834 resultados para Efficiency ranking


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Generally, in tropical and subtropical agroecosystems, the efficiency of nitrogen (N) fertilization is low, inducing a temporal variability of crop yield, economic losses, and environmental impacts. Variable-rate N fertilization (VRF), based on optical spectrometry crop sensors, could increase the N use efficiency (NUE). The objective of this study was to evaluate the corn grain yield and N fertilization efficiency under VRF determined by an optical sensor in comparison to the traditional single-application N fertilization (TSF). With this purpose, three experiments with no-tillage corn were carried out in the 2008/09 and 2010/11 growing seasons on a Hapludox in South Brazil, in a completely randomized design, at three different sites that were analyzed separately. The following crop properties were evaluated: aboveground dry matter production and quantity of N uptake at corn flowering, grain yield, and vegetation index determined by an N-Sensor® ALS optical sensor. Across the sites, the corn N fertilizer had a positive effect on corn N uptake, resulting in increased corn dry matter and grain yield. However, N fertilization induced lower increases of corn grain yield at site 2, where there was a severe drought during the growing period. The VRF defined by the optical crop sensor increased the apparent N recovery (NRE) and agronomic efficiency of N (NAE) compared to the traditional fertilizer strategy. In the average of sites 1 and 3, which were not affected by drought, VRF promoted an increase of 28.0 and 41.3 % in NAE and NRE, respectively. Despite these results, no increases in corn grain yield were observed by the use of VRF compared to TSF.

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In order to select soil management practices that increase the nitrogen-use efficiency (NUE) in agro-ecosystems, the different indices of agronomic fertilizer efficiency must be evaluated under varied weather conditions. This study assessed the NUE indices in no-till corn in southern Paraguay. Nitrogen fertilizer rates from 0 to 180 kg ha-1 were applied in a single application at corn sowing and the crop response investigated in two growing seasons (2010 and 2011). The experimental design was a randomized block with three replications. Based on the data of grain yield, dry matter, and N uptake, the following fertilizer indices were assessed: agronomic N-use efficiency (ANE), apparent N recovery efficiency (NRE), N physiological efficiency (NPE), partial factor productivity (PFP), and partial nutrient balance (PNB). The weather conditions varied largely during the experimental period; the rainfall distribution was favorable for crop growth in the first season and unfavorable in the second. The PFP and ANE indices, as expected, decreased with increasing N fertilizer rates. A general analysis of the N fertilizer indices in the first season showed that the maximum rate (180 kg ha-1) obtained the highest corn yield and also optimized the efficiency of NPE, NRE and ANE. In the second season, under water stress, the most efficient N fertilizer rate (60 kg ha-1) was three times lower than in the first season, indicating a strong influence of weather conditions on NUE. Considering that weather instability is typical for southern Paraguay, anticipated full N fertilization at corn sowing is not recommended due the temporal variability of the optimum N fertilizer rate needed to achieve high ANE.

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Vendor Ranking for letting reports from the Iowa Department of Transportation.

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Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

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This Phase II project follows a previous project titled Strategies to Address Nighttime Crashes at Rural, Unsignalized Intersections. Based on the results of the previous study, the Iowa Highway Research Board (IHRB) indicated interest in pursuing further research to address the quality of lighting, rather than just the presence of light, with respect to safety. The research team supplemented the literature review from the previous study, specifically addressing lighting level in terms of measurement, the relationship between light levels and safety, and lamp durability and efficiency. The Center for Transportation Research and Education (CTRE) teamed with a national research leader in roadway lighting, Virginia Tech Transportation Institute (VTTI) to collect the data. An integral instrument to the data collection efforts was the creation of the Roadway Monitoring System (RMS). The RMS allowed the research team to collect lighting data and approach information for each rural intersection identified in the previous phase. After data cleanup, the final data set contained illuminance data for 101 lighted intersections (of 137 lighted intersections in the first study). Data analysis included a robust statistical analysis based on Bayesian techniques. Average illuminance, average glare, and average uniformity ratio values were used to classify quality of lighting at the intersections.

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Develop, in conjunction with the regional planning affiliations and metropolitan planning organizations and other stakeholder groups, a process to exchange STP federal funds for Primary Highway System funds for the purpose of reducing the number of small projects that have to meet onerous federal requirements. In order to implement this recommendation, legislative action is required to eliminate the restriction on using Primary Road Fund revenue on local jurisdiction roadways in exchange for a portion of their federal STP funding. This past session, Iowa DOT worked with legislators to introduce a bill in both the House and the Senate to eliminate this Code restriction. Bills were discussed at the subcommittee level in both the House and Senate but did not proceed because of the need to have further discussions with impacted parties. Prior to next session, discussions will occur amongst all impacted parties to reach consensus on how this recommendation could be implemented. With that consensus in place prior to next session, it is anticipated that the bills can be reintroduced next session.

