Density Data of Two (H2 + CO2) Mixtures and a (H2 + CO2 + CH4) Mixture by a Modified Burnett Method at Temperature 673 K and Pressures up to 25 MPa

2019 ◽  
Vol 64 (4) ◽  
pp. 1693-1704 ◽  
Author(s):  
Siyuan Cheng ◽  
Fei Shang ◽  
Weigang Ma ◽  
Hui Jin ◽  
Naoya Sakoda ◽  
...  
Keyword(s):  
Density Data ◽  
Burnett Method

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

scholarly journals Analysis of the Vertical Air Motions and Raindrop Size Distribution Retrievals of a Squall Line Based on Cloud Radar Doppler Spectral Density Data

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 348
Author(s):  
Ningkun Ma ◽  
Liping Liu ◽  
Yichen Chen ◽  
Yang Zhang
Keyword(s):  
Spectral Density ◽  
Size Distribution ◽  
Squall Line ◽  
Vertical Profiles ◽  
Air Velocity ◽  
Density Data ◽  
Cloud Radar ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Reflectivity Factor

A squall line is a type of strongly organized mesoscale convective system that can cause severe weather disasters. Thus, it is crucial to explore the dynamic structure and hydrometeor distributions in squall lines. This study analyzed a squall line over Guangdong Province on 6 May 2016 that was observed using a Ka-band millimeter-wave cloud radar (CR) and an S-band dual-polarization radar (PR). Doppler spectral density data obtained by the CR were used to retrieve the vertical air motions and raindrop size distribution (DSD). The results showed the following: First, the CR detected detailed vertical profiles and their evolution before and during the squall line passage. In the convection time segment (segment B), heavy rain existed with a reflectivity factor exceeding 35 dBZ and a velocity spectrum width exceeding 1.3 m s−1. In the PR detection, the differential reflectivity factor (Zdr) was 1–2 dB, and the large specific differential phase (Kdp) also represented large liquid water content. In the transition and stratiform cloud time segments (segments B and C), the rain stabilized gradually, with decreasing cloud tops, stable precipitation, and a 0 °C layer bright band. Smaller Kdp values (less than 0.9) were distributed around the 0 °C layer, which may have been caused by the melting of ice crystal particles. Second, from the CR-retrieved vertical air velocity, before squall line passage, downdrafts dominated in local convection and weak updrafts existed in higher-altitude altostratus clouds. In segment B, the updraft air velocity reached more than 8 m s−1 below the 0 °C layer. From segments C to D, the updrafts changed gradually into weak and wide-ranging downdrafts. Third, in the comparison of DSD values retrieved at 1.5 km and DSD values on the ground, the retrieved DSD line was lower than the disdrometer, the overall magnitude of the DSD retrieved was smaller, and the difference decreased from segments C to D. The standardized intercept parameter (Nw) and shape parameter (μ) of the DSD retrieved at 1.8 km showed good agreement with the disdrometer results, and the mass-weighted mean diameter (Dm) was smaller than that on the ground, but very close to the PR-retrieved Dm result at 2 km. Therefore, comparing with the DSD retrieved at around 2 km, the overall number concentration remained unchanged and Dm got larger on the ground, possibly reflecting the process of raindrop coalescence. Lastly, the average vertical profiles of several quantities in all segments showed that, first of all, the decrease of Nw and Dm with height in segments C and D was similar, reflecting the collision effect of falling raindrops. The trends were opposite in segment B, indicating that raindrops underwent intense mixing and rapid collision and growth in this segment. Then, PR-retrieved Dm profiles can verify the rationality of the CR-retrieved Dm. Finally, a vertical velocity profile peak generated a larger Dm especially in segments C and D.

Development of a robust soft-SAFT model for protic ionic liquids using new high-pressure density data

2021 ◽  
pp. 113036
Author(s):  
Emanuel A. Crespo ◽  
Liliana P. Silva ◽  
Cristina I.P. Correia ◽  
Mónia A.R. Martins ◽  
Ramesh L. Gardas ◽  
...  
Keyword(s):  
High Pressure ◽  
Ionic Liquids ◽  
Protic Ionic Liquids ◽  
Density Data

Short-term forecast model of taxi demand based on time and space heterogeneity

2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Urban Traffic ◽  
Percentage Error ◽  
Density Level ◽  
Traffic Data ◽  
Short Term ◽  
Time And Space ◽  
Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

On the calculation of partial molar volumes at infinite dilution from experimental density data

