Uranium reserve, nuclear fuel cycle delusion, CO2 emission from the sea, and electricity supply: reflections after the fuel meltdown of Fukushima nuclear power units

The Great Tohoku-Kanto earthquake and resulting tsunami has brought considerable attention to the issue of the construction of new power plants. We argue in this paper, nuclear power is not a sustainable solution to energy problems. First, we explore the stock of uranium-235 and the different schemes developed by the nuclear power industry to exploit this resource. Second, we show that these methods, fast breeder and MOX fuel reactors, are not feasible. Third, we show that the argument that nuclear energy can be used to reduce CO2 emissions is false: the emissions from the increased water evaporation from nuclear power generation must be accounted for. In the case of Japan, water from nuclear power plants is drained into the surrounding sea, raising the water temperature which has an adverse affect on the immediate ecosystem, as well as increasing CO2 emissions from increased water evaporation from the sea. Next, a short exercise is used to show that nuclear power is not even needed to meet consumer demand in Japan. Such an exercise should be performed for any country considering the construction of additional nuclear power plants. Lastly, the paper is concluded with a discussion of the implications of our findings.

Ticks (Acari: Ixodidae) on wild animals from the Porto-Primavera Hydroelectric power station area, Brazil

From June 2000 to June 2001, a total of 741 ticks were collected from 51 free-living wild animals captured at the Porto-Primavera Hydroelectric power station area, located alongside an approximately 180 km course of the Paraná river, between the states of São Paulo and Mato Grosso do Sul, comprising 9 species of 3 genera: Ambly-omma (7 species), Boophilus (1) and Anocentor (1). A total of 421 immature Amblyomma ticks were reared in laboratory until the adult stage, allowing identification of the species. A. cajennense was the most frequent tick species (mostly immature stages) collected on 9 host species: Myrmecophaga tridactyla, Tamandua tetradactyla,Cerdocyon thous, Puma concolor,Tayassu tajacu, Mazama gouazoubira,Hydrochaeris hydrochaeris,Alouatta caraya, Cebus apella. Other tick species were less common, generally restricted to certain host taxa.

Large-Area Photo-Mosaics Using Global Alignment and Navigation Data

Seafloor imagery is a rich source of data for the study of biological and geological processes. Among several applications, still images of the ocean floor can be used to build image composites referred to as photo-mosaics. Photo-mosaics provide a wide-area visual representation of the benthos, and enable applications as diverse as geological surveys, mapping and detection of temporal changes in the morphology of biodiversity. We present an approach for creating globally aligned photo-mosaics using 3D position estimates provided by navigation sensors available in deep water surveys. Without image registration, such navigation data does not provide enough accuracy to produce useful composite images. Results from a challenging data set of the Lucky Strike vent field at the Mid Atlantic Ridge are reported

A survey on voltage sag events in power systems

This paper aims to survey the techniques and methods described in literature to analyse and characterise voltage sags and the corresponding objectives of these works. The study has been performed from a data mining point of view

Unusual voltage sag event detection in power systems

Three multivariate statistical tools (principal component analysis, factor analysis, analysis discriminant) have been tested to characterize and model the sags registered in distribution substations. Those models use several features to represent the magnitude, duration and unbalanced grade of sags. They have been obtained from voltage and current waveforms. The techniques are tested and compared using 69 registers of sags. The advantages and drawbacks of each technique are listed

A hybrid method for sag source location in power network

The work presented in this paper belongs to the power quality knowledge area and deals with the voltage sags in power transmission and distribution systems. Propagating throughout the power network, voltage sags can cause plenty of problems for domestic and industrial loads that can financially cost a lot. To impose penalties to responsible party and to improve monitoring and mitigation strategies, sags must be located in the power network. With such a worthwhile objective, this paper comes up with a new method for associating a sag waveform with its origin in transmission and distribution networks. It solves this problem through developing hybrid methods which hire multiway principal component analysis (MPCA) as a dimension reduction tool. MPCA reexpresses sag waveforms in a new subspace just in a few scores. We train some well-known classifiers with these scores and exploit them for classification of future sags. The capabilities of the proposed method for dimension reduction and classification are examined using the real data gathered from three substations in Catalonia, Spain. The obtained classification rates certify the goodness and powerfulness of the developed hybrid methods as brand-new tools for sag classification

Comparison of the polynomial model against explicit measurements of noise components for different mammography systems.

