Experimental Evaluation of the Transient Behavior of a Compressor Station During Emergency Shutdowns

2010 ◽  
Vol 132 (6) ◽  
Author(s):  
J. Jeffrey Moore ◽  
Rainer Kurz ◽  
Augusto Garcia-Hernandez ◽  
Klaus Brun
Keyword(s):  
Test Data ◽  
Transient Behavior ◽  
Primary Objective ◽  
Full Scale ◽  
Test Facility ◽  
Data Sets ◽  
Domain Modeling ◽  
Validation Data ◽  
The Public ◽  
Compressor Surge

The transient behavior of compressor stations, particularly under rapidly changing conditions, is of vital interest to operators. Predicting transient behavior is an important factor in avoiding damage during events such as emergency shutdowns. A limited number of “accidental” data sets from compressor manufacturers and users are available in the public literature domain. A variety of simulations and modeling approaches have been presented over the last few years at industry conferences. The available experimental data is not of sufficient quality and resolution to properly compare predictions with analytical results or simulations available in current software packages. Necessary information about the compressor, driver, valves, and geometry of the system is often missing. Currently utilized software has not been adequately validated with full-scale realistic benchmark data, as this data is not available in the public domain. Modeling procedures and results of surge control system simulations seldom contain validation data achieved through actual testing. This type of transient test data for a dynamic surge condition is often difficult to obtain. The primary objective of this work is to develop experimental transient compressor surge data on a full-scale test facility, which would facilitate the verification and comparison of existing and future transient surge models. Results of the testing and model comparisons will be documented. Relevant, dimensionless parameters will be presented and validated utilizing the test data. Conclusions from the testing and recommendations for the transient analysis software will be provided.

Experimental Evaluation of the Transient Behavior of a Compressor Station During Emergency Shutdowns

2009 ◽  
Author(s):  
J. Jeffrey Moore ◽  
Rainer Kurz ◽  
Augusto Garcia-Hernandez ◽  
Klaus Brun
Keyword(s):  
Test Data ◽  
Transient Behavior ◽  
Primary Objective ◽  
Full Scale ◽  
Test Facility ◽  
Data Sets ◽  
Domain Modeling ◽  
Validation Data ◽  
The Public ◽  
Compressor Surge

The transient behavior of compressor stations, particularly under rapidly changing conditions, is of vital interest to operators. Predicting transient behavior is an important factor in avoiding damage during events such as emergency shutdowns. A limited number of “accidental” data sets from compressor manufacturers and users are available in the public literature domain. A variety of simulations and modeling approaches have been presented over the last few years at industry conferences. The available experimental data is not of sufficient quality and resolution to properly compare predictions with analytical results or simulations available in current software packages. Necessary information about the compressor, the driver, the valves, and the geometry of the system is often missing. Currently utilized software has not been adequately validated with full-scale realistic benchmark data, as this data is not available in the public domain. Modeling procedures and results of surge control system simulations seldom contain validation data achieved through actual testing. This type of transient test data for a dynamic surge condition is often difficult to obtain. The primary objective of this work is to develop experimental transient compressor surge data on a full scale test facility, which would facilitate the verification and comparison of existing and future transient surge models. Results of the testing and model comparisons will be documented. Relevant, dimensionless parameters will be presented and validated utilizing the test data. Conclusions from the testing and recommendations for the transient analysis software will be provided.

scholarly journals DNA Methylation Markers for Pan-Cancer Prediction by Deep Learning

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 778 ◽  
Author(s):  
Liu ◽  
Liu ◽  
Pan ◽  
Li ◽  
Yang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Test Data ◽  
Data Sets ◽  
Methylation Data ◽  
Average Sensitivity ◽  
Validation Data ◽  
Data Set ◽  
Cancer Types

For cancer diagnosis, many DNA methylation markers have been identified. However, few studies have tried to identify DNA methylation markers to diagnose diverse cancer types simultaneously, i.e., pan-cancers. In this study, we tried to identify DNA methylation markers to differentiate cancer samples from the respective normal samples in pan-cancers. We collected whole genome methylation data of 27 cancer types containing 10,140 cancer samples and 3386 normal samples, and divided all samples into five data sets, including one training data set, one validation data set and three test data sets. We applied machine learning to identify DNA methylation markers, and specifically, we constructed diagnostic prediction models by deep learning. We identified two categories of markers: 12 CpG markers and 13 promoter markers. Three of 12 CpG markers and four of 13 promoter markers locate at cancer-related genes. With the CpG markers, our model achieved an average sensitivity and specificity on test data sets as 92.8% and 90.1%, respectively. For promoter markers, the average sensitivity and specificity on test data sets were 89.8% and 81.1%, respectively. Furthermore, in cell-free DNA methylation data of 163 prostate cancer samples, the CpG markers achieved the sensitivity as 100%, and the promoter markers achieved 92%. For both marker types, the specificity of normal whole blood was 100%. To conclude, we identified methylation markers to diagnose pan-cancers, which might be applied to liquid biopsy of cancers.

