The composition and origination of particles from surface water in the Chukchi Sea, Arctic Ocean

Suspended particle samples were collected at 11 stations on the shelf and slope regions of the Chukchi Sea and the central Arctic Ocean during the fifth Chinese National Arctic Research Expedition (summer 2012). The particle concentration, total organic carbon (TOC), total nitrogen (TN) and the isotopic composition of the samples were analyzed. The suspended particle concentration varied between 0.56 and 4.01 mg.L-1; the samples collected from the sea ice margin have higher concentrations. The organic matter content is higher in the shelf area (TOC: 9.78%–20.24%; TN: 0.91%–2.31%), and exhibits heavier isotopic compositions (δ13C: –23.29‰ to –26.33‰ PDB; δ15N: 6.14‰–7.78‰), indicating that the organic matter is mostly marine in origin with some terrigenous input. In the slope and the central Arctic Ocean, the organic matter content is lower (TOC: 8.06% – 8.96%; TN: 0.46%–0.72%), except for one sample (SR15), and has lighter isotopic compositions (δ13C: –26.93‰ to – 27.78‰ PDB; δ15N: 4.13‰–4.84‰). This indicates that the organic matter is mostly terrestrially-derived in these regions. The extremely high amount of terrigenous organic matter (TOC: 27.94%; TN: 1.16%; δ13C: –27.43‰ PDB; δ15N: 3.81‰) implies that it was carried by transpolar currents from the East Siberian Sea. Material, including sea ice algae, carried by sea ice are the primary source for particles in the sea ice margins. Sea ice melting released a substantial amount of biomass into the shelf, but a large amount of detrital and clay minerals in the slope and the central Arctic Ocean

DCTE-LLIE: A Dual Color-and-Texture-Enhancement-Based Method for Low-Light Image Enhancement

The enhancement of images captured under low-light conditions plays a vitally important role in the area of image processing and can significantly affect the performance of following operations. In recent years, deep learning techniques have been leveraged in the area of low-light image enhancement tasks, and deep-learning-based low-light image enhancement methods have been the mainstream for low-light image enhancement tasks. However, due to the inability of existing methods to effectively maintain the color distribution of the original input image and to effectively handle feature descriptions at different scales, the final enhanced image exhibits color distortion and local blurring phenomena. So, in this paper, a novel dual color-and-texture-enhancement-based low-light image enhancement method is proposed, which can effectively enhance low-light images. Firstly, a novel color enhancement block is leveraged to help maintain color distribution during the enhancement process, which can further eliminate the color distortion effect; after that, an attention-based multiscale texture enhancement block is proposed to help the network focus on multiscale local regions and extract more reliable texture representations automatically, and a fusion strategy is leveraged to fuse the multiscale feature representations automatically and finally generate the enhanced reflection component. The experimental results on public datasets and real-world low-light images established the effectiveness of the proposed method on low-light image enhancement tasks

Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes

Multi-stage growth and fluid evolution of a hydrothermal sulphide chimney in the East Pacific Ridge 1-2 degrees S hydrothermal field: constraints from in situ sulphur isotopes

Sulphur isotopes can be used as a powerful tool to trace fluid evolution and explore the formation of chimneys. To clarify the in situ S isotopic variations of sulphides at the micro-scale, we analyzed a sulphide chimney collected from the hydrothermal field in the East Pacific Rise 1–2° S using a sensitive high-mass-resolution ion micro-probe for stable isotopes (SHRIMP SI). Three mineral zones can be identified in the chimney: an external outer wall of porous anhydrite and colloform pyrite, an internal middle zone of sub-euhedral pyrite and massive chalcopyrite, and an inner zone of massive pyrite. The δ34SV-CDT values of the sulphides fall within the range 1.83–7.51‰ (avg. 4.05‰, n = 16), and S isotopic values increase from the core (3.09‰, n = 3) to the middle (3.78‰, n = 11) to the edge (6.99‰, n = 2). These results illustrate mineral crystallization processes and the mixing between seawater-derived S and magmatic–hydrothermal fluids during the growth of the chimney. The zones from the edge to the core are characterized by crystal morphologies of colloform/anhedral pyrite to massive pyrite with decreasing δ34S values, revealing multi-stage mineral deposition and sulphur isotopic fractionation. In contrast to the increase in δ34S values from the core to the edge in one profile (profile A), anomalously low δ34S values in fine-grained pyrite relative to chalcopyrite in another profile (profile B) in the middle zone result from S isotopic exchange between seawater SO4 2− and fluid H2S due to different fluid–seawater mixing, possibly caused by variations in permeability and porosity across the chimney.This work was financially supported by the National Natural Science Foundation of China (No. 41276055 & 41406066, the Fundamental Research Funds for the Central Public-Interest Scientific Institution (No. JT1701) and the China Ocean Mineral R&D Association (COMRA) project (No. DY135-G2-1-01, 03 & DY135-S2- 2-05)

