Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo

O conhecimento do valor da erosividade da chuva (R) de determinada localidade é fundamental para a estimativa das perdas de solo feitas a partir da Equação Universal de Perdas de Solo, sendo, portanto, de grande importância no planejamento conservacionista. A fim de obter estimativas do valor de R para localidades onde este é desconhecido, desenvolveu-se uma rede neural artificial (RNA) e analisou-se a acurácia desta com o método de interpolação "Inverso de uma Potência da Distância" (ID). Comparando a RNA desenvolvida com o método de interpolação ID, verificou-se que a primeira apresentou menor erro relativo médio na estimativa de R e melhor índice de confiança, classificado como "Ótimo", podendo, portanto, ser utilizada no planejamento de uso, manejo e conservação do solo no Estado de São Paulo.

On the artificial creation of the EL2 center by means of boron implantation in gallium arsenide

In the present work, an analysis of the dark and optical capacitance transients obtained from Schottky Au:GaAs barriers implanted with boron has been carried out by means of the isothermal transient spectroscopy (ITS) and differential and optical ITS techniques. Unlike deep level transient spectroscopy, the use of these techniques allows one to easily distinguish contributions to the transients different from those of the usual deep trap emission kinetics. The results obtained show the artificial creation of the EL2, EL6, and EL5 defects by the boron implantation process. Moreover, the interaction mechanism between the EL2 and other defects, which gives rise to the U band, has been analyzed. The existence of a reorganization process of the defects involved has been observed, which prevents the interaction as the temperature increases. The activation energy of this process has been found to be dependent on the temperature of the annealing treatment after implantation, with values of 0.51 and 0.26 eV for the as‐implanted and 400 °C annealed samples, respectively. The analysis of the optical data has corroborated the existence of such interactions involving all the observed defects that affect their optical parameters

Rugosidade da superfície do solo sob diferentes sistemas de manejo e influenciada por chuva artificial

A rugosidade da superfície do solo é influenciada pelo manejo, formada em especial pelo tipo de preparo e reduzida pela ação da chuva, principalmente. O objetivo deste estudo foi avaliar a influência de diferentes sistemas de manejo do solo e da aplicação de chuva artificial na rugosidade da superfície do solo. Os tratamentos estudados resultaram da combinação de três sistemas de manejo do solo, semeadura direta (SD), preparo convencional (PC) e cultivo mínimo (CM), com três doses de resíduo vegetal seco de soja (Glycine max L. Merrill): 0, 2 e 4 Mg ha-1. Nas unidades experimentais foram aplicadas sete chuvas, com intensidade de precipitação pluvial de 60 mm h-1 e duração de 60 min cada, totalizando 420 mm de lâmina de chuva. A rugosidade foi avaliada imediatamente antes e após o preparo do solo e imediatamente após a aplicação de cada uma das sete chuvas artificiais. Obtiveram-se valores do índice de rugosidade ao acaso entre 1,88 e 5,41 mm na semeadura direta; entre 3,88 e 8,30 mm no preparo convencional; e entre 8,99 e 17,45 mm no cultivo mínimo. Concluiu-se que: as operações de preparo do solo aumentaram a rugosidade da sua superfície, em geral; o cultivo mínimo foi o sistema de preparo do solo que proporcionou os maiores valores de rugosidade ao acaso; e nos tratamentos semeadura direta com cobertura do solo a ação da chuva não promoveu decaimento do microrrelevo do solo.

Integer filling facfor phases and isospin in vertical diatomic artificial molecules

Integer filling factor phases of many-electron vertically coupled diatomic artificial quantum dot molecules are investigated for different values of the interdot coupling. The experimental results are analyzed within local-spin density functional theory for which we have determined a simple lateral confining potential law that can be scaled for the different coupling regimes, and Hartree-Fock theory. Maximum density droplets composed of electrons in both bonding and antibonding or just bonding states are revealed, and interesting isospin-flip physics appears for weak interdot coupling when the systematic depopulation of antibonding states leads to changes in isospin.

Dissociation of vertical semiconductor diatomic artificial molecules

We investigate the dissociation of few-electron circular vertical semiconductor double quantum dot artificial molecules at 0 T as a function of interdot distance. A slight mismatch introduced in the fabrication of the artificial molecules from nominally identical constituent quantum wells induces localization by offsetting the energy levels in the quantum dots by up to 2 meV, and this plays a crucial role in the appearance of the addition energy spectra as a function of coupling strength particularly in the weak coupling limit.

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.

Magnetoresistance at artificial interfaces in the itinerant SrRuO3 ferromagnet

The magnetoresistance across interfaces in the itinerant ferromagnetic oxide SrRuO3 have been studied. To define appropriately the interfaces, epitaxial thin films have been grown on bicrystalline and laser-patterned SrTiO3 substrates. Comparison is made with results obtained on similar experiments using the double-exchange ferromagnetic oxide La2/3Sr1/3MnO3. It is found that in SrRuO3, interfaces induce a substantial negative magnetoresistance, although no traces of the low-field spin tunneling magnetoresistance are found. We discuss these results on the basis of the distinct degree of spin polarization in ruthenates and manganites and the different nature of the surface magnetic layer formed at interfaces.

Artificial zinc finger fusions targeting Sp1-binding sites and the trans-activator-responsive element potently repress transcription and replication of HIV-1.

