176 research outputs found
Human activities accelerated the degradation of saline seepweed red beaches by amplifying topâdown and bottomâup 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 topâdown and bottomâup forces that regulate the distribution and succession of these salt marshes and lead to the degradation of the red beaches. We examined how bottomâup forces influenced the germination, emergence, and colonization of saline seepweed with field measurements and a laboratory experiment. We also examined whether topâdown 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 topâdown forces such as predators and herbivores that drive the degradation of the coastal red beaches. Our results show that sediment accretion and plantâherbivory 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 topâdown and bottomâup 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
Reduced neural responses to reward reflect anhedonia and inattention: an ERP study
An inhibited neural response to reward is typical of clinical depression and can predict an individual's overall depressive symptoms. However, the mechanism underlying this are unclear. Previous studies have found that anhedonia and inattention may mediate the relationship between reward sensitivity and depressive symptoms. Therefore, this study aimed to verify the relationship between reward sensitivity and overall depressive symptoms in a depressive tendency sample as well as to explore the mechanism underlying the ability of neural responses to reward to predict overall depressive symptoms via a mediation model. Sixty-four participants (33 with depressive tendencies and 31 without; dichotomized by BDI-II) finished simple gambling tasks while their event-related potential components (ERPs) were recorded and compared. Linear regression was conducted to verify the predictive effect of ERPs on overall depressive symptoms. A multiple mediator model was used, with anhedonia and distractibility as mediators reward sensitivity and overall depressive symptoms. The amplitude of reward positivity (ÎRewP) was greater in healthy controls compared to those with depressive tendencies (pâ=â0.006). Both the gain-locked ERP component (bâ=âââ1.183, pâ=â0.007) and the ÎRewP (bâ=âââ0.991, pâ=â0.024) could significantly negatively predict overall depressive symptoms even after controlling for all anxiety symptoms. The indirect effects of anhedonia and distractibility were significant (both confidence intervals did not contain 0) while the direct effect of reward sensitivity on depressive symptom was not significant (lower confidence intervalâ=âââ0.320, upper confidence intervalâ=â0.065). Individuals with depressive tendencies display impaired neural responses to reward compared to healthy controls and reduced individual neural responses to reward may reflect the different biotypes of depression such as anhedonia and inattention.publishedVersio
Text detection and recognition based on a lensless imaging system
Lensless cameras are characterized by several advantages (e.g.,
miniaturization, ease of manufacture, and low cost) as compared with
conventional cameras. However, they have not been extensively employed due to
their poor image clarity and low image resolution, especially for tasks that
have high requirements on image quality and details such as text detection and
text recognition. To address the problem, a framework of deep-learning-based
pipeline structure was built to recognize text with three steps from raw data
captured by employing lensless cameras. This pipeline structure consisted of
the lensless imaging model U-Net, the text detection model connectionist text
proposal network (CTPN), and the text recognition model convolutional recurrent
neural network (CRNN). Compared with the method focusing only on image
reconstruction, UNet in the pipeline was able to supplement the imaging details
by enhancing factors related to character categories in the reconstruction
process, so the textual information can be more effectively detected and
recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed
lensless images. By performing experiments on datasets of different
complexities, the applicability to text detection and recognition on lensless
cameras was verified. This study reasonably demonstrates text detection and
recognition tasks in the lensless camera system,and develops a basic method for
novel applications
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 studentsâ 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 9â13 healthy lifestyle behaviours were 3.25 times more likely to achieve high academic grades than IUHSSs with 1â6 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
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