275,032 research outputs found
Joshua Davis: Author of Spare Parts
No full textCitation: K-State First (2016). Joshua Davis: Author of Spare Parts [Flier]. Manhattan, Kansas: K-State First.Flyer advertising Joshua Davis's author talk at Kansas State University
Steven Johnson Author Talk Poster
No full textK-State Book NetworkA poster advertising an author talk by Steven Johnson at Kansas State University on September 3, 2014. Steven Johnson's book "The Ghost Map" was the 2014-2015 common book
DBLP-derived labeled data for author name disambiguation
No full textThis is a DBLP-derived labeled data originally created by Dr. C. Lee Giles at Penn State University and filtered for duplicate removal and error correction by Dr. Jinseok Kim at University of Michigan. For more details, see references below.1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9Each row refers to an author name instance with following feature information separated by tab.author name: full name string extracted from DBLPunique author id: labels assigned manually by Dr. C. Lee Giles's teampaper id: assigned by Dr. Jinseok Kimauthor list: names of authors in the byline of the paperyear: publication yearvenue: conference or journal namestitle: stopwords removed and stemmed by the Porter's stemmerIf you want to use this dataset, please consider to cite papers below.For the original dataset: Han, H., Giles, L., Zha, H., Li, C., & Tsioutsiouliklis, K. (2004). Two Supervised Learning Approaches for Name Disambiguation in Author Citations. JCDL 2004: Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries, 296-305. doi:10.1145/996350.996419For the filtered dataset: 1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 or2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9</div
Susan Kim and Asa Simon Mittman, "Keeping History: Images, Texts, Ciphers, and the Franks Casket," with Susan Kim, in A Material History of Medieval and Early Modern Ciphers, ed. K Ellison and S Kim (New York: Routledge, 2017)
No full textSusan Kim and Asa Simon Mittman, "Keeping History: Images, Texts, Ciphers, and the Franks Casket," with Susan Kim, in A Material History of Medieval and Early Modern Ciphers, ed. K Ellison and S Kim (New York: Routledge, 2017
Analysis of Noise Coupling From a Power Distribution Network to Signal Traces in High-Speed Multilayer Printed Circuit Boards
No full textAs layout density increases in highly integrated multilayer printed circuit boards (PCBs), the noise that exists in the power distribution network (PDN) is increasingly coupled to the signal traces, and precise modeling to describe the coupling phenomenon becomes necessary. This paper presents a model to describe noise coupling between the power/ground planes and signal traces in multilayer systems. An analytical model for the coupling has been successfully derived, and the coupling mechanism was rigorously analyzed and clarified. Wave equations for a signal trace with power/ground noise were solved by imposing boundary conditions. Measurements in both the frequency and time domains have been conducted to confirm the validity of the proposed model
A note on Kim-Ma characterization of the Hilbert ball
No full textThis is an open access article under the CC BY license.[No abstract available]Kortney Rose Foundation, KRF, (2002-070-C00005); National Research Foundation of Korea, NRF* Corresponding author. E-mail addresses: [email protected] (K.-T. Kim), [email protected] (D. Ma). 1 Research supported in part by the grant KRF 2002-070-C00005 from The Korea Research Foundation
Jonguk Kim
No full text학위논문(박사)--아주대학교 일반대학원 :생명과학과,2014. 2DoctoralFive rice cDNAs that are most likely to encode a putative voltage-gated shaker type K+ channel were selected by in silico sequence homology and membrane topology analyses with respect to the number of transmembrane domains (TMs) and the presence of a well-preserved K+ selectivity filter (TXXTXGYG) in reference to Arabidopsis shaker type K+ channel (AKT1). The five candidate cDNAs were further subcloned into pSP64T vector, a Xenopus expression vector, and then in vitro transcribed to generate cRNAs. Each cRNAs were microinjected into Xenopus oocytes, and their K+ channel conductance was measured electrophysiologically by using two electrode voltage clamping (TEVC). Among them, one of the rice cDNAs gave rise to a K+ current with biophysical characteristics similar to those of the shaker type K+ channel.
Five bacterial species that are most likely to have putative prokaryotic inward rectifier K+ (Kir) channels were selected by in silico sequence homology and membrane topology analyses with respect to the number of transmembrane domains (TMs) and the presence of K+ selectivity filter and/or ATP binding sites in reference to rabbit heart inward rectifier K+ channel (Kir 6.2). A dot blot assay with genomic DNAs when probed with whole rabbit Kir6.2 cDNA further supported the in silico analysis by exhibiting a stronger hybridization in species with putative Kir’s compared to one without a Kir. Among them, Chromobacterium violaceum gave rise to a putative Kir channel gene, which was PCR-cloned into the bacterial expression vector pET30b(+), and its expression was induced in Escherichia coli and confirmed by gel purification and immunoblotting. On the other hand, this putative bacterial Kir channel was functionally expressed in Xenopus oocytes and its channel activity was measured electrophysiologically by using two electrode voltage clamping (TEVC). Results revealed a K+ current with characteristics similar to those of the ATP-sensitive K+ (K-ATP) channel.
