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Based on the concept of ecosystem service value, this study proposes a method for estimating the net value of ecosystem services considering cost inputs, and estimates and analyzes the net value of ecosystem services in the Yellow River Estuary wetland from 2019 to 2022 through time series remote sensing monitoring. The results indicate that:(1) Among the 11 types of ecosystem services in the Yellow River Estuary coastal wetland, biodiversity, carbon sequestration, food supply, and wave attenuation and protection are the dominant ecosystem services. The nearshore waters, reed beds, salt-alkali plants, and tidal flats show strong net value capacity. The net value of ecosystem services in 2022 decreased by 4.08×108 yuan compared with that in 2021, mainly due to the disappearance of a large area of salt-alkali plants with strong net value in 2022. (2) Despite the negative net benefits observed in the first year after the construction of typical ecological restoration projects in both 2019 and 2020, the net value of ecosystem services has reached the expected level. (3) Sensitivity analysis demonstrates the significant role of wave attenuation and protection, food supply, carbon sequestration, and biodiversity among the various ecosystem services. The estimation of net value of ecosystem services enables the comparison and analysis of the maximum net value generated by different types of wetlands, providing a scientific basis for the rational planning and management of coastal wetland ecosystems.

The global proliferation of natural predators, including Terpios hoshinota sponge and crown-of-thorns starfish, poses a significant threat to the ecological vitality of coral reefs. However, coral reef research currently lacks comprehensive quantitative studies on the ramifications of predator outbreaks in various coral reef landform types. To bridge this knowledge gap, this study utilized a dataset comprising 26 Sentinel-2 remote sensing images spanning the years 2016 to 2022, both pre- and post-two distinct episodes of natural predator outbreaks involving the Terpios hoshinota sponge and crown-of-thorns starfish, within the vicinity of Taiping Island. High-resolution GF-2 (PMS) remote-sensing images and satellite data were used to augment the analysis. Based on data obtained from the Google Earth platform of the Geographic Information System for remote sensing imagery, we conducted an experiment involving the classification of coral reef landform types in Taiping Island in the South China Sea. During the Support Vector Machine (SVM) classification experiments, we performed a meticulous examination of the image interpretation outcomes, making comparisons and rectifications in consultation with domain experts. Classification was performed by analyzing remote sensing images, and the identified landform types included areas characterized by dense coral sedimentation, sparse coral sedimentation, coral clusters, sand flats, and shallow reef front slopes. Subsequent analyses focused on discerning distinctive characteristics and tracing their evolutionary patterns. The findings reveal that (1) this study effectively integrates expert interpretation knowledge and an SVM classification algorithm to classify the various landform types present in Taiping Island's coral reef ecosystem. Remarkably, the classification achieved an unprecedented level of accuracy, with an overall accuracy rate of 96.46% and a kappa coefficient of 0.94. (2) The analysis showed that during the two natural predator outbreaks, the coral reef landforms on Taiping Island, which included dense coral reef areas, dense coral sedimentation areas, and sparse coral sedimentation areas, displayed significant decreases in their respective areas. The outbreak of Terpios hoshinota sponge had the most profound impact on the dense coral sedimentation area, resulting in a 72.92% decrease in the total area. Meanwhile, the outbreak of crown-of-thorns starfish had the most significant effect on the coral cluster area, causing a 59.17% reduction in total area. (3) Over the period spanning from 2016 to 2022, the degradation rate of the coral reefs on Taiping Island exceeded the recovery rate. Notably, the highest degradation rate (23.88%) occurred between March-June 2017. Conversely, the highest rate of recovery (18.03%) was observed from June-September 2017. We analyzed the dynamic characteristics of coral reef landform types, including changes in type distribution, areal extent, degradation rate, and recovery rate. Furthermore, we assessed the influence of natural disturbances on the degradation of coral reef ecosystems, thereby providing valuable insights for conservation and scientific inquiries pertaining to coral reef ecological resources in the waters surrounding Taiping Island. This effort is underpinned by a solid theoretical foundation. The Chinese and English literature cited in this article have contributed significantly to various facets, including coral reef monitoring methodologies, construction of coral reef classification systems, and assessment of coral reef ecosystem health.

