Eficiência de uso de boro no crescimento de clones de eucalipto em vasos

This paper evaluated the critical level, responsivity and boron use effciency on growth and the biomass production in six Eucalyptus grandis x Eucalyptus urophylla clones (Ca, Cb, Cc, Cd, Ce and Cf). An experiment was carried out in pots containing ground silicon, under greenhouse conditions using four boron rates per solution (0; 0.135; 0.27; 0.54 mg L -1 of B). The treatments were combined in a randomized block experimental design in a 4 × 6 factorial scheme with three replications. Plant height, stem diameter, dry matter production in the shoots of the plant and boron use effciency in the leaves, stem and total dry matter at the 8th month of age were evaluated. Due to boron fertilization, growth and shoot biomass were observed in the Eucalyptus plants after 240 days of being planted in pots. The increases in growth and biomass were 35 to 54% and 21 to 64%, respectively. The boron rates that promoted major growth of the plants were 0.33 to 0.44 mg L -1 of B and in this range the most effcient clone for dry matter production of leaves was Cf and the least effcient one to stem biomass production and the shoot biomass was Cd, no signifcant differences among other clones were observed. The critical level of boron in solution was 0.09 to 0.24 mg L -1 of B in the growth of the plants.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

Age of No-Till System and Nitrogen Management on Common Bean Nutrition and Yield

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter.

Resource management in location aware cognitive radio networks

Dynamic spectrum access (DSA) aims at utilizing spectral opportunities both in time and frequency domains at any given location, which arise due to variations in spectrum usage. Recently, Cognitive radios (CRs) have been proposed as a means of implementing DSA. In this work we focus on the aspect of resource management in overlaid CRNs. We formulate resource allocation strategies for cognitive radio networks (CRNs) as mathematical optimization problems. Specifically, we focus on two key problems in resource management: Sum Rate Maximization and Maximization of Number of Admitted Users. Since both the above mentioned problems are NP hard due to presence of binary assignment variables, we propose novel graph based algorithms to optimally solve these problems. Further, we analyze the impact of location awareness on network performance of CRNs by considering three cases: Full location Aware, Partial location Aware and Non location Aware. Our results clearly show that location awareness has significant impact on performance of overlaid CRNs and leads to increase in spectrum utilization effciency.

Implementation of porous silicon technology for a fluidic flow-through optical sensor for pH measurements

This work presents an innovative integration of sensing and nano-scaled fluidic actuation in the combination of pH sensitive optical dye immobilization with the electro-osmotic phenomena in polar solvents like water for flow-through pH measurements. These flow-through measurements are performed in a flow-through sensing device (FTSD) configuration that is designed and fabricated at MTU. A relatively novel and interesting material, through-wafer mesoporous silica substrates with pore diameters of 20 -200 nm and pore depths of 500 µm are fabricated and implemented for electro-osmotic pumping and flow-through fluorescence sensing for the first time. Performance characteristics of macroporous silicon (> 500 µm) implemented for electro-osmotic pumping include, a very large flow effciency of 19.8 µLmin-1V-1 cm-2 and maximum pressure effciency of 86.6 Pa/V in comparison to mesoporous silica membranes with 2.8 µLmin-1V-1cm-2 flow effciency and a 92 Pa/V pressure effciency. The electrical current (I) of the EOP system for 60 V applied voltage utilizing macroporous silicon membranes is 1.02 x 10-6A with a power consumption of 61.74 x 10-6 watts. Optical measurements on mesoporous silica are performed spectroscopically from 300 nm to 1000 nm using ellipsometry, which includes, angularly resolved transmission and angularly resolved reflection measurements that extend into the infrared regime. Refractive index (n) values for oxidized and un-oxidized mesoporous silicon sample at 1000 nm are found to be 1.36 and 1.66. Fluorescence results and characterization confirm the successful pH measurement from ratiometric techniques. The sensitivity measured for fluorescein in buffer solution is 0.51 a.u./pH compared to sensitivity of ~ 0.2 a.u./pH in the case of fluorescein in porous silica template. Porous silica membranes are efficient templates for immobilization of optical dyes and represent a promising method to increase sensitivity for small variations in chemical properties. The FTSD represents a device topology suitable for application to long term monitoring of lakes and reservoirs. Unique and important contributions from this work include fabrication of a through-wafer mesoporous silica membrane that has been thoroughly characterized optically using ellipsometry. Mesoporous silica membranes are tested as a porous media in an electro-osmotic pump for generating high pressure capacities due to the nanometer pore sizes of the porous media. Further, dye immobilized mesoporous silica membranes along with macroporous silicon substrates are implemented for continuous pH measurements using fluorescence changes in a flow-through sensing device configuration. This novel integration and demonstration is completely based on silicon and implemented for the first time and can lead to miniaturized flow-through sensing systems based on MEMS technologies.

