• Nutrition Research and Practice
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• v.5 no.5
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• pp.450-454
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• 2011

The beneficial effects of total parenteral nutrition (TPN) in improving the nutritional status of malnourished patients during hospital stays have been well established. However, recent randomized trials and meta-analyses have reported an increased rate of TPN-associated complications and mortality in critically ill patients. The increased risk of complications during TPN therapy has been linked to the development of hyperglycemia, especially during the first few days of TPN therapy. This retrospective study was conducted to determine whether the amount of dextrose from TPN in the 1st week in the intensive care unit (ICU) was related to the development of hyperglycemia and the clinical outcome. We included 88 non-diabetic critically ill patients who stayed in the medical ICU for more than two days. The subjects were 65 ${\pm}$ 16 years old, and the mean APACHE (Acute Physiology and Chronic Health Evaluation) II score upon admission was 20.9${\pm}$7.1. The subjects received 2.3${\pm}$1.4 g/kg/day of dextrose intravenously. We divided the subjects into two groups according to the mean blood glucose (BG) level during the 1st week of ICU stay: <140 mg/dl vs ${\geq}$ 140 mg/dl. Baseline BG and the amount of dextrose delivered via TPN were significantly higher in the hyperglycemia group than those in the normoglycemia group. Mortality was higher in the hyperglycemia group than in the normoglycemia group (42.4% vs 12.8%, P=0.008). The amount of dextrose from TPN was the only significant variable in the multiple linear regression analysis, which included age, APACHE II score, baseline blood glucose concentration and dextrose delivery via TPN as independent variables. We concluded that the amount of dextrose delivered via TPN might be associated with the development of hyperglycemia in critically ill patients without a history of diabetes mellitus. The amount of dextrose in TPN should be decided and adapted carefully to maintain blood glucose within the target range.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.653-660
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• 2008

Recently, Service-oriented computing is the emerging computing paradigm. In this paradigm, we require the practical process model to support service oriented software development. The well-known development methods e.g., Unified Software Development Process, UML components have been proposed focused on component. So, these methods cannot support service-oriented computing concepts such as service definition, binding and composition concepts using Business Process Execution Language (BPEL). Also, a few proposed service-oriented approach, for example Service Oriented Unified process (SOUP), and Service Oriented Modeling and Architecture (SOMA) have appeared. However, these approaches do not explicitly represent detailed guideline, artifacts and approach. Therefore, in this paper we propose a practical and simple process model to support service oriented software development. Also, we explicitly represent process model and artifact using Software Process Engineering Metamodel (SPEM) which is proposed by OMG. By using our approach, it can enhance systematization and effectiveness for service-oriented software development.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.4
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• pp.483-491
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• 2017

The interdigitated germanium (Ge) meta-lsemiconductor-metal (MSM) photodetectors (PDs) with and without an $SiO_2$ anti-reflection (AR) layer was fabricated, and the effect of $SiO_2$ AR layer on their optoelectronic response properties were investigated in detail. The lowest reflectance of 15.6% at the wavelength of 1550 nm was obtained with a $SiO_2$ AR layer with a thickness of 260 nm, which was in a good agreement with theoretically calculated film thickness for minimizing the reflection of Ge surface. The Ge MSM PD with 260 nm-thick $SiO_2$ AR layer exhibited enhanced device performance with the maximum values of responsivity of 0.65 A/W, the quantum efficiency of 52.2%, and the detectivity of $2.49{\times}10^9cm\;Hz^{0.5}W^{-1}$ under the light illumination with a wavelength of 1550 nm. Moreover, time-dependent switching analysis of Ge MSM PD with 260 nm- thick $SiO_2$ AR layer showed highest on/off ratio with excellent stability and reproducibility. All this investigation implies that 260 nm-thick $SiO_2$ AR layer, which is effective in the reduction in the reflection of Ge surface, has a great potential for Ge based optoelectronic devices.

• Journal of Environmental Health Sciences
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• v.27 no.4
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• pp.25-34
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• 2001

Heavy metal level and distribution in sediment and bivalves from the tidal flats along the mid-western coast, Korea. were investigated in this study. The averages of heavy metal concentrations in sediments from 5 sampling sites ranged from 86.35-187.77mg/kg for Mn. 13.25-17.73mg/kg for Zn.6.80-11.85mg/kg for Cr,4.28-6.25mg/kg for Ni, 1.85-3.09mg/kg for Cu, 0.004-0.02()mg/kg for Cd and 5.70-10.41 mg/kg for Pb, respectively. From the results of heavy metal concentrations in mussels, the levels of Mn and Fe concentrations in S. strictus collected from S1 antral S2 sites were particularly high. Deviations of heavy metal concentrations am(Ing the individual mussels from the same site could be largely minimized by selecting mussel samples of the limited range in body size(8.5-11cm). Among the mussels of the limited body size, heavy metal concentrations both in the flesh and in the whole soft part were not strongly related to the body size. It was shown that ,in S. strictus, Cu moves fast into the flesh parts after absorption, while Pb and Fe move fast into the non-flesh parts. Overally. the heavy metal concentration in the tends to increase with that in the whole soft parts. The concentration factors(heavy metal concentration in 5. strictus/heavy metal concentration in sediment) showed that, of the examined meta1s. Cd is the most cumulative in the body of S. strictus followed by Zn and Cu, while Mn, Cr. Ni and Pb are not cumulative.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

• Molecules and Cells
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• v.37 no.2
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• pp.118-125
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• 2014

