A fifty year record of winter glacier melt events in southern Chile, 38°–42°S

Little is known about the frequency and potential mass balance impact of winter glacier melt events. In this study, daily atmospheric temperature soundings from the Puerto Montt radiosonde (41.43°S) are used to reconstruct winter melting events at the glacier equilibrium line altitude in the 38°–42°S region of southern Chile, between 1960 and 2010. The representativeness of the radiosonde temperatures to near-surface glacier temperatures is demonstrated using meteorological records from close to the equilibrium line on two glaciers in the region over five winters. Using a degree-day model we estimate an average of 0.28 m of melt and 21 melt days in the 15 June–15 September period each year, with high inter-annual variability. The majority of melt events are associated with midlatitude migratory high pressure systems crossing Chile and northwesterly flows, that force adiabatic compression and warm advection, respectively. There are no trends in the frequency or magnitude of melt events over the period of record, but the annual frequency of winter melt days shows a significant, although rather weak and probably non-linear, relationship to late winter and early spring values of a multivariate El Niño Southern Oscillation Index (MEI)

Investigating the persistence of tick-borne pathogens via the R0 model

In the epidemiology of infectious diseases, the basic reproduction number,R0, has a number of important applications, most notably it can be used to predict whether a pathogen is likely to become established, or persist, in a given area. We used the R0 model to investigate the persistence of 3 tick-borne pathogens; Babesia microti, Anaplasma phagocytophilum and Borrelia burgdorferi sensu lato in an Apodemus sylvaticus-Ixodes ricinus system. The persistence of these pathogens was also determined empirically by screening questing ticks and wood mice by PCR. All 3 pathogens behaved differently in response to changes in the proportion of transmission hosts on which I. ricinus fed, the efficiency of transmission between the host and ticks and the abundance of larval and nymphal ticks found on small mammals. Empirical data supported theoretical predictions of the R0 model. The transmission pathway employed and the duration of systemic infection were also identified as important factors responsible for establishment or persistence of tick-borne pathogens in a given tick-host system. The current study demonstrates how the R0 model can be put to practical use to investigate factors affecting tick-borne pathogen persistence, which has important implications for animal and human health worldwide

A simple inverse method for the interpretation of pumped flowing fluid electrical conductivity logs

Pumped flowing fluid electrical conductivity (FFEC) logs, also known as pumped borehole dilution testing, is an experimentally easy‐to‐perform approach to evaluating vertical variations in the hydraulic conductivity of an aquifer. In contrast to the simplicity of the logging equipment, analysis of the data is complex and laborious. Current methods typically require repeated solution of the advection‐dispersion equation (ADE) for describing the flow in the borehole and comparison with the experimental results. In this paper, we describe a direct solution for determining borehole fluid velocity that bypasses the need for complex numerical computation and repetitive optimization. The method rests on the observation that, while solving the ADE for concentration profile in the borehole (as required for modeling and combined methods) is computationally challenging, the solution for flow distribution along the length of the borehole given concentration data is straightforward. The method can accommodate varying borehole diameters, and uses the fact that multiple profiles are taken in the standard logging approach to reduce the impact of noise. Data from both a simulated borehole and from a field test are successfully analyzed. The method is implemented in a spreadsheet, which is available as supporting information material to this paper

Knowledge-assisted ranking: A visual analytic application for sports event data

© 2016 IEEE. Organizing sports video data for performance analysis can be challenging, especially in cases involving multiple attributes and when the criteria for sorting frequently changes depending on the user's task. The proposed visual analytic system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. The authors use regression techniques to train different analytical models for different types of sorting requirements and use visualization to facilitate knowledge discovery at different stages of the process. They demonstrate the system with a rugby case study to find key instances for analyzing team and player performance. Organizing sports video data for performance analysis can be challenging in cases with multiple attributes, and when sorting frequently changes depending on the user's task. As this video shows, the proposed visual analytic system allows interactive data sorting and exploration

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. © 1995-2012 IEEE