This research was focused on increasing the efficiency of upland cotton production in the northern cotton belt through the use of new fertilizer formulations, placement, and timings. The objectives of the experiments reported in this thesis were to: 1) evaluate the effects of side-dress potassium (K), sulfur (S), and boron (B) formulation and application timing on tissue nutrient levels during the bloom period; 2) evaluate lint yield response of cotton to different formulations of nitrogen (N), K, S and B applied at side-dress; and 3) compare 5x5 banding (5 cm beside and 5cm below the seed) and deep placement of complete N-P-K-S blends to current nutrient management strategies on early season plant growth, nodes above white flower, total nodes, petiole nutrient concentrations during bloom, and lint yield. Tissue S and B concentrations were increased more often than K concentrations when the nutrients were applied with side-dress N. When evaluating P and K placement, petiole P levels were found to be significantly higher in unfertilized plots when no side-dress N was applied. Phosphorus and K placement and/or rate had no effect on lint yield when N was applied at side-dress during the study. Environmental conditions potentially influenced the response to P and K placement as 5x5 placement produced yields significantly higher during 2013 growing season at location 1, while deep placement produced significantly higher yields in 2014 at location 3. As a result, Virginia nutrient management recommendations for cotton have been updated to incorporate management strategies to maximize lint yields. / Master of Science

Precision management of cotton production can increase profitability by decreasing inputs. The overall objective of this project is to improve cotton production by minimizing seeding rates while still maximizing yields and lint quality in water-limited soils. The research for this study was conducted at the Texas AgriLife Research IMPACT Center located in the Brazos River floodplain. In 2008 and 2009, 27 measurement locations were selected in production-sized center-pivot irrigated fields and planted in cotton variety Deltapine 164 roundup ready flex / bollgard II. Sites were selected based on soil apparent electrical conductivity (ECa) values, in a low, medium, and high ECa zones. Three seeding rates (74,100; 98,800; and 123,500 seeds ha-1) were established in each of the three ECa zones with three replications. In 2009, an additional seeding rate was added at 49,400 seeds ha-1. At each measurement location, soil texture, soil moisture (weekly), lint quantity and quality (High Volume Instrument) were measured. An additional replication for each ECa zone and seeding rate was selected for lint quantity and quality (HVI) measurements. Results indicated that cotton lint yield increased as ECa values, clay content, and water holding capacity of the soil increased. The seeding rates did not consistently affect cotton lint yield or quality. Seeding rates of 74,100 and 49,400 seeds ha-1 in a low and medium ECa zone for IMPACT-08 and -09 yielded more lint (300 kg ha-1), respectively. HVI lint quality parameters, such as, micronaire, fiber length, strength, uniformity, and elongation were significantly better in ECa zone 3. While the seeding rates did not affect the amount of soil water used throughout the season, lint yield variations between ECa zones can be explained by the rate at which soil water was used. Lower rates at which soil water was used within ECa zone 3 resulted in higher lint yields when compared to ECa zones 1 and 2, which used soil water faster and at greater depths. The findings suggest that irrigation applied to the low ECa zone was not sufficient to meet the plants demand, while in a high ECa zone, irrigation could have been reduced, resulting in cost savings through reduced inputs.

No description available.

