Carte générale et détaillée de la Bretagne représentant en grande partie la région occidentale de la France comprise dans 16 départements entiers ou partiels : avec les distances précises entre les principaux endroits / dessiné et gravé par E Collin

Échelle(s) : [ca 1:710 000], échelle de 10 myriamètres [= 14,1 cm]

Nouvelle carte routière de la France : d'après celle revue par de Simencourt indiquant les routes royales et départementales, chemins de traverse, ch[em]ins de fer et canaux navigables, avec les distances en chiffres / dressée par Frémin, géographe ; Gravé par Ch. Dyonnet ; Écrit sur acier par Ch. Simon

Échelle(s) : [ca 1:2 440 000], échelle 100 kilomètres [= 4,1 cm]

Détail pour les distances qu'il y a d'un col ou d'un poste à un autre en lignes horizontalle et le tems qu'il faut pour aller d'un passage à l'autre d'un pas reglé

Ancien possesseur : Argenson, Antoine-René de Voyer (1722-1787 ; marquis de Paulmy d')

Soil fertility, organic carbon and fractions of the organic matter at different distances from eucalyptus stumps

Knowledge on variations in vertical, horizontal and temporal characteristics of the soil chemical properties under eucalyptus stumps left in the soil is of fundamental importance for the management of subsequent crops. The objective of this work was to evaluate the effect of eucalyptus stumps (ES) left after cutting on the spatial variability of chemical characteristics in a dystrophic Yellow Argisol in the eastern coastal plain region of Brazil. For this purpose, ES left for 31 and 54 months were selected in two experimental areas with similar characteristics, to assess the decomposition effects of the stumps on soil chemical attributes. Soil samples were collected directly around these ES, and at distances of 30, 60, 90, 120 and 150 cm away from them, in the layers 0-10, 10-20 and 20-40 cm along the row of ES, which is in-between the rows of eucalyptus trees of a new plantation, grown at a spacing of 3 x 3 m. The soil was sampled in five replications in plots of 900 m² each and the samples analyzed for pH, available P and K (Mehlich-1), exchangeable Al, Ca and Mg, total organic carbon (TOC) and C content in humic substances (HS) and in the free light fraction. The pH values and P, K, Ca2+, Mg2+ and Al3+ contents varied between the soil layers with increasing distance from the 31 and 54-monthold stumps. The highest pH, P, K, Ca2+ and Mg2+ values and the lowest Al3+ content were found in the surface soil layer. The TOC of the various fractions of soil organic matter decreased with increasing distance from the 31 and 54-month-old ES in the 0-10 and 10-20 cm layers, indicating that the root (and stump) cycling and rhizodeposition contribute to maintain soil organic matter. The C contents of the free light fraction, of the HS and TOC fractions were higher in the topsoil layer under the ES left for 31 months due to the higher clay levels of this layer, than in those found under the 54-month-old stumps. However, highest C levels of the different fractions of soil organic matter in the topsoil layer reflect the deposition and maintenance of forest residues on the soil surface, mainly after forest harvest.

Landscape structure affects dispersal in the greater white-toothed shrew: inference between genetic and simulated ecological distances

Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled. We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path

Inferring landscape effects on dispersal from genetic distances: how far can we go?

Functional connectivity affects demography and gene dynamics in fragmented populations. Besides species-specific dispersal ability, the connectivity between local populations is affected by the landscape elements encountered during dispersal. Documenting these effects is thus a central issue for the conservation and management of fragmented populations. In this study, we compare the power and accuracy of three methods (partial correlations, regressions and Approximate Bayesian Computations) that use genetic distances to infer the effect of landscape upon dispersal. We use stochastic individual-based simulations of fragmented populations surrounded by landscape elements that differ in their permeability to dispersal. The power and accuracy of all three methods are good when there is a strong contrast between the permeability of different landscape elements. The power and accuracy can be further improved by restricting analyses to adjacent pairs of populations. Landscape elements that strongly impede dispersal are the easiest to identify. However, power and accuracy decrease drastically when landscape complexity increases and the contrast between the permeability of landscape elements decreases. We provide guidelines for future studies and underline the needs to evaluate or develop approaches that are more powerful.