Given the adverse impact of image noise on the perception of important clinical details in digital mammography, routine quality control measurements should include an evaluation of noise. The European Guidelines, for example, employ a second-order polynomial fit of pixel variance as a function of detector air kerma (DAK) to decompose noise into quantum, electronic and fixed pattern (FP) components and assess the DAK range where quantum noise dominates. This work examines the robustness of the polynomial method against an explicit noise decomposition method. The two methods were applied to variance and noise power spectrum (NPS) data from six digital mammography units. Twenty homogeneously exposed images were acquired with PMMA blocks for target DAKs ranging from 6.25 to 1600 µGy. Both methods were explored for the effects of data weighting and squared fit coefficients during the curve fitting, the influence of the additional filter material (2 mm Al versus 40 mm PMMA) and noise de-trending. Finally, spatial stationarity of noise was assessed.Data weighting improved noise model fitting over large DAK ranges, especially at low detector exposures. The polynomial and explicit decompositions generally agreed for quantum and electronic noise but FP noise fraction was consistently underestimated by the polynomial method. Noise decomposition as a function of position in the image showed limited noise stationarity, especially for FP noise; thus the position of the region of interest (ROI) used for noise decomposition may influence fractional noise composition. The ROI area and position used in the Guidelines offer an acceptable estimation of noise components. While there are limitations to the polynomial model, when used with care and with appropriate data weighting, the method offers a simple and robust means of examining the detector noise components as a function of detector exposure.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

Leaf area of common bean genotypes during early pod filling as related to plant adaptation to limited phosphorus supply

Low phosphorus supply markedly limits leaf growth and genotypes able to maintain adequate leaf area at low P could adapt better to limited-P conditions. This work aimed to investigate the relationship between leaf area production of common bean (Phaseolus vulgaris) genotypes during early pod filling and plant adaptation to limited P supply. Twenty-four genotypes, comprised of the four growth habits in the species and two weedy accessions, were grown at two P level applied to the soil (20 and 80 mg kg-1) in 4 kg pots and harvested at two growth stages (pod setting and early pod filling). High P level markedly increased the leaf number and leaf size (leaf area per leaf), slightly increased specific leaf area but did not affect the net assimilation rate. At low P level most genotypic variation for plant dry mass was associated with leaf size, whereas at high P level this variation was associated primarily with the number of leaves and secondarily with leaf size, specific leaf area playing a minor role at both P level. Determinate bush genotypes presented a smaller leaf area, fewer but larger leaves with higher specific leaf area and lower net assimilation rate. Climbing genotypes showed numerous leaves, smaller and thicker leaves with a higher net assimilation rate. Indeterminate bush and indeterminate prostrate genotypes presented the highest leaf area, achieved through intermediate leaf number, leaf size and specific leaf area. The latter groups were better adapted to limited P. It is concluded that improved growth at low P during early pod filling was associated with common bean genotypes able to maintain leaf expansion through leaves with greater individual leaf area.

Variability of root traits in common bean genotypes at different levels of phosphorus supply and ontogenetic stages

Selection of common bean (Phaseolus vulgaris L.) cultivars with enhanced root growth would be a strategy for increasing P uptake and grain yield in tropical soils, but the strong plasticity of root traits may compromise their inclusion in breeding programs. The aim of this study was to evaluate the magnitude of the genotypic variability of root traits in common bean plants at two ontogenetic stages and two soil P levels. Twenty-four common bean genotypes, comprising the four growth habits that exist in the species and two wild genotypes, were grown in 4 kg pots at two levels of applied P (20 and 80 mg kg-1) and harvested at the stages of pod setting and early pod filling. Root area and root length were measured by digital image analysis. Significant genotype × P level and genotype × harvest interactions in analysis of variance indicate that the genotypic variation of root traits depended on soil nutrient availability and the stage at which evaluation was made. Genotypes differed for taproot mass, basal and lateral root mass, root area and root length at both P levels and growth stages; differences in specific root area and length were small. Genotypes with growth habits II (upright indeterminate) and III (prostrate indeterminate) showed better adaptation to limited P supply than genotypes of groups I (determinate) and IV (indeterminate climbing). Between the two harvests, genotypes of groups II and III increased the mass of basal and lateral roots by 40 and 50 %, respectively, whereas genotypes of groups I and IV by only 7 and 19 %. Values of the genotypic coefficient of determination, which estimates the proportion of phenotypic variance resulting from genetic effects, were higher at early pod filling than at pod setting. Correlations between shoot mass and root mass, which could indicate indirect selection of root systems via aboveground biomass, were higher at early pod filling than at pod setting. The results indicate that selection for root traits in common bean genotypes should preferentially be performed at the early pod-filling stage.

