• Archives of Plastic Surgery
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• v.44 no.5
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• pp.428-433
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• 2017

Background Candidate characteristics for hand surgery fellowship training remains unknown, as very little data is available in the literature. This study aims to provide information on the criteria that are employed to select candidates for the hand surgery fellowship match. Methods A 38-question survey was sent in April 2015 to all Accreditation Council for Graduate Medical Education recognized hand surgery fellowship program directors (n=81) involved in the U.S. match. The survey investigated factors used for the selection of applicants, including medical school, residency training, research experience, fellowship interview, and candidate characteristics. A 5-point Likert scale was used to grade 33 factors from "not at all important" (1) to "essential in making my decision" (5); or for five controversial factors from "very negative impact" (1) to "very positive impact in making my decision" (5). Results A total of 52% (42 out of 81) of responses were received from hand surgery fellowship program directors. The most important influential factors were interactions with faculty during interview and visit ($4.6{\pm}0.6$), interpersonal skills ($4.6{\pm}0.5$), overall interview performance in the selection process ($4.6{\pm}0.6$), professionalism and ethics ($4.6{\pm}0.7$), and letters of recommendation from hand surgeons ($4.5{\pm}0.7$). Factors that have a negative impact on the selection process include visa requirement ($2.1{\pm}1.2$), graduate of non-plastic surgery residency program ($2.4{\pm}1.3$), and graduate of a foreign medical school ($2.4{\pm}1.1$). Conclusions This study provides data on hand surgery fellowship directors' perception on the criteria important for fellowship applicant selection, and showed that interview-related criteria and letters of recommendation are the important factors.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.197-204
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• 2009

In the model selection problem, the main objective is to choose the true model from a manageable set of candidate models. An information criterion gauges the validity of a statistical model and judges the balance between goodness-of-fit and parsimony; "how well observed values ran approximate to the true values" and "how much information can be explained by the lower dimensional model" In this study, we introduce some information criteria modified from the Akaike Information Criterion (AIC) and the Bayesian Information Criterion(BIC). The information criteria considered in this study are compared via simulation studies and real application.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.159-165
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• 2007

In this paper, we propose a new pre-selection of candidate units that is suitable for the unit selection based Japanese TTS system. General pre-selection method performed by calculating a context-dependent cost within IP (Intonation Phrase). Different from other languages, however. Japanese has an accent represented as the height of a relative pitch, and several words form a single accentual phrase. Also. the prosody in Japanese changes in accentual phrase units. By reflecting such prosodic change in pre-selection. the qualify of synthesized speech can be improved. Furthermore, by calculating a context-dependent cost within accentual phrase, synthesis speed can be improved than calculating within intonation phrase. The proposed method defines AP. analyzes AP in context and performs pre-selection using accentual phrase matching which calculates CCL (connected context length) of the Phoneme's candidates that should be synthesized in each accentual phrase. The baseline system used in the proposed method is VoiceText, which is a synthesizer of Voiceware. Evaluations were made on perceptual error (intonation error, concatenation mismatch error) and synthesis time. Experimental result showed that the proposed method improved the qualify of synthesized speech. as well as shortened the synthesis time.

• Food Science of Animal Resources
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• v.31 no.3
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• pp.325-338
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• 2011

Investigations into fatty acid composition in meats are becoming more important due to consumer demand for high quality healthy food. Marker-assisted selection has been applied to livestock to improve meat quality by directly selecting animals for favorable alleles that affect economic traits. Quantitative trait loci affecting fatty acid composition in cattle and pigs were investigated, and five candidate genes (ACACA, FASN, SCD, FABPs, and SREBP-1) were significantly associated with fatty acid composition. The information presented here should provide valuable guidelines to detect causative mutations affecting fatty acid composition in cattle and pigs.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.161-168
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• 2014

In mixture experiments with process variables, we consider the case that some of process variables are either uncontrollable or hard to control, which are called noise variables. Given the such mixture experimental data with process variables, first we study how to search for candidate models. Good candidate models are screened by the sequential variables selection method and checking the residual plots for the validity of the model assumption. Two methods, which use numerical optimization methods proposed by Derringer and Suich (1980) and minimization of the weighted expected loss, are proposed to find a cost-effective robust optimal condition in which the performance of the mean as well as the variance of the response for each of the candidate models is well-behaved under the cost restriction of the mixture. The proposed methods are illustrated with the well known fish patties texture example described by Cornell (2002).