Optimally Combining Censored and Uncensored Datasets

Economists and other social scientists often face situations where they have access to two datasets that they can use but one set of data suffers from censoring or truncation. If the censored sample is much bigger than the uncensored sample, it is common for researchers to use the censored sample alone and attempt to deal with the problem of partial observation in some manner. Alternatively, they simply use only the uncensored sample and ignore the censored one so as to avoid biases. It is rarely the case that researchers use both datasets together, mainly because they lack guidance about how to combine them. In this paper, we develop a tractable semiparametric framework for combining the censored and uncensored datasets so that the resulting estimators are consistent, asymptotically normal, and use all information optimally. When the censored sample, which we refer to as the master sample, is much bigger than the uncensored sample (which we call the refreshment sample), the latter can be thought of as providing identification where it is otherwise absent. In contrast, when the refreshment sample is large and could typically be used alone, our methodology can be interpreted as using information from the censored sample to increase effciency. To illustrate our results in an empirical setting, we show how to estimate the effect of changes in compulsory schooling laws on age at first marriage, a variable that is censored for younger individuals. We also demonstrate how refreshment samples for this application can be created by matching cohort information across census datasets.

New method for analytical photovoltaic parameters identification: meeting manufacturer’s datasheet for different ambient conditions

At present, photovoltaic energy is one of the most important renewable energy sources. The demand for solar panels has been continuously growing, both in the industrial electric sector and in the private sector. In both cases the analysis of the solar panel efficiency is extremely important in order to maximize the energy production. In order to have a more efficient photovoltaic system, the most accurate understanding of this system is required. However, in most of the cases the only information available in this matter is reduced, the experimental testing of the photovoltaic device being out of consideration, normally for budget reasons. Several methods, normally based on an equivalent circuit model, have been developed to extract the I-V curve of a photovoltaic device from the small amount of data provided by the manufacturer. The aim of this paper is to present a fast, easy, and accurate analytical method, developed to calculate the equivalent circuit parameters of a solar panel from the only data that manufacturers usually provide. The calculated circuit accurately reproduces the solar panel behavior, that is, the I-V curve. This fact being extremely important for practical reasons such as selecting the best solar panel in the market for a particular purpose, or maximize the energy extraction with MPPT (Maximum Peak Power Tracking) methods.

Multiscale histograms: Summarizing topological relations in large spatial datasets

Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this paper, we present several novel techniques to eectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important topological relations: contains, contained, overlap, and disjoint. We rst present a novel framework to construct a multiscale histogram composed of multiple Euler histograms with the guarantee of the exact summarization results for aligned windows in constant time. Then we present an approximate algorithm, with the approximate ratio 19/12, to minimize the storage spaces of such multiscale Euler histograms, although the problem is generally NP-hard. To conform to a limited storage space where only k Euler histograms are allowed, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy. Finally, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. Our extensive experiments against both synthetic and real world datasets demonstrated that the approximate mul- tiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost effciency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for the real datasets.