Monitoring the Coastal Environment Using Remote Sensing and GIS Techniques

The coastal zone has been of importance for economic development and ecological restoration due to their rich natural resources and vulnerable ecosystems. Remote sensing techniques have proven to be powerful tools for the monitoring of the Earth’s surface and atmosphere on a global, regional, and even local scale, by providing important coverage, mapping and classification of land cover features such as vegetation, soil, water and forests. This chapter introduced the methods for monitoring the coastal environment using remote sensing and GIS techniques. Case studies of port expansion monitoring in typical coastal regions, together with the coastal environment changes analysis were also presented

Diffusion of False Information During Public Crises: Analysis Based on the Cellular Automaton Method

The progress of false information diffusion in the public crisis is harmful to the society. When the public crisis occurs, the public respond in different ways and the public also want to tell others what they think right. But what they think is right is not recognized by the government. Thus the false information forms and it begins to diffuse. As the false information spreads, the harm to society magnifies gradually. Particularly in network society, false information diffusion can easily cause secondary hazards and accelerate public crises to a devastating degree. Thus intervening and controlling the false information diffusion is an important aspect of the public crisis management. From the perspective of the social network theory, this study analyzes the progress of false information diffusion in terms of different public crisis management strategies and presents the result of false information diffusion through simulation on cellular automaton of different public crisis management strategies. In simulations on cellular automaton, interventions are also carried to control false information diffusion and alternatives are proposed to help reduce public crises. This study also extends the theory of false information management, which is significant for the government to improve the ability to evaluate the false information and carry out interventions effectively to control the false information when it begins to diffuse

Photocatalytic nitrogen reduction to ammonia: Insights into the role of defect engineering in photocatalysts

Engineering of defects in semiconductors provides an effective protocol for improving photocatalytic N2 conversion efficiency. This review focuses on the state-of-the-art progress in defect engineering of photocatalysts for the N2 reduction toward ammonia. The basic principles and mechanisms of thermal catalyzed and photon-induced N2 reduction are first concisely recapped, including relevant properties of the N2 molecule, reaction pathways, and NH3 quantification methods. Subsequently, defect classification, synthesis strategies, and identification techniques are compendiously summarized. Advances of in situ characterization techniques for monitoring defect state during the N2 reduction process are also described. Especially, various surface defect strategies and their critical roles in improving the N2 photoreduction performance are highlighted, including surface vacancies (i.e., anionic vacancies and cationic vacancies), heteroatom doping (i.e., metal element doping and nonmetal element doping), and atomically defined surface sites. Finally, future opportunities and challenges as well as perspectives on further development of defect-engineered photocatalysts for the nitrogen reduction to ammonia are presented. It is expected that this review can provide a profound guidance for more specialized design of defect-engineered catalysts with high activity and stability for nitrogen photochemical fixation

GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model

Trajectory prediction has been a long-standing problem in intelligent systems such as autonomous driving and robot navigation. Recent state-of-the-art models trained on large-scale benchmarks have been pushing the limit of performance rapidly, mainly focusing on improving prediction accuracy. However, those models put less emphasis on efficiency, which is critical for real-time applications. This paper proposes an attention-based graph model named GATraj with a much higher prediction speed. Spatial-temporal dynamics of agents, e.g., pedestrians or vehicles, are modeled by attention mechanisms. Interactions among agents are modeled by a graph convolutional network. We also implement a Laplacian mixture decoder to mitigate mode collapse and generate diverse multimodal predictions for each agent. Our model achieves performance on par with the state-of-the-art models at a much higher prediction speed tested on multiple open datasets

ThumbNet: One Thumbnail Image Contains All You Need for Recognition

Although deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks, its real-world application is still impeded by its voracious demand of computational resources. Current works mostly seek to compress the network by reducing its parameters or parameter-incurred computation, neglecting the influence of the input image on the system complexity. Based on the fact that input images of a CNN contain substantial redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet, to simultaneously accelerate and compress CNN models by enabling them to infer on one thumbnail image. We provide three effective strategies to train ThumbNet. In doing so, ThumbNet learns an inference network that performs equally well on small images as the original-input network on large images. With ThumbNet, not only do we obtain the thumbnail-input inference network that can drastically reduce computation and memory requirements, but also we obtain an image downscaler that can generate thumbnail images for generic classification tasks. Extensive experiments show the effectiveness of ThumbNet, and demonstrate that the thumbnail-input inference network learned by ThumbNet can adequately retain the accuracy of the original-input network even when the input images are downscaled 16 times

