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Following the climate-related disasters considered by several efforts, the nuclear capacity needs to double by 2050 compared to 2015. So, it is reasonable to investigate global warming incorporated with the fuzzy set theory for nuclear energy consumption in the aspect of fuzziness and nonlinearity of temperature variations. The complex modeling is proposed for the enhanced assessment of climate change where simulations indicate the degree of influence with the Boolean values between 0.0 and 1.0 in the designed variables. In the case of OIL, there are many 1.0 values between 20th and 60th months in the simulations where there are 10 times more for a 1.0 value in influence. Hence, the temperature variable can give the effective time using this study for 100 months. In the analysis, the 1.0 value in NUCLEAR means the highest influence of the modeling as the temperature increases resulting in global warming. In detail, the first influence happens near the 8th month and then there are four times more influences than effects in the early part of the temperature mitigation. Eventually, in the GLOBAL WARMING, the highest peak is around the 20th month, and then it is stabilized.

Abstract The nuclear dismantling assessment is analyzed with the fuzzy set and deep‐learning algorithm, where the project preference time search (PPTS) has been constructed. Artificial intelligence (AI) management is applied to nuclear decommissioning for the best estimation. The basic data are from three factors, Licensee, NRC, and Public. In the analysis, the highest OUTPUT value is 0.0513151 in the 72nd year, which is the best time for decommissioning, because the confidence value in the 37th year is 78.76% compared to that of the 72nd year. In the other case of membership functions with right‐shift values, the change rate is higher in the 72nd year as being similar to the value in the 37th year near 0.09 in final confidence value. The trend is a new function that shows two peaks compared to the previous one. The other cases could be made by comparing in the interested time. Finally, the list of reactor decommissions processes with numbering is used to find out the very confident time using the final confidence value as the PPTS method.

It is analyzed for the energy pattern affected by the social matters of the disease trend where the epidemic behavior is significantly important to present the prospect especially for the epidemic of the Coronavirus Disease 2019 (COVID-19). In this work, there are three kinds of data for the modeling where the power transactions, power plant type, and pandemic data are considered by the energy mix of South Korea. The COVID-19 based modeling for Electricity and Nuclear Energy vs. Energy shows that the energy trend decreases until 16.50th week and then it is in the peak point at 32.50th week. Otherwise, the nuclear energy trend decreases until 11.50th week and then it is in the peak point at 27.50th week. The nuclear energy trend increases after 44.25th week. So, the nuclear energy has a better prospect in energy markets. Therefore, the characteristics of an energy source can make an important impact on the market.

This paper proposes an emergency locking unit (ELU) for a seat belt retractor which is mounted on the back frame of a vehicle seat. The proposed unit uses a recliner sensor based on a MEMS acceleration sensor and solenoid mechanism. The seat has an upper frame supported to tilt on a lower frame. The retractor in belt in seat (BIS) system is supported by the upper frame. The proposed recliner sensor based on a MEMS acceleration sensor comprises orientation means for maintaining a predetermined orientation of emergency relative to the lower frame independently of the force of gravity when the upper frame tilts on the lower frame. Experimental results show that the developed recliner sensor unit operates effectively with respect to rollover angles. Thus, the developed unit will have a considerable potential to offer a new design concept in BIS system.

This study empirically investigates the effect of a CEO turnover on audit report lag (ARL), discretionary report lag (DRL) and total report lag (TRL). The object of this study is to provide empirical evidence for the responses of both the CEO and the external auditor on audit risk increases and information asymmetry that occur as a result of a CEO turnover. According to the previous study on CEO turnovers, the CEO turnover would increase audit risk and information asymmetry (Sohn et al., 2014). In this situation, the CEO has an incentive to provide timely information to decrease the monitoring costs and cost of debt (Lee et al., 2008). It is expected that an external auditor spends a large amount of time on audit procedures to lower the audit risk when the CEO changes. Therefore, the CEO turnover would have a conflicting effect on the ARL and DRL. The results of the analysis are as follows. First, the ARL increases and DRL decreases when the CEO changes, which suggests that an external auditor spends a great amount of time on audit procedures to lower the audit risk because the audit risk increases when the CEO changes. A new CEO provides information faster to reduce monitoring costs and cost of debt that occur due to information asymmetry. Second, the ARL increases and DRL decreases as the frequency of CEO turnover increases. An external auditor would estimate the audit risk as being high if the CEO changes more frequently. To lower the audit risk to an acceptable level, many audit hours are spent on audit procedures by an external auditor, which increases the ARL. A new CEO has an incentive to provide timely information when the CEO changes more frequently. Thus, the DRL decreases as the frequency of CEO turnover increases