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Abstract The aim of this study is to evaluate the incremental innovation performance of nuclear energy projects. Within this context, a novel model is generated which consists of two different stages, and large nuclear reactors are taken into consideration. Firstly, the Pythagorean fuzzy DEMATEL is used to weight the phases of technology S-Curve for nuclear energy projects. Moreover, the second stage includes the ranking two-generation technology S-curve with integer patterns for nuclear energy projects. In this framework, the best combinations are selected for innovation life cycle pattern with the integer code series. The findings demonstrate that the nuclear energy companies need to consider the two-generation technology S-Curve because continuous technological developments are occurring for nuclear power generation. It is also determined that aging in the first generation is the most significant period of two-generation technology S-Curve for nuclear energy projects. In this process, critical decisions should be made regarding future technological investments. In addition, the growth phase in the second generation is also important for the effectiveness of the nuclear energy technology. Conducting effective evaluations in these processes will contribute to increasing the efficiency of companies.

Abstract The aim of this study is to evaluate the incremental innovation performance of nuclear energy projects. Within this context, a novel model is generated which consists of two different stages, and large nuclear reactors are taken into consideration. Firstly, the Pythagorean fuzzy DEMATEL is used to weight the phases of technology S-Curve for nuclear energy projects. Moreover, the second stage includes the ranking two-generation technology S-curve with integer patterns for nuclear energy projects. In this framework, the best combinations are selected for innovation life cycle pattern with the integer code series. The findings demonstrate that the nuclear energy companies need to consider the two-generation technology S-Curve because continuous technological developments are occurring for nuclear power generation. It is also determined that aging in the first generation is the most significant period of two-generation technology S-Curve for nuclear energy projects. In this process, critical decisions should be made regarding future technological investments. In addition, the growth phase in the second generation is also important for the effectiveness of the nuclear energy technology. Conducting effective evaluations in these processes will contribute to increasing the efficiency of companies.

Moisture availability is a strong determinant of decomposition rates in forests worldwide. Climate models suggest that many terrestrial ecosystems are at risk from future droughts, suggesting moisture limiting conditions will develop across a range of forests worldwide. The impacts of increasing drought conditions on forest carbon (C) fluxes due to shifts in organic matter decay rates may be poorly characterised due to limited experimental research. To appraise this question, we conducted a meta-analysis of forest drought experiment studies worldwide, examining spatial limits, knowledge gaps and potential biases. To identify limits to experimental knowledge, we projected the global distribution of forest drought experiments against spatially modelled estimates of (i) future precipitation change, (ii) ecosystem total above-ground C and (iii) soil C storage. Our assessment, involving 115 individual experimental study locations, found a mismatch between the distribution of forest drought experiments and regions with higher levels of future drought risk and C storage, such as Central America, Amazonia, the Atlantic Forest of Brazil, equatorial Africa and Indonesia. Decomposition rate responses in litter and soil were also relatively under-studied, with only 30 experiments specifically examining the potential experimental impacts of drought on C fluxes from soil or litter. We propose new approaches for engaging experimentally with forest drought research, utilising standardised protocols to appraise the impacts of drought on the C cycle, while targeting the most vulnerable and relevant forests.

Moisture availability is a strong determinant of decomposition rates in forests worldwide. Climate models suggest that many terrestrial ecosystems are at risk from future droughts, suggesting moisture limiting conditions will develop across a range of forests worldwide. The impacts of increasing drought conditions on forest carbon (C) fluxes due to shifts in organic matter decay rates may be poorly characterised due to limited experimental research. To appraise this question, we conducted a meta-analysis of forest drought experiment studies worldwide, examining spatial limits, knowledge gaps and potential biases. To identify limits to experimental knowledge, we projected the global distribution of forest drought experiments against spatially modelled estimates of (i) future precipitation change, (ii) ecosystem total above-ground C and (iii) soil C storage. Our assessment, involving 115 individual experimental study locations, found a mismatch between the distribution of forest drought experiments and regions with higher levels of future drought risk and C storage, such as Central America, Amazonia, the Atlantic Forest of Brazil, equatorial Africa and Indonesia. Decomposition rate responses in litter and soil were also relatively under-studied, with only 30 experiments specifically examining the potential experimental impacts of drought on C fluxes from soil or litter. We propose new approaches for engaging experimentally with forest drought research, utilising standardised protocols to appraise the impacts of drought on the C cycle, while targeting the most vulnerable and relevant forests.

