Human activities accelerated the degradation of saline seepweed red beaches by amplifying top‐down and bottom‐up forces

Salt marshes dominated by saline seepweed (Suaeda heteroptera) provide important ecosystem services such as sequestering carbon (blue carbon), maintaining healthy fisheries, and protecting shorelines. These salt marshes also constitute stunning red beach landscapes, and the resulting tourism significantly contributes to the local economy. However, land use change and degradation have led to a substantial loss of the red beach area. It remains unclear how human activities influence the top‐down and bottom‐up forces that regulate the distribution and succession of these salt marshes and lead to the degradation of the red beaches. We examined how bottom‐up forces influenced the germination, emergence, and colonization of saline seepweed with field measurements and a laboratory experiment. We also examined whether top‐down forces affected the red beach distribution by conducting a field survey for crab burrows and density, laboratory feeding trials, and waterbird investigations. The higher sediment accretion rate induced by human activities limited the establishment of new red beaches. The construction of tourism facilities and the frequent presence of tourists reduced the density of waterbirds, which in turn increased the density of crabs, intensifying the top‐down forces such as predators and herbivores that drive the degradation of the coastal red beaches. Our results show that sediment accretion and plant–herbivory changes induced by human activities were likely the two primary ecological processes leading to the degradation of the red beaches. Human activities significantly shaped the abundance and distribution of the red beaches by altering both top‐down and bottom‐up ecological processes. Our findings can help us better understand the dynamics of salt marshes and have implications for the management and restoration of coastal wetlands

Adsorption and Desorption Characteristics of Arsenic on Soils: Kinetics, Equilibrium, and Effect of Fe(OH)3 Colloid, H2SiO3 Colloid and Phosphate

AbstractAdsorption and desorption of arsenic on different soils may affect the mobility, toxicity and bioavailability of arsenic in soil meia. In this study, laboratory batch experiments were carried out to study the adsorption and desorption of arsenic in three soils in China with different physicochemical properties. The results show that the adsorption was relatively fast for Beijing soil and Hainan soil, the reactions almost completed within the first few hours, while it was relatively slow for Jilin soil. The adsorption isotherms for three soils fitted very well to both the Langmuir and Freundlich models. The content of organic mater in the soils was of the major factor to determine the adsorption capacity. The thermodynamic parameters for the adsorption of arsenic were determined at three different temperatures of 283K, 303K and 323K. The adsorption reactions were endothermic and the process of adsorption was favored at high temperature. The adsorption behavior of arsenic on soils was strongly dependent on the concentrations of Fe(OH)3 and H2SiO3 colloid. Phosphate suppressed the adsorption of arsenite and arsenate, especially for BJ soil. The desorption data showed that desorption hysteresis occurred at the concentration studied. These findings improve our knowledge in modeling arsenic adsorption to common soil minerals

Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional Network (GCN) to model and process multi-targets appearing in sentences at the same time based on the positional relationship, and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target words. In addition to the standard target-dependent sentiment classification task, an auxiliary node relation classification task is constructed. Experiments demonstrate that our model achieves new comparable performance on the benchmark datasets: SemEval-2014 Task 4, i.e., reviews for restaurants and laptops. Furthermore, the method of dividing the target words into isolated individuals has disadvantages, and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model

Independent and Combined Associations Between Multiple Lifestyle Behaviours and Academic Grades of Inner Urban and Peri-Urban High School Students: A Cross-Sectional Study in Chongqing, China

Objectives This study aims to assess the independent and combined associations between multiple lifestyle behaviours and academic grades of inner urban high school students (IUHSSs) and peri-urban high school students (PUHSSs). Design A cross-sectional study was conducted. Participants There are 1481 high school students (49.9% boys) in this study, who were enrolled from one inner urban and two peri-urban schools in Chongqing, China. Outcome measures Academic grades were assessed based on the students’ self-reported grade ranking in the last cumulative examination. Results In IUHSSs and PUHSSs, high frequency of sugar-sweetened beverage consumption was unlikely to obtain high academic grades (OR 0.56, 95% CI 0.32 to 0.99 and 0.63, 95% CI 0.42 to 0.96), respectively). Among IUHSSs, meeting the recommendations for weekday screen time and egg consumption (OR 1.57, 95% CI 1.06 to 2.34 and 1.60, 95% CI 1.04 to 2.47, respectively) and high frequency of fruit consumption (1.67, 95% CI 1.11 to 2.50) were significantly associated with high academic grades; meeting the recommendation for weekday sleep duration was unlikely to obtain high academic grades (0.46, 95% CI 0.21 to 0.98). Among PUHSSs, meeting the recommendations for weekend sleep duration (1.40, 95% CI 1.02 to 1.93) and eating dinner regularly (1.55, 95% CI 1.01 to 2.37) had significant associations with high academic grades. No significant associations were found between physical activity and academic grades in both IUHSSs and PUHSSs (p \u3e 0.05). Moreover, IUHSSs with 9–13 healthy lifestyle behaviours were 3.25 times more likely to achieve high academic grades than IUHSSs with 1–6 healthy lifestyle behaviours (3.25, 95% CI 1.96 to 5.40). No significant associations were found in the combined associations between multiple lifestyle behaviours and academic grades among PUHSSs (p \u3e 0.05). Conclusions Correlations were observed between lifestyle behaviours and academic grades among high school students, and cumulative associations between multiple healthy lifestyle behaviours and academic outcomes appear to be stronger than the independent associations. These findings are particularly applicable to IUHSSs

ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding

Recent efforts of multimodal Transformers have improved Visually Rich Document Understanding (VrDU) tasks via incorporating visual and textual information. However, existing approaches mainly focus on fine-grained elements such as words and document image patches, making it hard for them to learn from coarse-grained elements, including natural lexical units like phrases and salient visual regions like prominent image regions. In this paper, we attach more importance to coarse-grained elements containing high-density information and consistent semantics, which are valuable for document understanding. At first, a document graph is proposed to model complex relationships among multi-grained multimodal elements, in which salient visual regions are detected by a cluster-based method. Then, a multi-grained multimodal Transformer called mmLayout is proposed to incorporate coarse-grained information into existing pre-trained fine-grained multimodal Transformers based on the graph. In mmLayout, coarse-grained information is aggregated from fine-grained, and then, after further processing, is fused back into fine-grained for final prediction. Furthermore, common sense enhancement is introduced to exploit the semantic information of natural lexical units. Experimental results on four tasks, including information extraction and document question answering, show that our method can improve the performance of multimodal Transformers based on fine-grained elements and achieve better performance with fewer parameters. Qualitative analyses show that our method can capture consistent semantics in coarse-grained elements.Comment: Accepted by ACM Multimedia 202