Morphological, pedigree, and molecular distances and their association with hybrid wheat performance

The objectives of this work were to estimate the genetic distance among wheat genotypes using morphological, pedigree, molecular, and combined morphological and molecular measures, to determine the correlations between these measures, and to evaluate the combining ability of the genotypes. Three generations and two planting designs were studied. Six wheat genotypes were crossed using a diallel design. The F1, F2 and F3generations were evaluated in the field, in the crop seasons of 2003, 2004 and 2005, under spaced plant and full-row planting designs. The estimated general and specific combining abilities of tested hybrids were influenced both by the generation and the planting design. The correlation coefficients among the distance measures and between these measures and genotype performances of different generations for the two planting designs were low to moderate. In order to obtain a more precise estimate of the genetic distance among cultivars and its association with the hybrid performance, more than one generation, planting design, and genetic distance estimation technique should be employed.

Classification of textures seen from different distances and under varying illumination direction

Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image

Potassium distribution in drip irrigation with fertigation for different injection distances in the main line

The purpose of this research was to evaluate the K2O distribution uniformity by surface drip irrigation at Universitat Politecnica de Valencia, Valencia, Spain (39º 29′ N, 0º 23′ W, 20 m). The irrigation was performed by drip lines with not-compensated emitters, spaced 0.3 m. The fertigation was realized using a fertilizer injector pump of electric action with injection of 0.25 h. The experimental design used completely randomized blocks with five treatments and four replications. The treatments consisted of injection in five distances, located at 10; 20; 30; 40; 50 m of the first drip line. Samples were collected in emitters located at the start, at 1/3, at 2/3 and at the end of the drip lines. The nutrient concentration was determined by flame spectrophotometry. The Christiansen's uniformity coefficients (CUC), of distribution (DUC), of statistical (SUC) and of emission (eUC) were estimated. The K2O concentration and distribution decreased linearly with the increase of the injection distance. In all treatments, the CUC, SUC and DUC were described as 'excellent'. The eUC was described as 'recommended' only at smaller injection distances.

Vascular trauma in the Amazon - the challenge of great distances

ABSTRACTObjective:to evaluate the incidence of unfavorable outcomes in vascular trauma patients and their possible correlation to the distance between the city where the injury was sustained and the hospital where the patient received definitive treatment.Methods:descriptive and retrospective study. Data were collected from medical records of patients submitted to surgical procedures for arterial or venous injuries from February 2011 to February 2013 at the only trauma center providing vascular surgery in a vast area of the Amazon region. Trauma date, patient gender and age, mechanism and anatomic topography of injury, surgical management, need for surgical re-intervention, hospitalization period, postoperative complications, mortality and limb amputation rates were analyzed. The incidence of unfavorable outcomes was assessed according to the distance between the city where the vascular injury was sustained and the trauma center.Results: One hundred seventy-three patients with 255 vascular injuries were analyzed; 95.95% were male (p<0.05), mean age of 28.92 years; 47.4% were caused by firearm projectiles (p<0.05); topographic distribution: 45.66% lower limbs (p<0.05), 37.57% upper limbs, 6.94% abdominal, 5.2% thoracic and 4.62% were cervical vascular injuries; 51.42% of patients required hospitalization for seven days or less (p<0.05); limb amputation was necessary in 15.6% and the overall mortality was 6.36%.Conclusion:distances greater than 200Km were associated to longer hospitalization period; distances greater than 300Km were associated to increased limb amputation probability; severe vascular trauma have an increased death probability when patients need to travel more than 200Km for surgical treatment.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Miss [Arthur] Hamilton [née Violet Antonia Fairlie-Cuninghame] (cliché C.N. Londres) [nageuse de longues distances] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 59246

[Mrs Arthur] Hamilton [nageuse de longues distances] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 60185