Tensile Strength Statistical Analysis of B610CF Steel

2011 ◽  
Vol 194-196 ◽  
pp. 310-315
Author(s):  
Bin Xue ◽  
Tian Hui Zhang ◽  
Ren Ping Xu

B610CF steel is a newly steel, and is widely used. The test data of tensile strength for B610CF steel is fitted several kinds of probability statistical models. In this paper, the correlation coefficient method and K-S method were used to test the fitting effects. It is concluded that three-parameter Weibull distribution is the optimal distribution for the test data, and the security tensile strength of 48mm B610CF steel is 627.54MPa under the condition of 99.9% reliability. Through the study of tensile strength confidence interval for B610CF steel, it is obtained that confidence interval is [601.47MPa, 644.86MPa] under the condition of 99.9% reliability and 80% confidence level. The results are important for the structural reliability analysis of B610CF steel.

2020 ◽  
Author(s):  
Hideya Kawasaki ◽  
Hiromi Suzuki ◽  
Masato Maekawa ◽  
Takahiko Hariyama

BACKGROUND As pathogens such as influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can easily cause pandemics, rapid diagnostic tests are crucial for implementing efficient quarantine measures, providing effective treatments to patients, and preventing or containing a pandemic infection. Here, we developed the immunochromatography-NanoSuit® method, an improved immunochromatography method combined with a conventional scanning electron microscope (SEM), which enables observation of immunocomplexes labeled with a colloidal metal. OBJECTIVE A total of 197 clinical samples from patients suspected to be suffering from influenza were provided by a general hospital at the Hamamatsu University School of Medicine for examination using the Flu kit. METHODS Immunochromatography kit The ImunoAce® Flu kit (NP antigen detection), a human influenza commercial diagnosis kit, was purchased from TAUNS Laboratories, Inc. (Shizuoka, Japan). Au/Pt nanoparticles were utilized to visualize the positive lines. A total of 197 clinical samples from patients suspected to be suffering from influenza were provided by a general hospital at the Hamamatsu University School of Medicine for examination using the Flu kit. After macroscopic diagnosis using the Flu kit, the samples were stored in a biosafety box at room temperature (20-25 °C / 68 - 77 °F). The IgM detection immunochromatography kit against SARS-CoV-2 was obtained from Kurabo Industries, Ltd. (Osaka, Japan). One step rRT-PCR for influenza A rRT-PCR for influenza A was performed as described previously using Flu A universal primers. A Ct within 38.0 was considered as positive according to the CDC protocol. The primer/probe set targeted the human RNase P gene and served as an internal control for human nucleic acid as described previously. SEM image acquisition The immunochromatography kit was covered with a modified NanoSuit® solution based on previously published components (Nisshin EM Co., Ltd., Tokyo, Japan), placed first onto the wide stage of the specimen holder, and then placed in an Lv-SEM (TM4000Plus, Hitachi High-Technologies, Tokyo, Japan). Images were acquired using backscattered electron detectors with 10 or 15 kV at 30 Pa. Particle counting In fields containing fewer than 50 particles/field, the particles were counted manually. Otherwise, ImageJ/Fiji software was used for counting. ImageJ/Fiji uses comprehensive particle analysis algorithms that effectively count various particles. Images were then processed and counting was performed according to the protocol. Diagnosis and statistics The EM diagnosis and criteria for a positive test were defined as follows: particle numbers from 6 fields from the background area and test-line were statistically analyzed using the t-test. If there were more than 5 particles in one visual field and a significant difference (P < 0.01) was indicated by the t-test, the result was considered positive. Statistical analysis using the t-test was performed in Excel software. Statistical analysis of the assay sensitivity and specificity with a 95% confidence interval (95% CI) was performed using the MedCalc statistical website. The approximate line, correlation coefficient, and null hypothesis were calculated with Excel software. RESULTS Our new immunochromatography-NanoSuit® method suppresses cellulose deformity and makes it possible to easily focus and acquire high-resolution images of gold/platinum labeled immunocomplexes of viruses such as influenza A, without the need for conductive treatment as with conventional SEM. Electron microscopy (EM)-based diagnosis of influenza A exhibited 94% clinical sensitivity (29/31) (95% confidence interval [95%CI]: 78.58–99.21%) and 100% clinical specificity (95%CI: 97.80–100%). EM-based diagnosis was significantly more sensitive (71.2%) than macroscopic diagnosis (14.3%), especially in the lower influenza A-RNA copy number group. The detection ability of our method is comparable to that of real-time reverse transcription-polymerase chain reaction. CONCLUSIONS This simple and highly sensitive quantitative analysis method involving immunochromatography can be utilized to diagnose various infections in humans and livestock, including highly infectious diseases such as COVID-19.