• Journal of the military operations research society of Korea
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• v.25 no.1
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• pp.29-36
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• 1999

In this paper, we are concerned with the K-shortest loopless path problem. The MPS algorithm, recently proposed by Martins et al., finds paths efficiently because it solves the shortest path problem only one time unlike other algorithms. But its computational complexity has not been known yet. We propose a few techniques by which the MPS algorithm can be implemented efficiently. First, we use min-heap data structure for the storage of candidate paths in order to reduce searching time for finding minimum distance path. Second, we prevent the eliminated paths from reentering in the list of candidate paths by lower bounding technique. Finally, we choose the source mode as a deviation node, by which selection time for the deviation node is reduced and the performance is improved in spite of the increase of the total number of candidate paths.

• Journal of Korean Society of Forest Science
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• v.96 no.2
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• pp.125-130
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• 2007

The principal morphological characters used for identification of hydrangea cultivars are often dependent on agroclimatic conditions. Furthermore, information on the selection or the genetic background of the hydrangea breeding is so rare that a molecular marker system for cultivar identification is needed. Amplified fragment length polymorphism (AFLP) markers were employed for fingerprinting Hydrangea macrophylla cultivars and candidate cultivars of H. macrophylla selected in Korea. One AFLP primer combination was sufficient to distinguish 17 H. macrophylla cultivars and 4 candidate cultivars. The profile of 19 loci that can minimize the error of amplification peak detection was constructed. AFLP markers were efficient for identification, estimation of genetic distances between cultivars, and cultivar discrimination. Based on the observed AFLP markers, genetic relationship was reconstructed by the UPGMA method. Seventeen H. macrophylla cultivars and H. macrophylla for. normalis formed a major cluster, and candidate cultivars selected in Korea formed another cluster.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4771-4787
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• 2019

Aimed at the disclosure risk of mobile terminal user's location privacy in location-based services, a location-privacy protection scheme based on similar trajectory substitution is proposed. On the basis of the anonymized identities of users and candidates who request LBS, this scheme adopts trajectory similarity function to select the candidate whose trajectory is the most similar to user's at certain time intervals, then the selected candidate substitutes user to send LBS request, so as to protect user's privacy like identity, query and trajectory. Security analyses prove that this scheme is able to guarantee such security features as anonymity, non-forgeability, resistance to continuous query tracing attack and wiretapping attack. And the results of simulation experiment demonstrate that this scheme remarkably improve the optimal candidate' trajectory similarity and selection efficiency.

• BMB Reports
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• v.51 no.1
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• pp.1-2
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• 2018

Positive selection on a new beneficial mutation generates a characteristic pattern of DNA sequence polymorphism when it reaches an intermediate allele frequency. On genome sequences of African Drosophila melanogaster, we detected such signatures of selection at 37 candidate loci and identified "sweeping haplotypes (SHs)" that are increasing or have increased rapidly in frequency due to hitchhiking. Based on geographic distribution of SH frequencies, we could infer whether selective sweeps occurred starting from de novo beneficial mutants under simple constant selective pressure. Single SHs were identified at more than half of loci. However, at many other loci, we observed multiple independent SHs, implying soft selective sweeps due to a high beneficial mutation rate or parallel evolution across space. Interestingly, SH frequencies were intermediate across multiple populations at about a quarter of the loci despite relatively low migration rates inferred between African populations. This invokes a certain form of frequency-dependent selection such as heterozygote advantage. At one locus, we observed a complex pattern of multiple independent that was compatible with recurrent frequency-dependent positive selection on new variants. In conclusion, genomic patterns of positive selection are very diverse, with equal contributions of hard and soft sweeps and a surprisingly large proportion of frequency-dependent selection in D. melanogaster populations.