2020 ◽  
pp. 1-7
Author(s):  
Jorge Álvarez Juliá ◽  
María Del Carmen Grande ◽  
Carmen Raquel Barrero ◽  
Carlos Miguel Marschoff
Keyword(s):  
Infinite Dilution ◽  
Partial Molar Volumes ◽  
Density Data ◽  
Molar Volumes ◽  
Experimental Density

scholarly journals SAT0372 PATIENTS WITH PSORIATIC ARTHRITIS SHOW HIGHER BONE DENSITY COMPARED TO AGE AND GENDER MATCHED PATIENTS WITH ANKYLOSING SPONDYLITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1133.2-1134
Author(s):  
D. Freier ◽  
E. Wiebe ◽  
R. Biesen ◽  
T. Buttgereit ◽  
S. Hermann ◽  
...  
Keyword(s):  
Back Pain ◽  
Psoriatic Arthritis ◽  
Ankylosing Spondylitis ◽  
Bone Density ◽  
Rheumatic Diseases ◽  
Inflammatory Rheumatic Diseases ◽  
Age And Gender ◽  
Gender Distribution ◽  
Density Data ◽  
And Gender

Background:The prevalence of osteoporosis in inflammatory rheumatic diseases such as psoriatic arthritis (PsA) has not been sufficiently clarified yet, and the data in the literature are heterogeneous. In addition, it is still unclear to what extent patients with PsA differ in terms of bone density from patients with other forms of spondyloarthritis such as ankylosing spondylitis (AS).Objectives:In an interim analysis of the Rh-GIOP Study (ClinicalTrials.gov IdentifierNCT02719314), we observed that PsA patients demonstrated more frequently normal bone density than any other patient group analyzed (suffering from e.g. rheumatoid arthritis or systemic sclerosis). The main objective of this investigation was to compare bone density data from patients with PsA and AS, as both diseases belong to the spondyloarthritis group. 1100 patients with inflammatory rheumatic diseases provided the basis of Rh-GIOP, a prospective study monitoring glucocorticoid (GC)-induced osteoporosis in patients with rheumatic diseases. Rh-GIOP was established in 2015 at the Charité University Hospital. Bone mineral density data were measured by dual x-ray absorptiometry (DXA).Methods:92 patients with PsA (65% female) were compared with 51 patients suffering from AS (35% female). Potential risk and protective factors (e.g. data on GC treatment, anti-rheumatic therapy), laboratory parameters (e.g. Vitamin D, alkaline phosphatase, calcium and inflammatory markers) and functional status (e.g. Health Assessment Questionnaire, sporting activities, back pain) were compared between these groups. Statistical analysis was performed descriptively using mean and standard deviation, t-tests for metric variables, and chi-square tests for nominal variables. Due to the heterogeneous gender distribution, an additional statistical matching was performed to compare patients matched by age and gender.Results:Patients with PsA displayed significantly higher minimal T-scores than patients with AS (p=0.003) even though patients with AS were younger and more often male (p<0.001). AS patients showed a higher frequency of osteopenic bone densities (p<0.05), however, no differences in the frequency of osteoporotic bone densities were found. Body-mass-index (BMI) was significantly higher (p<0.001) in PsA patients. PsA patients demonstrated a higher frequency of csDMARD use (p<0.001). Additional analyses among PsA patients with and without csDMARDs revealed also significantly higher minimal T-scores in PsA patients taking csDMARDs (90% Methotrexate), and both groups showed the same average of age and gender distribution. Furthermore, AS patients complained significantly more often of back pain (96 % vs. 74%, p=0.001) than PsA patients. No differences in GC use or cumulative GC dose were found. All results could be confirmed when groups were matched by age and gender.Conclusion:Our results demonstrate that patients with PsA display higher bone density compared to age and gender matched patients with ankylosing spondylitis. Possible influencing factors could be the higher frequency of csDMARD use, higher BMI or the lower frequency of back pain in PsA patients. Multivariate tests and additional biomarker investigations in larger cohorts are necessary to corroborate these findings and to identify underlying pathogenic differences which could serve for an explanation.Disclosure of Interests:Desiree Freier: None declared, Edgar Wiebe: None declared, Robert Biesen: None declared, Thomas Buttgereit: None declared, Sandra Hermann: None declared, Timo Gaber: None declared, Frank Buttgereit Grant/research support from: Amgen, BMS, Celgene, Generic Assays, GSK, Hexal, Horizon, Lilly, medac, Mundipharma, Novartis, Pfizer, Roche, and Sanofi.

An assessment of new satellite total density data for improving upper atmosphere models

1999 ◽  
Vol 47 (12) ◽  
pp. 1465-1473 ◽  
Author(s):  
S.L Bruinsma ◽  
P Exertier ◽  
R Biancale
Keyword(s):  
Upper Atmosphere ◽  
Total Density ◽  
Density Data
Sign in / Sign up

Export Citation Format

Share Document