Detection of Phosphorus and Nitrogen Deficiencies in Corn Using Spectral Radiance Measurements

Applications of remote sensing in crop production are becoming increasingly popular due in part to an increased concern with pollution of surface and ground waters due to over-fertilization of agricultural lands and the need to compensate for spatial variability in a field. Past research in this area has focused primarily on N stress in crops. Other stresses and the interactions have not been fully evaluated. A field experiment was conducted to determine wavelengths and/or combinations of wavelengths that are indicative of P and N deficiency and also the interaction between these in corn (Zea mays L.). The field experiment was a randomized complete block design with four replications using a factorial arrangement of treatments in an irrigated continuous corn system. The treatment included four N rates (0, 67, 134, and 269 kg N ha-1) and four P rates (0, 22, 45, and 67 kg P ha-1). Spectral radiance measurements were taken at various growth stages in increments from 350 to 1000 nm and correlated with plant N and P concentration, plant biomass, grain N and P concentration, and grain yield. Reflectance in the near-infrared (NIR) and blue regions was found to predict early season P stress between growth stages V6 and V8. Late season detection of P stress was not achieved. Plant N concentration was best predicted using reflectance in the red and green regions of the spectrum, while grain yield was estimated using reflectance in the NIR region, with the particular wavelengths of importance changing with growth stage

On the equivalence of two deformation schemes in quantum field theory

Two recent deformation schemes for quantum field theories on the two-dimensional Minkowski space, making use of deformed field operators and Longo-Witten endomorphisms, respectively, are shown to be equivalent.Comment: 14 pages, no figure. The final version is available under Open Access. CC-B

Biomateriais: polímeros e compósitos.

bitstream/item/115782/1/cot10-01.pd

A data mining approach to the SAR values over large MR image repositories

Purpose: In magnetic resonance imaging, the radiofrequency energy absorption arises as one of the main safety concerns, being mainly related with increased body temperature. Monitoring radiofrequency absorption is achieved by the estimation of specific absorption rate (SAR), whose implementation lies on equipment manufacturers, which in turn are not totally enlightening about its calculus. This work presents an exploratory approach of whole-body SAR values stored in DICOM metadata aiming to find correlation with body weight, body mass index (BMI), gender and pulse sequences for abdominal/pelvic (17.812 series) and head (29.907 series) studies. Methods and Materials: All studies were acquired in a 3 Tesla scanner with high-performance gradients. Data were extracted using Dicoogle, a DICOM metadata mining tool. Several DICOM tags were analysed (e.g. patient weight, height, gender, sequence name). For each study type, specifically weighted pulse sequences were related with weight, BMI and gender through boxplot diagrams, statistical and effect size analysis. Results: SAR limits were never exceeded. Generally, SAR values tended to decrease with increasing body weight and BMI values for abdominal/pelvic studies. On the other hand, head studies showed different trends regarding distinct pulse sequences. SAR values tend to be higher in male individuals (p<0,05). As expected, turbo spin echo sequences present the highest SAR values. The values found for echo gradient spoiled sequence (FLASH) were also high. Conclusion: It is confirmed that SAR estimates are related with the analysed variables. An individual examination of pulse sequences is recommended to observe trends regarding weight, BMI or gender.publishe

Adaptivity as a Property to Achieve Resilience of Load-Carrying Systems

Load-carrying systems often suffer from unexpected disruptions which can cause damages or system breakdowns if they were neglected during product development. In this context, unexpected disruptions summarize unpredictable load conditions, external disturbances or failures of system components and can be comprehended as uncertainties caused by nescience. While robust systems can cope with stochastic uncertainties, uncertainties caused by nescience can be controlled only by resilient load-carrying systems. This paper gives an overview of the characteristics of resilience as well as the time-dependent resilient behaviour of subsystems. Based on this, the adaptivity of subsystems is classified and can be distinguished between autonomous and externally induced adaption and the temporal horizon of adaption. The classification of adaptivity is explained using a simple example of a joint brake application

