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This paper presents a Semantic Positioning System (SPS) to enhance the accuracy of mobile device geo-localization in outdoor urban environments. Although the traditional Global Positioning System (GPS) can offer a rough localization, it lacks the necessary accuracy for applications such as Augmented Reality (AR). Our SPS integrates Geographic Information System (GIS) data, GPS signals, and visual image information to estimate the 6 Degree-of-Freedom (DoF) pose through cross-view semantic matching. This approach has excellent scalability to support GIS context with Levels of Detail (LOD). The map data representation is Digital Elevation Model (DEM), a cost-effective aerial map that allows for fast deployment for large-scale areas. However, the DEM lacks geometric and texture details, making it challenging for traditional visual feature extraction to establish pixel/voxel level cross-view correspondences. To address this, we sample observation pixels from the query ground-view image using predicted semantic labels. We then propose an iterative homography estimation method with semantic correspondences. To improve the efficiency of the overall system, we further employ a heuristic search to speedup the matching process. The proposed method is robust, real-time, and automatic. Quantitative experiments on the challenging Bund dataset show that we achieve a positioning accuracy of 73.24%, surpassing the baseline skyline-based method by 20%. Compared with the state-of-the-art semantic-based approach on the Kitti dataset, we improve the positioning accuracy by an average of 5%.

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Cadmium (Cd) poses great threats to human health as a major contaminant in agricultural soil. Biochar shows great potential in the remediation of agricultural soil. However, it remains unclear whether the remediation effect of biochar on Cd pollution is affected by various cropping systems. Here, this study used 2007 paired observations from 227 peer-reviewed articles and employed hierarchical meta-analysis to investigate the response of three types of cropping systems to the remediation of Cd pollution by using biochar. As a result, biochar application significantly reduced the Cd content in soil, plant roots and edible parts of various cropping systems. The decrease in Cd level ranged from 24.9% to 45.0%. The feedstock, application rate, and pH of biochar as well as soil pH and cation exchange capacity were dominant factors for Cd remediation effect of biochar, and their relative importance all exceeded 37.4%. Lignocellulosic and herbal biochar were found to be suitable for all cropping systems, while the effects of manure, wood and biomass biochar were limited in cereal cropping systems. Furthermore, biochar exhibited a more long-lasting remediation effect on paddy soils than on dryland. This study provides new insights into the sustainable agricultural management of typical cropping systems.

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The contamination of soils with petroleum and its derivatives is a longstanding, widespread, and worsening environmental issue. However, efforts to remediate petroleum hydrocarbon-polluted soils often neglect or overlook the interference of heavy metals that often co-contaminate these soils and occur in petroleum itself. Here, we identified Acinetobacter baumannii strain JYZ-03 according to its Gram staining, oxidase reaction, biochemical tests, and FAME and 16S rDNA gene sequence analyses and determined that it has the ability to degrade petroleum hydrocarbons. It was isolated from soil contaminated by both heavy metals and petroleum hydrocarbons. Strain JYZ-03 utilized diesel oil, long-chain n-alkanes, branched alkanes, and polycyclic aromatic hydrocarbons (PAHs) as its sole carbon sources. It degraded 93.29% of the diesel oil burden in 7 days. It also had a high tolerance to heavy metal stress caused by arsenic (As). Its petroleum hydrocarbon degradation efficiency remained constant over the 0-300 mg/L As(V) range. Its optimal growth conditions were pH 7.0 and 25-30 °C, respectively, and its growth was not inhibited even by 3.0% (w/v) NaCl. Strain JYZ-03 effectively bioremediates petroleum hydrocarbon-contaminated soil in the presence of As stress. Therefore, strain JYZ-03 may be of high value in petroleum- and heavy-metal-contaminated site bioremediation.

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The rapid spread of the coronavirus disease (COVID-19) pandemic in over 200 countries poses a substantial threat to human health. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, can be discharged with feces into the drainage system. However, a comprehensive understanding of the occurrence, presence, and potential transmission of SARS-CoV-2 in sewers, especially in community sewers, is still lacking. This study investigated the virus occurrence by viral nucleic acid testing in vent stacks, septic tanks, and the main sewer outlets of community where confirmed patients had lived during the outbreak of the epidemic in Wuhan, China. The results indicated that the risk of long-term emission of SARS-CoV-2 to the environment via vent stacks of buildings was low after confirmed patients were hospitalized. SARS-CoV-2 were mainly detected in the liquid phase, as opposed to being detected in aerosols, and its RNA in the sewage of septic tanks could be detected for only four days after confirmed patients were hospitalized. The surveillance of SARS-CoV-2 in sewage could be a sensitive indicator for the possible presence of asymptomatic patients in the community, though the viral concentration could be diluted more than 10 times, depending on the sampling site, as indicated by the Escherichia coli (E. coli) test. The comprehensive investigation of the community sewage drainage system is helpful to understand the occurrence characteristics of SARS-CoV-2 in sewage after excretion with feces and the feasibility of sewage surveillance for COVID-19 pandemic monitoring.

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Compositing is one of the most important editing operations for images and videos. The process of improving the realism of composite results is often called harmonization. Previous approaches for harmonization mainly focus on images. In this paper, we take one step further to attack the problem of video harmonization. Specifically, we train a convolutional neural network in an adversarial way, exploiting a pixel-wise disharmony discriminator to achieve more realistic harmonized results and introducing a temporal loss to increase temporal consistency between consecutive harmonized frames. Thanks to the pixel-wise disharmony discriminator, we are also able to relieve the need of input foreground masks. Since existing video datasets which have ground-truth foreground masks and optical flows are not sufficiently large, we propose a simple yet efficient method to build up a synthetic dataset supporting supervised training of the proposed adversarial network. The experiments show that training on our synthetic dataset generalizes well to the real-world composite dataset. In addition, our method successfully incorporates temporal consistency during training and achieves more harmonious visual results than previous methods.

