Le vélo électrique: un outil pour la santé ou un gadget "écolo" [Electrically assisted bicycles: health enhancement or "green" gadget?].

Electrically assisted bicycles (EAB) are flourishing in cities throughout the world and capitalize on ecological and practical advantages, helping in the fight against pollution, CO2 emissions and traffic jam. Human power is necessary to activate the electrical support, so that it equals to a moderate intensity physical activity (> 3 MET), or a vigorous one on hilly courses (>6 MET). The ecological benefits are obvious and transportation departments tend to support citizens who purchase one. EAB offer increased mobility at speeds of 15 to 25 km/h depending on hills and fitness of the rider, but could cause more accidents. EAB is linked to a real physical activity beneficial for health, but potentially more dangerous than a traditional bicycle.

Transition to parenthood: The role of social interaction and endogenous networks

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdepen- dencies across individuals' transition to parenthood and its timing we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics regarding age, intended education and parity. Agents endogenously form their network based on social closeness. Network members then may influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social inter- actions can explain the shift of first-birth probabilities in Austria over the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

Recovery of soil physical properties by green manure, liming, gypsum and pasture and spontaneous native species¹

Inadequate usage can degrade natural resources, particularly soils. More attention has been paid to practices aiming at the recovery of degraded soils in the last years, e.g, the use of organic fertilizers, liming and introduction of species adapted to adverse conditions. The purpose of this study was therefore to investigate the recovery of physical properties of a Red Latosol (Oxisol) degraded by the construction of a hydroelectric power station. In the study area, a soil layer about 8m thick had been withdrawn by heavy machines leading not only to soil compaction, but resulting in high-degree degradation. The experiment was arranged in a completely randomized design with nine treatments and four replications. The treatments consisted of: 1- soil mobilization by tilling (to ensure the effect of mechanical mobilization in all treatments) without planting, but growth of spontaneous vegetation; 2- Black velvet bean (Stizolobium aterrimum Piper & Tracy); 3- Pigeonpea (Cajanus cajan (L.) DC); 4- Liming + black velvet bean; 5-Liming + pigeonpea until 1994, when replaced by jack bean (Canavalia ensiformis); 6- Liming + gypsum + black velvet bean; 7- Liming + gypsum + pigeonpea until 1994, when replaced by jack bean; and two controls as reference: 8- Native Cerrado vegetation and 9- bare soil (no tilling and no planting), left under natural conditions and in this situation, without spontaneous vegetation. In treatments 1 through 7, the soil was tilled. Treatments were installed in 1992 and left unmanaged for seven years, until brachiaria (Brachiaria decumbens) was planted in all plots in 1999. Seventeen years after implantation, the properties soil macroporosity, microporosity, total porosity, bulk density and aggregate stability were assessed in the previously described treatments in the soil layers 0.00-0.10; 0.10-0.20 and 0.20-0.40 m, and soil Penetration Resistance and soil moisture in 0.00-0.15 and 0.15-0.30 m. The plants were evaluated for: brachiaria dry matter and spontaneous growth of native tree species in the plots as of 2006. Results were analyzed by variance analysis and Tukey´s test at 5 % for mean comparison. In all treatments, except for the bare soil (no recovery measures), ongoing recovery of the degraded soil physical properties was observed. Macroporosity, soil bulk density and total porosity were good soil quality indicators. The occurrence of spontaneous native species indicated the soil recovery process. The best adapted species was Machaerium acutifolium Vogel, with the largest number of plants and most advanced development; the dry matter production of B. decumbens in recovering soil was similar to normal conditions, evidencing soil recovery.

