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Abstract: Local power density (LPD) at the hottest part of a hot nuclear fuel rod should be estimated accurately to confirm that the rod does not melt. The power peaking factor (PPF) is defined as the highest LPD divided by the average power density in the reactor core. In this paper, the PPF is calculated by support vector regression (SVR) models using numerous measured signals from the reactor cooling system. SVR models are regression analysis models using a kernel function for artificial neural networks. Their neural network weights are found by solving a quadratic programming problem under linear constraints. SVR models are trained using a training data set and then verified against another test data set. The proposed SVR models were applied to the first fuel cycle of the Yonggwang nuclear power plant unit 3. The root mean square errors of the SVR model, with and without in-core neutron flux sensor signal inputs, were 0.1113% and 0.0968%, respectively. This level of errors is sufficiently low for use in LPD monitoring. [Copyright &y& Elsevier]

Abstract: In this work, a model predictive control method combined with fuzzy identification, is applied to the design of the thermoelectric (TE) power control in the SP-100 space reactor. The future TE power is predicted by using the fuzzy model identified by a subtractive clustering method of a fast and robust algorithm. The objectives of the proposed fuzzy model predictive controller are to minimize both the difference between the predicted TE power and the desired power, and the variation of control drum angle that adjusts the control reactivity. Also, the objectives are subject to maximum and minimum control drum angle and maximum drum angle variation speed. The genetic algorithm that is effective in accomplishing multiple objectives is used to optimize the fuzzy model predictive controller. A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed controller. The results of numerical simulations to check the performance of the proposed controller show that the TE generator power level controlled by the proposed controller could track the target power level effectively, satisfying all control constraints. [Copyright &y& Elsevier]

Abstract: Ketoprofen, a non-steroidal anti-inflammatory drug with well-known anti-inflammatory, antipyretic and analgesic properties, has low solubility in water and causes local or systemic disturbance in the gastrointestinal tract. In the present study we investigated the potential of polyamidoamine (PAMAM) dendrimers as drug carriers of ketoprofen by in vitro and in vivo studies. The in vitro release of ketoprofen from the drug–dendrimer complex is significantly slower compared to pure ketoprofen. Anti-nociceptive studies using the acetic acid-induced writhing model in mice showed a prolonged pharmacodynamic behavior for the ketoprofen–PAMAM dendrimer complex. Also, the blood level studies were investigated. We concluded that PAMAM dendrimers might be considered as a potential drug carrier of ketoprofen with a sustained release behavior under suitable conditions. [Copyright &y& Elsevier]

Nuclear power plants (NPPs) are designed in consideration of design basis accidents (DBAs). However, if the safety injection system (SIS) is not working properly in a loss-of-coolant-accident (LOCA) situation, it can induce a severe accident that exceeds DBAs. Therefore, it is important to properly actuate the SIS before a DBA becomes a severe accident. If the SIS is not working in time, the reactor core may be uncovered and the reactor vessel (RV) may be damaged. In this paper, we defined the golden time as the available time from an initial SIS malfunction for actuating the SIS to prevent reactor core uncovery and RV failure. A support vector regression (SVR) model was applied to predict the golden time. The input variables and parameters of the SVR model were selected and optimized by using a genetic algorithm. The data set of severe accident scenarios was obtained by using the Modular Accident Analysis Program (MAAP) code. An optimized power reactor (OPR1000) was used for the simulations. It was shown that that the proposed SVR model could predict the golden time accurately. [ABSTRACT FROM AUTHOR]

Highlights: [•] A model predictive controller for load-following operation was developed. [•] Genetic algorithm optimizes the five nonlinear discrete control rod speeds. [•] The boron concentration is adjusted with automatic adjustment logic. [•] The proposed controller reflects the realistic control rod drive mechanism movement. [•] The performance was confirmed to be satisfactory by simulation from BOC to EOC. [Copyright &y& Elsevier]

This paper presents a methodology utilizing an accident management strategy in order to determine accident environmental conditions to be used as inputs to equipment survivability assessments. In the case that there is a well-established accident management strategy for a specific nuclear power plant (NPP), an application of this tool can provide a technical rationale on equipment survivability assessment so that plant-specific and time-dependent accident environmental conditions could be practically and realistically defined in accordance with the equipment and instrumentation required for the accident management strategy or appropriate actions. For this work, three different tools are introduced; probabilistic safety assessment (PSA) outcomes, major accident management strategy actions, and accident environmental stages (AESs). In order to quantitatively investigate an applicability of accident management strategy on equipment survivability, the accident simulation for most likely scenario in Korean standard nuclear power plants (KSNPs) is performed with the MAAP4 code. The accident management guideline (AMG) actions such as the reactor coolant system (RCS) depressurization, water injection into the RCS, the containment pressure and temperature control, and hydrogen concentration control in containment are applied. The effects of these AMG actions on the accident environmental conditions are investigated by comparison to actions from previous normal accident simulation, especially focusing on equipment survivability assessment. As a result, the AMG-involved case shows the higher accident consequences along the accident environmental stages. This implies that plant-specific AMG actions need to be considered in order to determine accident environmental conditions in equipment survivability assessments. [Copyright &y& Elsevier]