Research on measurement and correction of a fish-eye image distortion

Fisheye lenses have the advantages of short focal length and large field of view. However, by using the "non-similar" imaging principle, they artificially introduce a large barrel distortion. In order to improve the quality of the images correction of distortion is required. This article analyzes the polar distortion correction model, raised a simple distortion coefficient calibration method and the use of bilinear interpolation method for gray level interpolation. Compared to other methods, this method is easier to reinforce and achieves high accuracy, and it can be easily implemented in the hardware system. At the end of the paper we introduced a device correction for a fisheye CCD camera. Based on the original data, a distortion correction model is established. In order to minimize the error, the correction was divided into three sections, and the image is well recovered

Determination of the Active Soap Number of Crude Oil and Soap Partitioning Behavior

The optimal salinity of the alkali/surfactant/crude oil system in an alkali/surfactant/polymer (ASP) flooding process was found previously to be a function of the soap/surfactant ratio. Therefore, the soap number is of great importance in formulation design and simulation of ASP flooding processes for enhanced oil recovery. However, there is as yet no established way to quantitatively determine the amount of soap in crude oil relevant to an ASP process. Soaps are the salts of fatty acids, a definition generalized here to include the salts of naphthenic acids. In this paper, we present a method to determine the amount of “active soap”, which consists only of soap that partitions into the aqueous phase at low ionic strength and transfers into the oleic phase at high ionic strength. Two fast and accurate methods, aqueous-phase potentiometric titration and two-phase colorimetric titration, were used to determine the water-soluble active soap number (WSASN), a measure of the active soap. Both methods were proven to be sufficiently precise by titrating a model oil containing known concentrations of oleic acid, both with and without isopropyl alcohol (IPA) present. The total soap number (TSN) with IPA present and water-soluble soap number (WSSN) and WSASN of a crude oil without IPA were measured in Na₂CO₃ and NaOH solutions. The partition of soap between oil and brine phases was also investigated. It was found that the partition coefficient of water-soluble active soap (WSAS) is near unity at optimal salinity as determined by IFT measurements, a result that supports the use of WSASN to represent the amount of active soap. Moreover, it was found that the logarithm of optimal salinity versus soap fraction for a soap/surfactant mixture followed the previously proposed mole fraction mixing rule more closely when WSASN was used than if total acid number (TAN) or TSN were used as in previous studies. It was also found that the values of WSSN and WSASN measured at room temperature were different from those measured at high temperature and that the soap generated by NaOH was more hydrophobic than that generated by Na₂CO₃. Results of this work are helpful for formulation design and simulation of ASP flooding processes

Large-Scale Synthesis and Mechanism of β‑SiC Nanoparticles from Rice Husks by Low-Temperature Magnesiothermic Reduction

Silicon carbide (SiC) nanomaterials have many applications in semiconductor, refractories, functional ceramics, and composite reinforcement due to their unique chemical and physical properties. However, large-scale and cost-effective synthesis of SiC nanomaterials at a low temperature is still challenging. Herein, a low-temperature and scalable process to produce β-phase SiC nanoparticles from rice husks (RHs) by magnesiothermic reduction (MR) at a relative low temperature of 600 °C is described. The SiC nanoparticles could inherit the morphology of biogenetic nano-SiO₂ in RHs with a size of about 20–30 nm. The MR reaction mechanism and role of intermediate species are investigated. The result shows that SiO₂ is first reduced to Mg₂Si in the rapid exothermic process and the intermediate product, Mg₂Si, further reacts with residual SiO₂ and C to produce SiC. Moreover, the SiC shows considerable electromagnetic wave absorption with a minimum reflection loss of −5.88 dB and reflection loss bandwidth < −5 dB of 1.78 GHz. This paper provides a large-scale, cost-effective, environmental friendly, and sustainable process to produce high-quality β-phase SiC nanoparticles from biomass at a low temperature, which is applicable to functional ceramics and optoelectronics