Small-Scale Modeling of Pharmaceutical Powder Compression from Tap Density Testers, to Roller Compactors, and to the Tablet Press Using Big Data

2016 ◽  
Vol 12 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Zhenqi Shi ◽  
Jon L Hilden
Keyword(s):  
Big Data ◽  
Small Scale ◽  
Scale Modeling ◽  
Tap Density ◽  
Tablet Press ◽  
Pharmaceutical Powder ◽  
Powder Compression

scholarly journals Challenges of Big Data analysis

2014 ◽  
Vol 1 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Jianqing Fan ◽  
Fang Han ◽  
Han Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Measurement Errors ◽  
Modern Society ◽  
Big Data Analysis ◽  
Small Scale ◽  
Paradigm Change ◽  
Statistical Inferences ◽  
The One ◽  
And Storage

Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

scholarly journals Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

2014 ◽  
Vol 23 (01) ◽  
pp. 21-26 ◽  
Author(s):  
T. Miron-Shatz ◽  
A. Y. S. Lau ◽  
C. Paton ◽  
M. M. Hansen
Keyword(s):  
Health Care ◽  
Big Data ◽  
Health Care Providers ◽  
Data Privacy ◽  
Recent Literature ◽  
Unstructured Data ◽  
Small Scale ◽  
Small Data ◽  
Quantified Self ◽  
Care Providers

Summary Objectives: As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods: A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results: Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful.

scholarly journals Importance of Sub-Grid Scale Modeling for Accurate Aerodynamic Simulations

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Petter Ekman ◽  
James Venning ◽  
Torbjörn Virdung ◽  
Matts Karlsson
Keyword(s):  
The Body ◽  
Small Scale ◽  
Piv Measurements ◽  
Vortical Structures ◽  
Scale Modeling ◽  
Simple Geometry ◽  
Image Velocimetry ◽  
Large Eddy ◽  
Near Wall ◽  
Ahmed Body

Abstract The Ahmed body is one of the most well-investigated vehicle bodies for aerodynamic purposes. Despite its simple geometry, the flow around the body, especially at the rear, is very complex as it is dominated by a large wake with strong interaction between vortical structures. In this study, the flow around the 25 deg Ahmed body has been investigated using large eddy simulations and compared to high-resolution particle image velocimetry (PIV) measurements. Special emphasis was put on studying three commonly used sub-grid scale (SGS) models and their ability to capture vortical structures around the Ahmed body. The ability of the SGS models to capture the near-wall behavior and small-scale dissipation is crucial for capturing the correct flow field. Very good agreement between simulations and PIV measurements were seen when using the dynamic Smagorinsky-Lilly and the wall-adopting local eddy-viscosity SGS models, respectively. However, the standard Smagorinsky-Lilly model was not able to capture the flow patterns when compared to the PIV measurements due to shortcomings in the near-wall modeling in the standard Smagorinsky-Lilly model, resulting in overpredicted separation.

scholarly journals Monitoring Water‐Soil Dynamics and Tree Survival Using Soil Sensors under a Big Data Approach

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4634
Author(s):  
Pascual ◽  
Rivera ◽  
Gómez ◽  
Domínguez-Lerena
Keyword(s):  
Big Data ◽  
Soil Water ◽  
Real Time ◽  
Water Dynamics ◽  
Species Selection ◽  
Soil Dynamics ◽  
Small Scale ◽  
Soil Water Dynamics ◽  
Soil Sensors ◽  
Tree Survival

The high importance of green urban planning to ensure access to green areas requires modern and multi-source decision-support tools. The integration of remote sensing data and sensor developments can contribute to the improvement of decision-making in urban forestry. This study proposes a novel big data-based methodology that combines real-time information from soil sensors and climate data to monitor the establishment of a new urban forest in semi-arid conditions. Water‐soil dynamics and their implication in tree survival were analyzed considering the application of different treatment restoration techniques oriented to facilitate the recovery of tree and shrub vegetation in the degraded area. The synchronized data-capturing scheme made it possible to evaluate hourly, daily, and seasonal changes in soil‐water dynamics. The spatial variation of soil‐water dynamics was captured by the sensors and it highly contributed to the explanation of the observed ground measurements on tree survival. The methodology showed how the efficiency of treatments varied depending on species selection and across the experimental design. The use of retainers for improving soil moisture content and adjusting tree-watering needs was, on average, the most successful restoration technique. The results and the applied calibration of the sensor technology highlighted the random behavior of water‐soil dynamics despite the small-scale scope of the experiment. The results showed the potential of this methodology to assess watering needs and adjust watering resources to the vegetation status using real-time atmospheric and soil data.

