The duration of the school-to-work transition in Italy and in other European countries: a flexible baseline hazard interpretation

Purpose: The Italian school-to-work transition (STWT) is astonishingly slow and long in comparison to the other EU countries. We analyze its determinants comparing the Italian case with Austria, Poland and the UK. Design/methodology/approach: The analysis is based on a Cox survival model with proportional hazard. The smoothed hazard estimates allow us to identify the nonlinear path of the hazard function. Findings: The authors reckon that the actual length of the transition to a stable job is around 30 months in Italy. Conversely, it is less than one year in the other countries. Women are particularly penalized, despite being on average more educated than men. Tertiary or vocational education at high secondary school strongly increases the hazard rate to a regular job. The smoothed hazard estimates suggest positive duration dependence at the beginning of the transition and slightly negative thereafter. Practical implications: Stimulating economic growth and investing in education and training are important pre-conditions for shortening the transition. Originality/value: Despite the duration of the STWT is one of the most important indicators to measure the efficiency of the STWT, it is not easy to measure. The authors build on their previous research work on this topic, but relaxing the assumption of a monotonic hazard rate and using the flexible baseline hazard approach to test for the existence of nonlinear duration dependence. Furthermore, they extend the analysis by including student-workers who attended a vocational path of education, in order to detect its effectiveness in allowing young people finding a job sooner

Cartografía de severidad de incendios forestales a partir de la combinación del modelo de mezclas espectrales y la clasificación basada en objetos

This study shows an accurate and fast methodology in order to evaluate fire severity classes of large forest fires. A single Landsat Enhanced Thematic Mapper multispectral image was utilized in this study with the aim of mapping fire severity classes (high, moderate and low) using a combined-approach based in an spectral mixing model and object-based image analysis. A large wildfire in the Northwest of Spain is used to test the model. Fraction images obtained by Landsat unmixing were used as input data in the object-based image analysis. A multilevel segmentation and a classification were carried out by using membership functions. This method was compared with other simplest ones in order to evaluate the suitability to distinguish between the three fire severity classes above mentioned. McNemar’s test was used to evaluate the statistical significance of the difference between approaches tested in this study. The combined approach achieved the highest accuracy reaching 97.32% and kappa index of agreement of 95.96% and improving accuracy of individual classes.Este estudio presenta una metodología rápida y precisa para la evaluación de los niveles de severidad que afectan a grandes incendios forestales. El trabajo combina un modelo de mezclas espectrales y un análisis de imágenes basado en objetos con el objetivo de cartografiar distintos niveles de severidad (alto, moderado y bajo) empleando una imagen multiespectral Landsat Enhanced Thematic Mapper. Este modelo es testado en un gran incendio forestal ocurrido en el noroeste de España. Las imágenes fracción obtenidas tras aplicar el modelo de mezclas a la imagen Landsat fueron utilizadas como datos de entrada en el análisis basado en objetos. En este se llevó a cabo una segmentación multinivel y una posterior clasificación usando funciones de pertenencia. Esta metodología fue comparada con otras más simples con el fin de evaluar su conveniencia a al hora de distinguir entre los tres niveles de severidad anteriormente mencionados. El test de McNemar fue empleado para evaluar la significancia estadística de la diferencia entre los métodos testados en el estudio. El método combinado alcanzó la más alta precisión con un 97,32% y un índice Kappa del 95,96%, además de mejorar la precisión de los niveles individualmente

Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data

P. 102-118All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR.S

Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature

14 p.Forest fires are incidents of great importance in Mediterranean environments. Landsat data have proven to be suitable for evaluating post-fire vegetation damage and determining different levels of burn severity, which is crucial for planning post-fire rehabilitation. This study assessed the utility of combined Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images and Land Surface Temperature (LST) to accurately map burn severity. We studied a large convection- dominated wildfire, which occurred on 19–21 September 2012 in Spain, in a zone dominated by Pinus pinaster Ait. Burn severity degree (low, moderate, and high) was measured 2–3 months after fire in 111 field plots using the Composite Burn Index (CBI). Four fraction images were generated using MESMA from the reflective bands of a post-fire Landsat 7 Enhanced Thematic Mapper (ETM +) image: 1.-char, 2.-green vegetation (GV), 3.-non-photosynthetic vegetation and soil (NPVS) and 4.-shade. The thermal band was converted to LST using a single channel algorithm. Next, Multinomial Logistic Regression (MLR) was used to obtain the probability of each burn severity level from MESMA fraction images and LST. Finally, a burn severity map was generated from the probability images and independently validated using an error matrix, producer and user accuracies per class, and κ statistic. MLR identified the char fraction image and LST as the only significant explanatory variables when burn severity acted as the response variable. Two burn severity degrees (low-moderate and high) were finally considered to build the final burn severity map. In this way, we reached a higher accuracy (κ = 0.79) than using the original three burn severity levels (κ = 0.66). Our study demonstrates the validity of combining fraction images and LST from Landsat data to map burn severity accurately in Mediterranean countriesS