Water Erosion on an Oxisol under Integrated Crop-Forest Systems in a Transitional Area between the Amazon and Cerrado Biomes

ABSTRACT Water erosion is one of the main factors driving soil degradation, which has large economic and environmental impacts. Agricultural production systems that are able to provide soil and water conservation are of crucial importance in achieving more sustainable use of natural resources, such as soil and water. The aim of this study was to evaluate soil and water losses in different integrated production systems under natural rainfall. Experimental plots under six different land use and cover systems were established in an experimental field of Embrapa Agrossilvipastoril in Sinop, state of Mato Grosso, Brazil, in a Latossolo Vermelho-Amarelo Distrófico (Udox) with clayey texture. The treatments consisted of perennial pasture (PAS), crop-forest integration (CFI), eucalyptus plantation (EUC), soybean and corn crop succession (CRP), no ground cover (NGC), and forest (FRS). Soil losses in the treatments studied were below the soil loss limits (11.1 Mg ha-1 yr-1), with the exception of the plot under bare soil (NGC), which exhibited soil losses 30 % over the tolerance limit. Water losses on NGC, EUC, CRP, PAS, CFI and FRS were 33.8, 2.9, 2.4, 1.7, 2.4, and 0.5 % of the total rainfall during the period of study, respectively.

Remote Control of Mobile Devices in Android Platform

Remote control systems are a very useful element to control and monitor devices quickly and easily. This paper proposes a new architecture for remote control of Android mobile devices, analyzing the different alternatives and seeking the optimal solution in each case. Although the area of remote control, in case of mobile devices, has been little explored, it may provide important advantages for testing software and hardware developments in several real devices. It can also allow an efficient management of various devices of different types, perform forensic security tasks, etc ... The main idea behind the proposed architecture was the design of a system to be used as a platform which provides the services needed to perform remote control of mobile devices. As a result of this research, a proof of concept was implemented. An Android application running a group of server programs on the device, connected to the network or USB interface, depending on availability. This servers can be controlled through a small client written in Java and runnable both on desktop and web systems.

Use of Remote Sensing for Collection of Data Elements for Linear Referencing Systems, 2001

This report evaluates the use of remotely sensed images in implementing the Iowa DOT LRS that is currently in the stages of system architecture. The Iowa Department of Transportation is investing a significant amount of time and resources into creation of a linear referencing system (LRS). A significant portion of the effort in implementing the system will be creation of a datum, which includes geographically locating anchor points and then measuring anchor section distances between those anchor points. Currently, system architecture and evaluation of different data collection methods to establish the LRS datum is being performed for the DOT by an outside consulting team.

Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in closed-loop schemes robust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimizationcriteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed,and their relationship with existing algorithms is indicated. As withother previously proposed solutions, the minimum bit error rate algorithmconverges to the open-loop transmission scheme for very poor CSI estimates.

Upgrading of thethird generation of the Remote Diagnostic Systems for ABB Marine Propulsion Systems

Remote diagnostics has become very popular in marine industry. Diagnostic systems are improved all the time and the newest monitoring systems are constantly taken into operation. Most vessels are, however, rigged up by outmoded facilities, which should be updated. In this work the principles of operating of such a remote diagnostic system as the ABB's third generation remote diagnostic system (RDS) and the newest Propulsion Condition Management System (PCMS) are studied. As a result of the thesis the ways of upgrading the old systems of RDS are presented. Specifically, the work focuses on the establishment of the connection between Advant controller AC-110 from the old system and PCMS server, and in other words how to get Modbus data by OPC server.