Evaluation of a Method Combining Physical Experiment and Data Insertion For the Assessment of PET-CT Systems

Background: To evaluate and compare Positron Emission Tomography (PET) devices among them, tests are performed on phantoms that generally consist in simple geometrical objects, fillable with radiotracers. On one hand, those tests bring a control over the experiment through the operator preparation but on the other hand, they are limited in terms of reproducibility, repeatability and are time-consuming, in particular, if several replications are required. To overcome these restrictions, we designed a method combining physical experiment and data insertion that aims to avoid experimental repetitions while testing multiple configurations for the performance evaluation of PET scanners.Methods: Based on the National Electrical Manufacturers Association Image Quality standard, four experiments, with different spheres-to-background ratios: 2:1, 4:1, 6:1 and 8:1, were performed. An additional acquisition was done with a radioactive background and no activity within the spheres. It was created as a baseline to artificially simulate the radioactive spheres and reproduce initial experiments. Standard sphere set was replaced by smaller target sizes (4, 5, 6, 8, 10 and 13 mm) to match current detectability performance of PET scanners. Images were reconstructed following standard guidelines, i.e. using OSEM algorithm, and an additional BPL reconstruction was performed. We visually compared experimental and simulated images. We measured the activity concentration values into the spheres to calculate the mean and maximum recovery coefficient (RCmean and RCmax ) which we used in a quantitative analysis.Results: No significant visual discrepancies were identified between experimental and simulated series. Mann-Whitney U tests comparing simulated and experimental distributions showed no statistical differences for both RCmean (P value = 0.611) and RCmax (P value = 0.720). Spearman tests revealed high correlation for RCmean (ρ = 0.974, P value < 0.001) and RCmax (ρ = 0.974, P value < 0.001) between both datasets. According to Bland-Altman plots, we highlighted slight shifts in RCmean and RCmax of respectively 2.1 ± 16.9 % and 3.3 ± 22.3 %.Conclusions: The method produced realistic results compared to experimental data. Known synthesized information fused with original data allows full exploration of the system's capabilities while avoiding the limitations associated with repeated experiments.

Evaluation of Plotting Position Formulae for Pearson Type 3 Distribution in Three Hydrological Stations on the Niger River

In this study, eight unbiased plotting position formulae recommended for Pearson Type 3 distribution were evaluated by comparing the simulated series of each formula with the annual maximum series (AMS) of River Niger at Baro, Koroussa and Shintaku hydrological stations, each having data length of 51years, 53 years and 58 years respectively. The parameters of Pearson Type 3 distribution were computed by the method of moments with corrections for skewness. While the fitting of Pearson Type 3 distribution proceeds with the development of flood – return period (Q-T) relationship, followed by application of the derived Q- T relation to compute simulated discharges for comparison with AMS of the study stations. The plotting position formulae were evaluated on the basis of optimum values of the statistically goodness-of-fit of probability plot correlation coefficient (PPCC), relative root mean square error (RRMSE), percent bias (PBIAS), mean absolute error (MAE) and Nash-sutcliffe efficiency (NSE), across the stations. The plotting position formulae were ranked on scale of 1 to 8. Thus a plotting formula that best simulates the empirical observations using the goodness-of-measures was scored “1” and so on. The individual scores per plotting position were summed across the gof tests to obtain the total score. The study show that Chegodayev is the best plotting position formula for Baro, Weibull is the best plotting position Formula for Kourassou and Shintaku hydrological stations. The overall performances of the eight plotting position formulae across the hydrological stations show that weibull distribution is the overall best having scored 27, seconded by Chegodayev with 30 and thirdly, Beard with 38. The Pearson Type 3 distribution had been found one of the best probability distribution model of flood flow in Nigeria and this study was conducted to gain in-depth knowledge of the distribution. Finally, this study recommends extension of the studies to Log-Pearson Type 3 distribution.

A Two-Steps-Ahead Estimator for Bubble Entropy

Aims: Bubble entropy (bEn) is an entropy metric with a limited dependence on parameters. bEn does not directly quantify the conditional entropy of the series, but it assesses the change in entropy of the ordering of portions of its samples of length m, when adding an extra element. The analytical formulation of bEn for autoregressive (AR) processes shows that, for this class of processes, the relation between the first autocorrelation coefficient and bEn changes for odd and even values of m. While this is not an issue, per se, it triggered ideas for further investigation. Methods: Using theoretical considerations on the expected values for AR processes, we examined a two-steps-ahead estimator of bEn, which considered the cost of ordering two additional samples. We first compared it with the original bEn estimator on a simulated series. Then, we tested it on real heart rate variability (HRV) data. Results: The experiments showed that both examined alternatives showed comparable discriminating power. However, for values of 10<m<20, where the statistical significance of the method was increased and improved as m increased, the two-steps-ahead estimator presented slightly higher statistical significance and more regular behavior, even if the dependence on parameter m was still minimal. We also investigated a new normalization factor for bEn, which ensures that bEn=1 when white Gaussian noise (WGN) is given as the input. Conclusions: The research improved our understanding of bubble entropy, in particular in the context of HRV analysis, and we investigated interesting details regarding the definition of the estimator.