The purpose of this study was to examine the effects of alcohol hangover on simulated highway driving performance.Driving performance of forty-two social drinkers was tested the morning following an evening of consuming on average 10.2 (SD = 4.2) alcoholic drinks (alcohol hangover) and on a control day (no alcohol consumed). Subjects performed a standardized 100-km highway driving test in the STISIM driving simulator. In addition to the standard deviation of lateral position (SDLP; i.e., the weaving of the car), lapses of attention were examined. Self-reported driving quality and driving style were scored, as well as mental effort to perform the test, sleepiness before and after driving, and hangover severity.Driving performance was significantly impaired during alcohol hangover as expressed by an SDLP increase of +1.9 cm (t (1,41) = 2.851, p = 0.007), increased number of lapses relative to the control day (7.7 versus 5.3 lapses, t (1,41) = 2.125, p = 0.019), and an increased total lapse time (182.7 versus 127.3 s, p = 0.040). During alcohol hangover, subjects reported their driving quality to be significantly poorer (t (1,41) = 4.840, p = 0.001) and less safe (t (1,41) = 5.078, p = 0.001), wise (t (1,41) = 4.061, p = 0.001), predictable (t (1,41) = 3.475, p = 0.001), and responsible (t (1,41) = 4.122, p = 0.001). Subjects further reported being significantly more tense while driving (t (1,41) = 3.280, p = 0.002), and more effort was needed to perform the driving test (t (1,41) = 2.941, p = 0.001). There was a significant interaction with total sleep time and hangover effects on SDLP and the number of lapses.In conclusion, driving is significantly impaired during alcohol hangover, as expressed in an elevated SDLP and increased number of lapses. Total sleep time has a significant impact on the magnitude of driving impairment.

The purpose of this study was to examine the effects of alcohol hangover on simulated highway driving performance.Driving performance of forty-two social drinkers was tested the morning following an evening of consuming on average 10.2 (SD = 4.2) alcoholic drinks (alcohol hangover) and on a control day (no alcohol consumed). Subjects performed a standardized 100-km highway driving test in the STISIM driving simulator. In addition to the standard deviation of lateral position (SDLP; i.e., the weaving of the car), lapses of attention were examined. Self-reported driving quality and driving style were scored, as well as mental effort to perform the test, sleepiness before and after driving, and hangover severity.Driving performance was significantly impaired during alcohol hangover as expressed by an SDLP increase of +1.9 cm (t (1,41) = 2.851, p = 0.007), increased number of lapses relative to the control day (7.7 versus 5.3 lapses, t (1,41) = 2.125, p = 0.019), and an increased total lapse time (182.7 versus 127.3 s, p = 0.040). During alcohol hangover, subjects reported their driving quality to be significantly poorer (t (1,41) = 4.840, p = 0.001) and less safe (t (1,41) = 5.078, p = 0.001), wise (t (1,41) = 4.061, p = 0.001), predictable (t (1,41) = 3.475, p = 0.001), and responsible (t (1,41) = 4.122, p = 0.001). Subjects further reported being significantly more tense while driving (t (1,41) = 3.280, p = 0.002), and more effort was needed to perform the driving test (t (1,41) = 2.941, p = 0.001). There was a significant interaction with total sleep time and hangover effects on SDLP and the number of lapses.In conclusion, driving is significantly impaired during alcohol hangover, as expressed in an elevated SDLP and increased number of lapses. Total sleep time has a significant impact on the magnitude of driving impairment.

The aim of this study is to define strategies to increase green nuclear energy investments. Within this context, a model is suggested that includes two different stages. Firstly, the consensus-based fuzzy preferences are determined for these investments. For this purpose, four different TRIZ-based strategies are identified, which have an influence on the effectiveness of these projects. Furthermore, in the second stage, the significance weights of these strategies are calculated by considering the spherical fuzzy DEMATEL approach. The findings indicate that dynamicity is the most appropriate TRIZ strategy for the effectiveness of green nuclear energy investments. In addition, the prior action is another significant strategy that should also be considered in these projects. It is obvious that sufficient technological infrastructure is very important for the performance of green nuclear power plants and there is a rapid development of this technology. Because of this situation, these nuclear power plants should be designed effectively so that they can be adoptable to any changes in the technology. In this scope, qualified personnel should be employed, and communication between departments should be effective and of high quality. With the help of these issues, technological changes can be adopted more easily.

The aim of this study is to define strategies to increase green nuclear energy investments. Within this context, a model is suggested that includes two different stages. Firstly, the consensus-based fuzzy preferences are determined for these investments. For this purpose, four different TRIZ-based strategies are identified, which have an influence on the effectiveness of these projects. Furthermore, in the second stage, the significance weights of these strategies are calculated by considering the spherical fuzzy DEMATEL approach. The findings indicate that dynamicity is the most appropriate TRIZ strategy for the effectiveness of green nuclear energy investments. In addition, the prior action is another significant strategy that should also be considered in these projects. It is obvious that sufficient technological infrastructure is very important for the performance of green nuclear power plants and there is a rapid development of this technology. Because of this situation, these nuclear power plants should be designed effectively so that they can be adoptable to any changes in the technology. In this scope, qualified personnel should be employed, and communication between departments should be effective and of high quality. With the help of these issues, technological changes can be adopted more easily.