Although the modelling of the spreading of a forest fire has made considerable progress recently, there remains a lack of reliable field measurements of thermodynamic quantities. We propose in this paper a method and a set of measuring structures built in order to improve the knowledge on the fundamental physical mechanisms that control the propagation of wildland fires. These experimental apparatus are designed to determine: the fire front shape, its rate of spread, the amount of energy impinging ahead of it, the vertical distribution of the temperature within the fire plume as well as the wind velocity and direction. The methodology proposed was applied to a fire spreading across the Corsican scrub on a test site.The recorded data allowed us to reconstruct the fire behaviour and provide its main properties. Wind and vegetation effects on fire behaviour were particularly addressed.

We compare the Calvo and Rotemberg price‐setting mechanisms in a New Keynesian model with trend inflation. We show that: the long‐run relationship between inflation and output is positive in Rotemberg and negative in Calvo; the dynamics of the two models differ even to a first‐order approximation; positive trend inflation enlarges the determinacy region in the Rotemberg model, whereas it shrinks it in the Calvo model; the responses of output and inflation to technology shocks are amplified by trend inflation in Calvo, whereas they are dampened in Rotemberg; the two models imply differing non‐linear adjustments after a disinflation.

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Recently, computer vision-based methods have started to replace conventional sensor based fire detection technologies. In general, visible band image sequences are used to automatically detect suspicious fire events in indoor or outdoor environments. There are several methods which aim to achieve automatic fire detection on visible band images, however, it is difficult to identify which method is the best performing as there is no fire image dataset which can be used to test the different methods. This paper presents a benchmarking of state of the art wildland fire color segmentation algorithms using a new 1 fire dataset introduced for the first time in this paper. The dataset contains images of wildland fire in different contexts (fuel, background, luminosity, smoke). All images of the dataset are characterized according to the principal color of the fire, the luminosity and the presence of smoke in the fire area. With this characterization, it has been possible to determine on which kind of images each algorithm is efficient. Also a new probabilistic fire segmentation algorithm is introduced and compared to the other techniques. Benchmarking is performed in order to assess performances of twelve algorithms that can be used for the segmentation of wildland fire images.

We study the effects of progressive labor income taxation in an otherwise standard New Keynesian (NK) model. We show that progressive taxation (i) introduces a trade‐off between output and inflation stabilization and affects the slope of the Phillips Curve, (ii) acts as automatic stabilizer changing the responses to technology shocks and demand shocks, and (iii) alters the prescription for the optimal monetary policy. The welfare gains from commitment decrease as labor income taxes become more progressive. Quantitatively, the model reproduces the observed negative correlation between the volatility of output, hours, and inflation and the degree of progressivity of labor income taxation.

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This note considers the Leduc and Liu (JME, 2016) model and studies the e¤ects of their uncertainty shock under di¤erent Taylor-types rules. It shows that both the responses of real and nominal variables highly depend on the Taylor rule considered. Remarkably, in ‡ation reacts positively so that uncertainty shocks look more like supply shocks, once an empirically plausible degree of interest rate smoothness is taken into account. This result is reinforced with less reactive monetary rules. Overall, these rules bring about a less severe recession.