Country macroeconomic indicators modeling features

Economic time series have repeatable or non-repeatable fluctuation. A pattern of a time series, which repeats at regular intervals every year, same direction, and similar magnitude is defined as seasonality. The seasonal component represents intra-year fluctuations that are more or less stable year after in a time series. Possible causes of these variations are a systematic and calendar related effects and include natural factors (for instance seasonalweather patterns), administrativemeasures (for example the starting and ending dates of the school year), social/cultural/religious traditions (fixed holidays such as Christmas), the length of the months (28, 29, 30 or 31 days) or quarters (90, 91 or 92 days).Analysts, economists, police makers use time series to make conclusions and decisions in respective area. They tray to identify important features of economic series such as short term changes, directions, turning points and consistency between other economic indicators. These points are usually in interest. Sometimes seasonal movements can make these features difficult to see and this type of analysis is not easy using raw time series data.Deterministic, TRAMO-SEATS and ARIMA-X-12 seasonal adjustment methods are analysed in this article. 1600 time serieswere simulated for solvingwhich seasonal adjustmentmethod is precise. TRAMOSEATS and ARIMA-X-12 both perform similarly for the simulated series. Econometric models of macroeconomic indicators of Lithuania reveal that modeling with seasonal adjusted data is more accurate.

Penalty of model misspecification in time series dominated with trend

Model specification is consequential in mathematical science and statistics in particular. This work seeks to ascertain the consequences of model mis-specification in the analysis of a time series dominated by trend. It further discusses the statistical properties of various types of trend as well as when they are combine with AR (1) and MA (1) process. It recommends the use of spectrum analysis in detection of trend type in a given series.Illustrations were carried out using simulated series. The results from the simulated series was in harmony with the theoretical results.

A comparative study on combinations of forecasts and their individual forecasts by means of simulated series

Over the years, several studies that compare individual forecasts with the combination of forecasts were published. There is, however, no unanimity in the conclusions. Furthermore, methods of combination by regression are poorly explored. This paper presents a comparative study of three methods of combination and their individual forecasts. Based on simulated data, it is evaluated the accuracy of Artificial Neural Networks, ARIMA and exponential smoothing models; calculating the combined forecasts through simple average, minimum variance and regression methods. Four accuracy measurements, MAE, MAPE, RMSE and Theil’s U, were used for choosing the most accurate method. The main contribution is the accuracy of the combination by regression methods.

Rainfall trends for the State of Paraná: present and future climate

This paper analyzes the variability and the precipitation trend of the State of Paraná, in Brazil. For that, monthly precipitation data belonging to 24 precipitation stations in a 30-year period (1980-2010) were analyzed and they were compared with projections of precipitation for the years 2016-2050. These data were simulated by Eta/Miroc5 for RCP 4.5 (Representative Concentration Pathways) from the Center for Weather Forecasting and Climate Studies CPTEC/INPE and the historical data of precipitation were taken from National Water Agency (ANA). The Mann-Kendall non-parametric test and the Sen’s slope estimator were applied to detect trends and magnitudes, respectively. The Mann-Whitney test was used to compare the median of the historical series (1980-2010) with the simulated series (2016-2050) and the comparison of the means between the two series was performed by Test t. The results draw attention to the great variability and significant changes in the monthly average rainfall that may occur, if the climate change scenarios that were considered become a reality in the near future.

Testing the Tests: What Are the Impacts of Incorrect Assumptions When Applying Confidence Intervals or Hypothesis Tests to Compare Competing Forecasts?

Which of two competing continuous forecasts is better? This question is often asked in forecast verification, as well as climate model evaluation. Traditional statistical tests seem to be well suited to the task of providing an answer. However, most such tests do not account for some of the special underlying circumstances that are prevalent in this domain. For example, model output is seldom independent in time, and the models being compared are geared to predicting the same state of the atmosphere, and thus they could be contemporaneously correlated with each other. These types of violations of the assumptions of independence required for most statistical tests can greatly impact the accuracy and power of these tests. Here, this effect is examined on simulated series for many common testing procedures, including two-sample and paired t and normal approximation z tests, the z test with a first-order variance inflation factor applied, and the newer Hering–Genton (HG) test, as well as several bootstrap methods. While it is known how most of these tests will behave in the face of temporal dependence, it is less clear how contemporaneous correlation will affect them. Moreover, it is worthwhile knowing just how badly the tests can fail so that if they are applied, reasonable conclusions can be drawn. It is found that the HG test is the most robust to both temporal dependence and contemporaneous correlation, as well as the specific type and strength of temporal dependence. Bootstrap procedures that account for temporal dependence stand up well to contemporaneous correlation and temporal dependence, but require large sample sizes to be accurate.

Simulation of the minimum annual river flows based on the RCP climatic scenario, time horizon up to 2060-2080 and the Kaczawa River

In this paper a new simulations of minimum daily flow for Kaczawa River a left side tributary of the Odra River in south-west Poland are presented. Generated data were made based on very long series of 35 years of observed data and 24 sites of meteorological stations for south-west Poland gathered from the the Institute of Meteorology and Water Management National Research Institute (IMGW). For the data generation the spatial weather generator SWGEN producing the multisite daily time series was applied. Data were generated for the present (the year 2000 are used as a background) as well for future climate condition for 2060 and 2080 according Representative Concentration Pathways (RCPs) scenarios. The flow simulation in the river catchment is made using MIKE SHE hydrological model. Simulations are done for 2060 and 2080. The large number of new simulated series determined by the lead time, two climate change scenarios (RCP4.5 and RCP6.0), and number of generated years (1000 for each case) is equal to 5000 for a single station. Finally, Lognormal Pdf function for the minimum flow is presented as well probability of exceedance of minimum values.