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We present a novel algorithm for semantic segmentation and labeling of 3D point clouds of indoor scenes, where objects in point clouds can have significant variations and complex configurations. Effective segmentation methods decomposing point clouds into semantically meaningful pieces are highly desirable for object recognition, scene understanding, scene modeling, etc. However, existing segmentation methods based on low-level geometry tend to either under-segment or over-segment point clouds. Our method takes a fundamentally different approach, where semantic segmentation is achieved along with labeling. To cope with substantial shape variation for objects in the same category, we first segment point clouds into surface patches and use unsupervised clustering to group patches in the training set into clusters, providing an intermediate representation for effectively learning patch relationships. During testing, we propose a novel patch segmentation and classification framework with multiscale processing, where the local segmentation level is automatically determined by exploiting the learned cluster based contextual information. Our method thus produces robust patch segmentation and semantic labeling results, avoiding parameter sensitivity. We further learn object-cluster relationships from the training set, and produce semantically meaningful object level segmentation. Our method outperforms state-of-the-art methods on several representative point cloud datasets, including S3DIS, SceneNN, Cornell RGB-D and ETH.

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Bisphenol A (BPA), diethyl phthalate (DEP), and carbamazepine (CBZ) have been widely used in chemical and pharmaceutical fields, and their residues are detected in various environments. Therefore, to find a suitable method for removing the compounds from an aqueous solution, an adsorption method by granular activated charcoal (AC) was studied. To investigate the adsorption properties of AC, its kinetics, equilibrium, pH effects, and regeneration of AC were examined. Moreover, its surface properties (i.e., surface area, pore volume, functional groups, and surface charge) were characterized by N2 adsorption and desorption isotherm, Fourier transform infrared (FTIR), and zeta potential analyses. Experimental results show that AC has high removal efficiencies for the target compounds at the low initial concentration as well as high estimated adsorption capacities (qm) for DEP, BPA, and CBZ, whose values were 293.4 ± 18.8, 254.9 ± 16.2, and 153.3 ± 1.61 mg/g, respectively. In comparison with other adsorbents based on previously reported results, AC was shown to have generally higher removability for the three compounds than others. Moreover, it was observed that AC's ability to adsorb DEP and BPA was dependent on pH because of hydrolysis and ionization, respectively. Meanwhile, there is no pH effect for CBZ adsorption by AC. After 3 cycles of adsorption/desorption, AC still maintained 92, 100, and 82% of initial adsorption capacities for DEP, BPA, and CBZ, respectively. Therefore, the AC is an effective adsorbent for the removal of endocrine-disrupting chemicals and pharmaceuticals from aqueous solution.

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The metal-organic framework (MOF) materials, MIL-101(Cr), and copper-doped MIL-101(Cr) (Cu@MIL-101(Cr)) were prepared through hydrothermal method and were used to remove volatile organic compounds (VOCs) in this study. Morphological characterization demonstrated that MIL-101(Cr) and Cu-3@MIL-101(Cr) were octahedral crystal, with specific surface area of 3367 and 2518 m2/g, respectively. The results of XRD, TG, and FTIR showed that the copper doping procedure would not alter the skeleton structure, but it would affect the crystallinity and thermal stability of MIL-101(Cr). Besides, MIL-101(Cr) and Cu-3@MIL-101(Cr) displayed good removal efficiencies on benzene sorption, and the maximum sorption capacity was 103.4 and 114.4 mg/g, respectively. In competitive adsorptions, the order of adsorption priority on Cu-3@MIL-101(Cr) was as follows: ethylbenzene > toluene > benzene. Hence, it could be concluded that MIL-101(Cr) and copper-doped MIL-101(Cr) demonstrated good performance in VOCs adsorption and showed a promising potential for large-scale applications in the removal of VOCs. Graphical abstract ᅟ.

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The evapotranspiration (ET) cover system,as an alternative cover system of landfill, has been used in many remediation projects since 2003. It is an inexpensive, practical,and easily maintained biological system, but is mainly favorable in arid and semiarid sites due to limited water-holding capacity of the single loam layer and limited transpiration of grass. To improve the effectiveness of percolation control, an innovative scheme of ET was suggested in this paper: (1) a clay liner was added under the single loam layer to increase the water-holding capacity; (2) combined vegetation consisting of shrub and grass was used to replace the grass cover. Hydrologic evaluation of conventional cover,ET cover and the innovative ET cover under the same condition was performed using the computer program HELP, which showed the performance of the innovative ET cover is obviously superior to that of ET cover and conventional cover.

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The sludge, which was collected from a biological coke wastewater treatment plant, was used as a low-cost adsorbent in the removal of reactive dyes (Methylene Blue (MB) and Reactive Red 4 (RR4)) from aqueous solution. The pH of dye solution played an important role on the dye uptake. With the solution pH increase, the MB uptake increased; whereas the RR4 uptake decreased. The maximum uptake of RR4 by protonated sludge was 73.7 mg/g at pH 1, and the maximum uptake of MB by sludge was 235.3 mg/g at pH 9. Three functional groups, including carboxyl, phosphonate, and amine group, were identified by potentiometric titration, fourier transform infrared (FT-IR) spectrometry, and X-ray photoelectron spectroscopy (XPS). The anionic functional groups, phosphonate and carboxyl group, were identified as the binding sites for the cationic MB. Amine groups were identified to bind RR4. The main mechanism of the reactive dyestuffs adsorption is electrostatic interaction.

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