Correlation of Statistical Distributions of the Dimension of Yeast Cells Attached to the Substrate and Its Surface Electrical Potential

The ability of cells to adhere to substrates is an important factor for the effectiveness of biotechnologies and bioimplants. This research demonstrates that the statistical distribution of the sizes of the cells (Saccharomyces cerevisiae) attached to the substrate surface correlates with the statistical distribution of electrical potential on the substrate’s surface. Hypothetically, this behavior should be taken into consideration during the processing of surfaces when cell adhesion based on cell size is required.

Statistical Distribution of Lassa Fever in Edo State, Nigeria

Lassa fever is a severe viral infection caused by the Lassa virus and spread by contact with excretions or secretions of infected rats gaining access to food and water inside human houses and other human activity areas. Sierra Leone, the Republic of Guinea, Nigeria, and Liberia are among the nations where it is endemic with a high number of deaths recorded yearly due to Lassa fever. In Nigeria, one of the states with the highest incidence is Edo. In order to reduce and predict the spread of Lassa fever in Edo state, the trend of the disease needs to be understood. Knowledge of the statistical distribution of a disease is one of the best ways to understand the trend of the disease. Currently, existing research on the statistical distribution of Lassa fever is very rare. The present work is an attempt to initiate research on the statistical distribution of Lassa fever with data obtained on weekly cases of Lassa Fever in Edo State, Nigeria. Based on the Kolmogorov Smirnoff and Anderson Darling’s goodness of fit test for fitting distribution, the Geometric distribution outfitted the weekly confirmed incidences of Lassa fever in Edo State, Nigeria when compared with the Discrete Uniform and Poisson distributions. The study further revealed that on the average, two Lassa fever cases is recorded per week in Edo State within the study period. This number of cases per week is on the high side and should be immediately looked into.

Health index prediction of dissolved gases in transformer oil based on statistical distribution model

In this paper, dissolved gas analysis (DGA) and statistical distribution model (SDM) were used to predict the health index (HI) of dissolved gas in transformer oil. First, the individual DGA data are classified according to transformer ages ranging from 1 to 4 years. Then, representative fitting models were selected and extrapolated from 5 to 25 years. The inverse cumulative distribution function (ICDF) of the selected distribution model was used to calculate the single conditional parameter data from 5 to 25 years. Finally, the traditional scoring method is used to estimate the future HI value. The results show that DGA parameters can be expressed by exponential equation based on statistical model. The predicted values of DGA health index of transformer oil from 1 to 7 years were basically consistent with the calculated values, and the DGA score was 100 points. By the 20th year, the DGA score had dropped to 75, requiring timely monitoring. The research results can provide powerful data support and theoretical reference for transformer life prediction.

The measurements of vacuum arc behaviour at threshold current

The measurements of vacuum arc current parameters at the threshold current were made. The threshold currents for Cu, W, graphite cathodes were measured. It was shown that the current that vacuum arc chops with, has a statistical distribution. The vacuum arc current chopping is accompanied with significant ion current burst. The ion current of W and Cu cathodes contains intensive peaks with 30-50 ns duration. It was shown that the cathode materials that have intense peaks have a significant threshold current.

A mathematical model of relation and origin of variation between CRI and CSR indexes

The aim of this work is both the mathematical relation and the value variation analysis between CRI and CSR indexes. For this aim the physical mathematical model is proposed on the basis of the ISO-test. The physical basis of the model is a material balance of a one piece of coke from the ISO sample. Results of calculating by the model are curves of CSR=f(CRI) which reproduces the regressions in analogy with CSR=a+b.CRI for most coke-producing countries. The model showed that a larger part of CSR=f(CRI) curve is linear and that a universal regression in analogy with CSR=a+b.CRI does not exist. As follows from the model, every piece of coke from the ISO sample has its own CSR=f(CRI) curve with a CRI and CSR point. Between pieces of coke, variations of CRI and CSR values can be explained by the open pore amount, the coke pores’ surface area, the statistical distribution of molecular oriented domains on the basis of Lc and the coke piece mass. In our results, pores with a geometrical orientation from the outside to the center of a coke piece and having a minimum length significantly influence on the coke quality according to CRI and CSR indexes.

Non-stationary statistical estimation of wind speeds of rare recurrence in the Azov and Black Sea region

In the paper statistical distribution of the highest wind speed per year in the Azov and Black Sea region was analyzed using the data of 33 meteorological stations for 1958-2013. A statistical estimation of the wind speed extremes was carried out by approximation of the empirical sample with a function of Generalized distribution of Extreme Values (GEV) and by extrapolating it to the low probabilities region. We used two methodologies and applied statistical distribution functions corresponding to them. The first method is based on the assumption of stationarity of parameters of the GEV function. The second one is based on the non-stationary assumption of time dependence of extremum localization parameter μ. It was found, that for 13 out of 33 stations of the region, non-stationary GEV-function turned out to be adequate to describe extreme wind speeds.