• Childhood Kidney Diseases
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• v.28 no.1
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• pp.35-43
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• 2024

Purpose: Recurrent urinary tract infections (UTIs) in children is a major challenge for pediatricians. This study was designed to investigate the risk factors for recurrent UTIs and determine the association between recurrent UTIs and clinical findings, including growth patterns in infants and children younger than 24 months of age. Methods: We retrospectively reviewed the medical records of 147 patients <24 months of age with UTIs who were hospitalized between August 2018 and October 2021. The patients were divided into recurrent and single UTI episode groups. Clinical findings and anthropometric and laboratory data were compared between the two groups. Results: In the recurrent UTI group, the weight-for-length (WFL) percentile at the first UTI diagnosis was lower compared to the single UTI episode group, and the weight-for-age percentile at 3-month and 6-month follow-ups after the first UTI decreased (all P<0.05). In univariable logistic regression analysis, higher birth weight, lower WFL percentile, the presence of hydronephrosis, acute pyelonephritis or vesicoureteral reflux, the use of prophylactic antibiotics, and non-Escherichia coli infections were associated with the development of recurrent UTIs (all P<0.05). However, in the multivariable analysis, only the presence of hydronephrosis and prophylactic antibiotic use were independently related to UTI recurrence (P<0.05). Conclusions: The presence of hydronephrosis at the first UTI can be helpful for predicting UTI recurrence in young children aged <24 months. Antibiotic prophylaxis may be associated with UTI recurrence. Potential growth delay should be carefully monitored in infants with recurrent UTI.

• Honam Mathematical Journal
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• v.43 no.3
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• pp.539-545
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• 2021

In this paper, we study some symmetric and recurrent conditions of nearly cosymplectic manifolds. We prove that Ricci-semisymmetric and Ricci-recurrent nearly cosymplectic manifolds are Einstein and conformal flat nearly cosymplectic manifold is locally isometric to Riemannian product ℝ × N, where N is a nearly Kähler manifold.

• Communications of the Korean Mathematical Society
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• v.38 no.4
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• pp.1249-1260
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• 2023

In the present paper, we study a semi-symmetric recurrent metric connection and verify its various geometric properties.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.51-55
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• 1990

In this study, a peripheral auditory model used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean placenames and syllables. As a result of this study, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation. The recurrent network in this study reflects well time information of temporal speech signal.

• Bulletin of the Korean Mathematical Society
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• v.45 no.4
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• pp.689-700
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• 2008

In this paper we prove that a $\phi$-recurrent (k, $\mu$)-contact metric manifold is an $\eta$-Einstein manifold with constant coefficients. Next, we prove that a three-dimensional locally $\phi$-recurrent (k, $\mu$)-contact metric manifold is the space of constant curvature. The existence of $\phi$-recurrent (k, $\mu$)-manifold is proved by a non-trivial example.

• Journal of Gastric Cancer
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• v.4 no.3
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• pp.176-179
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• 2004

Surgical treatment for afferent loop syndrome (ALS) in patients with recurrent gastric cancer is usually not feasible because of the recurrent tumor mass at the anastomosis site and/or extensive carcinomatosis resulting in bowel loop fixation. Furthermore, ALS usually makes oral intake impossible, resulting in a rapid deterioration in general condition. In this situation, gastroscopic stenting at the anastomotic site and/or percutaneous external drainage may be a more feasible alternative for palliation. We herein report a recurrent gastric cancer whose ALS was successfully treated with internal and external drainage procedures.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1231-1238
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• 1999

This paper presents an approach for the optimal heating load Identification using Diagonal Recurrent Neural Networks(DRNN). In this paper, the DRNN captures the dynamic nature of a system and since it is not fully connected, training is much faster than a fully connected recurrent neural network. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself. In this study, A dynamic backpropagation (DBP) with delta-bar-delta learning method is used to train an optimal heating load identifier. Delta-bar-delta learning method is an empirical method to adapt the learning rate gradually during the training period in order to improve accuracy in a short time. The simulation results based on experimental data show that the proposed model is superior to the other methods in most cases, in regard of not only learning speed but also identification accuracy.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.283-290
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• 2013

Subjects can experience two types of recurrent events in a longitudinal study. In addition, there may exist intermittent dropouts that results in repeated observation gaps during which no recurrent events are observed. Therefore, theses periods are regarded as non-risk status. In this paper, we consider a special case where information on the observation gap is incomplete, that is, the termination time of observation gap is not available while the starting time is known. For a statistical inference, incomplete termination time is incorporated in terms of interval-censored data and estimated with two approaches. A shared frailty effect is also employed for the association between two recurrent events. An EM algorithm is applied to recover unknown termination times as well as frailty effect. We apply the suggested method to young drivers' convictions data with several suspensions.

• Sleep Medicine and Psychophysiology
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• v.2 no.1
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• pp.91-96
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• 1995

A 24-year-old woman complained of recurrent episodes of hypersomnia lasting on the average about 15 days with mild mood alternation such as depression and irritability. During interepisode interval, she was free of any symptoms. Depending on the absence of excessive eating and hypersexuality, she was clinically diagnosed as recurrent monosymptomatic hypersomnia or the incomplete form of Kleine-Levin syndrome. When nocturnal polysomnography and multiple sleep latency test were performed 10 days after her recovery from a hypersomnic episode, reduced slow wave sleep % and pathologic daytime sleepiness were still noted. The authors suggest that the clinical recovery in recurrent monosymptomatic hypersomnia precede electrophysiological normalization by several days.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.157-162
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• 1990

In this study, a peripheral auditory model is used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean place names and syllables. In the case of using the general learning rule, it is found that the weights are diverged for a long sequence because of the characteristics of the node function in the hidden and output layers. So, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation and to use long data. The recognition results are considerably good, even if time worping and endpoint detection are omitted and learning patterns and test patterns are made of average length of data. The recurrent network used in this study reflects well time information of temporal speech signal.