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

14 p.Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999–2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forestsS

Long-range selective transport of anions and cations in graphene oxide membranes, causing selective crystallization on the macroscale

Monoatomic nanosheets can form 2-dimensional channels with tunable chemical properties, for ion storage and filtering applications. Here, we demonstrate transport of K+, Na+, and Li+ cations and F- and Cl- anions on the centimeter scale in graphene oxide membranes (GOMs), triggered by an electric bias. Besides ion transport, the GOM channels foster also the aggregation of the selected ions in salt crystals, whose composition is not the same as that of the pristine salt present in solution, highlighting the difference between the chemical environment in the 2D channels and in bulk solutions

Vegetation and soil fire damage analysis based on species distribution modeling trained with multispectral satellite data

Producción CientíficaForest managers demand reliable tools to evaluate post-fire vegetation and soil damage. In this study, we quantify wildfire damage to vegetation and soil based on the analysis of burn severity, using multitemporal and multispectral satellite data and species distribution models, particularly maximum entropy (MaxEnt). We studied a mega-wildfire (9000 ha burned) in North-Western Spain, which occurred from 21 to 27 August 2017. Burn severity was measured in the field using the composite burn index (CBI). Burn severity of vegetation and soil layers (CBIveg and CBIsoil) was also differentiated. MaxEnt provided the relative contribution of each pre-fire and post-fire input variable on low, moderate and high burn severity levels, as well as on all severity levels combined (burned area). In addition, it built continuous suitability surfaces from which the burned surface area and burn severity maps were built. The burned area map achieved a high accuracy level (κ = 0.85), but slightly lower accuracy when differentiating the three burn severity classes (κ = 0.81). When the burn severity map was validated using field CBIveg and CBIsoil values we reached lower κ statistic values (0.76 and 0.63, respectively). This study revealed the effectiveness of the proposed multi-temporal MaxEnt based method to map fire damage accurately in Mediterranean ecosystems, providing key information to forest managers.Ministerio de Economía, Industria y Competitividad (project 559 AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17)Ministerio de Educación, Cultura y Deporte (grants PRX17/00234 and PRX17/00133

Evidence for the Gompertz Curve in the Income Distribution of Brazil 1978-2005

This work presents an empirical study of the evolution of the personal income distribution in Brazil. Yearly samples available from 1978 to 2005 were studied and evidence was found that the complementary cumulative distribution of personal income for 99% of the economically less favorable population is well represented by a Gompertz curve of the form $G(x)=\exp [\exp (A-Bx)]$, where $x$ is the normalized individual income. The complementary cumulative distribution of the remaining 1% richest part of the population is well represented by a Pareto power law distribution $P(x)= \beta x^{-\alpha}$. This result means that similarly to other countries, Brazil's income distribution is characterized by a well defined two class system. The parameters $A$, $B$, $\alpha$, $\beta$ were determined by a mixture of boundary conditions, normalization and fitting methods for every year in the time span of this study. Since the Gompertz curve is characteristic of growth models, its presence here suggests that these patterns in income distribution could be a consequence of the growth dynamics of the underlying economic system. In addition, we found out that the percentage share of both the Gompertzian and Paretian components relative to the total income shows an approximate cycling pattern with periods of about 4 years and whose maximum and minimum peaks in each component alternate at about every 2 years. This finding suggests that the growth dynamics of Brazil's economic system might possibly follow a Goodwin-type class model dynamics based on the application of the Lotka-Volterra equation to economic growth and cycle.Comment: 22 pages, 15 figures, 4 tables. LaTeX. Accepted for publication in "The European Physical Journal B

Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Producción CientíficaPrescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (project AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17

Self-reported daily walking time in COPD : relationship with relevant clinical and functional characteristics

Altres ajuts: The FyCEPOC and INSEPOC studies were funded by Laboratorios Esteve SA (Barcelona, Spain). The NEREA study was funded by an unrestricted grant from J Uriach y Compañía SA. The DEPREPOC study was funded by Grupo Ferrer (Barcelona, Spain). The funding bodies have no involvement in the analysis and interpretation of data, the writing of the report, or in the decision to submit the article for publication.Quantifying physical activity in chronic obstructive pulmonary disease (COPD) is important as physical inactivity is related to poor health outcomes. This study analyzed the relationship between patients' self-reported daily walking time and relevant characteristics related to COPD severity. Pooled analysis was performed on data from four observational studies on which daily walking time was gathered from a personal interview. Patients were classified as physically inactive if walking time was 3, post-bronchodilator forced expiratory volume in the first second 4, and CAT score >30. Lower self-reported walking times are related to worse markers of disease severity in COPD