Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
64,233
result(s) for
"Chen, W."
Sort by:
A quinary WTaCrVHf nanocrystalline refractory high-entropy alloy withholding extreme irradiation environments
In the quest of new materials that can withstand severe irradiation and mechanical extremes for advanced applications (
e.g
. fission & fusion reactors, space applications, etc.), design, prediction and control of advanced materials beyond current material designs become paramount. Here, through a combined experimental and simulation methodology, we design a nanocrystalline refractory high entropy alloy (RHEA) system. Compositions assessed under extreme environments and in situ electron-microscopy reveal both high thermal stability and radiation resistance. We observe grain refinement under heavy ion irradiation and resistance to dual-beam irradiation and helium implantation in the form of low defect generation and evolution, as well as no detectable grain growth. The experimental and modeling results—showing a good agreement—can be applied to design and rapidly assess other alloys subjected to extreme environmental conditions.
Refractory high entropy alloys (RHEAs) have recently been developed in the context of high-temperature and severe environmental applications. Here the authors, by combining simulation and experiments, develop an irradiation resistant, thermally stable, and strong RHEA for nuclear application.
Journal Article
Practical business negotiation
\"Practical Business Negotiation introduces university students to business negotiation as practiced in the globalized business world. There are no other textbooks which take on this topic in depth with non-native English speakers in mind. Current textbooks about negotiation tend to be dense, academic and less than practical in content. Many are demotivating to students who are not easily able to consume a few hundred pages of academic writing. This textbook takes a step by step approach providing bite-sized presentation of negotiation concepts with practical exercises that include linguistic as well as negotiation content. Explanations are reinforced with practical questions and problem solving and recent examples drawn from a business world that includes much more than North American and Europe\"-- Provided by publisher.
Book
Design and Operation of the ATLAS Transient Science Server
The Asteroid Terrestrial impact Last Alert System (ATLAS) system consists of two 0.5 m Schmidt telescopes with cameras covering 29 square degrees at plate scale of 1.86 arcsec per pixel. Working in tandem, the telescopes routinely survey the whole sky visible from Hawaii (above δ > − 50 ° ) every two nights, exposing four times per night, typically reaching o < 19 magnitude per exposure when the moon is illuminated and c < 19.5 magnitude per exposure in dark skies. Construction is underway of two further units to be sited in Chile and South Africa which will result in an all-sky daily cadence from 2021. Initially designed for detecting potentially hazardous near earth objects, the ATLAS data enable a range of astrophysical time domain science. To extract transients from the data stream requires a computing system to process the data, assimilate detections in time and space and associate them with known astrophysical sources. Here we describe the hardware and software infrastructure to produce a stream of clean, real, astrophysical transients in real time. This involves machine learning and boosted decision tree algorithms to identify extragalactic and Galactic transients. Typically we detect 10-15 supernova candidates per night which we immediately announce publicly. The ATLAS discoveries not only enable rapid follow-up of interesting sources but will provide complete statistical samples within the local volume of 100 Mpc. A simple comparison of the detected supernova rate within 100 Mpc, with no corrections for completeness, is already significantly higher (factor 1.5 to 2) than the current accepted rates.
Journal Article
Quality teaching in primary science education : cross-cultural perspectives
This edited volume explores how primary school teachers create rich opportunities for science learning, higher order thinking and reasoning, and how the teaching of science in Australia, Germany and Taiwan is culturally framed.
Book
Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods
Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.
Journal Article
SP1-induced upregulation of the long noncoding RNA TINCR regulates cell proliferation and apoptosis by affecting KLF2 mRNA stability in gastric cancer
The long noncoding RNA
TINCR
shows aberrant expression in human squamous carcinomas. However, its expression and function in gastric cancer remain unclear. We report that
TINCR
is strongly upregulated in human gastric carcinoma (GC), where it was found to contribute to oncogenesis and cancer progression. We also revealed that
TINCR
overexpression is induced by nuclear transcription factor SP1. Silencing
TINCR
expression inhibited cell proliferation, colony formation, tumorigenicity and apoptosis promotion, whereas
TINCR
overexpression promoted cell growth, as documented in the SGC7901 and BGC823 cell lines. Mechanistic analyses indicated that
TINCR
could bind to STAU1 (staufen1) protein, and influence
KLF2
mRNA stability and expression, then KLF2 regulated cyclin-dependent kinase genes
CDKN1A/P21
and
CDKN2B/P15
transcription and expression, thereby affecting the proliferation and apoptosis of GC cells. Together, our findings suggest that
TINCR
contributes to the oncogenic potential of GC and may constitute a potential therapeutic target in this disease.
Journal Article
Deep learning predicts path-dependent plasticity
Plasticity theory aims at describing the yield loci and work hardening of a material under general deformation states. Most of its complexity arises from the nontrivial dependence of the yield loci on the complete strain history of a material and its microstructure. This motivated 3 ingenious simplifications that underpinned a century of developments in this field: 1) yield criteria describing yield loci location; 2) associative or nonassociative flow rules defining the direction of plastic flow; and 3) effective stress–strain laws consistent with the plastic work equivalence principle. However, 2 key complications arise from these simplifications. First, finding equations that describe these 3 assumptions for materials with complex microstructures is not trivial. Second, yield surface evolution needs to be traced iteratively, i.e., through a return mapping algorithm. Here, we show that these assumptions are not needed in the context of sequence learning when using recurrent neural networks, diverting the above-mentioned complications. This work offers an alternative to currently established plasticity formulations by providing the foundations for finding history- and microstructure-dependent constitutive models through deep learning.
Journal Article
Arctic amplification modulated by Atlantic Multidecadal Oscillation and greenhouse forcing on multidecadal to century scales
Enhanced warming in the Arctic (Arctic amplification, AA) in the last decades has been linked to several factors including sea ice and the Atlantic Multidecadal Oscillation (AMO). However, how these factors contributed to AA variations in a long-term perspective remains unclear. By reconstructing a millennial AA index combining climate model simulations with recently available proxy data, this work determines the important influences of the AMO and anthropogenic greenhouse gas forcing on AA variations in the last millennium, leading to identification of a significant downward trend of AA on top of a sustained strong AMO modulation at the multidecadal scales. The decreased AA during the industrial era was strongly associated with the anthropogenic forcing, proving the emerging role of the forcing in reducing the AA strength.
Reconstructed Arctic amplification shows a significant downward trend over the past millennium which can to a large part be explained by the strength of the Atlantic Multidecadal Oscillation and recent anthropogenic greenhouse gas forcing.
Journal Article
Arctic Warming Revealed by Multiple CMIP6 Models
The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models’ simulation and projection of the Arctic near-surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread.
Journal Article