In order to study the interaction between pictures and words, we investigated the variations of their actual arousal levels through the observation of the positivity offset and negativity bias under different conditions. We used emotional pictures and emotional Chinese words to construct stimuli under four conditions: (1) only a word was presented (Word Only condition), (2) only a picture was presented (Picture Only condition), (3) a word was presented before a picture (Word Before Picture condition) and (4) a picture was presented before a word (Picture Before Word condition). The picture and word in each target pair under the conditions (3) and (4) were congruous in content and emotion. Significant negativity bias is noticed under the Picture Only and the Word Only conditions. Effects analogous to a positivity offset are observed under the other two conditions. It is suggested that the actual arousal levels of multiple stimuli, such as those presented under the conditions (3) and (4) are different from those of individual pictures and individual words, while the actual arousal levels of individual pictures do not differ significantly from those of individual words. The results indicate that content consistency can lead to a reduction in emotional attention, and also that the influence of pictures on words will differ from the opposite condition.
The monitoring and analysis of the temperature field in the reservoir area is gaining importance as it provides insights regarding structural safety and environmental impact. Most studies focus on earthen dams and the seasonal fluctuation of the thermal field induced by the ambient temperature. Few studies analyse the long-term trend of the bedrock temperature after high-arch dam impoundment as the involved geological elements are likely to be unexpected and unknown. In this paper, a unique geothermal evolution model in the downstream bedrock of a 285.5 m high-arch dam is reported based on continuous field observations during the past nine years, which cannot be explained by available hydrogeological data. To clarify the phenomenon, a research framework that comprises field measurements, machine learning interpretation, and hydrothermal coupling simulation is proposed. Borehole surveys and water chemistry analyses are performed to gather more data from critical areas. The proposed machine learning pipelines attempt to interpret the field test data. These are integrated with a double-loop grid-search process and can achieve hyperparameter searching and model evaluation simultaneously. It proves to be robust to local minima and more applicable to groundwater source discrimination when compared to other models. The contribution of the newly discovered features is further discussed through comparative and sensitivity analysis based on the hydrothermal coupling theory. With the proposed hybrid analysis framework, the shallow buried limestones and the confined hot groundwater have been identified as the causes of the unique phenomenon. These factors should be of concern to researchers facing similar situation in the future.
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