• Korean Journal of Psychosomatic Medicine
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• 제31권2호
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• pp.108-117
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• 2023

Objectives : Somatic symptom disorder (SSD) is characterized by the manifestation of a variety of physical symptoms, but little is known about differences in autonomic nervous system activity according to symptom severity, especially within patient groups. In this study, we examined differences in heart rate variability (HRV) across symptom severity in a group of SSD patients to analyze a representative marker of autonomic nervous system changes by symptoms severity. Methods : Medical records were retrospectively reviewed for patients who were diagnosed with SSD based on DSM-5 from September 18, 2020 to October 29, 2021. We applied inverse probability of treatment weighting (IPTW) methods to generate more homogeneous comparisons in HRV parameters by correcting for selection biases due to sociodemographic and clinical characteristic differences between groups. Results : There were statistically significant correlations between the somatic symptom severity and LF (nu), HF (nu), LF/HF, as well as SD1/SD2 and Alpha1/Alpha2. After IPTW estimation, the mild to moderate group was corrected to 27 (53.0%) and the severe group to 24 (47.0%), and homogeneity was achieved as the differences in demographic and clinical characteristics were not significant. The analysis of inverse probability weighted regression adjustment model showed that the severe group was associated with significantly lower RMSSD (β=-0.70, p=0.003) and pNN20 (β=-1.04, p=0.019) in the time domain and higher LF (nu) (β=0.29, p<0.001), lower HF (nu) (β=-0.29, p<0.001), higher LF/HF (β=1.41, p=0.001), and in the nonlinear domain, significant differences were tested for SampEn15 (β=-0.35, p=0.014), SD1/SD2 (β=-0.68, p<0.001), and Alpha1/Alpha2 (ß=0.43, p=0.001). Conclusions : These results suggest that differences in HRV parameters by SSD severity were showed in the time, frequency and nonlinear domains, specific parameters demonstrating significantly higher sympathetic nerve activity and reduced ability of the parasympathetic nervous system in SSD patients with severe symptoms.

• Korean Journal of Remote Sensing
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• 제38권5_1호
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of Intelligence and Information Systems
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• 제28권2호
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Animal Science and Technology
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• 제44권2호
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• pp.165-170
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• 2002

Mature weight (A) and rate of maturing (k) estimated by nonlinear regression were studied to determine the optimum age range over which the estimate of growth curve parameters can be estimated. The weight-age data from 1,133 Hanwoo bulls at Hanwoo Improvement Center of N.A.C.F. were used to fit the growth curve using Gompertz model. All available weight data from birth to the specific age of months were used for the estimation of parameters: the six specific ages used were 12, 14, 16, 18, 20 22 and 24 months of age. The mean estimates of mature weight (A) were 966.5, 1,255.9, 1,126.2, 916.5, 842.2, 780.9 and 767.0kg for ages 12 through 24 months, respectively. The mean estimates of mature weight (A) to 22 and 24 months of age were not different from each other. However, they were different from the estimates based on the data to other ages. Mean estimates of rate of maturing (k) were 3.362, 3.595, 3.536, 3.421, 3.403, 3.409 and 3.411 for ages 12 through 24 months, respectively. The mean estimates of maturing rate (k) for ages 18 through 24 months of age were not significantly different from each other. However, they were different from the estimates based on the data to other ages. Correlations among estimates of A at various ages showed the highest value of 0.93 between 22 and 24 months. Correlations among estimates of k at various ages were highest ranging from 0.91 to 0.99 among 18 to 24 months. The correlations between A and k were positive and tended to decrease with the increase of the age from 0.84 for the age of 12 months to 0.10 for the age of 24 months. Thus, the estimates of growth curve parameters, A and k, suitable for genetic studies can be derived from accumulated Hanwoo bulls after 22 months of age.

• Korean journal of applied entomology
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• 제57권4호
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• pp.261-269
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• 2018

The cowpea aphid Aphis craccivora Koch (Hemiptera: Aphididae) is a polyphagous species with a worldwide distribution. We investigated the temperature effects on development periods of nymphs, and the longevity and fecundity of apterous female of A. craccivora. The study was conducted at six constant temperatures of 10.0, 15.0, 20.0, 25, 30.0, and $32.5^{\circ}C$. A. craccivora developed successfully from nymph to adult stage at all temperatures subjected. The developmental rate of A. craccivora increased as temperature increased. The lower developmental threshold (LT) and thermal constant (K) of A. craccivora nymph stage were estimated by linear regression as $5.3^{\circ}C$ and 128.4 degree-days (DD), respectively. Lower and higher threshold temperatures (TL, TH and TH-TL, respectively) were calculated by the Sharpe_Schoolfield_Ikemoto (SSI) model as $17.0^{\circ}C$, $34.6^{\circ}C$ and $17.5^{\circ}C$. Developmental completion of nymph stages was described using a three-parameter Weibull function. Life table parameters were estimated. The intrinsic rate of increase was highest at $25^{\circ}C$, while the net reproductive rate was highest at $20^{\circ}C$. Biological characteristics of A. craccivora populations from different geographic areas were discussed.