Osteosarcoma is the most common primary malignant bone tumor with a very poor prognosis. Treating osteosarcoma remains a challenge due to its high transitivity. Tenascin-C, with large molecular weight variants including different combinations of its alternative spliced FNIII repeats, is specifically over expressed in tumor tissues. This study examined the expression of Tenascin-C FNIIIA1 in osteosarcoma tissues, and estimated the effect of mechanical stimulation on A1 expression in MG-63 cells. Through immunohistochemical analysis, we found that the A1 protein was expressed at a higher level in osteosarcoma tissues than in adjacent normal tissues. By cell migration assay, we observed that there was a significant correlation between A1 expression and MG-63 cell migration. The relation is that Tenascin-C FNIIIA1 can promote MG-63 cell migration. According to our further study into the effect of mechanical stimulation on A1 expression in MG-63 cells, the mRNA and protein levels of A1 were significantly up-regulated under mechanical stress with the mTOR molecule proving indispensable. Meanwhile, 4E-BP1 and S6K1 (downstream molecule of mTOR) are necessary for A1 normal expression in MG-63 cells whether or not mechanical stress has been encountered. We found that Tenascin-C FNIIIA1 is over-expressed in osteosar-coma tissues and can promote MG-63 cell migration. Furthermore, mechanical stress can facilitate MG-63 cell migration though facilitating A1 overexpression with the necessary molecules (mTOR, 4E-BP1 and S6K1). In con-clusion, high expression of A1 may promote the meta-stasis of osteosarcoma by facilitating MG-63 cell migration. Tenascin-C FNIIIA1 could be used as an indicator in metastatic osteosarcoma patients.

• Communications of Mathematical Education
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• v.27 no.4
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• pp.407-428
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• 2013

The purpose of this research is to develop an effective teaching-learning model and suggest efficient methods for improving the learning abilities in mathematics of the students of engineering college. For this purpose, we examined their learning styles and learning attitudes toward Mathematics, which are important factors in teaching-learning process, and analyzed the relation between them. As a result, we found that participants had a disposition of being dependent and highly participating, so we made an effort to develop a teaching-learning model which can make the students active and highly involved in. After applying the developed teaching-learning model to Engineering Mathematics class for one semester, we conducted survey of the participants' responses. In conclusion, we found that this model is very helpful for Engineering students to utilize a self-directed learning by diagnosing and adjusting their learning process in Meta-cognitive way. Also, it is confirmed that this model has a considerable influence in which students can actively participate in class and have positive attitude to Mathematics learning.

• Communications of Mathematical Education
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• v.31 no.4
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• pp.457-485
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• 2017

The Self-reported Evaluation tool developed in this study allows the learners to check and evaluate their own learning by determining the details that are self-assessed. Also this tool allows learners to receive feedback on their self - evaluation results. In this study pre - post test was performed to investigate the effect of self - assessment on the learners' tendency of studying math. The result showed that Self-reported evaluation improved self - confidence, self - strategy on learning mathematics, and meta-cognitive ability. Also by conducting a qualitative analysis of the Self-reported evaluation, students practiced the cognitive activities such as summarizing the contents they have learned that day. They also tried to understand and improve the learning habit, attitude, and learning state. Teachers were also able to communicate with students by providing individual questions and feedback through student's individual Self-reported Evaluation.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6407-6412
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• 2015

Purpose: To predict prostatic carcinoma using a logistic regression model on prebiopsy peripheral blood samples. Materials and Methods: Data of a total of 873 patients who consulted Urology Outpatient Clinics of Fatih Sultan Mehmet Training and Research Hospital between February 2008 and April 2014 scheduled for prostate biopsy were screened retrospectively. PSA levels, prostate volumes, prebiopsy whole blood cell counts, neutrophil and platelet counts, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), biopsy results and Gleason scores in patients who had established diagnosis of prostate cancer (PCa) were evaluated. Results: This study was performed on a total of 873 cases, with an age range 48-76 years, divided into three groups as for biopsy results. with diagnoses of benign prostatic hyperplasia (BPH) (n=304, 34.8 %), PCa (n=265, 30.4 %) and histological prostatitis (n=304; 34.8 %). Intra- and intergroup comparative evaluations were performed. White blood cell and neutrophil counts in the histological prostatitis group were significantly higher than those of the BPH and PCa groups (p=0.001; p=0.004; p<0.01). A statistically significant intergroup difference was found for PLR (p=0.041; p<0.05) but not lymphocyte count (p>0.05). According to pairwise comparisons, PLR were significantly higher in the PCa group relative to BPH group (p=0.018, p<0.05, respectively). Though not statistically significant, higher PLR in cases with PCa in comparison with the prostatitis group was remarkable (p=0.067, and p>0.05, respectively). Conclusions: Meta-analyses showed that in patients with PSA levels over 4 ng/ml, positive predictive value of PSA is only 25 percent. Therefore, novel markers which can both detect clinically significant prostate cancer, and also prevent unnecessary biopsies are needed. Relevant to this issue in addition to PSA density, velocity, and PCA3, various markers have been analyzed. In the present study, PLR were found to be the additional predictor of prostatic carcinoma.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.203-210
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• 2014

This study proposes adaptive decision making method having feed-back structure of regression and simulation models to support the quick decision making of production managers by managing and integrating the mutual relationship among historical data. For that, from historical data that have extracted and accumulated from each process, we first selected major constraint resources that are used as independent variables in regression model. The regression model is designed by using the dependent variables (objectives) that defined above by managers and independent variables selected in previous step and simulation model that are composed of constraint resources is designed. In process of simulation run, we obtain the multiple feasible solutions (alternatives) by using meta-heuristic method. Each solution is substituted by regression equation and we found the optimal solution that is minimum of difference between values obtained by regression model and simulation results. The optimal solution is delivered and incorporated to production site and current operation results from production site is used to generate new regression model after that time.