• IMMUNE NETWORK
• /
• v.15 no.2
• /
• pp.83-90
• /
• 2015

Evaluation and screening of vaccines against tuberculosis depends on development of proper cost effective disease models along with identification of different immune markers that can be used as surrogate endpoints of protection in preclinical and clinical studies. The objective of the present study was therefore evaluation of subcutaneous model of M.tuberculosis infection along with investigation of different immune biomarkers of tuberculosis infection in BALB/c mice. Groups of mice were infected subcutaneously with two different doses : high ($2{\times}10^6CFU$) and low doses ($2{\times}10^2CFU$) of M.tuberculosis and immune markers including humoral and cellular markers were evaluated 30 days post M.tuberculosis infections. Based on results, we found that high dose of subcutaneous infection produced chronic disease with significant (p<0.001) production of immune markers of infection like $IFN{\gamma}$, heat shock antigens (65, 71) and antibody titres against panel of M.tuberculosis antigens (ESAT-6, CFP-10, Ag85B, 45kDa, GroES, Hsp-16) all of which correlated with high bacterial burden in lungs and spleen. To conclude high dose of subcutaneous infection produces chronic TB infection in mice and can be used as convenient alternative to aerosol models in resource limited settings. Moreover assessment of immune markers namely mycobacterial antigens and antibodies can provide us valuable insights on modulation of immune response post infection. However further investigations along with optimization of study protocols are needed to justify the outcome of present study and establish such markers as surrogate endpoints of vaccine protection in preclinical and clinical studies in future.

• Fashion & Textile Research Journal
• /
• v.20 no.4
• /
• pp.464-473
• /
• 2018

The objective of this study is to develop a sizing system of fitness clothing that can properly accommodate various body sizes of Korean senior women. The sizing system of upper and lower fitness clothing was developed in the present study by selection of key variables, identification of size category candidates, and determination of an optimal sizing system. First, key anthropometric dimensions (stature and bust circumference for upper clothing and stature; waist circumference for lower clothing) were identified by factor analysis on the direct body measurements (n = 272) and 3D whole-body scan data (n = 271) of Korean senior women in Size Korea. Second, sizing system candidates based on the key dimensions of upper and lower clothing were explored using a grid method and an optimization method. Lastly, among the sizing system candidates, optimal sizing systems of upper and lower clothing were selected in terms of accommodation rate. Five size categories (short/small, short/medium, tall/small, tall/medium, and tall/large) were selected as the optimal sizing systems of upper and lower clothing with 89% and 78% of accommodation rate, respectively, for the Korean senior women. The anthropometric characteristics of the representative humans of the optimal size categories would be of use in the design of fitness compressive wear for the better fit and effectiveness of exercise and health of Korean senior women.

• Korean Journal of Environmental Agriculture
• /
• v.20 no.5
• /
• pp.310-316
• /
• 2001

To find out the effect of low temperature on the regulation of tomato chloroplast genes, the optimization of the system in chloroplast protein synthesis and the identification of the changes in chloroplast protein synthesis induced by chilling were studied. Incorporation reaction occurred rapidly at the first 30 minutes and was constantly maintained after 60 minutes. A broad optimal temperature on protein synthesis was found around 20 to $30^{\circ}C$. No difference was shown in the chloroplast protein synthesis under high light intensity (1600 ${\mu}E/m^2/s$) as well as under low light intensity (400 ${\mu}E/m^2/s$) even darkness. $K^+$, $Mg^{++}$ and ATP at an optimal concentration act as an activator, while DTT, chloramphenicol, cycloheximide, $Ca^{++}$ and inorganic phosphate act as an inhibitor in the chloroplast protein synthesis. Synthesis of 15, 55 and 60 kd chloroplast encoded stromal proteins and 18, 24, 33 and 55 kd chloroplast encoded thylakoid membrane proteins were reduced by chilling, while 17 kd chloroplast encoded stromal protein and 16 kd chloroplast encoded thylakoid membrane protein was induced by chilling. It was expected that the 55 kd stromal protein would be the large subunit of rubisco and the 33 kd thylakoid membrane protein would be the D1 protein which was drastically reduced by chilling.

• Korean Journal of Food Science and Technology
• /
• v.31 no.6
• /
• pp.1648-1653
• /
• 1999

A strain of potential producer of fibrinolytic enzyme was isolated from shrimp Jeot-Gal, a tiny salted shrimps, and identified as Bacillus sp.. The preliminary experiment showed an enzyme yield of 18 U/mL in medium for screening. The carbon, nitrogen and salts significantly influenced the fibrinolytic enzyme production. An optimized medium containing 2% skim milk, 2% soluble starch and 3% NaCl (pH 7.5) after 72 hrs fermentation time at $37^{\circ}C$ yielded 3-fold increase in enzyme production, 62 U/mL.

• Smart Structures and Systems
• /
• v.33 no.4
• /
• pp.291-300
• /
• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of Power Electronics
• /
• v.17 no.4
• /
• pp.1014-1026
• /
• 2017

The traditional fault diagnosis method for photovoltaic (PV) inverters has a difficult time meeting the requirements of the current complex systems. Its main weakness lies in the study of nonlinear systems. In addition, its diagnosis time is long and its accuracy is low. To solve these problems, a hidden Markov model (HMM) is used that has unique advantages in terms of its training model and its recognition for diagnosing faults. However, the initial value of the HMM has a great influence on the model, and it is possible to achieve a local minimum in the training process. Therefore, a genetic algorithm is used to optimize the initial value and to achieve global optimization. In this paper, the HMM is combined with a genetic algorithm (GHMM) for PV inverter fault diagnosis. First Matlab is used to implement the genetic algorithm and to determine the optimal HMM initial value. Then a Baum-Welch algorithm is used for iterative training. Finally, a Viterbi algorithm is used for fault identification. Experimental results show that the correct PV inverter fault recognition rate by the HMM is about 10% higher than that of traditional methods. Using the GHMM, the correct recognition rate is further increased by approximately 13%, and the diagnosis time is greatly reduced. Therefore, the GHMM is faster and more accurate in diagnosing PV inverter faults.