Preparation and Characterization of Barite/TiO 2

To make full use of barite mineral and obtain a kind of composite particles material which has the property of both barite and TiO2, the composite particles material with TiO2 coated on the surface of barite particle was prepared by the method of TiOSO4 solution chemical hydrolysis and precipitation to form hydrolysis composite, removing the impurities of hydrolysis composite, drying, and calcination in this study. The results were evaluated by the covering power of composites. Composite structure and properties were characterized by means of XRD, SEM, FTIR, and XPS. The results showed that the surface of barite had been coated with rutile TiO2 uniformly and compactly and the hiding power value and oil absorption value of the composite powder were 18.50 g/m2 and 15.5 g/100 g, respectively, which had similar pigment performances to TiO2. The results also showed that it was mainly the strong chemical bond between barite and TiO2 that combined them firmly in barite/TiO2 composite particle (B/TCP)

Analysis of factors influencing the efficacy of vagus nerve stimulation for the treatment of drug-resistant epilepsy in children and prediction model for efficacy evaluation

ObjectiveVagus nerve stimulation (VNS) has been widely used in the treatment of drug-resistant epilepsy (DRE) in children. We aimed to explore the efficacy and safety of VNS, focusing on factors that can influence the efficacy of VNS, and construct a prediction model for the efficacy of VNS in the treatment of DRE children.MethodsRetrospectively analyzed 45 DRE children who underwent VNS at Qilu Hospital of Shandong University from June 2016 to November 2022. A ≥50% reduction in seizure frequency was defined as responder, logistic regression analyses were performed to analyze factors affecting the efficacy of VNS, and a predictive model was constructed. The predictive model was evaluated by receiver operating characteristic curve (ROC), calibration curves, and decision curve analyses (DCA).ResultsA total of 45 DRE children were included in this study, and the frequency of seizures was significantly reduced after VNS treatment, with 25 responders (55.6%), of whom 6 (13.3%) achieved seizure freedom. There was a significant improvement in the Quality of Life in Childhood Epilepsy Questionnaire (15.5%) and Seizure Severity Score (46.2%). 16 potential factors affecting the efficacy of VNS were included, and three statistically significant positive predictors were ultimately screened: shorter seizure duration, focal seizure, and absence of intellectual disability. We developed a nomogram for predicting the efficacy of VNS in the treatment of DRE children. The ROC curve confirmed that the predictive model has good diagnostic performance (AUC = 0.864, P < 0.05), and the nomogram can be further validated by bootstrapping for 1,000 repetitions, with a C-index of 0.837. Besides, this model showed good fitting and calibration and positive net benefits in decision curve analysis.ConclusionVNS is a safe and effective treatment for DRE children. We developed a predictive nomogram for the efficacy of VNS, which provides a basis for more accurate selection of VNS patients

Spatiotemporal patterns and spatial risk factors for visceral leishmaniasis from 2007 to 2017 in Western and Central China: a modelling analysis

Visceral leishmaniasis (VL) is a neglected disease caused by trypanosomatid protozoa in the genus Leishmania, which is transmitted by phlebotomine sandflies. Although this vector-borne disease has been eliminated in several regions of China during the last century, the reported human VL cases have rebounded in Western and Central China in recent decades. However, understanding of the spatial epidemiology of the disease remains vague, as the spatial risk factors driving the spatial heterogeneity of VL. In this study, we analyzed the spatiotemporal patterns of annual human VL cases in Western and Central China from 2007 to 2017. Based on the related spatial maps, the boosted regression tree (BRT) model was adopted to explore the relationships between VL and spatial correlates as well as predicting both the existing and potential infection risk zones of VL in Western and Central China. The mined links reveal that elevation, minimum temperature, relative humidity, and annual accumulated precipitation make great contributions to the spatial heterogeneity of VL. The maps show that Xinjiang Uygur Autonomous Region, Gansu, western Inner Mongolia Autonomous Region, and Sichuan are predicted to fall in the highest infection risk zones of VL. Approximately 61.60 million resident populations lived in the high-risk regions of VL in Western and Central China. Our results provide a better understanding of how spatial risk factors driving VL spread as well as identifying the potential endemic risk region of VL, thereby enhancing the biosurveillance capacity of public health authorities