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Senate File 2314, 84th General Assembly, states the Iowa Department of Transportation shall submit quarterly reports regarding the implementation of efficiency measures identified in the “Road Use Tax Fund Efficiency Report,” January 2012. This report shall provide details of activities undertaken in the previous quarter relating to one-time and long-term program efficiencies and partnership efficiencies. Issues covered include savings realized from the implementation of particular efficiency measures; updates concerning measures that have not been implemented; efforts involving cities, counties, other jurisdictions, or stakeholder interest groups; any new efficiency measures identified or undertaken; and identification of any legislative action that may be required to achieve efficiencies.

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Senate File 2314, 84th General Assembly, states the Iowa Department of Transportation shall submit quarterly reports regarding the implementation of efficiency measures identified in the "Road Use Tax Fund Efficiency Report," January 2012. This report shall provide details of activities undertaken in the previous quarter relating to one-time and long-term program efficiencies and partnership efficiencies. Issues covered include savings realized from the implementation of particular efficiency measures; updates concerning measures that have not been implemented; efforts involving cities, counties, other jurisdictions, or stakeholder interest groups; any new efficiency measures identified or undertaken; and identification of any legislative action that may be required to achieve efficiencies.

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Senate File 2314, 84th General Assembly, states the Iowa Department of Transportation shall submit quarterly reports regarding the implementation of efficiency measures identified in the "Road Use Tax Fund Efficiency Report," January 2012. This report shall provide details of activities undertaken in the previous quarter relating to one-time and long-term program efficiencies and partnership efficiencies. Issues covered include savings realized from the implementation of particular efficiency measures; updates concerning measures that have not been implemented; efforts involving cities, counties, other jurisdictions, or stakeholder interest groups; any new efficiency measures identified or undertaken; and identification of any legislative action that may be required to achieve efficiencies.

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Several agencies specify AASHTO T283 as the primary test for field acceptance of moisture susceptibility in hot mix asphalt. When used in this application, logistical difficulties challenge its practicality, while repeatability is routinely scrutinized by contractors. An alternative test is needed which can effectively demonstrate the ability to screen mixtures based on expected performance. The ideal replacement can be validated with field performance, is repeatable, and allows for prompt reporting of results. Dynamic modulus, flow number, AASHTO T283, Hamburg wheel tracking device (HWTD), and the moisture induced sensitivity test (MIST) were performed on plant produced surface mixes in Iowa. Follow-up distress surveys were used to rank the mixes by their performance. The rankings indicate both the quantity of swelling from MIST conditioning and submersed flow number matched the performance ranking of all but one mixture. Hamburg testing parameters also appear effective, namely the stripping inflection point and the ratio between stripping slope and the creep slope. Dynamic modulus testing was ineffective, followed by AASHTO T283 and ratios produced from flow number results of conditioned samples.

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Selostus: Lypsylehmien valkuaisruokinnan ja hedelmällisyyden yhteys: kirjallisuustutkimus valkuaisruokinnan vaikutuksista Suomen olosuhteissa

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Senate File 2314, 84th General Assembly, states the Iowa Department of Transportation shall submit quarterly reports regarding the implementation of efficiency measures identified in the "Road Use Tax Fund Efficiency Report," January 2012. This report shall provide details of activities undertaken in the previous quarter relating to one-time and long-term program efficiencies and partnership efficiencies. Issues covered include savings realized from the implementation of particular efficiency measures; updates concerning measures that have not been implemented; efforts involving cities, counties, other jurisdictions, or stakeholder interest groups; any new efficiency measures identified or undertaken; and identification of any legislative action that may be required to achieve efficiencies.

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Intracardiac organization indices such as atrial fibrillation (AF) cycle length (AFCL) have been used to track the efficiency of stepwise catheter ablation (step-CA) of longstanding persistent AF, however with limited success. The morphology of AF activation waves reflects the underlying activation patterns. Its temporal evolution is a local organization indicator that could be potentially used for tracking the efficiency of step-CA. We report a new method for characterizing the structure of the temporal evolution of activation wave morphology. Using recurrence plots, novel organization indices are proposed. By computing their relative evolution during the first step of ablation vs baseline, we found that these new parameters are superior to AFCL to track the effect of step-CA "en route" to AF termination.