Coastal wetlands are a type of unique ecosystem, in which rhizosphere microorganisms of vegetation play a significant role in the overall ecology. Rhizosphere soil samples from the vegetation of Liaohekou Coastal Wetlands, Northeast China (40°54′44″ N, 121°47′51″ E), were collected in seven habitats (Suaeda and Phragmites community in different coverage, aquaculture ponds and farmland biotopes) to evaluate the diversity and structure of bacterial community using high throughput sequencing. Soil physicochemical characteristics and bacterial communities were found to be affected by vegetation coverage by ANOVA tests. As and Ni were the main heavy metal variables affecting the bacterial communities as demonstrated by RDA tests, while NO₃⁻-N were important variables in nutrient factors. Proteobacteria was the predominant phylum in all soils. Gillisia and Woeseia were the two most dominant genera peculiarly in Suaeda and Phragmites community. Meanwhile SparCC showed that Woeseia play a dominant role in wetland rhizosphere bacterial communities. The Chemoheterotrophic function was dominant in all communities with FAPROTAX results, while in wetland the cycle of Sulfur and Nitrogen were significantly affected by vegetation type and coverage. In conclusion, this study revealed the structural composition and diversity of rhizosphere bacterial communities under different vegetation types and coverage. This research could help deepen our understanding of the microbial ecology on the wetlands and provide information on bacterial communities in various habitats.

According to the Thailand’s National Strategy (2017-2036) and National Reform Plans, various tools, techniques, or methods are necessary to collect and investigate data for the effective preservation and protection of the country’s natural resources. We aimed to apply various tools and methods for integrated coastal management in Thailand. This study used the InVEST models, including the habitat quality (HQ) and habitat risk assessment (HRA) models, to evaluate the natural habitat quality and cumulative human activity risk in the Sriracha district and Sichang Islands, Thailand. The HQ model revealed the presence of abundant ecological services and high quality natural habitats. We observed habitat degradation in the mooring zone, city area, forests, and coral reefs, with moderate risk to distinct habitats. Our findings identified two potential scenarios. Conservation scenarios exhibited a lower HRA ratio compared to current and development scenarios. Overall, the results showed the effectiveness of the InVEST model in evaluating habitat risk under both present and simulated conditions. Our study highlights the importance of informed management plans and policy-making processes to achieve planned coastal management goals.

Due to the limitations of measurement equipment and the influence of factors such as the environment and target, measurement errors may occur during the data acquisition process of airborne LiDAR bathymetry (ALB). The refractive index of water is defined as the propagation ratio of the speed of light waves in a vacuum to that in water; this ratio influences not only the propagation speed of the laser pulse in water but also the propagation direction of the laser pulse entering water. Therefore, the influence of refractive index changes in water on the ALB errors needs to be analyzed. To this end, the principle of ALB is first briefly introduced. Then, the calculation method for the refractive index of water is described with Snell’s law and an empirical formula. Finally, the influence of refractive index changes on ALB errors is analyzed using the derived formula at the water–air interface and in the water column. The experimental results showed that in a constant elevation of 50 m for a bathymetric floor, the refractive index changes in water caused by temperature, salinity, and depth are less than 0.001. The maximum bathymetric error and maximum planimetric error caused by the refractive index changes at the water–air interface are 0.036 m and 0.015 m, respectively. The ALB errors caused by refractive index changes in the water column are relatively low, and the water column does not need to be layered to calculate the ALB errors. The influence of refractive index changes in water on the ALB error is minimal, accounting for only a small proportion of all bathymetric errors. Thus, it is necessary to determine whether the effect of the ALB error due to refractive index changes in water needs to be corrected based on the accuracy requirements of the data acquisition. This study and analysis can provide a reference basis for correcting ALB errors.

Understanding how people change their views during multiparty argumentative discussions is important in applications that involve human communication, e.g., in social media and education. Existing research focuses on lexical features of individual comments, dynamics of discussions, or the personalities of participants but deemphasizes the cumulative influence of the interplay of comments by different participants on a participant’s mindset. We address the task of predicting the points where a user’s view changes given an entire discussion, thereby tackling the confusion due to multiple plausible alternatives when considering the entirety of a discussion. We make the following contributions. (1) Through a human study, we show that modeling a user’s perception of comments is crucial in predicting persuasiveness. (2) We present a sequential model for cumulative influence that captures the interplay between comments as both local and nonlocal dependencies, and demonstrate its capability of selecting the most effective information for changing views. (3) We identify contextual and interactive features and propose sequence structures to incorporate these features. Our empirical evaluation using a Reddit Change My View dataset shows that contextual and interactive features are valuable in predicting view changes, and a sequential model notably outperforms the nonsequential baseline models.

Accurate shoreline extraction is critical for coastal engineering applications, including erosion monitoring, disaster response, and sustainable management of island ecosystems. However, traditional methods face challenges in large-scale monitoring due to high costs, environmental interference (e.g., cloud cover), and poor performance in complex terrains (e.g., bedrock coastlines). This study developed an optimized DeepLabV3+ model for the extraction of island shorelines, which improved model performance by replacing the backbone network with MobileNetV2, introducing a strip pooling layer into the ASPP module, and adding CBAM modules in both the shallow and deep stages of feature extraction from the backbone network. The model accuracy was verified using a self-built drone dataset of the shoreline of Koh Lan, Thailand, and the results showed: (1) Compared with the control model, the improved DeepLabV3+ model performs excellently in pixel accuracy (PA), recall, F1 score, and intersection over union (IoU), reaching 98.7%, 97.7%, 98.0%, and 96.2%, respectively. Meanwhile, the model has the lowest number of parameters and floating-point operations, at 6.61 M and 6.7 GFLOPS, respectively. (2) In terms of pixel accuracy (PA) and intersection over union (IoU), the CBAM attention mechanism outperforms the SE-Net and CA attention mechanisms. Compared with the original DeepLabV3+ network, PA increased by 3.1%, and IoU increased by 8.2%. (3) The verification results of different types of coastlines indicate that the improved model can effectively distinguish between shadows and water bodies, reducing the occurrence of false negatives and false positives, thereby lowering the risk of misclassification and obtaining better extraction results. This work provides a cost-effective tool for dynamic coastal management, particularly in data-scarce island regions.