Comparison of Breien and Canonball Volcanic Tuffs in Southern North Dakota

Volcanic tuffs of Cretaceous age are found sandwiched in many outcrops in southwestern North Dakota. The lateral extent of many of these tuffs has been mapped, but distinguishing discrete tuffs is a work in progress. This report looks at two tuffs found along the Cannonball River south of Bismarck. The Breien Tuff was collected in southeastern Morton County and the Cannonball Tuff was collected in northwestern Sioux County, but research had not yet been done to determine whether these two tuffs are distinct, or if one is merely an extension of the other. The proximity of the two sample sites allows the possibility that the Breien Tuff may be an extension of the Cannonball Tuff. In order to distinguish the tuffs multiple comparative and analytical tests must be performed on both tuffs. Conclusions were made about the possible distinction or correlation of the Cannonball and Breien Tuffs using grain size analysis, x-ray diffraction, magnetic separation, and grain mount petrographic analysis. The Breien and Cannonball Tuffs have few different properties when examined by the unaided eye. By the methods available for this research, insufficient evidence was found to show that the Cannonball Tuff and Breien Tuff were from the same depositional episode. However, further analytical tests of the two tuffs could determine the distinctness of these two tuffs. Scanning electron microscopy, as well as trace element and glass grain chemical analysis are some methods that could further the fingerprinting of these tuffs

Bayesian System Identification of Nonlinear Systems: Informative Training Data through Experimental Design

This paper addresses the situation where one is performing Bayesian system identification on a nonlinear dynamical system using a set of experimentally - obtained training data. To be more specifi c, an investigation is performed to find the optimum form of excitation that should be used during generation of the training data. To that end, the Shannon entr opy is used as an information measure such that, through analysing the information content of t he posterior parameter distribution, the `informativeness' of different sets of training data can be assessed. In the current work the form of excitation is parameterised thus allowing the choosing of an appropriate excitation to be phrased as an optimisat ion problem (where one is aiming to maximise the information content of the training data)

Emerging trends in optimal structural health monitoring system design: From sensor placement to system evaluation

This paper presents a review of advances in the field of Sensor Placement Optimisation (SPO) strategies for Structural Health Monitoring (SHM). This task has received a great deal of attention in the research literature, from initial foundations in the control engineering literature to adoption in a modal or system identification context in the structural dynamics community. Recent years have seen an increasing focus on methods that are specific to damage identification, with the maximisation of correct classification outcomes being prioritised. The objectives of this article are to present the SPO for SHM problem, to provide an overview of the current state of the art in this area, and to identify promising emergent trends within the literature. The key conclusions drawn are that there remains a great deal of scope for research in a number of key areas, including the development of methods that promote robustness to modelling uncertainty, benign effects within measured data, and failures within the sensor network. There also remains a paucity of studies that demonstrate practical, experimental evaluation of developed SHM system designs. Finally, it is argued that the pursuit of novel or highly efficient optimisation methods may be considered to be of secondary importance in an SPO context, given that the optimisation effort is expended at the design stage

Simplifying transformations for nonlinear systems: Part I, an optimisation-based variant of normal form analysis

This paper introduces the idea of a ‘simplifying transformation’ for nonlinear structural dynamic systems. The idea simply stated; is to bring under one heading, those transformations which ‘simplify’ structural dynamic systems or responses in some sense. The equations of motion may be cast in a simpler form or decoupled (and in this sense, nonlinear modal analysis is encompassed) or the responses may be modified in order to isolate and remove certain components. It is the latter sense of simplification which is considered in this paper. One can regard normal form analysis in a way as the removal of superharmonic content from nonlinear system response. In the current paper, this problem is cast in an optimisation form and the differential evolution algorithm is used

Competing Interactions among Supramolecular Structures on Surfaces

A simple model was constructed to describe the polar ordering of non-centrosymmetric supramolecular aggregates formed by self assembling triblock rodcoil polymers. The aggregates are modeled as dipoles in a lattice with an Ising-like penalty associated with reversing the orientation of nearest neighbor dipoles. The choice of the potentials is based on experimental results and structural features of the supramolecular objects. For films of finite thickness, we find a periodic structure along an arbitrary direction perpendicular to the substrate normal, where the repeat unit is composed of two equal width domains with dipole up and dipole down configuration. When a short range interaction between the surface and the dipoles is included the balance between the up and down dipole domains is broken. Our results suggest that due to surface effects, films of finite thickness have a none zero macroscopic polarization, and that the polarization per unit volume appears to be a function of film thickness.Comment: 3 pages, 3 eps figure

Fueling the Credit Crisis: Who Uses Consumer Credit and What Drives Debt Burden?

Excessive household debt contributed to the worst recession in decades. Insights about borrowing and spending behavior can inform economic recovery forecasts, policy decisions, and financial education. This study identifies life cycle and credit attitude as key determinants of who uses debt. Younger households are more likely to borrow for consumption, as are those who believe that it is all right to borrow to purchase luxury goods or cover living expenses. Furthermore, households that condone borrowing for these purposes have a higher consumer debt burden. Debt capacity (or creditworthiness) and financial discipline are also significant factors in determining household debt use

A Behavioral Life-Cycle Approach to Understanding the Wealth Effect

The somewhat surprising strength in consumer spending in recent years has focused renewed attention on the much-debated wealth effect, the notion that when individuals feel wealthier, they consume more. This study utilizes survey data to examine the wealth effect within the context of the behavioral life-cycle model of savings. The results indicate that the likelihood of households spending more when their assets increase in value decreases with the portion of assets held in home equity. This unexpected finding is due to homeowners responding to the perceived wealth gain from increased home values by cashing out their equity. The likelihood increases with the portion of assets held in stock outside of retirement accounts, but is not significantly related to the portion of assets held in stock overall. Moreover, households that have a full-time income earner, are homeowners, have more education, have a younger household head, or expect economic growth, are more likely to report a wealth effect. Households that utilize savings “rules of thumb” are less likely to report a wealth effect. These results can be used to improve the wealth effect specification in consumer demand models and assist firms to target consumer markets

A machine learning approach to nonlinear modal analysis

Although linear modal analysis has proved itself to be the method of choice for the analysis of linear dynamic structures, its extension to nonlinear structures has proved to be a problem. A number of competing viewpoints on nonlinear modal analysis have emerged, each of which preserves a subset of the properties of the original linear theory. From the geometrical point of view, one can argue that the invariant manifold approach of Shaw and Pierre is the most natural generalisation. However, the Shaw–Pierre approach is rather demanding technically, depending as it does on the analytical construction of a mapping between spaces, which maps physical coordinates into invariant manifolds spanned by independent subsets of variables. The objective of the current paper is to demonstrate a data-based approach motivated by Shaw–Pierre method which exploits the idea of statistical independence to optimise a parametric form of the mapping. The approach can also be regarded as a generalisation of the Principal Orthogonal Decomposition (POD). A machine learning approach to inversion of the modal transformation is presented, based on the use of Gaussian processes, and this is equivalent to a nonlinear form of modal superposition. However, it is shown that issues can arise if the forward transformation is a polynomial and can thus have a multi-valued inverse. The overall approach is demonstrated using a number of case studies based on both simulated and experimental data