Comparison of CART and discriminant analysis of morphometric data in foraminiferal taxonomy

2006 ◽  
Vol 29 (1) ◽  
pp. 153-162
Author(s):  
Pratul Kumar Saraswati ◽  
Sanjeev V Sabnis
Keyword(s):  
Discriminant Analysis ◽  
Test Data ◽  
Parametric Method ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Misclassification Error ◽  
Data Sets ◽  
Morphometric Data ◽  
Validation Data ◽  
Data Set

Paleontologists use statistical methods for prediction and classification of taxa. Over the years, the statistical analyses of morphometric data are carried out under the assumption of multivariate normality. In an earlier study, three closely resembling species of a biostratigraphically important genus Nummulites were discriminated by multi-group discrimination. Two discriminant functions that used diameter and thickness of the tests and height and length of chambers in the final whorl accounted for nearly 100% discrimination. In this paper Classification and Regression Tree (CART), a non-parametric method, is used for classification and prediction of the same data set. In all 111 iterations of CART methodology are performed by splitting the data set of 55 observations into training, validation and test data sets in varying proportions. In the validation data sets 40% of the iterations are correctly classified and only one case of misclassification in 49% of the iterations is noted. As regards test data sets, nearly 70% contain no misclassification cases whereas in about 25% test data sets only one case of misclassification is found. The results suggest that the method is highly successful in assigning an individual to a particular species. The key variables on the basis of which tree models are built are combinations of thickness of the test (T), height of the chambers in the final whorl (HL) and diameter of the test (D). Both discriminant analysis and CART thus appear to be comparable in discriminating the three species. However, CART reduces the number of requisite variables without increasing the misclassification error. The method is very useful for professional geologists for quick identification of species.

Evaluation of Asphalt Drainable Base Structural Performance for Flexible Airport Pavement Design

2018 ◽  
Vol 2672 (23) ◽  
pp. 128-136
Author(s):  
Qiang Li ◽  
Benjamin Mahaffay ◽  
Jeffrey Gagnon
Keyword(s):  
Data Analysis ◽  
Test Data ◽  
Full Scale ◽  
Pavement Design ◽  
Test Facility ◽  
Construction Specifications ◽  
Airport Pavement ◽  
Dry Conditions ◽  
Design Software ◽  
Aggregate Base

In 2014, the Federal Aviation Administration’s (FAA) National Airport Pavement Test Facility (NAPTF) completed construction of its flexible pavement test section, Construction Cycle 7 (CC7). Among the objectives of CC7 was to study the performance of an asphaltic drainable base pavement section compared with a P-209 aggregate base pavement section under full-scale airport loading. This research addresses the pre-traffic material characterization, full-scale traffic test data analysis and post-traffic test data analysis of these two pavement sections in dry conditions. This paper will discuss the preliminary results from laboratory testing and full-scale traffic tests for material properties which may be used in design and construction specifications to improve FAA pavement design software (FAARFIELD).

Trust in the Police: Cross-country Comparisons

2012 ◽  
pp. 24-47
Author(s):  
V. Gimpelson ◽  
G. Monusova
Keyword(s):  
Public Opinion ◽  
Public Attitudes ◽  
Crime Rates ◽  
Authoritarian Regimes ◽  
Data Sets ◽  
The Public ◽  
Positive Attitudes ◽  
Cross Country ◽  
Police Activity

Using different cross-country data sets and simple econometric techniques we study public attitudes towards the police. More positive attitudes are more likely to emerge in the countries that have better functioning democratic institutions, less prone to corruption but enjoy more transparent and accountable police activity. This has a stronger impact on the public opinion (trust and attitudes) than objective crime rates or density of policemen. Citizens tend to trust more in those (policemen) with whom they share common values and can have some control over. The latter is a function of democracy. In authoritarian countries — “police states” — this tendency may not work directly. When we move from semi-authoritarian countries to openly authoritarian ones the trust in the police measured by surveys can also rise. As a result, the trust appears to be U-shaped along the quality of government axis. This phenomenon can be explained with two simple facts. First, publicly spread information concerning police activity in authoritarian countries is strongly controlled; second, the police itself is better controlled by authoritarian regimes which are afraid of dangerous (for them) erosion of this institution.