scholarly journals Scale Modeling of Snow–Avalanche Impact on Structures

1980 ◽  
Vol 26 (94) ◽  
pp. 189-196
Author(s):  
T. E. Lang ◽  
J.D. Dent
Keyword(s):  
Froude Number ◽  
Computer Modeling ◽  
Snow Avalanche ◽  
Small Scale ◽  
Snow Avalanches ◽  
Scale Modeling ◽  
Impact Processes ◽  
Model Fluid ◽  
Impact Predictions

AbstractSmall–scale modeling of flow and impact of snow avalanches is demonstrated to be both feasible and accurate. Geometric, kinematic, and force variables are scaled correctly under equivalence of Froude number between prototype and model using sifted snow as the model fluid. Physical and computer–simulated impact processes show correspondence, so that computer modeling is demonstrated to be a viable tool in flow and impact predictions.

Drift of Piles Subjected to Cyclic Lateral Loading From a Varying Direction: System vs. Soil Element Behavior

2014 ◽  
Author(s):  
Christina Rudolph ◽  
Jürgen Grabe ◽  
Britta Bienen
Keyword(s):  
Cyclic Loading ◽  
Model Tests ◽  
Small Scale ◽  
Shear Tests ◽  
Centrifuge Testing ◽  
Scale Modeling ◽  
Highly Sensitive ◽  
Number Of Cycles ◽  
Laterally Loaded ◽  
Insight Into

Offshore monopiles are usually designed using the p-y method for cyclic loading. While the method works for static loading, it was not developed for high numbers of cycles. Since the turbines are highly sensitive towards tilting, cyclic loading must be considered. The static results should therefore be combined with results from cyclic model tests with a high number of cycles to account for the accumulation of displacement or rotation during the lifetime of these structures. These model tests can underestimate the accumulation, however, as it has recently been shown that a change of loading direction can increase the accumulation considerably. These results have been verified using small scale modeling and centrifuge testing. The results from modeling the full problem of a laterally loaded pile are compared here with results from cyclic simple shear tests with a change of shearing direction during the cyclic loading. For these tests, a newly developed apparatus is used. This allows further insight into the question how a soil can “retain a memory” of its loading history.

scholarly journals Sting Jets in Simulations of a Real Cyclone by Two Mesoscale Models

2010 ◽  
Vol 138 (11) ◽  
pp. 4054-4075 ◽  
Author(s):  
Oscar Martínez-Alvarado ◽  
Florian Weidle ◽  
Suzanne L. Gray
Keyword(s):  
Small Scale ◽  
Extratropical Cyclones ◽  
Vertical Resolution ◽  
Mesoscale Models ◽  
Physical Mechanisms ◽  
The United Kingdom ◽  
Scale Modeling ◽  
Surface Measurements ◽  
The Subject ◽  
Comparison Of The Results

Abstract The existence of sting jets as a potential source of damaging surface winds during the passage of extratropical cyclones has recently been recognized. However, there are still very few published studies on the subject. Furthermore, although it is known that other models are capable of reproducing sting jets, in the published literature only one numerical model [the Met Office Unified Model (MetUM)] has been used to numerically analyze these phenomena. This article aims to improve our understanding of the processes that contribute to the development of sting jets and show that model differences affect the evolution of modeled sting jets. A sting jet event during the passage of a cyclone over the United Kingdom on 26 February 2002 has been simulated using two mesoscale models, namely the MetUM and the Consortium for Small Scale Modeling (COSMO) model, to compare their performance. Given the known critical importance of vertical resolution in the simulation of sting jets, the vertical resolution of both models has been enhanced with respect to their operational versions. Both simulations have been verified against surface measurements of maximum gusts, satellite imagery, and Met Office operational synoptic analyses, as well as operational analyses from the ECMWF. It is shown that both models are capable of reproducing sting jets with similar, though not identical, features. Through the comparison of the results from these two models, the relevance of physical mechanisms, such as evaporative cooling and the release of conditional symmetric instability, in the generation and evolution of sting jets is also discussed.

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