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• A dynamic setting temperature that adapts to climate change was proposed. • The method was based on clothing adjustment behavior which adapts to thermal comfort. • The dynamic setting temperature had significant energy saving potential. • The dynamic setting temperature is useful for intelligent control of air-conditioner. Saving energy consumption of air-conditioning is essential to building energy conservation while satisfying the thermal comfort requirements of indoor environment. On the basis of human clothing adjustment behavior, a dynamic setting temperature (DST) that adapts to climate change was proposed in this paper, i.e. indoor air-conditioning setting temperature changes dynamically with outdoor air temperature; furthermore. A method was established to obtain the DST. The daily mean clothing insulation was predicted according to the historical temperature at first, then indoor thermal comfort temperature was calculated based on the variable clothing insulation, and finally the DST was determined. According to the value or range of predicted mean vote (0, -0.5~0.5, -1~+1) corresponding to the thermal comfort, three levels of DST were defined. The corresponding thermal acceptability rates were 95%, 90% and 73%, respectively. A simulation of DST was conducted in a public building in Changsha, China based on meteorological data of 2017. Three levels of DST ranges were obtained, varied from 20.1 to 27.8 °C in level I, 17.8 to 28.7 °C in level II and 15.4 to 29.7 °C in level III, respectively. The simulation results of operational air-conditioning energy consumption shows that the application of DST could save up to 65.5% of energy consumption compared with the traditional fixed setting temperature (FST), and the operational energy consumption decreased by 59.1% due to the application of level-III DST compared with level-I DST. Therefore, the DST proposed in this paper has significant energy saving potential while meeting the thermal comfort requirements. [ABSTRACT FROM AUTHOR]

• Subjects performed 10 kinds of cognitive tests in the three phase experiments. • The cognitive test scores had significant learning effect. • The speed of all tests and the accuracy of a few tests increased significantly. • The improvement rate of cognitive test speed was higher compared with accuracy. • The learning effect model based on logistic function had higher R2 (> 0.99). This study aimed to identify the law of cognitive test scores with the number of repetitions, and to establish a prediction model that quantitatively describes the changes in cognitive test scores. The experiments were conducted in a chamber that simulated an office environment. Participants performed neurobehavioral, Tsai-Partington, typing and d2 tests at 26 ℃. The speed and accuracy were used to reflect the cognitive test scores. The results demonstrated that a significant learning effect occurred in these cognitive test scores. The speed and accuracy of completing cognitive tests increased as the number of repetitions increased. The majority of cognitive tests could achieve stability after five repetitions and, at most, eight repetitions. The learning effect had different effects on the speed and accuracy. The percentage improvement of the speed was higher compared with accuracy, but it took more attempts to reach stability. The regression model based on the logistic function could accurately quantify the tendency for the variation in cognitive test scores with the number of repetitions. Compared with the power, linear and logarithmic functions, the logistic function had a higher R2(> 0.99). A method for correcting the learning effect of these cognitive tests was proposed based on the prediction model. [ABSTRACT FROM AUTHOR]