Today’s electronics in aviation (avionics) are more complex than ever before. With higher requirements on safety and reliability and with new SoC (System on Chip) technology, the validation and verification of designs meet new challenges. In commercial and military aircraft there are many safety-critical systems that need to be reliable. The consequences of a failure of a safety-critical system onboard a civil or military aircraft are immeasurably more serious than a glitch or a bit-flip in a consumer appliance or Internet service delivery. If possible hazards are found early in the design process, a lot of work can be saved later on. Certain structures in the code are prone to produce glitchy logic and timing problems and should be avoided. This thesis will strengthen Saab Avitronics knowledge of adaptable rule checking tools for HDL, with a market analysis of the tools available. Moreover will it evaluate two of the most suitable tools and finally it will describe some of the design issues that exist when coding safety-critical systems. Finally it is concluded that the introduction of static rule checking tools will help the validator to find dangerous constructs in the code. However, it will not be possible to fully automate rule checking for safety-critical systems, because of the high requirements on reliability.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-05-11T12:40:39Z No. of bitstreams: 1 arquivo total.pdf: 6105657 bytes, checksum: 8404a0fcb54e3893c95fdfb017f0ac96 (MD5) / Made available in DSpace on 2016-05-11T12:40:39Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 6105657 bytes, checksum: 8404a0fcb54e3893c95fdfb017f0ac96 (MD5) Previous issue date: 2015-08-31 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In recent years, commercial cotton lint have been developed with better quality, presenting different characteristics, but with similar coloring. This can be a problem because these samples is identified, large-scale, performed by a visual inspection, which is a very subjective method and error prone. Another way available for classification of samples is the use of HVI system (High Volume Instruments) to determine physical quality parameters. However, this apparatus has a high cost when compared to digital imaging technique, furthermore has the need for adequate infrastructure and a trained analyst for analysis procedure. This work proposes the development of a novel analytical method based on the use of digital image and multivariate analysis to (1) naturally colored cotton plumes classification according to the type of cultivar and (2) simultaneous determination of degree of yellowness (+b), reflectance (Rd) and wax content (WAX). The acquisition of digital images of cotton lints was carried out through a webcam and histograms containing distributions in levels of colors in standard RGB (red-green-blue), grayscale and HSV system (hue-saturation-value) they were obtained. In the classification of samples, models based discriminant analysis by partial least squares (PLS-DA) and linear discriminant analysis (LDA) with variable selection by the successive projections algorithm (SPA) or stepwise (SW) were evaluated. For the determination of the parameters +b, Rd and WAX, PLS models and multiple linear regression (MLR) with variable selection by the SPA were developed and compared. The best classification results were obtained with LDA / SW model with a correct classification rate (TCC) of 96% for the test group using the HSV combination. As the calibration methods, satisfactory prediction results were obtained for both models (PLS and MLR-SPA) with values of RMSEP near repeatability of the reference method. Furthermore, no systematic error was observed and there were no significant differences between the predicted values and reference, according to a paired t-test at 95% confidence. As advantages of the method is simple, low cost, does not use reagent, does not destroy the sample and realizes analysis at short time intervals. / Nos últimos anos, plumas de algodão comerciais têm-se desenvolvido com melhor qualidade, apresentando características diferentes, mas com coloração similar. Isto pode ser um problema porque a identificação destas amostras é, em larga escala, realizada por meio de uma inspeção visual, que é um método subjetivo e sujeito a erros. Outra forma disponível para classificação dessas amostras consiste no uso do sistema HVI (High Volume Instruments) na determinação de parâmetros físicos de qualidade. Contudo, tal equipamento apresenta um alto custo, se comparado a técnica de imagens digitais, além do mais tem-se a necessidade de uma infraestrutura adequada e de um analista treinado para o procedimento de análise. Este trabalho propõe o desenvolvimento de uma nova metodologia analítica baseada na utilização de imagens digitais e análise multivariada para (1) classificação de plumas de algodão naturalmente colorido de acordo com o tipo de cultivar e (2) determinação simultânea de grau de amarelamento (+b), reflectância (Rd) e teor de cera (WAX). A aquisição das imagens digitais das plumas de algodão foi realizada por meio de uma webcam e foram obtidos os histogramas contendo as distribuições nos níveis de cores no padrão RGB (vermelho-verde-azul), escala de cinza e o sistema HSV (matiz-saturação-valor). Na classificação das amostras, modelos baseados na análise discriminante pelos mínimos quadrados parciais (PLS-DA) e análise discriminante linear (LDA) com seleção de variáveis pelo algoritmo das projeções sucessivas (SPA) ou pelo stepwise (SW) foram avaliados. Para a determinação dos parâmetros de +b, Rd e WAX, modelos PLS e regressão linear múltipla (MLR) com seleção de variáveis pelo SPA foram desenvolvidos e comparados. Os melhores resultados de classificação foram obtidos com o modelo LDA/SW, com uma taxa de classificação correta (TCC) de 96% para o conjunto de teste utilizando a combinação HSV. Quanto aos métodos de calibração, resultados de previsão satisfatórios foram obtidos para ambos os modelos (PLS e MLR-SPA), com valores de RMSEP próximos à repetitividade do método de referência. Além disso, nenhum erro sistemático foi observado e não foram encontradas diferenças significativas entre os valores previstos e de referência, de acordo com um teste t-pareado ao nível de confiança de 95%. Como vantagens o método é simples, de baixo custo, não utiliza reagente, não destrói a amostra e realiza análise em curtos intervalos de tempo.

Static analysis tools can aid programmers by reporting potential programming mistakes prior to the execution of a program. Funqual is a static analysis tool that reads C++17 code ``in the wild'' and checks that the function call graph follows a set of rules which can be defined by the user. This sort of analysis can help the programmer to avoid errors such as accidentally calling blocking functions in time-sensitive contexts or accidentally allocating memory in heap-sensitive environments. To accomplish this, we create a type system whereby functions can be given user-defined type qualifiers and where users can define their own restrictions on the call graph based on these type qualifiers. We demonstrate that this tool, when used with hand-crafted rules, can catch certain types of errors which commonly occur in the wild. We claim that this tool can be used in a production setting to catch certain kinds of errors in code before that code is even run.

<p>Crowdsourcing allows users to offload tedious work to an on-demand workforce. However, the time saved by the requesters is often offset by the time they must spend preparing instructions and refining them to address the ambiguities that typically arise. If crowdsourcing is to become viable, and result in net gains for requesters, requesters must be able to obtain high-quality results with a low investment of time in writing instructions. That might mean finding ways to accommodate hastily written instructions. Instruction quality could be improved by resolving ambiguities either with help of crowd workers, or by using NLP-based tools. </p> <p><br></p> <p>In this dissertation, I present 1) a taxonomy of ambiguities that can occur in task instructions, 2) a workflow that enables requesters to resolve ambiguities before posting them to workers, 3) a set of methods to improve the quality of instructions while workers are</p> <p>working on the task, and finally, 4) a system that leverages current NLP technologies to detect ambiguities automatically before they are posted to the workers.</p>