3,410 research outputs found
Development of University Life-Science Programs and University-Industry Joint Research in Japan
How does the establishment of new university educational programs promote university-industry joint research? To study this question for the fields of life sciences and biotechnology, we first compile the data on the establishment of new undergraduate and graduate programs in these fields in Japanese universities since the 1950s. We then analyze statistically whether and how such establishment contributed to the occurrence and frequency of university-industry joint research in biotechnology. The results suggest that, first, the expansion of such university programs in fact contributed to the promotion of university-industry joint research and, second, these collaborations increased following the 1998 legislation to promote technology transfer from universities (the so-called TLO Act) and the 1999 legislation to allow universities to retain rights on their inventions made with government research funds (the so-called Japanese Bayh-Dole Act).
Late Superhumps in WZ Sge-Type Dwarf Novae
We report on the detection of very stable modulations with periods
unexpectedly (~0.5%) longer than superhump periods during the slowly fading
stage of WZ Sge-type superoutbursts in three systems, GW Lib, V455 And and WZ
Sge. These periods are naturally explained by assuming that these modulations
are superhumps arising from matter near the tidal truncation radius. This
finding provides an additional support to the hypothetical idea of expansion of
the accretion disk well beyond the 3:1 orbital resonance in some low mass-ratio
systems. Combined with the effect of 2:1 resonance, we present an explanation
of the origin of positive period derivatives in certain short-period SU
UMa-type dwarf novae.Comment: Accepted by PASJ (Letters), 4 pages, 3 figure
Characterization of Dwarf Novae Using SDSS Colors
We have developed a method for estimating the orbital periods of dwarf novae
from the Sloan Digital Sky Survey (SDSS) colors in quiescence using an
artificial neural network. For typical objects below the period gap with
sufficient photometric accuracy, we were able to estimate the orbital periods
with an accuracy to a 1 sigma error of 22 %. The error of estimation is worse
for systems with longer orbital periods. We have also developed a
neural-network-based method for categorical classification. This method has
proven to be efficient in classifying objects into three categories (WZ Sge
type, SU UMa type and SS Cyg/Z Cam type) and works for very faint objects to a
limit of g=21. Using this method, we have investigated the distribution of the
orbital periods of dwarf novae from a modern transient survey (Catalina
Real-Time Survey). Using Bayesian analysis developed by Uemura et al. (2010,
arXiv:1003.0945), we have found that the present sample tends to give a flatter
distribution toward the shortest period and a shorter estimate of the period
minimum, which may have resulted from the uncertainties in the neural network
analysis and photometric errors. We also provide estimated orbital periods,
estimated classifications and supplementary information on known dwarf novae
with quiescent SDSS photometry.Comment: 70 pages, 7 figures, Accepted for publication in PASJ, minor
correction
Does Founders’ Human Capital Matter for Innovation? Evidence from Japanese Start-ups
Using a sample from an original questionnaire survey in Japan, this paper explores whether and how founders’ human capital affects innovation outcomes by start-ups. The results provide evidence that founders with greater human capital are more likely to yield innovation outcome. However, because certain types of founders’ human capital may boost R&D investment, which possibly results in innovation outcomes, we estimate the determinants of innovation outcomes by an instrumental variable probit model taking into account the endogeneity of R&D investment. Our findings suggest that specific human capital for innovation, such as founders’ prior innovation experience, is directly associated with innovation outcomes after start-up, while generic human capital, such as founders’ educational background, indirectly affects innovation outcomes through R&D investment.Start-up, Founder, Human capital, Innovations, R&D investment
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