Use of Remote-Sensing Imagery to Estimate Corn Grain Yield

Remote sensing—the process of acquiring information about objects from remote platforms such as ground-based booms, aircraft, or satellites—is a potentially important source of data for site-specific crop management, providing both spatial and temporal information. Our objective was to use remotely sensed imagery to compare different vegetation indices as a means of assessing canopy variation and its resultant impact on corn (Zea mays L.) grain yield. Treatments consisted of five N rates and four hybrids, which were grown under irrigation near Shelton, NE on a Hord silt loam in 1997 and 1998. Imagery data with 0.5-m spatial resolution were collected from aircraft on several dates during both seasons using a multispectral, four-band [blue, green, red, and near-infrared reflectance] digital camera system. Imagery was imported into a geographical information system (GIS) and then geo-registered, converted into reflectance, and used to compute three vegetation indices. Grain yield for each plot was determined at maturity. Results showed that green normalized difference vegetation index (GNDVI) values derived from images acquired during midgrain filling were the most highly correlated with grain yield; maximum correlations were 0.7 and 0.92 in 1997 and 1998, respectively. Normalizing GNDVI and grain yield variability within hybrids improved the correlations in both years, but more dramatic increases were observed in 1997 (0.7 to 0.82) than in 1998 (0.92 to 0.95). This suggested GNDVI acquired during midgrain filling could be used to produce relative yield maps depicting spatial variability in fields, offering a potentially attractive alternative to use of a combine yield monitor

All-Optical Broadband Excitation of the Motional State of Trapped Ions

We have developed a novel all-optical broadband scheme for exciting, amplifying and measuring the secular motion of ions in a radio frequency trap. Oscillation induced by optical excitation has been coherently amplified to precisely control and measure the ion's secular motion. Requiring only laser line-of-sight, we have shown that the ion's oscillation amplitude can be precisely controlled. Our excitation scheme can generate coherent motion which is robust against variations in the secular frequency. Therefore, our scheme is ideal to excite the desired level of oscillatory motion under conditions where the secular frequency is evolving in time. Measuring the oscillation amplitude through Doppler velocimetry, we have characterized the experimental parameters and compared them with a molecular dynamics simulation which provides a complete description of the system.Comment: 8 pages, 10 figure

Phase III study of nilotinib versus best supportive care with or without a TKI in patients with gastrointestinal stromal tumors resistant to or intolerant of imatinib and sunitinib

Background This phase III open-label trial investigated the efficacy of nilotinib in patients with advanced gastrointestinal stromal tumors following prior imatinib and sunitinib failure. Patients and methods Patients were randomized 2:1 to nilotinib 400 mg b.i.d. or best supportive care (BSC; BSC without tyrosine kinase inhibitor, BSC+imatinib, or BSC+sunitinib). Primary efficacy end point was progression-free survival (PFS) based on blinded central radiology review (CRR). Patients progressing on BSC could cross over to nilotinib. Results Two hundred and forty-eight patients enrolled. Median PFS was similar between arms (nilotinib 109 days, BSC 111 days; P=0.56). Local investigator-based intent-to-treat (ITT) analysis showed a significantly longer median PFS with nilotinib (119 versus 70 days; P=0.0007). A trend in longer median overall survival (OS) was noted with nilotinib (332 versus 280 days; P=0.29). Post hoc subset analyses in patients with progression and only one prior regimen each of imatinib and sunitinib revealed a significant difference in median OS of >4 months in favor of nilotinib (405 versus 280 days; P=0.02). Nilotinib was well tolerated. Conclusion In the ITT analysis, no significant difference in PFS was observed between treatment arms based on CRR. In the post hoc subset analyses, nilotinib provided significantly longer median O

A Novel Robust Scene Change Detection Algorithm for Autonomous Robots Using Mixtures of Gaussians

Interest in change detection techniques has considerably increased during recent years in the field of autonomous robotics. This is partly because changes in a robot's working environment are useful for several robotic skills (e.g., spatial cognition, modelling or navigation) and applications (e.g., surveillance or guidance robots). Changes are usually detected by comparing current data provided by the robot's sensors with a previously known map or model of the environment. When the data consists of a large point cloud, dealing with it is a computationally expensive task, mainly due to the amount of points and the redundancy. Using Gaussian Mixture Models (GMM) instead of raw point clouds leads to a more compact feature space that can be used to efficiently process the input data. This allows us to successfully segment the set of 3D points acquired by the sensor and reduce the computational load of the change detection algorithm. However, the segmentation of the environment as a Mixture of Gaussians has some problems that need to be properly addressed. In this paper, a novel change detection algorithm is described in order to improve the robustness and computational cost of previous approaches. The proposal is based on the classic Expectation Maximization (EM) algorithm, for which different selection criteria are evaluated. As demonstrated in the experimental results section, the proposed change detection algorithm achieves the detection of changes in the robot's working environment faster and more accurately than similar approaches