IMPLEMENTATION OF FATWA ULAMA COUNCIL ACEH CONCERNING MAINTAINING FARDHU KIFAYAH FOR THE MUSLIM’S CORPSE INFECTED BY COVID 19 IN BANDA ACEH

2020 ◽  
Author(s):  
Andri Nirwana
Keyword(s):  
Health Workers ◽  
Descriptive Analysis ◽  
Body Fluids ◽  
The Body ◽  
Analysis Approach ◽  
Data Sets ◽  
Infected People ◽  
The Public ◽  
The People ◽  
Banda Aceh

Abstract: The phenomenon of the people who forcibly took covid's corpse 19 from the hospital to be taken care of by Fardhu Kifayah by his family and the community, became a conclusion that there was community doubt about the management of Tajhiz Mayat conducted by the hospital. Coupled with the circulation of the video of the Ruku movement 'in the corpse prayer conducted by unscrupulous parties at the Hospital, became added doubts from the public against the hospital. To solve this problem, this research uses a Descriptive Analysis approach, namely by formulating a question, namely How to arrange Covid 19's body in Banda Aceh and this question will be answered with several theories and data sets from the field. So it was concluded in a conclusion that answered the formulation of the problems mentioned. Theoretically the spread of covid 19 is very fast, the size of the virus is only 0.1 micrometer and is in body fluids, especially nasopharyngeal fluid and oropharyngeal fluids of infected people, fluids in the body of covid 19 bodies can get out through every gap of the body such as mouth, nose, eye and rectum, because it requires special techniques in its management. Fardhu kifayah to covid 19 bodies should be carried out by trained Ustad and trained health workers, so that the spread stopped. The results of this study concluded that the management of the Moslem bodies died at Zainal Abidin Hospital in Banda Aceh was in accordance with the Fatwa of the Aceh Ulama Council (MPU) and the bodies were handled by trained Ustad and health workers.

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

scholarly journals Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

scholarly journals COVID-19 in patients undergoing chronic kidney replacement therapy and kidney transplant recipients in Scotland: findings and experience from the Scottish renal registry

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Samira Bell ◽  
◽  
Jacqueline Campbell ◽  
Jackie McDonald ◽  
Martin O’Neill ◽  
...  
Keyword(s):  
Public Health ◽  
Replacement Therapy ◽  
Study Data ◽  
Data Sets ◽  
Kidney Transplant Recipients ◽  
The Public ◽  
Transplant Patients ◽  
Kidney Replacement Therapy ◽  
Observational Cohort ◽  
Respiratory Coronavirus

Abstract Background Infection with the severe acute respiratory coronavirus 2 (SARS-CoV-2) has led to a worldwide pandemic with coronavirus disease 2019 (COVID-19), the disease caused by SARS-CoV-2, overwhelming healthcare systems globally. Preliminary reports suggest a high incidence of infection and mortality with SARS-CoV-2 in patients receiving kidney replacement therapy (KRT). The aims of this study are to report characteristics, rates and outcomes of all patients affected by infection with SARS-CoV-2 undergoing KRT in Scotland. Methods Study design was an observational cohort study. Data were linked between the Scottish Renal Registry, Health Protection Scotland and the Scottish Intensive Care Society Audit Group national data sets using a unique patient identifier (Community Health Index (CHI)) for each individual by the Public Health and Intelligence unit of Public Health, Scotland. Descriptive statistics and survival analyses were performed. Results During the period 1st March 2020 to 31st May 2020, 110 patients receiving KRT tested positive for SARS-CoV-2 amounting to 2% of the prevalent KRT population. Of those affected, 86 were receiving haemodialysis or peritoneal dialysis and 24 had a renal transplant. Patients who tested positive were older and more likely to reside in more deprived postcodes. Mortality was high at 26.7% in the dialysis patients and 29.2% in the transplant patients. Conclusion The rate of detected SARS-CoV-2 in people receiving KRT in Scotland was relatively low but with a high mortality for those demonstrating infection. Although impossible to confirm, it appears that the measures taken within dialysis units coupled with the national shielding policy, have been effective in protecting this population from infection.

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