Conversion to soy on the Amazonian agricultural frontier increases streamflow without affecting stormflow dynamics

Large-scale soy agriculture in the southern Brazilian Amazon now rivals deforestation for pasture as the region`s predominant form of land use change. Such landscape-level change can have substantial consequences for local and regional hydrology, but these effects remain relatively unstudied in this ecologically and economically important region. We examined how the conversion to soy agriculture influences water balances and stormflows using stream discharge (water yields) and the timing of discharge (stream hydrographs) in small (2.5-13.5 km2) forested and soy headwater watersheds in the Upper Xingu Watershed in the state of Mato Grosso, Brazil. We monitored water yield for 1 year in three forested and four soy watersheds. Mean daily water yields were approximately four times higher in soy than forested watersheds, and soy watersheds showed greater seasonal variability in discharge. The contribution of stormflows to annual streamflow in all streams was low (< 13% of annual streamflow), and the contribution of stormflow to streamflow did not differ between land uses. If the increases in water yield observed in this study are typical, landscape-scale conversion to soy substantially alters water-balance, potentially altering the regional hydrology over large areas of the southern Amazon.

Stable isotopes of bulk organic matter to trace carbon and nitrogen dynamics in an estuarine ecosystem in Babitonga Bay (Santa Catarina, Brazil)

The biogeochemical processes affecting the transport and cycling of terrestrial organic carbon in coastal and transition areas are still not fully understood One means of distinguishing between the sources of organic materials contributing to particulate organic matter (POM) in Babitonga Bay waters and sediments is by the direct measurement of delta(13)C of dissolved inorganic carbon (DIC) and delta(13)C and delta(15)N in the organic constituents. An isotopic survey was taken from samples collected in the Bay in late spring of 2004. The results indicate that the delta(13)C and delta(15)N compositions of OM varied from -21.7 parts per thousand to -26 2 parts per thousand. and from + 9 2 parts per thousand. to -0 1 parts per thousand, respectively. delta(13)C from DIC ranges from +0.04 parts per thousand to -12.7 parts per thousand The difference in the isotope compositions enables the determination of three distinct end-members terrestrial, marine and urban Moreover, the evaluation of source contribution to the particulate organic matter (POM) in the Bay, enables assessment of the anthropogenic impact. Comparing the depleted values of delta(13)C(DIC) and delta(13)C(POC) it is possible to further understand the carbon dynamic within Babitonga Bay (C) 2010 Elsevier BV All rights reserved

Simulation of Soil Carbon Dynamics under Sugarcane with the CENTURY Model

Currently there is a trend for the expansion of the area cropped with sugarcane (Saccharum officinarum L.), driven by an increase in the world demand for biofuels, due to economical, environmental, and geopolitical issues. Although sugarcane is traditionally harvested by burning dried leaves and tops, the unburned, mechanized harvest has been progressively adopted. The use of process based models is useful in understanding the effects of plant litter in soil C dynamics. The objective of this work was to use the CENTURY model in evaluating the effect of sugarcane residue management in the temporal dynamics of soil C. The approach taken in this work was to parameterize the CENTURY model for the sugarcane crop, to simulate the temporal dynamics of soil C, validating the model through field experiment data, and finally to make predictions in the long term regarding soil C. The main focus of this work was the comparison of soil C stocks between the burned and unburned litter management systems, but the effect of mineral fertilizer and organic residue applications were also evaluated. The simulations were performed with data from experiments with different durations, from 1 to 60 yr, in Goiana and Timbauba, Pernambuco, and Pradopolis, Sao Paulo, all in Brazil; and Mount Edgecombe, Kwazulu-Natal, South Africa. It was possible to simulate the temporal dynamics of soil C (R(2) = 0.89). The predictions made with the model revealed that there is, in the long term, a trend for higher soil C stocks with the unburned management. This increase is conditioned by factors such as climate, soil texture, time of adoption of the unburned system, and N fertilizer management.

Dynamics of carbon, biomass, and structure in two Amazonian forests

Amazon forests are potentially globally significant sources or sinks for atmospheric carbon dioxide. In this study, we characterize the spatial trends in carbon storage and fluxes in both live and dead biomass (necromass) in two Amazonian forests, the Biological Dynamic of Forest Fragments Project (BDFFP), near Manaus, Amazonas, and the Tapajos National Forest (TNF) near Santarem, Para. We assessed coarse woody debris (CWD) stocks, tree growth, mortality, and recruitment in ground-based plots distributed across the terra firme forest at both sites. Carbon dynamics were similar within each site, but differed significantly between the sites. The BDFFP and the TNF held comparable live biomass (167 +/- 7.6 MgC.ha(-1) versus 149 +/- 6.0 MgC.ha(-1), respectively), but stocks of CWD were 2.5 times larger at TNF (16.2 +/- 1.5 MgC.ha(-1) at BDFFP, versus 40.1 +/- 3.9 MgC.ha(-1) at TNF). A model of current forest dynamics suggests that the BDFFP was close to carbon balance, and its size class structure approximated a steady state. The TNF, by contrast, showed rapid carbon accrual to live biomass (3.24 +/- 0.22 MgC.ha(-1).a(-1) in TNF, 2.59 +/- 0.16 MgC.ha(-1).a(-1) in BDFFP), which was more than offset by losses from large stocks of CWD, as well as ongoing shifts of biomass among size classes. This pattern in the TNF suggests recovery from a significant disturbance. The net loss of carbon from the TNF will likely last 10 - 15 years after the initial disturbance (controlled by the rate of decay of coarse woody debris), followed by uptake of carbon as the forest size class structure and composition continue to shift. The frequency and longevity of forests showing such disequilibruim dynamics within the larger matrix of the Amazon remains an essential question to understanding Amazonian carbon balance.