• Journal of Information Technology Applications and Management
• /
• v.31 no.1
• /
• pp.113-122
• /
• 2024

This comprehensive study delves into the analysis of user review data across various metaverse platforms, employing advanced text mining techniques such as TF-IDF and Word2Vec to gain insights into user perceptions. The primary objective is to uncover the factors that contribute to user satisfaction and dissatisfaction, thereby providing a nuanced understanding of user experiences in the metaverse. Through TF-IDF analysis, the research identifies key words and phrases frequently mentioned in user reviews, highlighting aspects that resonate positively with users, such as the ability to engage in creative activities and social interactions within these virtual environments. Word2Vec analysis further enriches this understanding by revealing the contextual relationships between words, offering a deeper insight into user sentiments and the specific features that enhance their engagement with the platforms. A significant finding of this study is the identification of common grievances among users, particularly related to the processes of refunds and login, which point to broader issues within payment systems and user interface designs across platforms. These insights are critical for developers and operators of metaverse platforms, suggesting a focused approach towards enhancing user experiences by amplifying positive aspects. The research underscores the importance of continuous improvement in user interface design and the transparency of payment systems to foster a loyal user base. By providing a comprehensive analysis of user reviews, this study offers valuable guidance for the strategic development and optimization of metaverse platforms, ensuring they remain responsive to user needs and continue to evolve as vibrant, engaging virtual environments.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.16
• /
• pp.6781-6785
• /
• 2014

Purpose: This study used receiver operating characteristic curves to analyze Surveillance, Epidemiology and End Results (SEER) medulloblastoma (MB) and primitive neuroectodermal tumor (PNET) outcome data. The aim of this study was to identify and optimize predictive outcome models. Materials and Methods: Patients diagnosed from 1973 to 2009 were selected for analysis of socio-economic, staging and treatment factors available in the SEER database for MB and PNET. For the risk modeling, each factor was fitted by a generalized linear model to predict the outcome (brain cancer specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A Monte Carlo algorithm was used to estimate the modeling errors. Results: There were 3,702 patients included in this study. The mean follow up time (S.D.) was 73.7 (86.2) months. Some 40% of the patients were female and the mean (S.D.) age was 16.5 (16.6) years. There were more adult MB/PNET patients listed from SEER data than pediatric and young adult patients. Only 12% of patients were staged. The SEER staging has the highest ROC (S.D.) area of 0.55 (0.05) among the factors tested. We simplified the 3-layered risk levels (local, regional, distant) to a simpler non-metastatic (I and II) versus metastatic (III) model. The ROC area (S.D.) of the 2-tiered model was 0.57 (0.04). Conclusions: ROC analysis optimized the most predictive SEER staging model. The high under staging rate may have prevented patients from selecting definitive radiotherapy after surgery.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.27 no.1
• /
• pp.45-54
• /
• 2024

This study developed a model to evaluate the co-benefits and trade-off effects between biodiversity and carbon storage capacity based on the implementation locations of nature-based solutions. The model aims to propose optimal implementation locations by using the conceptual idea of edge effects for carbon storage and connectivity for biodiversity. The co-benefits were considered by simultaneously taking into account two effects rather than a single effect. Trade-off effects were observed among optimal plans through a comparison of benefits. The NSGA-II multi-objective optimization algorithm was utilized, confirming the identification of Pareto-optimal solutions. The implementation patterns of Pareto-optimal solutions for green areas were examined. This study holds significance in proposing optimal locations by evaluating various co-benefits and trade-off effects of nature-based solutions. By advancing models based on this evaluation framework, it is anticipated that the assessment of co-benefits and trade-off effects among various benefits of nature-based solutions, such as climate change mitigation, enhancement of biodiversity, and provision of ecosystem services, can be accomplished.

• Journal of the Korean Ceramic Society
• /
• v.59
• /
• pp.338-349
• /
• 2020

Large scale with high-purity hexagonal aluminum nitride nanoparticles (AlN NPs) was synthesized using DC thermal plasma arc discharge method (TPAD). Argon gas was used as the plasma forming gas, while ammonia (NH₃) gas was used as the reactive gas, which was fed into the reactor at a constant flow rate of 5 LPM. In order to optimize the process for high yield, the experiments were carried out at various plasma input powers, such as 1.5, 3.0, and 4.5 kW. Following the optimization, to examine the influence of using pure nitrogen gas, an experiment was also carried out in the nitrogen ambience. The phase identification and structural determination of the synthesized NPs were carried out using XRD and Raman spectroscopic analyses. While the morphology, particle size, and elemental compositions of the synthesized NPs were observed from SEM, HRTEM, XPS, and EDX analyses. The photoluminescence response was confirmed from the PL spectrum. The PL emission peaks observed around 440 nm (2.8 eV) and 601 nm (2.07 eV), respectively, which correspond to the UV blue and red band emissions of both AlN and Al/AlN NPs. The results show that the synthesized nano-AlN NPs exhibit excellent crystallinity with a high yield of approximately 210 g/h. The current plasma technology can be regarded as a perfect potential process for developing nano-AlN powders with improved efficiency.