Suaeda salsa (S. salsa) is an important ecological barrier and tourism resource in coastal wetland resources, and assessing changes in its health is beneficial for protecting the ecological health of wetlands and increasing finances. The aim was to explore improvements in the degradation of S. salsa communities in the Liao River Estuary National Nature Reserve since a wetland restoration project was carried out in Panjin, Liaoning Province, China, in 2015. In this study, landscape changes in the reserve were assessed based on Sentinel-2 images classification results from 2016 to 2019. A pressure-state-response framework was constructed to assess the annual degradation of S. salsa communities within the wetlands. The assessment results show that the area of S. salsa communities and water bodies decreased annually from 2016 to 2019, and the increased degradation indicators indicate a state of continued degradation. The area of types such as aquaculture ponds and Phragmites australis communities did not change much, while the estuarine mudflats increased year by year. The causes of S. salsa community degradation include anthropogenic impacts from abandoned aquaculture ponds and sluice control systems but also natural impacts from changes in the tidal amplitude and soil properties of the mudflats. The results also indicate that the living conditions of S. salsa in the Liao River estuary wetlands are poor and that anthropogenic disturbance is necessary to restore the original vegetation abundance.
In two experiments, we investigated the correspondences between off‐line word segmentation and on‐line segmentation processing during Chinese reading. In Experiment 1, participants were asked to read sentences which contained critical four‐character strings, and then, they were required to segment the same sentences into words in a later off‐line word segmentation task. For each item, participants were split into 1‐word segmenters (who segmented four‐character strings as a single word) and 2‐word segmenters (who segmented four‐character strings as 2 two‐character words). Thus, we split participants into two groups (1‐word segmenters and 2‐word segmenters) according to their off‐line segmentation bias. The data analysis showed no reliable group effect on all the measures. In order to avoid the heterogeneity of participants and stimuli in Experiment 1, two groups of participants (1‐word segmenters and 2‐word segmenters) and three types of critical four‐character string (1‐word strings, ambiguous strings, and 2‐word strings) were identified in a norming study in Experiment 2. Participants were required to read sentences containing these critical strings. There was no reliable group effect in Experiment 2, as was the case in Experiment 1. However, in Experiment 2, participants spent less time and made fewer fixations on 1‐word strings compared to ambiguous and 2‐word strings. These results indicate that the off‐line word segmentation preferences do not necessarily reflect on‐line word segmentation processing during Chinese reading and that Chinese readers exhibit flexibility such that word, or multiple constituent, segmentation commitments are made on‐line.
To enhance features of different electromagnetic interference (EMI) signals, which are significant for further feature extraction and pattern recognition, the authors propose an EMI signal feature enhancement method based on extreme energy difference and a deep auto-encoder. Experimental results show that this method can effectively enhance features of EMI signals and improve recognition accuracy.
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