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Pressure Dependence of the Magneto-transport Properties in Fe/MgO Granular Systems
A. Garc´ıa-Garc´ıa,P. A. Algarabel,J. A. Pardo,Z. Arnold,J. Kamarad 한국물리학회 2013 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.62 No.10
The effect of hydrostatic pressure at room temperature on the resistance and magnetoresistance (MR) of a discontinuous metal-insulator multilayer (DMIM) of nominal composition [Fe(tFe = 0.7nm)/MgO(tMgO = 3 nm)]15 has been studied. The resistivity of the DMIM, ρ, decreases linearly with pressure indicating an increase in conduction via tunneling effect. The value of coefficient (1/ρ0)dρ/dP = –3.9 × 10−2 kbar−1 is higher than reported values in other granular films implying that the electronic state of the DMIM is close to the iron percolation threshold. At the maximum applied magnetic field (3 kOe) the MR ratio increases from 0.6% at ambient pressure to 1.1% at 7 kbar. This result can be explained by a reduction of the tunnel barrier width induced by the hydrostatic pressure.
ACO-based clustering for Ego Network analysis
Gonzalez-Pardo, A.,Jung, J.J.,Camacho, D. North-Holland 2017 Future generations computer systems Vol.66 No.-
<P>The unstoppable growth of Social Networks (SNs), and the huge number of connected users, have become these networks as one of the most popular and successful domains for a large number of research areas. The different possibilities, volume and variety that these SNs offer, has become them an essential tool for every-day working and social relationships. One of the basic features that any SN provides is to allow users to group, organize and classify their connections into different groups, or 'circles'. These circles can be defined using different characteristics as roommates, workmates, hobbies, professional skills, etc. The problem of finding these circles taking into account the variety, volume and dynamics of these SNs has become an important challenge for a wide number of Computer Science areas, as Big Data, Data Mining or Machine Learning among others. Problems related to pre-processing, fusion and knowledge discovering of information from these sources are still an open question. This paper presents a new Bioinspired method, based on Ant Colony Optimization (ACO) algorithms, that has been designed to find and analyze these circles. Given any user in a network, the new method is able to automatically determine the different users that compose his/her groups or circles of interest, so the network will be clustered into different components based on the users profiles and their dynamics. This algorithm has been applied to Ego Networks where the node centering the network (called 'Ego') represents the user being studied. In this work two different ACO algorithms, that differ in the source of information used to perform the community finding tasks, have been designed. The first ACO algorithm uses the information extracted from the topology of the network, whereas the second one uses the profile information provided by users. The proposed algorithms are able to detect the different circles in three popular Social Networks: Facebook, Twitter and Google+. Finally, and using several databases from previous SNs, an experimental evaluation of our methods has been carried out to show how the algorithms are currently working. (C) 2016 Elsevier B.V. All rights reserved.</P>
Neutron Capture on <SUP>209</SUP>Bi: Determination of the Production Ratio of ^(210m)Bi/^(210g)Bi
F. Gunsing,E. Berthoumieux,A. Borella,T. Belgya,L. Szentmiklosi,P. Schillebeeckx,J. C. Drohe,R. Wynants,N. Colonna,S. Marrone,G. Tagliente,R. Terlizzi,C. Domingo-pardo,J. Tain,T. Martinez,C. Massimi,P 한국물리학회 2011 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.59 No.23
Neutron capture on ^(209)Bi produces either an isomeric state ^(210m)Bi with a half life of 3×10^6 years, or the ground state ^(210g)Bi which decays with a half life of 5 days to the alpha emitter ^(210)Po. Therefore the neutron capture cross section ratio ^(209)Bi(n,γ) ^(210m)^(Bi)/^(210g)Bi plays an important role in predicting the short- and long-term radio-toxicity produced by ^(209)Bi under neutron irradiation. This ratio is dependent on the neutron energy. We have measured this ratio for cold neutrons at the cold neutron beam facility of the Budapest Neutron Centre by observing the population of the ground- and the metastable state using high resolution gamma-ray spectroscopy. The same technique hasbeen used at the pulsed white neutron source GELINA of the IRMM, Geel in combination with the neutron time-of-flight technique. Results for the neutron-energy dependent branching ratio will be presented. In addition we performed simulations using a statistical decay code.
Friesen, Melissa C,Locke, Sarah J,Zaebst, Dennis,Viet, Susan,Shortreed, Susan,Chen, Yu-Cheng,Koh, Dong-Hee,Pardo, Larissa,Schwartz, Kendra L,Davis, Faith G,Stewart, Patricia A,Colt, Joanne S,Purdue, M BMJ Publishing Group Ltd 2014 Occupational and environmental medicine Vol.71 No.suppl1
<P><B>Objectives</B></P><P>We applied machine learning approaches to efficiently assist multiple experts to transparently estimate occupational lead exposure in a case-control study of renal cell carcinoma.</P><P><B>Method</B></P><P>We used hierarchical cluster models to classify the 7154 study jobs with occupational history and job/industry questionnaires into 360 groups with similar responses. Each group was reviewed independently by two or three experts and was assigned probabilities of lead exposure (<5%, ≥5– <50%, ≥50%) for three time periods (<1980, 1980–1994, ≥1995). When the group’s mean response pattern suggested within-group exposure variability, experts identified programmable conditions that defined the rating differences where possible or flagged the group for further review. After splitting jobs that overlapped time periods at the calendar cut point, the 9992 job/time periods were assigned their relevant expert/group/time period estimate. Classification and regression tree (CART) models were developed to predict each expert’s expected assignment, based on previous decisions, to assign estimates for jobs in groups that expert had not assessed and for jobs requiring further review.</P><P><B>Results</B></P><P>In preliminary analyses, CART models predicted 91–96% of the experts’ pre-1995 estimates and 77–96% of ≥1995 estimates. CART estimates were assigned to 3–48% of the job/time periods, varying by expert. Overall, 92% of the job/time periods were assigned the same estimate by at least two experts.</P><P><B>Conclusions</B></P><P>Our framework reduced the number of exposure decisions needed from each expert compared to job-by-job assessment. Future work will use CART models to identify differences between experts to be resolved and incorporate frequency and intensity of lead exposure estimates.</P>