Demonstration of dual-band infrared thermal imaging at Grass Valley Creek bridges

We demonstrated dual-band infrared (DBIR) thermal imaging at the Grass Valley Creek Bridges near Redding CA. DBIR thermal imaging is an enabling technology for rapid, reliable, bridge deck inspections while minimizing lane closures. bridge-deck inspections were conducted from a mobile DBIR bridge inspection laboratory during November 2-3, 1995. We drove this self-contained unit at limited highway speeds over 0.4 lane miles of bridge deck. Using two thermal IR bands, we distinguished delaminations from clutter. Clutter, or unwanted thermal detail, occurs from foreign materials or uneven shade on the bridge deck surface. By mapping the DBIR spectral- response differences at 3-5 {mu}m and 8-12 {mu}m, we removed foreign material clutter. By mapping the deck diurnal thermal inertia variations, we removed clutter from uneven shade. Thermal inertia is a bulk deck property, the square root of thermal conductivity x density x heat capacity. Delaminated decks have below-average thermal inertias, or above-average day-night temperature excursions. Compared to normal decks areas, delaminated deck areas were typically 2 or 3 {degrees}C warmer at noon, and 0.5{degrees}C cooler at night. The mobile DBIR bridge inspection laboratory is currently undergoing extensive testing to examine bridges by the Federal Highway Administration

The International Postal Network and Other Global Flows as Proxies for National Wellbeing.

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals.Project LASAGNE Contract No. 318132 (STREP) - funded by the European CommissionThis is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.015597

Model selection in historical research using approximate Bayesian computation

Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to reevaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester's laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence.Funding for this work was provided by the SimulPast Consolider Ingenio project (CSD2010-00034) of the former Ministry for Science and Innovation of the Spanish Government and the European Research Council Advanced Grant EPNet (340828).Peer ReviewedPostprint (published version

Banks' Net Interest Margin and the Level of Interest Rates

The prevailing view in the literature is that, in the long run, an increase in the level of interest rates will impact positively on banks' net interest margins. Using a time series of more than 40 years for the German banking system, we confirm this effect (the net interest margin increases by 7 basis points for every 100 basis point increase in the interest rate level). What is more, we show that the opposite effect exists in the short run. In addition, we analyze the consequences of the low-interest-rate environment and find that banks' interest margins on retail deposits, especially term deposits, have declined by up to 97 basis points

Digital holographic imaging as a method for quantitative, live cell imaging of drug response to novel targeted cancer therapies

Digital holographic imaging (DHI) is a noninvasive, live cell imaging technique that enables long-term quantitative visualization of cells in culture. DHI uses phase-shift imaging to monitor and quantify cellular events such as cell division, cell death, cell migration, and drug responses. In recent years, the application of DHI has expanded from its use in the laboratory to the clinical setting, and currently it is being developed for use in theranostics. Here, we describe the use of the DHI platform HoloMonitorM4 to evaluate the effects of novel, targeted cancer therapies on cell viability and proliferation using the HeLa cancer cell line as a model. We present single cell tracking and population-wide analysis of multiple cell morphology parameters