Collectively, cloning and functional characterization of rice and bacterial ion channels could be greatly facilitated by combining the in silico analysis and heterologous expression in Xenopus oocytes
Selective recognition of NH4+ over K+ with tripodal oxazoline receptors
No full textBenzene-based tripodal tris(oxazolines) are found to be promising receptors for the selective recognition of NH4+ over K+ with high binding affinities.X1144sciescopu
Design of distributed JT (Joule-Thomson) effect heat exchanger for superfluid 2 K cooling device
No full textSuperfluid at 2 K or below is readily obtained from liquid helium at 4.2 K by reducing its vapour pressure. For better cooling performance, however, the cold energy of vaporized helium at 2 K chamber can be effectively utilized in a recuperator which is specially designed in this paper for accomplishing so-called the distributed Joule-Thomson (JT) expansion effect. This paper describes the design methodology of distributed JT effect heat exchanger for 2 K JT cooling device. The newly developed heat exchanger allows continuous significant pressure drop at high-pressure part of the recuperative heat exchanger by using a capillary tube. Being different from conventional recuperative heat exchangers, the efficient JT effect HX must consider the pressure drop effect as well as the heat transfer characteristic. The heat exchanger for the distributed JT effect actively utilizes continuous pressure loss at the hot stream of the heat exchanger by using an OD of 0.64 mm and an ID of 0.4 mm capillary tube. The analysis is performed by dividing the heat exchanger into the multiple sub-units of the heat exchange part and JT valve. For more accurate estimation of the pressure drop of spirally wound capillary tube, preliminary experiments are carried out to investigate the friction factor at high Reynolds number. By using the developed pressure drop correlation and the heat transfer correlation, the specification of the heat exchanger with distributed JT effect for 2 K JT refrigerator is determined
Missing-data handling methods for lifelogs-based wellness index estimation: Comparative analysis with panel data
No full textBackground: A lifelogs-based wellness index (LWI) is a function for calculating wellness scores based on health behavior lifelogs (eg, daily walking steps and sleep times collected via a smartwatch). A wellness score intuitively shows the users of smart wellness services the overall condition of their health behaviors. LWI development includes estimation (ie, estimating coefficients in LWI with data). A panel data set comprising health behavior lifelogs allows LWI estimation to control for unobserved variables, thereby resulting in less bias. However, these data sets typically have missing data due to events that occur in daily life (eg, smart devices stop collecting data when batteries are depleted), which can introduce biases into LWI coefficients. Thus, the appropriate choice of method to handle missing data is important for reducing biases in LWI estimations with panel data. However, there is a lack of research in this area. Objective: This study aims to identify a suitable missing-data handling method for LWI estimation with panel data. Methods: Listwise deletion, mean imputation, expectation maximization-based multiple imputation, predictive-mean matching-based multiple imputation, k-nearest neighbors-based imputation, and low-rank approximation-based imputation were comparatively evaluated by simulating an existing case of LWI development. A panel data set comprising health behavior lifelogs of 41 college students over 4 weeks was transformed into a reference data set without any missing data. Then, 200 simulated data sets were generated by randomly introducing missing data at proportions from 1% to 80%. The missing-data handling methods were each applied to transform the simulated data sets into complete data sets, and coefficients in a linear LWI were estimated for each complete data set. For each proportion for each method, a bias measure was calculated by comparing the estimated coefficient values with values estimated from the reference data set. Results: Methods performed differently depending on the proportion of missing data. For 1% to 30% proportions, low-rank approximation-based imputation, predictive-mean matching-based multiple imputation, and expectation maximization-based multiple imputation were superior. For 31% to 60% proportions, low-rank approximation-based imputation and predictive-mean matching-based multiple imputation performed best. For over 60% proportions, only low-rank approximation-based imputation performed acceptably. Conclusions: Low-rank approximation-based imputation was the best of the 6 data-handling methods regardless of the proportion of missing data. This superiority is generalizable to other panel data sets comprising health behavior lifelogs given their verified low-rank nature, for which low-rank approximation-based imputation is known to perform effectively. This result will guide missing-data handling in reducing coefficient biases in new development cases of linear LWIs with panel data.Methodologie en Organisatie van Desig
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