Finely tuning MOFs towards high-performance post-combustion CO2 capture materials

2016 ◽  
Vol 52 (3) ◽  
pp. 443-452 ◽  
Author(s):  
Qian Wang ◽  
Junfeng Bai ◽  
Zhiyong Lu ◽  
Yi Pan ◽  
Xiaozeng You
Keyword(s):  
Co2 Capture ◽  
High Performance ◽  
Fine Tuning ◽  
Schematic Drawing

Schematic drawing of six strategies for fine tuning of MOF structures from prototypical MOFs.

scholarly journals Deep Learning of COVID-19 Chest X-Rays: New Models or Fine Tuning?

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Data Augmentation ◽  
Dominant Role ◽  
Care Center ◽  
Characteristic Curve ◽  
Fine Tuning ◽  
Urgent Care ◽  
X Rays ◽  
Training Time

Chest X-rays have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. While many new DL models have been being developed for this purpose, this study aimed to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.

scholarly journals High Performance CO2 Capture by Autothermal AGR

2013 ◽  
Vol 37 ◽  
pp. 2284-2292
Author(s):  
Yasushi Mori ◽  
Jonathan Forsyth
Keyword(s):  
Co2 Capture ◽  
High Performance

HIERARCHICAL MAPPING FOR HPC APPLICATIONS

2011 ◽  
Vol 21 (03) ◽  
pp. 279-299 ◽  
Author(s):  
I-HSIN CHUNG ◽  
CHE-RUNG LEE ◽  
JIAZHENG ZHOU ◽  
YEH-CHING CHUNG
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Scale Up ◽  
Matrix Multiplication ◽  
Spectral Graph Theory ◽  
Communication Patterns ◽  
Fine Tuning ◽  
Mapping Algorithm ◽  
Communication Time ◽  
Run Time

As the high performance computing systems scale up, mapping the tasks of a parallel application onto physical processors to allow efficient communication becomes one of the critical performance issues. Existing algorithms were usually designed to map applications with regular communication patterns. Their mapping criterion usually overlooks the size of communicated messages, which is the primary factor of communication time. In addition, most of their time complexities are too high to process large scale problems. In this paper, we present a hierarchical mapping algorithm (HMA), which is capable of mapping applications with irregular communication patterns. It first partitions tasks according to their run-time communication information. The tasks that communicate with each other more frequently are regarded as strongly connected. Based on their connectivity strength, the tasks are partitioned into supernodes based on the algorithms in spectral graph theory. The hierarchical partitioning reduces the mapping algorithm complexity to achieve scalability. Finally, the run-time communication information will be used again in fine tuning to explore better mappings. With the experiments, we show how the mapping algorithm helps to reduce the point-to-point communication time for the PDGEMM, a ScaLAPACK matrix multiplication computation kernel, up to 20% and the AMG2006, a tier 1 application of the Sequoia benchmark, up to 7%.

scholarly journals Molten shell-activated, high-performance, un-doped Li4SiO4 for high-temperature CO2 capture at low CO2 concentrations

2020 ◽  
pp. 127353
Author(s):  
Ke Wang ◽  
Feng Gu ◽  
Peter T. Clough ◽  
Youwei Zhao ◽  
Pengfei Zhao ◽  
...  
Keyword(s):  
High Temperature ◽  
Co2 Capture ◽  
High Performance ◽  
Low Co2 ◽  
Co2 Concentrations

Investigation of Solid Solubility, Hardness, and Thermal Properties of Au-Ge-Sb System

2014 ◽  
Vol 1016 ◽  
pp. 336-341
Author(s):  
Kamolchanok Thipayarat ◽  
Ekasit Nisaratanaporn ◽  
Boonrat Lohwongwatana
Keyword(s):  
High Performance ◽  
Alloy System ◽  
Fine Tuning ◽  
Lead Free ◽  
Diagram Method ◽  
Average Value ◽  
Wide Range ◽  
Lead Free Solders ◽  
Hardness Values ◽  
Fine Tune

In recent years, the Au-Ge-Sb system has been studied as a possible alternative alloy for soldering applications [1-4]. The alloy has various fbenefits such as (i) low melting temperature which allows the alloy system to be used as a drop-in solution for high performance lead-free solders, (ii) three distinct phases of different hardness values (100, 150 and 500 HV) which offer the ability to fine tune the composition and microstructure to a wide range of properties, and (iii) limited solute solubility which offers ease of control and fine-tuning of microstructure, mechanical properties and colors. Gold compositions centered around 75wt% gold were modeled and selected using the CALPHAD (CALculation of PHAse Diagram) method. Predictions were later confirmed by experimental results. The alloy solidifies in the range of 242.5-261.7 °C. The overall hardness values were measured and confirmed to be within the volume average value of all the phases combined.

scholarly journals Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Vinícius da Fonseca Vieira ◽  
Carolina Ribeiro Xavier ◽  
Nelson Francisco Favilla Ebecken ◽  
Alexandre Gonçalves Evsukoff
Keyword(s):  
Spectral Method ◽  
Community Detection ◽  
High Performance ◽  
Large Scale ◽  
Fine Tuning ◽  
Detection Algorithms ◽  
Research Areas ◽  
Structure Detection ◽  
Wide Range ◽  
Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

High-Performance Self-Cross-Linked PGP–POEM Comb Copolymer Membranes for CO2 Capture

2017 ◽  
Vol 50 (22) ◽  
pp. 8938-8947 ◽  
Author(s):  
Na Un Kim ◽  
Byeong Ju Park ◽  
Yeji Choi ◽  
Ki Bong Lee ◽  
Jong Hak Kim
Keyword(s):  
Co2 Capture ◽  
High Performance ◽  
Comb Copolymer
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