Binding energy and momentum distribution of nuclear matter using Green's functions methods

The influence of hole-hole (h-h) propagation in addition to the conventional particle-particle (p-p) propagation, on the energy per particle and the momentum distribution is investigated for the v2 central interaction which is derived from Reid¿s soft-core potential. The results are compared to Brueckner-Hartree-Fock calculations with a continuous choice for the single-particle (SP) spectrum. Calculation of the energy from a self-consistently determined SP spectrum leads to a lower saturation density. This result is not corroborated by calculating the energy from the hole spectral function, which is, however, not self-consistent. A generalization of previous calculations of the momentum distribution, based on a Goldstone diagram expansion, is introduced that allows the inclusion of h-h contributions to all orders. From this result an alternative calculation of the kinetic energy is obtained. In addition, a direct calculation of the potential energy is presented which is obtained from a solution of the ladder equation containing p-p and h-h propagation to all orders. These results can be considered as the contributions of selected Goldstone diagrams (including p-p and h-h terms on the same footing) to the kinetic and potential energy in which the SP energy is given by the quasiparticle energy. The results for the summation of Goldstone diagrams leads to a different momentum distribution than the one obtained from integrating the hole spectral function which in general gives less depletion of the Fermi sea. Various arguments, based partly on the results that are obtained, are put forward that a self-consistent determination of the spectral functions including the p-p and h-h ladder contributions (using a realistic interaction) will shed light on the question of nuclear saturation at a nonrelativistic level that is consistent with the observed depletion of SP orbitals in finite nuclei.

Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

Summary

Soil, water and nutrient losses by interrill erosion from green cane cultivation

Interrill erosion occurs by the particle breakdown caused by raindrop impact, by particle transport in surface runoff, by dragging and suspension of particles disaggregated from the soil surface, thus removing organic matter and nutrients that are essential for agricultural production. Crop residues on the soil surface modify the characteristics of the runoff generated by rainfall and the consequent particle breakdown and sediment transport resulting from erosion. The objective of this study was to determine the minimum amount of mulch that must be maintained on the soil surface of a sugarcane plantation to reduce the soil, water and nutrient losses by decreasing interrill erosion. The study was conducted in Pradópolis, São Paulo State, in 0.5 x 1.0 m plots of an Oxisol, testing five treatments in four replications. The application rates were based on the crop residue production of the area of 1.4 kg m-2 (T1- no cane trash; T2-25 % of the cane trash; T3- 50 % trash; T4-75 % trash; T5-100 % sugarcane residues on the surface), and simulated rainfall was applied at an intensity of 65 mm h-1 for 60 min. Runoff samples were collected in plastic containers and soon after taken to the laboratory to quantify the losses of soil, water and nutrients. To minimize soil loss by interrill erosion, 75 % of the cane mulch must be maintained on the soil, to control water loss 50 % must be maintained and 25 % trash controls organic matter and nutrient losses. This information can contribute to optimize the use of this resource for soil conservation on the one hand and the production of clean energy in sugar and alcohol industries on the other.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Perturbation theory for classical thermodynamic Green's functions

A systematic time-dependent perturbation scheme for classical canonical systems is developed based on a Wick's theorem for thermal averages of time-ordered products. The occurrence of the derivatives with respect to the canonical variables noted by Martin, Siggia, and Rose implies that two types of Green's functions have to be considered, the propagator and the response function. The diagrams resulting from Wick's theorem are "double graphs" analogous to those introduced by Dyson and also by Kawasaki, in which the response-function lines form a "tree structure" completed by propagator lines. The implication of a fluctuation-dissipation theorem on the self-energies is analyzed and compared with recent results by Deker and Haake.

Timing incorporation of different green manure crops to minimize the risk of nitrogen leaching

Selostus: Viherlannoituskasvuston kynnön viivyttäminen vähentää typen huuhtoutumista

Communication in networks with hierarchical branching

We present a simple model of communication in networks with hierarchical branching. We analyze the behavior of the model from the viewpoint of critical systems under different situations. For certain values of the parameters, a continuous phase transition between a sparse and a congested regime is observed and accurately described by an order parameter and the power spectra. At the critical point the behavior of the model is totally independent of the number of hierarchical levels. Also scaling properties are observed when the size of the system varies. The presence of noise in the communication is shown to break the transition. The analytical results are a useful guide to forecasting the main features of real networks.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.