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The historical sedimentary and evolutionary characteristics of persistent organic pollutants and endocrine disruptors in typical regions of the Three Gorges Reservoir are scarcely studied. Herein, the 96-year data on contaminated sediment history were reconstructed using Caesium 137 isotope dating. Polychlorinated biphenyl concentrations in the involved sediment cores ranged from non-detected (ND) to 11.39 ng/g. The concentrations of polycyclic aromatic hydrocarbons ranged from ND to 2075.20 ng/g and peaked in the 1970s owing to natural, agricultural and human activities. Further, phthalate esters (PAEs) and heavy metals (HMs) were detected at concentrations ranging from ND to 589.2 ng/g and 12.10-93.67 µg/g, respectively, with highest values recorded in the 1980s owing to rapid industrialisation and insufficient management during China's early reform and development stages. PAE and HM concentrations have increased in recent years, suggesting the need to focus on industrial and agricultural activities that have caused this impact. Although current pollutant concentrations in sediments do not pose a risk to the aquatic ecosystem, they should be continuously monitored.
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Flood discharge atomization is a prevalent hydraulics phenomenon in reservoir scheduling operations, however, its effect on the migration and transformation behavior of pollutants has not been examined. In this study, the behaviors and mechanisms of the direct photodegradation of dibutyl phthalate (DBP) in atomized water and the indirect photodegradation of DBP in the presence of ferric ions and nitrate were investigated. The results showed that the photodegradation rate of DBP was accelerated under atomization conditions by sunlight irradiation. The photodegradation efficiency of DBP in the presence of ferric ions and nitrate under atomization conditions was increased by 2.20 times and 1.82 times compared with no-atomization conditions, respectively. The quencher experiments indicated that the main active species for DBP photodegradation in the presence of ferric ions were hydroxyl radicals (·OH) and superoxide radicals (·O2-) with atomization, while the main active species in the presence of nitrate were ·OH, ·O2- and electrons (e-). In addition, the differences were found in the photodegradation products and pathways of DBP between with and without atomization treatment. In the presence of ferric ions, the benzene ring of DBP was opened to produce fumaric acid, while phthalic acid bis(4-hydroxybutyl) ester was produced in the presence of nitrate under atomization conditions. The results of this study provide a scientific basis for assessing the effect of water conservancy projects on the migration and transformation behaviors of pollutants, which is of great theoretical significance and scientific value.
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Inflammation plays a significant role in the occurrence and development of acute kidney injury (AKI). Evidence regarding the prognostic effect of the systemic immune-inflammation index (SII) in critically ill patients with AKI is scarce. The aim of this study was to assess the association between SII and all-cause mortality in these patients. Detailed clinical data were extracted from the Medical Information Mart for Intensive Care Database (MIMIC)-IV. The primary outcome was set as the in-hospital mortality. A total of 10,764 AKI patients were enrolled in this study. The restricted cubic splines analyses showed a J-shaped curve between SII and the risk of in-hospital and ICU mortality. After adjusting for relevant confounders, multivariate Cox regression analysis showed that both lower and higher SII levels were associated with an elevated risk of in-hospital all-cause mortality. A similar trend was observed for ICU mortality. In summary, we found that the SII was associated in a J-shaped pattern with all-cause mortality among critically ill patients with AKI. SII appears to be have potential applications in the clinical setting as a novel and easily accessible biomarker for predicting the prognosis of AKI patients.
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The Wasserstein distance, especially among symmetric positive-definite matrices, has broad and deep influences on the development of artificial intelligence (AI) and other branches of computer science. In this paper, by involving the Wasserstein metric on SPD(n), we obtain computationally feasible expressions for some geometric quantities, including geodesics, exponential maps, the Riemannian connection, Jacobi fields and curvatures, particularly the scalar curvature. Furthermore, we discuss the behavior of geodesics and prove that the manifold is globally geodesic convex. Finally, we design algorithms for point cloud denoising and edge detecting of a polluted image based on the Wasserstein curvature on SPD(n). The experimental results show the efficiency and robustness of our curvature-based methods.
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Shape registration, finding the correct alignment of two sets of data, plays a significant role in computer vision such as objection recognition and image analysis. The iterative closest point (ICP) algorithm is one of well known and widely used algorithms in this area. The main purpose of this paper is to incorporate ICP with the fast convergent extended Hamiltonian learning (EHL), so called EHL-ICP algorithm, to perform planar and spatial rigid shape registration. By treating the registration error as the potential for the extended Hamiltonian system, the rigid shape registration is modelled as an optimization problem on the special Euclidean group S E ( n ) ( n = 2 , 3 ) . Our method is robust to initial values and parameters. Compared with some state-of-art methods, our approach shows better efficiency and accuracy by simulation experiments.
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Bayesian methods have been rapidly developed due to the important role of explicable causality in practical problems. We develope geometric approaches to Bayesian inference of Pareto models, and give an application to the analysis of sea clutter. For Pareto two-parameter model, we show the non-existence of α-parallel prior in general, hence we adopt Jeffreys prior to deal with the Bayesian inference. Considering geodesic distance as the loss function, an estimation in the sense of minimal mean geodesic distance is obtained. Meanwhile, by involving Al-Bayyati's loss function we gain a new class of Bayesian estimations. In the simulation, for sea clutter, we adopt Pareto model to acquire various types of parameter estimations and the posterior prediction results. Simulation results show the advantages of the Bayesian estimations proposed and the posterior prediction.