An opinion dynamics model for the diffusion of innovations

We study the dynamics of the adoption of new products by agents with continuous opinions and discrete actions (CODA). The model is such that the refusal in adopting a new idea or product is increasingly weighted by neighbor agents as evidence against the product. Under these rules, we study the distribution of adoption times and the final proportion of adopters in the population. We compare the cases where initial adopters are clustered to the case where they are randomly scattered around the social network and investigate small world effects on the final proportion of adopters. The model predicts a fat tailed distribution for late adopters which is verified by empirical data. (C) 2009 Elsevier B.V. All rights reserved.

Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.

Continuous opinions and discrete actions in opinion dynamics problems

A model where agents show discrete behavior regarding their actions, but have continuous opinions that are updated by interacting with other agents is presented. This new updating rule is applied to both the voter and Sznajd models for interaction between neighbors, and its consequences are discussed. The appearance of extremists is naturally observed and it seems to be a characteristic of this model.

Coordination dynamics and attentional costs of continuous and discontinuous bimanual circle drawing movements

It has been suggested that the temporal control of rhythmic unimianual movements is different between tasks requiring continuous (e.g., circle drawing) and discontinuous movements (e.g., finger tapping). Specifically, for continuous movements temporal regularities are ail emergent property, whereas for tasks that involve discontinuities timing is ail explicit part of the action goal. The present experiment further investigated the control of continuous and discontinuous movements by comparing the coordination dynamics and attentional demands of bimanual continuous circle drawing with bimanual intermittent circle drawing. The intermittent task required participants to insert a 400 ms pause between each cycle while circling. Using dual-task methodology, 15 right-handed participants performed the two circle drawing tasks, while vocally responding to randomly presented auditory probes. The circle drawing tasks were performed in symmetrical and asymmetrical coordination modes and at movement frequencies of 1 Hz and 1.7 Hz. Intermittent circle drawing exhibited superior spatial and temporal accuracy and stability than continuous circle drawing supporting the hypothesis that the two tasks have different underlying control processes. In terms of attentional cost, probe RT was significantly slower during the intermittent circle drawing task than the continuous circle drawing task across both coordination modes and movement frequencies. Of interest was the finding that in the intermittent circling task reaction time (RT) to probes presented during the pause between cycles did not differ from the RT to probes occurring during the circling movement. The differences in attentional demands between the intermittent and continuous circle drawing tasks may reflect the operation of explicit event timing and implicit emergent timing processes, respectively. (C) 2008 Elsevier B.V. All rights reserved.

Correlation of Modified Natural Rubber Properties by Artificial Neural Networks

Natural rubber (NR) is a raw material largely used by the modern industry; however, it is common that chemical modifications must be made to NR in order to improve properties such as hydrophobicity or mechanical resistance. This work deals with the correlation of properties of NR modified with dimethylaminoethylmethacrylate or methylmethacrylate as grafting agents. Dynamic-mechanical behavior and stress/strain relations are very important properties because they furnish essential characteristics of the material such as glass transition temperature and rupture point. These properties are concerned with different physical principles; for this reason, normally they are not related to each other. This work showed that they can be correlated by artificial neural networks (ANN). So, from one type of assay, the properties that as a rule only could be obtained from the other can be extracted by ANN correlation. POLYM. ENG. SCI., 49:499-505, 2009. (c) 2009 Society of Plastics Engineers

Optimization of the AZO dyes decoloration process through neural networks: Determination of the H2O2 addition critical point

The concentration of hydrogen peroxide is an important parameter in the azo dyes decoloration process through the utilization of advanced oxidizing processes, particularly by oxidizing via UV/H2O2. It is pointed out that, from a specific concentration, the hydrogen peroxide works as a hydroxyl radical self-consumer and thus a decrease of the system`s oxidizing power happens. The determination of the process critical point (maximum amount of hydrogen peroxide to be added) was performed through a ""thorough mapping"" or discretization of the target region, founded on the maximization of an objective function objective (constant of reaction kinetics of pseudo-first order). The discretization of the operational region occurred through a feedforward backpropagation neural model. The neural model obtained presented remarkable coefficient of correlation between real and predicted values for the absorbance variable, above 0.98. In the present work, the neural model had, as phenomenological basis the Acid Brown 75 dye decoloration process. The hydrogen peroxide addition critical point, represented by a value of mass relation (F) between the hydrogen peroxide mass and the dye mass, was established in the interval 50 < F < 60. (C) 2007 Elsevier B.V. All rights reserved.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Neural network model for designing automotive devices using SMD LED

The advantages offered by the electronic component LED (Light Emitting Diode) have resulted in a quick and extensive application of this device in the replacement of incandescent lights. In this combined application, however, the relationship between the design variables and the desired effect or result is very complex and renders it difficult to model using conventional techniques. This paper consists of the development of a technique using artificial neural networks that makes it possible to obtain the luminous intensity values of brake lights using SMD (Surface Mounted Device) LEDs from design data. This technique can be utilized to design any automotive device that uses groups of SMD LEDs. The results of industrial applications using SMD LED are presented to validate the proposed technique.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.

Experimental investigation on adaptive robust controller designs applied to a free-floating space manipulator

This paper aims to formulate and investigate the application of various nonlinear H(infinity) control methods to a fiee-floating space manipulator subject to parametric uncertainties and external disturbances. From a tutorial perspective, a model-based approach and adaptive procedures based on linear parametrization, neural networks and fuzzy systems are covered by this work. A comparative study is conducted based on experimental implementations performed with an actual underactuated fixed-base planar manipulator which is, following the DEM concept, dynamically equivalent to a free-floating space manipulator. (C) 2011 Elsevier Ltd. All rights reserved.