Melatonin, mostly released by the pineal gland, is a circadian rhythm-regulated and multifunctional hormone. Great advances in melatonin research have been made, including its role in rhythms of the sleep-wake cycle, retardation of ageing processes, as well as antioxidant or anti-inflammatory functions. Melatonin can scavenge free radicals such as reactive oxygen species (ROS), a key factor in reproductive functions. Melatonin plays an important role in oocyte maturation, fertilization and embryonic development as well. The concurrent use of melatonin increases the number of mature oocytes, the fertilization rate, and number of high-quality embryos, which improves the clinical outcome of assisted reproductive technology (ART). This review discusses the relationship between melatonin and human reproductive function, and potential clinical applications of melatonin in the field of reproductive medicine. • Melatonin is a very potent endogenous radical scavenger. • Melatonin can be taken in order to improve outcomes for in vitro fertilization-embryo transfer. • Melatonin promotes oocyte maturation. • Melatonin delays ovarian aging. • Melatonin benefits the development of early embryonic. [ABSTRACT FROM AUTHOR]

• A deep fuzzy neural network (DFNN) model is improved by optimally deactivating the fuzzy rule nodes. • The rule-dropout DFNN model is proposed to predict the internal containment states of a nuclear power plant (NPP). • The internal states are promptly and accurately predicted by the rule-dropout DFNN model in postulated NPP accidents. • This prompt and accurate prediction can be used to mitigate the severe accident circumstances of NPPs. A serious threat to the integrity of the reactor core, reactor coolant system, or containment is incurred if proper and essential actions to mitigate accidents cannot be taken owing to insufficient information about the internal states of the nuclear power plant (NPP). Therefore, this study was carried out to develop a model capable of mitigating the risk of severe accidents by accurately predicting the internal states of an NPP containment. A deep fuzzy neural network (DFNN) is a method in which syllogistic fuzzy reasoning is relatively efficient and inference capability is enhanced. In this study, the internal states of an NPP containment, hydrogen concentration and pressure, are predicted using a rule-dropout DFNN, as little NPP information is available under the circumstances of severe accidents. In addition, the performance of the proposed rule-dropout DFNN model is compared with that of other fuzzy neural network variations to verify the enhancement in the accuracy of the DFNN. The developed rule-dropout DFNN model is expected to be capable of providing accident monitoring information in advance for accident mitigation, as its prediction error for the hydrogen concentration and pressure in the containment is low. [ABSTRACT FROM AUTHOR]

• A simplified cascade fuzzy neural networks (SCFNNs) model is developed to predict critical flow. • The SCFNN model uses the data obtained from the Henry–Fauske model. • The SCFNN model enables a prompt prediction without any iterative calculations and any additional training. • The SCFNN model can be used as a universal standalone program that does not need the steam table. In order to identify and quantify reactor coolant leaks in nuclear power plants, it is important to understand and model the leak phenomena of the fluid. When reactor coolant leaks from a high pressure and temperature reactor coolant system, two-phase mixture or steam is jetted out by the flashing phenomenon and has critical flow characteristics. Therefore, critical flow analysis is needed to understand and quantify the leakage phenomena. In this study, simplified cascade fuzzy neural networks (SCFNNs) based on the artificial intelligence methods are developed to predict critical flow. The SCFNNs consist of syllogistic fuzzy reasoning and are the simplified model of the existing cascade fuzzy neural network (CFNN) model in terms of not using the outputs of all the preceding fuzzy neural network (FNN) modules but only the output of the very preceding FNN module. The SCFNN model is developed to predict the critical flow for the critical mass flux and critical pressure while repeating a stepwise fuzzy neural network. The proposed SCFNN model uses the data obtained from the Henry–Fauske model to understand the fluid properties and predict the critical flow. The SCFNN method is approximately 17 times more accurate than a previous correlation method and over 20 times more accurate than the FNN and support vector regression models. It is expected that effective information will be provided to quantify the reactor coolant leak from the SCFNN model. Also, the developed SCFNN model can be used as a universal standalone program to predict critical flow, which runs faster without iterative calculations, and without a need for steam tables. [ABSTRACT FROM AUTHOR]

Nuclear reactor cores should be maintained within various safety limits such as the local power density (LPD). Therefore, a detailed three-dimensional core power distribution monitoring is required during reactor operation. In addition, LPD must be predicted to prevent nuclear fuel melting. In this study, the most important parameter related to LPD—the power peaking factor—was predicted. A cascaded fuzzy neural network (CFNN) methodology was utilized to predict the power peaking factor in the reactor core. A CFNN model was developed using the numerical simulation data of the optimized power reactor 1000 and its performance was analyzed. Additionally, its uncertainty analysis was conducted to determine the prediction accuracy of the CFNN model. The prediction intervals were found to be pretty narrow, which confirms that the predicted value is reliable. The accuracy of the proposed CFNN model proves to be able to assist nuclear reactor operators in monitoring the power peaking factor. [ABSTRACT FROM AUTHOR]