972 resultados para Technical efficiency


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Due to losses caused by water erosion, the development of techniques that increase the efficiency of soil conservation practices is fundamental. Terracing of agricultural lands is an important conservation practice. Bearing in mind that improperly built terraces may negatively affect the landscape, the purpose of this work was to evaluate the efficiency as well as the adequacy of retention terraces. Assessments were performed in four terraces implanted in different states, all located in the mideastern region of the state of Minas Gerais. The water storage efficiency of the terraces was determined by comparing the effective with the required storage capacity, as established in the project. Proposals were also made for the adequacy of the assessed terraces, based on the correction of the characteristics that jeopardized storage efficiency. The storage efficiency of three of the four assessed terraces was below the required levels (0.5-13 %). The main properties influencing storage capacity were: uniformity of ridge crest height, terrace end closure, and the cross section finishing. In two of the three low-efficiency terraces, the correction of these characteristics proved sufficient to raise the storage efficiency to nearly 100 %.

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The laparoscopic approach has emerged as a valid option for surgical management of kidney cancer, as well as a few benign pathologies. The immediate benefits of laparoscopy are well established and include less estimated blood loss, decreased pain, shorter perioperative convalescence, and improved cosmesis. Long-term oncologic outcomes of patients treated laparoscopically for kidney tumors are similar to those of open surgery.

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High rates of phosphate fertilizers are applied to potato (Solanum tuberosum L.), which may cause antagonistic interactions with other nutrients and limit crop yields when over-supplied. The purpose of this study was to evaluate the influence of phosphorus (P) levels in nutrient solution on P use efficiency, nutritional status and dry matter (DM) accumulation and partitioning of potato plants cv. Ágata. The experiment was carried out in a greenhouse, arranged in a completely randomized block design with four replications. Treatments consisted of seven P levels in nutrient solution (0, 2, 4, 8, 16, 31, and 48 mg L-1). Plants were harvested after 28 days of growth in nutrient solution, and separated in roots, stems and leaves for evaluations. The treatment effects were analyzed by regression analysis. Phosphorus levels of up to 8 mg L-1 increased the root and shoot DM accumulation, but drastically decreased the root/shoot ratio of potato cv. Ágata. Higher P availability increased P concentration, accumulation and absorption efficiency, but decreased P use efficiency. Higher P levels increased the N, P, Mg, Fe, and Mn concentrations in roots considerably and decreased K, S, Cu, and Zn concentrations. In shoot biomass, N, P, K, and Ca concentrations were significantly increased by P applied in solution, unlike Mg and Cu concentrations. Although higher P levels (> 8 mg L-1) in nutrient solution increased P concentration, accumulation and absorption efficiency, the DM accumulation and partitioning of potato cv. Ágata were not affected.

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The soil water available to crops is defined by specific values of water potential limits. Underlying the estimation of hydro-physical limits, identified as permanent wilting point (PWP) and field capacity (FC), is the selection of a suitable method based on a multi-criteria analysis that is not always clear and defined. In this kind of analysis, the time required for measurements must be taken into consideration as well as other external measurement factors, e.g., the reliability and suitability of the study area, measurement uncertainty, cost, effort and labour invested. In this paper, the efficiency of different methods for determining hydro-physical limits is evaluated by using indices that allow for the calculation of efficiency in terms of effort and cost. The analysis evaluates both direct determination methods (pressure plate - PP and water activity meter - WAM) and indirect estimation methods (pedotransfer functions - PTFs). The PTFs must be validated for the area of interest before use, but the time and cost associated with this validation are not included in the cost of analysis. Compared to the other methods, the combined use of PP and WAM to determine hydro-physical limits differs significantly in time and cost required and quality of information. For direct methods, increasing sample size significantly reduces cost and time. This paper assesses the effectiveness of combining a general analysis based on efficiency indices and more specific analyses based on the different influencing factors, which were considered separately so as not to mask potential benefits or drawbacks that are not evidenced in efficiency estimation.

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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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The frequent lack of microbiological documentation of infection by blood cultures (BC) has a major impact on clinical management of febrile neutropenic patients, especially in cases of unexplained persistent fever. We assessed the diagnostic utility of the LightCycler SeptiFast test (SF), a multiplex blood PCR, in febrile neutropenia. Blood for BC and SF was drawn at the onset of fever and every 3 days of persistent fever. SF results were compared with those of BC, clinical documentation of infection, and standard clinical, radiological, and microbiological criteria for invasive fungal infections (IFI). A total of 141 febrile neutropenic episodes in 86 hematological patients were studied: 44 (31%) microbiologically and 49 (35%) clinically documented infections and 48 (34%) unexplained fevers. At the onset of fever, BC detected 44 microorganisms in 35/141 (25%) episodes. Together, BC and SF identified 78 microorganisms in 61/141 (43%) episodes (P = 0.002 versus BC or SF alone): 12 were detected by BC and SF, 32 by BC only, and 34 by SF only. In 19/52 (37%) episodes of persistent fever, SF detected 28 new microorganisms (7 Gram-positive bacterial species, 15 Gram-negative bacterial species, and 6 fungal species [89% with a clinically documented site of infection]) whereas BC detected only 4 pathogens (8%) (P = 0.001). While BC did not detect fungi, SF identified 5 Candida spp. and 1 Aspergillus sp. in 5/7 probable or possible cases of IFI. Using SeptiFast PCR combined with blood cultures improves microbiological documentation in febrile neutropenia, especially when fever persists and invasive fungal infection is suspected. Technical adjustments may enhance the efficiency of this new molecular tool in this specific setting.

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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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Determination of the precise composition and variation of microbiota in cystic fibrosis lungs is crucial since chronic inflammation due to microorganisms leads to lung damage and ultimately, death. However, this constitutes a major technical challenge. Culturing of microorganisms does not provide a complete representation of a microbiota, even when using culturomics (high-throughput culture). So far, only PCR-based metagenomics have been investigated. However, these methods are biased towards certain microbial groups, and suffer from uncertain quantification of the different microbial domains. We have explored whole genome sequencing (WGS) using the Illumina high-throughput technology applied directly to DNA extracted from sputa obtained from two cystic fibrosis patients. To detect all microorganism groups, we used four procedures for DNA extraction, each with a different lysis protocol. We avoided biases due to whole DNA amplification thanks to the high efficiency of current Illumina technology. Phylogenomic classification of the reads by three different methods produced similar results. Our results suggest that WGS provides, in a single analysis, a better qualitative and quantitative assessment of microbiota compositions than cultures and PCRs. WGS identified a high quantity of Haemophilus spp. (patient 1) or Staphylococcus spp. plus Streptococcus spp. (patient 2) together with low amounts of anaerobic (Veillonella, Prevotella, Fusobacterium) and aerobic bacteria (Gemella, Moraxella, Granulicatella). WGS suggested that fungal members represented very low proportions of the microbiota, which were detected by cultures and PCRs because of their selectivity. The future increase of reads' sizes and decrease in cost should ensure the usefulness of WGS for the characterisation of microbiota.

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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.