A robust procedure for verifying TomoTherapy Hi-Art™ source models for small fields

2011 ◽  
Vol 56 (12) ◽  
pp. 3685-3699 ◽  
Author(s):  
B Hundertmark ◽  
E Sterpin ◽  
T Mackie
Keyword(s):  
1990 ◽  
Vol 29 (04) ◽  
pp. 282-288 ◽  
Author(s):  
A. van Oosterom

AbstractThis paper introduces some levels at which the computer has been incorporated in the research into the basis of electrocardiography. The emphasis lies on the modeling of the heart as an electrical current generator and of the properties of the body as a volume conductor, both playing a major role in the shaping of the electrocardiographic waveforms recorded at the body surface. It is claimed that the Forward-Problem of electrocardiography is no longer a problem. Several source models of cardiac electrical activity are considered, one of which can be directly interpreted in terms of the underlying electrophysiology (the depolarization sequence of the ventricles). The importance of using tailored rather than textbook geometry in inverse procedures is stressed.


Author(s):  
A.F. Andreev ◽  
◽  
A.A. Daudova ◽  
A.A. Biketova ◽  
◽  
...  
Keyword(s):  

2007 ◽  
Author(s):  
Chew Hong Sia ◽  
Azhar Md Ali ◽  
Nurul Ezalina Hamzah ◽  
Mohd Shafie Jumaat

2021 ◽  
Vol 81 ◽  
pp. 191-196
Author(s):  
S. Dufreneix ◽  
J. Bellec ◽  
S. Josset ◽  
L. Vieillevigne

2021 ◽  
Vol 89 ◽  
pp. 140-146
Author(s):  
José M. Lárraga-Gutiérrez ◽  
Olivia A. García-Garduñoa ◽  
José A. Herrera-González ◽  
Olga O. Galván de la Cruz

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann

AbstractThe necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.


2021 ◽  
Vol 13 (8) ◽  
pp. 1424
Author(s):  
Lucas Terres de Lima ◽  
Sandra Fernández-Fernández ◽  
João Francisco Gonçalves ◽  
Luiz Magalhães Filho ◽  
Cristina Bernardes

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of free and open-source models to estimate the sea-level impact can contribute to improve coastal management. This study aims to develop and validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE)—a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the sea-level rise impact module of TerrSet—Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses the Bruun rule formula implemented in GEE and can determine the coastline retreat of a profile by creatting a simple vector line from topo-bathymetric data. The model shows a very high correlation (0.97) with a classical Bruun rule study performed in the Aveiro coast (NW Portugal). Therefore, the achieved results disclose that the GEE platform is suitable to perform these analysis. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific community.


2020 ◽  
Vol 1 (3) ◽  
pp. 1283-1297
Author(s):  
Mike Thelwall ◽  
Pardeep Sud

Ongoing problems attracting women into many Science, Technology, Engineering and Mathematics (STEM) subjects have many potential explanations. This article investigates whether the possible undercitation of women associates with lower proportions of, or increases in, women in a subject. It uses six million articles published in 1996–2012 across up to 331 fields in six mainly English-speaking countries: Australia, Canada, Ireland, New Zealand, the United Kingdom and the United States. The proportion of female first- and last-authored articles in each year was calculated and 4,968 regressions were run to detect first-author gender advantages in field normalized article citations. The proportion of female first authors in each field correlated highly between countries and the female first-author citation advantages derived from the regressions correlated moderately to strongly between countries, so both are relatively field specific. There was a weak tendency in the United States and New Zealand for female citation advantages to be stronger in fields with fewer women, after excluding small fields, but there was no other association evidence. There was no evidence of female citation advantages or disadvantages to be a cause or effect of changes in the proportions of women in a field for any country. Inappropriate uses of career-level citations are a likelier source of gender inequities.


Birds ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 114-126
Author(s):  
Marek Panek

Predators can modify their diet and demography in response to changes in food availability and habitat quality. I tested the prediction that some species can change their predation pattern, between specialist type and generalist type, depending on the complexity of habitat structure. It was hypothesized that their dietary response is stronger in diversified habitats than in simplified ones, but the opposite tendency occurs in the case of reproductive response. The nestling diet and breeding success of the Eurasian Buzzard Buteo buteo, the abundance of its main prey (the common vole Microtus arvalis), and that of the most important alternative prey group (passerines) were estimated over ten years in two types of agricultural habitat in western Poland, i.e., in the diversified habitat of small fields and the simplified habitat of large fields. The vole abundance was higher in large fields, but the abundance of passerines was greater in small fields. The frequency of voles in the Eurasian Buzzard nestling diet was higher in large fields than in small fields and increased with the abundance of this prey in crop fields. However, no difference in the relationship between the vole frequency in the diet of Eurasian Buzzards and the abundance of voles was found between the two habitat types. The breeding success of Eurasian Buzzards was dependent on the vole abundance, but this relationship did not differ between the two field types. It seems that the pattern of dietary and reproductive response of Eurasian Buzzards depends on the actual availability of individual prey species, which can be modified by habitat quality, rather than on relative prey abundance.


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