in our study. Our presented workflow should be considered for future studies, thereby facilitating the analysis of high-throughput 16S rRNA (gene) sequencing data substantially, while maximizing reliability and confidence in microbial community data analysis.
MicroProteins are short, single domain proteins that act by sequestering larger, multi-domain proteins into non-functional complexes. MicroProteins have been identified in plants and animals, where ...they are mostly involved in the regulation of developmental processes. Here we show that two Arabidopsis thaliana microProteins, miP1a and miP1b, physically interact with CONSTANS (CO) a potent regulator of flowering time. The miP1a/b-type microProteins evolved in dicotyledonous plants and have an additional carboxy-terminal PF(V/L)FL motif. This motif enables miP1a/b microProteins to interact with TOPLESS/TOPLESS-RELATED (TPL/TPR) proteins. Interaction of CO with miP1a/b/TPL causes late flowering due to a failure in the induction of FLOWERING LOCUS T (FT) expression under inductive long day conditions. Both miP1a and miP1b are expressed in vascular tissue, where CO and FT are active. Genetically, miP1a/b act upstream of CO thus our findings unravel a novel layer of flowering time regulation via microProtein-inhibition.
At geotechnical sites, deformation measurements are routinely performed during the construction process. In this paper, it is shown how information from such measurements can be utilized to update ...the reliability estimate of the geotechnical site at future construction stages. A recently proposed method for Bayesian updating of the reliability is successfully applied in conjunction with a stochastic nonlinear geotechnical finite element model. Therein, uncertainty in the soil material properties is modelled by non-Gaussian random fields. The structural reliability evaluations required for the Bayesian updating are carried out by means of subset simulation, an efficient adaptive Monte Carlo method. The approach is demonstrated through an application to a sheet pile wall at a deformation-sensitive geotechnical construction site.
•A new method for performing global reliability sensitivity analysis is proposed.•It requires as an input only samples that fall in the failure domain.•It can be carried out without extra model ...evaluations following reliability analysis.•It can be implemented with any sampling-based reliability analysis method.•It is much more efficient than currently existing methods.
Global reliability sensitivity analysis (RSA) can help to assess the effects of input random variables X on the probability of failure Pr(F) of an engineering system. Conventionally, this requires repeated evaluations of the conditional failure probability Pr(F|Xi = xi) for multiple values of the input random variable Xi and for all Xi of interest. Such a solution is straightforward but computationally expensive. In this paper, we propose a new method to perform global RSA, which requires as an input only samples of X that fall in the failure domain. Such samples are a by-product of many sampling-based reliability analysis methods. The proposed method constructs the Pr(F|Xi = xi) by application of Bayes’ rule, based on the probability density function (PDF) of X conditioned on system failure F. This conditional PDF is approximated with a kernel density estimation from the failure samples. In this way, the reliability sensitivities of all the input random variables can be computed following a sampling-based reliability analysis with no additional computation cost. The approach is investigated on numerical examples in conjunction with crude Monte Carlo simulation, importance sampling and subset simulation. The results demonstrate the computational advantages over existing single-loop sampling methods for global RSA.
We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I &M) planning. Unlike other DRL ...algorithms for (I &M) planning, the proposed +RQN architecture dispenses with computing the belief state and directly handles erroneous observations instead. We apply the algorithm to a basic I &M planning problem for a one-component system subject to deterioration. In addition, we investigate the performance of Monte Carlo tree search for the I &M problem and compare it to the +RQN. The comparison includes a statistical analysis of the two methods’ resulting policies, as well as their visualization in the belief space.
High arsenic (As) concentrations in groundwater are a worldwide problem threatening the health of millions of people. Microbial processes are central in the (trans)formation of the As-bearing ferric ...and ferrous minerals, and thus regulate dissolved As levels in many aquifers. Mineralogy, microbiology and dissolved As levels can vary sharply within aquifers, making high-resolution measurements particularly valuable in understanding the linkages between them. We conducted a high spatial resolution geomicrobiological study in combination with analysis of sediment chemistry and mineralogy in an alluvial aquifer system affected by geogenic As in the Red River delta in Vietnam. Microbial community analysis revealed a dominance of fermenters, methanogens and methanotrophs whereas sediment mineralogy along a 46 m deep core showed a diversity of Fe minerals including poorly crystalline Fe (II/III) and Fe(III) (oxyhydr)oxides such as goethite, hematite, and magnetite, but also the presence of Fe(II)-bearing carbonates and sulfides which likely formed as a result of microbially driven organic carbon (OC) degradation. A potential important role of methane (CH4) as electron donor for reductive Fe mineral (trans)formation was supported by the high abundance of Candidatus Methanoperedens, a known Fe(III)-reducing methanotroph. Overall, these results imply that OC turnover including fermentation, methanogenesis and CH4 oxidation are important mechanisms leading to Fe mineral (trans)formation, dissolution and precipitation, and thus indirectly affecting As mobility by changing the Fe-mineral inventory.
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•Fermentation, methanogenesis and methanotrophy prevail at the As-contaminated site.•Reducing aquifer consists of gray sediment and is dominated by Fe(III) (oxyhydr)oxides and As(III).•Less reducing aquifer consists of yellow-brown sediment and is dominated by goethite and As(V).•Anaerobic CH4 oxidation likely supports carbonate mineral formation.•Methanotrophic Fe(III)-reducer Ca. Methanoperedens coincides with As and Fe peaks in sediments.
•Value of information (VoI) analysis allows to quantify the benefits of monitoring.•A framework for modeling and computing VoI with structural reliability methods is presented.•A computationally ...efficient algorithm is introduced, based on importance sampling.•The framework is illustrated through an application to fatigue monitoring.
When designing monitoring systems and planning inspections, engineers must assess the benefits of the additional information that can be obtained and weigh them against the cost of these measures. The value of information (VoI) concept of the Bayesian statistical decision analysis provides a formal framework to quantify these benefits. This paper presents the determination of the VoI when information is collected to increase the reliability of engineering systems. It is demonstrated how structural reliability methods can be used to effectively model the VoI and an efficient algorithm for its computation is proposed. The theory and the algorithm are demonstrated by an illustrative application to monitoring of a structural system subjected to fatigue deterioration.
Acoustic emissions (AE) are stress waves initiated by sudden strain releases within a solid body. These can be caused by internal mechanisms such as crack opening or propagation, crushing, or rubbing ...of crack surfaces. One application for the AE technique in the field of Structural Engineering is Structural Health Monitoring (SHM). With piezo-electric sensors mounted to the surface of the structure, stress waves can be detected, recorded, and stored for later analysis. An important step in quantitative AE analysis is the estimation of the stress wave source locations. Commonly, source location results are presented in a rather deterministic manner as spatial and temporal points, excluding information about uncertainties and errors. Due to variability in the material properties and uncertainty in the mathematical model, measures of uncertainty are needed beyond best-fit point solutions for source locations. This paper introduces a novel holistic framework for the development of a probabilistic source location algorithm. Bayesian analysis methods with Markov Chain Monte Carlo (MCMC) simulation are employed where all source location parameters are described with posterior probability density functions (PDFs). The proposed methodology is applied to an example employing data collected from a realistic section of a reinforced concrete bridge column. The selected approach is general and has the advantage that it can be extended and refined efficiently. Results are discussed and future steps to improve the algorithm are suggested.
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