Tat activates transcription by interacting with Sp1, NF-kappaB, positive transcription elongation factor b, and trans-activator-responsive element (TAR). Tat and Sp1 play major roles in transcription by protein-protein interactions at human immunodeficiency virus, type 1 (HIV-1) long terminal repeat. Sp1 activates transcription by interacting with cyclin T1 in the absence of Tat. To disrupt the transcription activation by Tat and Sp1, we fused Sp1-inhibiting polypeptides, zinc finger polypeptide, and the TAR-binding mutant Tat (TatdMt) together. A designed or natural zinc finger and Tat mutant fusion was used to target the fusion to the key regulatory sites (GC box and TAR) on the long terminal repeat and nascent short transcripts to disrupt the molecular interaction that normally result in robust transcription. The designed zinc finger and TatdMt fusions were targeted to the TAR, and they potently repressed both transcription and replication of HIV-1. The Sp1-inhibiting POZ domain, TatdMt, and zinc fingers are key functional domains important in repression of transcription and replication. The designed artificial zinc fingers were targeted to the high affinity Sp1-binding site, and by being fused with TatdMt and POZ domain, they strongly block both Sp1-cyclin T1-dependent transcription and Tat-dependent transcription, even in the presence of excess expressed Tat.

Urinary Sphincter Based on Electronics and Artificial Muscles

Objectives: The AMS 800 is the current artifi cial urinary sphincter (AUS) forincontinence due to intrinsic sphincter defi ciency. Despite good clinical results,technical failures inherent to the hydraulic mechanism or urethral ischemicinjury contribute to revisions up to 60%. We are developing an electronic AUS,called ARTUS to overcome the rigors of AMS. The objective of this study wasto evaluate the technical effi cacy and tissue tolerance of the ARTUS systemin an animal model.Methods: The ARTUS is composed by three parts: thecontractile unit, a series of rings and an integrated microprocessor. The contractileunit is made of Nitinol fi bers. The rings are placed around the urethrato control the fl ow of urine by squeezing the urethra. They work in a sequentialalternative mode and are controlled by a microprocessor. In the fi rst phase athree-rings device was used while in the second phase a two-rings ARTUS wasused. The device was implanted in 14 sheep divided in two groups of six andeight animals for study purpose. The fi rst group aimed at bladder leak pointpressure (BLPP) measurement and validation of the animal model; the secondgroup aimed at verifying midterm tissue tolerance by explants at twelve weeks.General animal tolerance was also evaluated.Results: The ARTUS systemimplantation was uneventful. When the system was activated, the BLPP wasmeasured at 1.038 ± 0.044 bar (mean ± SD). Urethral tissue analysis did notshow signifi cant morphological changes. No infection and no sign of discomfortwere noted in animals at 12 weeks.Conclusions: The ARTUS proved to beeffective in continence achievement in this study. Histological results supportour idea that a sequential alternative mode can avoid urethral atrophy andischemia. Further technical developments are needed to verify long-termoutcome and permit human use.

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.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Guide Specification for Highway Construction Texturing Concrete Pavement for Reduced Tire/Pavement Noise using Artificial Turf Drag, March 1, 2011

This document provides language that can be used by an Owner-Agency to develop materials and construction specifications with the objective of reducing tire/pavement noise. While the practices described herein are largely prescriptive, they have been demonstrated to increase the likelihood of constructing a durable, quieter concrete surface. Guidance is provided herein for texturing the concrete surface since texture geometry has a paramount effect on tire/pavement noise. Guidance for curing is also provided to improve strength and durability of the surface mortar, and thus to improve texture durability.

Artificial-Intelligence-Based Optimization of the Management of Snow Removal Assets and Resources Final Report, October 2002

Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent snow removal asset management system (SRAMS). The system has been evaluated through a case study examining snow removal from the roads in Black Hawk County, Iowa, for which the Iowa Department of Transportation (Iowa DOT) is responsible. The SRAMS is comprised of an expert system that contains the logical rules and expertise of the Iowa DOT’s snow removal experts in Black Hawk County, and a geographic information system to access and manage road data. The system is implemented on a mid-range PC by integrating MapObjects 2.1 (a GIS package), Visual Rule Studio 2.2 (an AI shell), and Visual Basic 6.0 (a programming tool). The system could efficiently be used to generate prioritized snowplowing routes in visual format, to optimize the allocation of assets for plowing, and to track materials (e.g., salt and sand). A test of the system reveals an improvement in snowplowing time by 1.9 percent for moderate snowfall and 9.7 percent for snowstorm conditions over the current manual system.

Artificial muscle: the human chimera is the future.

Severe heart failure and cerebral stroke are broadly associated with the impairment of muscular function that conventional treatments struggle to restore. New technologies enable the construction of "smart" materials that could be of great help in treating diseases where the main problem is muscle weakness. These materials "behave" similarly to biological systems, because the material directly converts energy, for example electrical energy into movement. The extension and contraction occur silently like in natural muscles. The real challenge is to transfer this amazing technology into devices that restore or replace the mechanical function of failing muscle. Cardiac assist devices based on artificial muscle technology could envelope a weak heart and temporarily improve its systolic function, or, if placed on top of the atrium, restore the atrial kick in chronic atrial fibrillation. Artificial sphincters could be used to treat urinary incontinence after prostatectomy or faecal incontinence associated with stomas. Artificial muscles can restore the ability of patients with facial paralysis due to stroke or nerve injury to blink. Smart materials could be used to construct an artificial oesophagus including peristaltic movement and lower oesophageal sphincter function to replace the diseased oesophagus thereby avoiding the need for laparotomy to mobilise stomach or intestine. In conclusion, in the near future, smart devices will integrate with the human body to fill functional gaps due to organ failure, and so create a human chimera.