Uncertainty In Online Dating

Relationship initiation is a moment typically characterized by high uncertainty, and online dating platforms have the potential to heighten uncertainty and thus deeply affect relationship formation dynamics. While previous research has focused on other-uncertainty and on its reduction through information-seeking, this qualitative study adopts Babrow’s (2001) problematic integration (PI) theory to expand our understanding of uncertainty in online dating beyond other-focused uncertainty, by exploring the meanings and sources of uncertainty in online dating, how uncertainty is appraised, and what strategies daters adopt to cope with it. Data obtained from 13 semi-structured interviews with active online daters was analyzed using thematic analysis. The analysis uncovered multiple sources of uncertainty related to the self, the other, and the relationship, and multiple epistemological and ontological meanings daters ascribed to uncertainty that validated PI’s perspective. Daters often appraised uncertainty as undesirable, but they also appeared willing to tolerate unsolved uncertainty they deemed acceptable; furthermore, uncertainty was not always salient in their interactions, suggesting the need for further investigation into the biopsychological aspects of the appraisal process. Daters’ varied coping strategies revealed that uncertainty reduction was often forgone when it conflicted with communication goals, and the reliance on decision-making heuristics reflected the recognition that uncertainty could rarely be solved in full, especially in a context characterized by an emphasis on mate selection and abundance of choice. The analysis also revealed that the online dating process has the potential to increase the negative effects of uncertainty by delaying interpersonal communication and thus problematizing the most significant coping strategy available to daters

Basic Types of Coarse-Graining

We consider two basic types of coarse-graining: the Ehrenfests' coarse-graining and its extension to a general principle of non-equilibrium thermodynamics, and the coarse-graining based on uncertainty of dynamical models and Epsilon-motions (orbits). Non-technical discussion of basic notions and main coarse-graining theorems are presented: the theorem about entropy overproduction for the Ehrenfests' coarse-graining and its generalizations, both for conservative and for dissipative systems, and the theorems about stable properties and the Smale order for Epsilon-motions of general dynamical systems including structurally unstable systems. Computational kinetic models of macroscopic dynamics are considered. We construct a theoretical basis for these kinetic models using generalizations of the Ehrenfests' coarse-graining. General theory of reversible regularization and filtering semigroups in kinetics is presented, both for linear and non-linear filters. We obtain explicit expressions and entropic stability conditions for filtered equations. A brief discussion of coarse-graining by rounding and by small noise is also presented.Comment: 60 pgs, 11 figs., includes new analysis of coarse-graining by filtering. A talk given at the research workshop: "Model Reduction and Coarse-Graining Approaches for Multiscale Phenomena," University of Leicester, UK, August 24-26, 200

SEDIMENT NITROGEN DYNAMICS IN BACKWATER WETLAND CONFLUENCES OF A REGULATED RIVER

As harmful algal blooms in regulated river systems have increased in the past decade, the importance of understanding sediment nutrients has also increased. Research linking nutrient processes and fine sediment dynamics to harmful algal blooms in confluence wetlands along regulated rivers has recently become apparent. However, the relationship between sediment nutrient dynamics in confluence wetlands has been understudied. Utilization of sediment fingerprinting, high-frequency water quality monitoring, and tracer unmixing mass-balance modeling, was able to suggest sediment N mineralization in Appalachia confluence riparian wetland was not a dominate source of nitrate downstream. Further measures of supplementary tracers and additional sediment sources were coupled with stable isotope unmixing modeling. The use of a dual-isotope unmixing method reduced uncertainty for majority of events where internal sediment sources were prominent. Potential sources are especially important to characterize when using stable isotope tracing techniques to continue the reduction of model uncertainty. Dual-isotope unmixing results process through mass-balance modeling demonstrated small tributaries may have larger depositional impacts on a confluence wetland rather than the localized regulated river, emphasizing the importance of nutrient reduction in small scale waterways. The use of this data significantly improves mass-balance unmixing modeling for confluence wetlands and implementation of floodplains in watershed management practices

Multiple Model Adaptive Estimator Target Tracker for Maneuvering Targets in Clutter

The task of tracking a target in the presence of measurement clutter is a two-fold problem: one of handling measurement association uncertainty (due to clutter), and poorly known or significantly varying target dynamics. Measurement association uncertainty does not allow conventional tracking algorithms (such as Kalman filters) to be implemented directly. Poorly known or varying target dynamics complicate the design of any tracking filter, and filters using only a single dynamics model can rarely handle anything beyond the most benign target maneuvers. In recent years, the Multiple Hypothesis Tracker (MHT) has gained acceptance as a means of handling targets in a measurement-clutter environment. MHT algorithms rely on Gaussian mixture representations of a target\u27s current state estimate, and the number of components within these mixtures grows exponentially with each successive sensor scan. Previous research into techniques that limit the growth of Gaussian mixture components proved that the Integral Square Error cost-function-based algorithm performs well in this role. Also, multiple-model adaptive algorithms have been shown to handle poorly known target dynamics or targets that exhibit a large range of maneuverability over time with excellent results. This research integrates the ISE mixture reduction algorithm into Multiple-Model Adaptive Estimator (MMAE) and Interacting Mixed Model (IMM) tracking algorithms. The algorithms were validated to perform well at a variety of measurement clutter densities by using a Monte Carlo simulation environment based on the C++ language. Compared to single-dynamics-model MHT trackers running against a maneuvering target, the Williams-filter-based, multiple-model algorithms exhibited superior tracking performance