Chaotic Behaviour of Active Magnetic Bearing System by Time Series Analysis

Chaotic behaviour is one of the hardest problems that can happen in nonlinear dynamical systems with severe nonlinearities. It makes the system's responses unpredictable. It makes the system's responses to behave similar to noise. In some applications it should be avoided. One of the approaches to detect the chaotic behaviour is nding the Lyapunov exponent through examining the dynamical equation of the system. It needs a model of the system. The goal of this study is the diagnosis of chaotic behaviour by just exploring the data (signal) without using any dynamical model of the system. In this work two methods are tested on the time series data collected from AMB (Active Magnetic Bearing) system sensors. The rst method is used to nd the largest Lyapunov exponent by Rosenstein method. The second method is a 0-1 test for identifying chaotic behaviour. These two methods are used to detect if the data is chaotic. By using Rosenstein method it is needed to nd the minimum embedding dimension. To nd the minimum embedding dimension Cao method is used. Cao method does not give just the minimum embedding dimension, it also gives the order of the nonlinear dynamical equation of the system and also it shows how the system's signals are corrupted with noise. At the end of this research a test called runs test is introduced to show that the data is not excessively noisy.

Selectivity of glyphosate in tank mixtures for RR soybean in sequential applications with mixtures only in the first or second application

Tank mixtures among herbicides of different action mechanisms might increase weed control spectrum and may be an important strategy for preventing the development of resistance in RR soybean. However, little is known about the effects of these herbicide combinations on soybean plants. Hence, two experiments were carried out aiming at evaluating the selectivity of glyphosate mixtures with other active ingredients applied in postemergence to RR soybean. The first application was carried out at V1 to V2 soybean stage and the second at V3 to V4 (15 days after the first one). For experiment I, treatments (rates in g ha-1) evaluated were composed by two sequential applications: the first one with glyphosate (720) in tank mixtures with cloransulam (30.24), fomesafen (125), lactofen (72), chlorimuron (12.5), flumiclorac (30), bentazon (480) and imazethapyr (80); the second application consisted of isolated glyphosate (480). In experiment II, treatments also consisted of two sequential applications, but tank mixtures as described above were applied as the second application. The first one in this experiment consisted of isolated glyphosate (720). For both experiments, sequential applications of glyphosate alone at 720/480, 960/480, 1200/480 and 960/720 (Expt. I) or 720/480, 720/720, 720/960 and 720/1200 (Expt. II) were used as control treatments. Applications of glyphosate tank mixtures with other herbicides are more selective to RR soybean when applied at younger stages whereas applications at later stages might cause yield losses, especially when glyphosate is mixed with lactofen and bentazon.

Selectivity of glyphosate tank mixtures for RR soybean

An active ingredients mixture of different action mechanisms is an essential tool to prevent or manage areas with resistant weeds. However, it is important that such a mixture provides adequate selectivity to the crop. The aim of this work was to evaluate glyphosate selectivity to glyphosate-resistant (RR) soybean, and also verify if there is selectivity in mixtures with other active ingredients applied postemergence aimed at new control strategies, which might be used in RR soybean cultivation. The herbicides and respective rates (g ha-1) evaluated were: glyphosate (720, 960, 1,200, and 1,440), and the mixtures of glyphosate (960) with cloransulam-methyl (30.24), fomesafen (125), lactofen (72), chlorimuron-ethyl (12.5), flumiclorac-pentyl (30), bentazon (480), or imazethapyr (80). All treatments were applied in postemergence when the soybean crop was at V2 to V3 stage. Treatments with glyphosate or in mixtures with postemergent herbicides showed visual effects of phytotoxicity when applied to the glyphosate-resistant soybean. Effects such as reduction in plant height, crop closure, number of pods per plant, and hundred grain weight could be observed. However, the effects related to plant development were mostly transient and did not persist during the crop cycle. Among the studied treatments, only the mixture of glyphosate and lactofen was not selective to the crop, promoting negative effects on most characteristics analyzed and consequently reducing grain yield.