2017 ◽  
Vol 928 (10) ◽  
pp. 58-63 ◽  
Author(s):  
V.I. Salnikov

The initial subject for study are consistent sums of the measurement errors. It is assumed that the latter are subject to the normal law, but with the limitation on the value of the marginal error Δpred = 2m. It is known that each amount ni corresponding to a confidence interval, which provides the value of the sum, is equal to zero. The paradox is that the probability of such an event is zero; therefore, it is impossible to determine the value ni of where the sum becomes zero. The article proposes to consider the event consisting in the fact that some amount of error will change value within 2m limits with a confidence level of 0,954. Within the group all the sums have a limit error. These tolerances are proposed to use for the discrepancies in geodesy instead of 2m*SQL(ni). The concept of “the law of the truncated normal distribution with Δpred = 2m” is suggested to be introduced.


2000 ◽  
Vol 22 (2) ◽  
pp. 209-228 ◽  
Author(s):  
John C. Paolillo

Felix (1988) claimed to demonstrate that UG-based knowledge of grammaticality causes nonnative speakers (NNSs) to have more accurate grammaticality judgments on sentences that are ungrammatical according to UG than on those that are grammatical. Birdsong (1994) criticized the methodology employed, noting that it ignores “response bias” (a propensity to judge sentences as ungrammatical) as a potential explanation. Felix and Zobl (1994) dismissed this criticism as merely methodological. In this paper, Birdsong's criticism is upheld by considering a statistical model of the data. At the same time, a more complete logistic regression model allows a fuller statistical analysis, revealing tentative support for the asymmetry claim, as well as differential learning states for different constructions and a tendency toward transfer avoidance. These theoretically significant effects were unnoticed in the earlier discussion of this research. For SLA research on grammaticality judgments to proceed fruitfully, appropriate statistical models need to be considered in designing the research.


2021 ◽  
Vol 28 ◽  
pp. 146-150
Author(s):  
L. A. Atramentova

Using the data obtained in a cytogenetic study as an example, we consider the typical errors that are made when performing statistical analysis. Widespread but flawed statistical analysis inevitably produces biased results and increases the likelihood of incorrect scientific conclusions. Errors occur due to not taking into account the study design and the structure of the analyzed data. The article shows how the numerical imbalance of the data set leads to a bias in the result. Using a dataset as an example, it explains how to balance the complex. It shows the advantage of presenting sample indicators with confidence intervals instead of statistical errors. Attention is drawn to the need to take into account the size of the analyzed shares when choosing a statistical method. It shows how the same data set can be analyzed in different ways depending on the purpose of the study. The algorithm of correct statistical analysis and the form of the tabular presentation of the results are described. Keywords: data structure, numerically unbalanced complex, confidence interval.


2021 ◽  
Author(s):  
Yulius Luturmas

ABSTRACTThe Village Government apparatus of Analutur, Southwest Maluku Regency is not excellent, and one of the influencing factors is that the Village Apparatus Recruitment is not carried out properly. By using associative research methods that link recruitment and performance. Data were collected by conducting structured interviews, observations, literature study, and distributing a list of questions to 50 respondents. then analyzed quantitatively (Product Moment Correlation Statistical Analysis). The results show that the correlation between apparatus recruitment and government performance in Analutur Village, Southwest Maluku Regency is 0.857. Based on the coefficient of determination, it is proven that recruitment contributes to performance by 62.2% and the remaining 37.8% is determined by other variables which are constant. Furthermore, a significant test was carried out using t-count at a confidence level of 0.05%. And the result is t-count of 8.888&gt; t-table 1.68, which means that the hypothesis is accepted. Keywords: Apparatus Recruitment, Performance and Village Improvement


2021 ◽  
Vol 12 ◽  
Author(s):  
Annika Fredén ◽  
Sverker Sikström

We propose that leaders play a more important role in voters’ party sympathy in proportional representation systems (PR) than previous research has suggested. Voters, from the 2018 Swedish General Election, were in an experiment asked to describe leaders and parties with three indicative keywords. Statistical models were conducted on these text data to predict their vote choice. The results show that despite that the voters vote for a party, the descriptions of leaders predicted vote choice to a similar extent as descriptions of parties. However, the order of the questions mattered, so that the first questions were more predictive than the second question. These analyses indicate that voters tend to conflate characteristics of leaders with their parties during election campaigns, and that leaders are a more important aspect of voting under PR than previous literature has suggested. Overall, this suggests that statistical analysis of words sheds new light of underlying sympathies related to voting.


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