Purpose: Aiming to investigate the combined association of sleep quality and outdoor activity with the risk of asthma and allergic rhinitis in children. Patients and Methods: 16,936 children from kindergartens and primary schools in 13 administrative districts of Shanghai, China were involved in the analyses. The Children's Sleep Habits Questionnaire (CSHQ) and the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire were respectively used to evaluate the sleep quality, allergic rhinitis and asthma. Multivariable logistic regression models were used to analyze the correlation of sleep quality, outdoor activities and their combination with children's asthma and allergic rhinitis. Results: The overall prevalence of asthma and allergic rhinitis in our sampled children were 10.2% and 17.6%, respectively. Poor sleep quality (asthma: aOR, 1.45; 95% CI: 1.23 to 1.73; allergic rhinitis: aOR, 1.16; 95% CI: 1.03 to 1.31) and low outdoor activity (asthma: aOR, 1.30; 95% CI: 1.14 to 1.49, allergic rhinitis: aOR, 1.18; 95% CI: 1.07 to 1.32) were separately associated with higher risk of asthma and allergic rhinitis, and when the two were superimposed, an additive effect (asthma: aOR, 1.76; 95% CI: 1.30 to 2.39; allergic rhinitis: aOR, 1.46; 95% CI: 1.17 to 1.82) was revealed. These associations were independent of sleep duration, but being stronger in sleep sufficiency group. And after stratification by gender, premature birth and family history of allergy, similar associations were validated in general. Conclusion: Poor sleep quality and low levels of outdoor activity, when combined, have a stronger association with asthma and allergic rhinitis than each factor independently, and this relationship is not influenced by sleep duration. Our findings highlight the need for public health interventions that simultaneously address multiple lifestyle factors to reduce the risk of allergic diseases. [ABSTRACT FROM AUTHOR]

The Transformer has become pivotal for the integrated analysis of multi-source remote-sensing (RS) data in Earth observation, particularly in applications such as the fusion classification of hyperspectral images (HSI) and Light Detection and Ranging (LiDAR) data. However, Transformers are often employed as effective feature extractors by adopting similar processing blocks for different modalities from multi-source sensors, overlooking differences in imaging principles and data characteristics. Moreover, in the feature extraction process across different sensor data, there is a lack of necessary cross-modal information interaction, leading to insufficient utilization of complementary information between different sensors and resulting in suboptimal fusion outcomes. In this paper, we propose an interactive Transformer and CNN network for the fusion classification of HSI and LiDAR data. Specifically, a heterogeneous three-branch network architecture is designed for HSI and LiDAR data, where Transformers and CNNs encapsulate global contextual spatial and spectral information for HSI and capture geometric elevation patterns for LiDAR data, respectively. Elevation-Spatial Interaction (ESI) and Spectral-Spatial Interaction (SSI) modules are then introduced for multi-stage feature interaction. ESI enables the CNN-Transformer network to focus on essential local elevation details while simultaneously modelling global contextual spatial information. SSI facilitates the Transformer-Transformer network to cyclically intertwine spectral and spatial information for long-range spectral-spatial feature fusion. Finally, the interacted elevation, spatial, and spectral features undergo the Gated Fusion module to achieve hierarchical fusion adaptively, resulting in an elevation-spatial-spectral representation. Experiments conducted on three benchmark HSI-LiDAR datasets demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Introduction: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter‐observer consistency. Methods: A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U‐Net, ResUNet and U‐Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter‐observer was also evaluated. Results: The AttSEResUNet with post‐processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). Conclusion: Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter‐observer. [ABSTRACT FROM AUTHOR]

Cable force of bridge cable is an important standard to measure the safety of bridge. According to the working principle of laser Doppler vibration measurement, a small and high-precision laser Doppler vibration measurement system is designed and applied to cable tension measurement. Firstly, the frequency stability characteristics of the heterodyne acousto-optic frequency shifter laser Doppler vibration system are analysed, and the heterodyne laser Doppler vibrometer which is composed of the direct digital frequency synthesizer and the serial dual frequency shifter is then proposed, and the adaptive filter is used to eliminate the disturbance caused by the environmental vibration in this vibration measurement system. The low frequency vibration measurement experiments and real cable force measurement experiments show that the frequency shift system based on DDS has high frequency stability. The background noise of the vibration meter shows a flat distribution in the low frequency range, and the vibration measurement system itself has very strong seismic ability. It provides an effective method for bridge cable force monitoring.