Interactions between soybean and weeds in a replacement series system, considering the effects of water stress

Due to the increase of water deficiency in many farm regions and its meaning on weed interference, competitive interactions between soybean and three weeds were evaluated under water stress (20 to 40 days after transplanting) and no stress conditions. Three independent experiments were carried out in a growth chamber, being each one composed by the weeds Alternanthera tenella, Tridax procumbens or Digitaria ciliaris, along with the crop, in which soil water condition and plant composition effects were evaluated while in competition. A replacement series system was used, including both monoculture of each species and a mixture with a ratio of 50% between weed and soybean. A completely randomized design was used in factorial arrangement, with treatments distributed in three levels for plant composition factor (soybean and weeds monocultures, in addition to the soybean + weed mixture) and two levels for the water factor (with or without stress), amounting six treatments in each experiment. Soybean dry mass was higher than weed dry mass, when growing without water stress. However, under water stress conditions, the dry mass of soy was reduced in all experiments, mainly in the D. ciliaris comparative experiment. Water restriction was also significant in the plants' photosynthesis reduction in most of the experiments, reducing leaf area duration and efficiency of water use. Analysing all variables shows greater weed tolerance than soybean when submitted to water deficit and with distinct changes of their interactions and mechanism of competition, in each experiment.

A comparative analysis of preprocessing techniques of cardiac event series for the study of heart rhythm variability using simulated signals

In the present study, using noise-free simulated signals, we performed a comparative examination of several preprocessing techniques that are used to transform the cardiac event series in a regularly sampled time series, appropriate for spectral analysis of heart rhythm variability (HRV). First, a group of noise-free simulated point event series, which represents a time series of heartbeats, was generated by an integral pulse frequency modulation model. In order to evaluate the performance of the preprocessing methods, the differences between the spectra of the preprocessed simulated signals and the true spectrum (spectrum of the model input modulating signals) were surveyed by visual analysis and by contrasting merit indices. It is desired that estimated spectra match the true spectrum as close as possible, showing a minimum of harmonic components and other artifacts. The merit indices proposed to quantify these mismatches were the leakage rate, defined as a measure of leakage components (located outside some narrow windows centered at frequencies of model input modulating signals) with respect to the whole spectral components, and the numbers of leakage components with amplitudes greater than 1%, 5% and 10% of the total spectral components. Our data, obtained from a noise-free simulation, indicate that the utilization of heart rate values instead of heart period values in the derivation of signals representative of heart rhythm results in more accurate spectra. Furthermore, our data support the efficiency of the widely used preprocessing technique based on the convolution of inverse interval function values with a rectangular window, and suggest the preprocessing technique based on a cubic polynomial interpolation of inverse interval function values and succeeding spectral analysis as another efficient and fast method for the analysis of HRV signals

The time series convergence of dispersion in financial analysts' forecasts

Finansanalytiker har en stor betydelse för finansmarknaderna, speciellt igenom att förmedla information genom resultatprognoser. Typiskt är att analytiker i viss grad är oeniga i sina resultatprognoser, och det är just denna oenighet analytiker emellan som denna avhandling studerar. Då ett företag rapporterar förluster tenderar oenigheten gällande ett företags framtid att öka. På ett intuitivt plan är det lätt att tolka detta som ökad osäkerhet. Det är även detta man finner då man studerar analytikerrapporter - analytiker ser ut att bli mer osäkra då företag börjar gå med förlust, och det är precis då som även oenigheten mellan analytikerna ökar. De matematisk-teoretiska modeller som beskriver analytikers beslutsprocesser har däremot en motsatt konsekvens - en ökad oenighet analytiker emellan kan endast uppkomma ifall analytikerna blir säkrare på ett individuellt plan, där den drivande kraften är asymmetrisk information. Denna avhandling löser motsägelsen mellan ökad säkerhet/osäkerhet som drivkraft bakom spridningen i analytikerprognoser. Genom att beakta mängden publik information som blir tillgänglig via resultatrapporter är det inte möjligt för modellerna för analytikers beslutsprocesser att ge upphov till de nivåer av prognosspridning som kan observeras i data. Slutsatsen blir därmed att de underliggande teoretiska modellerna för prognosspridning är delvis bristande och att spridning i prognoser istället mer troligt följer av en ökad osäkerhet bland analytikerna, i enlighet med vad analytiker de facto nämner i sina rapporter. Resultaten är viktiga eftersom en förståelse av osäkerhet runt t.ex. resultatrapportering bidrar till en allmän förståelse för resultatrapporteringsmiljön som i sin tur är av ytterst stor betydelse för prisbildning på finansmarknader. Vidare används typiskt ökad prognosspridning som en indikation på ökad informationsasymmetri i redovisningsforskning, ett fenomen som denna avhandling därmed ifrågasätter.

