• Journal of Applied Biological Chemistry
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• v.48 no.4
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• pp.207-212
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• 2005

Prolyl endopeptidase (PEP, EC 3.4.21.26), also referred to as prolyl oligopeptidase, has been suggested to participate in learning and memory processes by cleaving peptide bonds on carboxyl side of prolyl residue within neuropeptides of less than 30 amino acids, and is abundant in brains of amnestic patients. Therefore, compounds possessing PEP inhibitory activity can be good candidate of drug against memory loss. Upon examination for PEP inhibition from traditional medicinal plants having tonic, stimulating, and anti-amnestic effects, Corni Fructus (Cornus officinallis) showed significant PEP inhibition. Ursolic and oleanolic acids, components of Corni Fructus, inhibited PEP with $IC_{50}$ values of $17.2\;{\pm}\;0.5$ and $22.5\;{\pm}\;0.7\;{\mu}M$, respectively.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.839-849
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• 2023

The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.

• Molecules and Cells
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• v.41 no.7
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• pp.646-652
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• 2018

Neurodegeneration can result in memory loss in the central nervous system (CNS) and impairment of taste and smell in the peripheral nervous system (PNS). The neurodegeneration seen in Parkinson's disease (PD) is characterized by functional loss of dopaminergic neurons. Recent studies have also found a role for dopaminergic neurons in regulating taste memory rewards in insects. To investigate how taste memories and sugar sensitivity can be affected in PD, we utilized the $DJ-1{\beta}$ mutant fruit fly, $DJ-1{\beta}^{ex54}$, as a PD model. We performed binary choice feeding assays, electrophysiology and taste-mediated memory tests to explore the function of the $DJ-1{\beta}$ gene in terms of sugar sensitivity as well as associative taste memory. We found that PD flies exhibited an impaired ability to discriminate sucrose across a range of sugar concentrations, with normal responses at only very high concentrations of sugar. They also showed an impairment in associative taste memory. We highlight that the taste impairment and memory defect in $DJ-1{\beta}^{ex54}$ can be recovered by the expression of wild-type $DJ-1{\beta}$ gene in the dopaminergic neurons. We also emphasized the role of dopaminergic neurons in restoring taste memory function. This impaired memory property of $DJ-1{\beta}^{ex54}$ flies also allows them to be used as a model system for finding supplementary dietary foods that can improve memory function. Here we provide evidence that the associative taste memory of both control and $DJ-1{\beta}^{ex54}$ flies can be enhanced with dietary supplementation of the medicinal plant, omija.

• Nuclear Engineering and Technology
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• v.49 no.8
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• pp.1589-1599
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• 2017

Field programmable gate arrays (FPGAs) are getting more attention in safety-related and safety-critical application development of nuclear power plant instrumentation and control systems. The high logic density and advancements in architectural features make static random access memory (SRAM)-based FPGAs suitable for complex design implementations. Devices deployed in the nuclear environment face radiation particle strike that causes transient and permanent failures. The major reasons for failures are total ionization dose effects, displacement damage dose effects, and single event effects. Different from the case of space applications, soft errors are the major concern in terrestrial applications. In this article, a review of radiation effects on FPGAs is presented, especially soft errors in SRAM-based FPGAs. Single event upset (SEU) shows a high probability of error in the dependable application development in FPGAs. This survey covers the main sources of radiation and its effects on FPGAs, with emphasis on SEUs as well as on the measurement of radiation upset sensitivity and irradiation experimental results at various facilities. This article also presents a comparison between the major SEU mitigation techniques in the configuration memory and user logics of SRAM-based FPGAs.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.488-491
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• 1997

The PMGS(Plant Model Generating System) was developed based on modular modeling method and fluid network calculation concept. Fluid network calculation is used as a method of real-time computation of fluid network, and the module which has a topology with node and branch is defined to take advantages of modular modeling. Also, the database which have a shared memory as an instance is designed to manage simulation data in real-time. The applicability of the PMGS was examined implementing the HRSG(Heat Recovery Steam Generator) control logic on DCS.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.216-220
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• 1987

A graphic display and operator console system is developed for monitoring the operation of power plant. It has multiprocessor structure using VME bus and common memory. The graphic monitoring system is applied to fault tolerant control system for enhancing reliability of boiler analog controller. As a result, it displays all the operating date as color graphic images with 14 pages. Moreover, it can transfer the operator commands to the other micro-processors through common memory.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Advances in Traditional Medicine
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• v.6 no.4
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• pp.253-263
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• 2006

The plant is used in India as well as several countries since several centuries for treating different types of ailments. The plant is an important constituent of the Ayurvedic Materia Medica and finds mention in several ancient texts including Caraka Sanhita ($6^{th}$ century A.D.) and the Bhavprakasa ($16^{th}$ century A.D.). The scientific studies on this plant have reported several activities of this plant. Though the plant has cardiotonic, vasoconstrictor, sedative, neuro-muscular blocking, and anticancer activities, it is more popular as memory enhancer. Traditionally, a poultice made of the boiled plant is placed on the chest in acute bronchitis and coughs of children. The plant contains saponins: bacosides A and B, hersaponin, sapogenins: bacogenin $A_{1}$, $A_{2}$, and $A_{3}$ stigmasterol, and flavonoids: luteolin and luteolin-7 glucoside, nicotine, brahmine, and herpestine. This review focuses on the scientific data published since 1931.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1055-1060
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• 2009

Noise is one of the major environmental problems in human life. But hot water distributers with the flow rate control valve bring about often noise according to the heating control condition in residential buildings. The sound power level increased as the flow rate and pressure difference increased. And thus, experimental analyses for the flow rate control and the pressure difference control were carried out in this study to reduce the noise emitted from the flow rate control valve. As the results, the flow rate control method using a SMA(Shape Memory Alloy)-valve and the flow rate control system using a pressure difference sensor can be expected to control noise in the region of below 50 dB of sound power level.