In vitro antimicrobial activity of a new series of 1,4-naphthoquinones

The antibacterial activity of a series of 1,4-naphthoquinones was demonstrated. Disk diffusion tests were carried out against several Gram-positive and Gram-negative bacteria. The compound 5-amino-8-hydroxy-1,4-naphthoquinone was the most effective, presenting inhibition zones measuring 20 mm against staphylococci, streptococci and bacilli at 50 µg/ml. Methicillin-resistant Staphylococcus aureus and several clinical isolates of this bacterium were also inhibited. Naphthazarin, 5-acetamido-8-hydroxy-1,4-naphthoquinone, and 2,3-diamino-1,4-naphthoquinone were the next most active compounds. The minimal inhibitory concentration of the active compounds was determined against S. aureus, ranging from 30 to 125 µg/ml. All compounds presented a minimal bactericidal concentration higher than 500 µg/ml, indicating that their effect was bacteriostatic. The EC50, defined as the drug concentration that produces 50% of maximal effect, was 8 µg/ml for 5-amino-8-hydroxy-1,4-naphthoquinone against S. aureus, S. intermedius, and S. epidermidis. These results indicate an effective in vitro activity of 5-amino-8-hydroxy-1,4-naphthoquinone and encourage further studies for its application in antibiotic therapy.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Integrated energy storage tank for large scale power-to-gas applications

The global interest towards renewable energy production such as wind and solar energy is increasing, which in turn calls for new energy storage concepts due to the larger share of intermittent energy production. Power-to-gas solutions can be utilized to convert surplus electricity to chemical energy which can be stored for extended periods of time. The energy storage concept explored in this thesis is an integrated energy storage tank connected to an oxy-fuel combustion plant. Using this approach, flue gases from the plant could be fed directly into the storage tank and later converted into synthetic natural gas by utilizing electrolysis-methanation route. This work utilizes computational fluid dynamics to model the desublimation of carbon dioxide inside a storage tank containing cryogenic liquid, such as liquefied natural gas. Numerical modelling enables the evaluation of the transient flow patterns caused by the desublimation, as well as general fluid behaviour inside the tank. Based on simulations the stability of the cryogenic storage and the magnitude of the key parameters can be evaluated.

Sentinel node biopsy in breast cancer: results in a large series

Sentinel lymph node biopsy (SLNB) is an appropriate method for the evaluation of axillary status in cases of early breast cancer. We report our experience in treating cases evaluated using SLNB. We analyzed a total of 1192 cases assessed by means of SLNB from July 1999 to December 2007. SLNB processing was successfully completed in 1154 cases with the use of blue dye or radiolabeled 99mTc-Dextran-500, or both. Of these 1154 patients, 857 were N0(i-) (no regional lymph node metastasis, negative immunohistochemistry, IHC), 96 were N0(i+) (no regional lymph node metastasis histologically, positive IHC, no IHC cluster greater than 0.2 mm) and 201 were N1mi (greater than 0.2 mm, none greater than 2.0 mm). Most of the tumors (70%) were invasive ductal carcinomas and tumors were staged as T1 in 770 patients (65%). A total of 274 patients underwent SLNB and axillary dissections up to April 2003. The inclusion criteria were tumor size equal to or less than 3 cm in diameter, no clinically palpable axillary lymph nodes, no neoadjuvant therapy. In 19 cases, the SLN could not be identified intraoperatively. A false-negative rate of 11% and a negative predictive value of 88.2% were obtained for the 255 assessable patients. The overall concordance between SLNB and axillary lymph node status was 92%. SLNB sensitivity for nodes was 81% and specificity was 100%. The higher sensitivity, specificity, accuracy, and lower false-negative rates of SLNB suggest that this method may be an appropriate alternative to total axillary dissection in early breast cancer patients.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

A time series analysis of meteorological factors and hospital outpatient admissions for cardiovascular disease in the Northern district of Guizhou Province, China

Findings on the effects of weather on health, especially the effects of ambient temperature on overall morbidity, remain inconsistent. We conducted a time series study to examine the acute effects of meteorological factors (mainly air temperature) on daily hospital outpatient admissions for cardiovascular disease (CVD) in Zunyi City, China, from January 1, 2007 to November 30, 2009. We used the generalized additive model with penalized splines to analyze hospital outpatient admissions, climatic parameters, and covariate data. Results show that, in Zunyi, air temperature was associated with hospital outpatient admission for CVD. When air temperature was less than 10°C, hospital outpatient admissions for CVD increased 1.07-fold with each increase of 1°C, and when air temperature was more than 10°C, an increase in air temperature by 1°C was associated with a 0.99-fold decrease in hospital outpatient admissions for CVD over the previous year. Our analyses provided statistically significant evidence that in China meteorological factors have adverse effects on the health of the general population. Further research with consistent methodology is needed to clarify the magnitude of these effects and to show which populations and individuals are vulnerable.