On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.Comment: 82 pages, 32 figure

A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines

Traditional thermodynamic models for military turbofans suffer from non-convergence and inaccuracy due to inaccuracy of the component maps and the instability of the iterative process. To address these problems, a thermodynamically oriented and neural network-based hybrid model for military turbofans is proposed. Different from iteration-based thermodynamic models, the proposed hybrid model transforms the iteration process into a multi-objective optimization and training process for a component-level neural network in order to improve convergence and modeling accuracy. The experiment shows that the accuracy of the proposed hybrid model can reach about 7%, 5% better than the map-fitting-based thermodynamic model and 8% better than the purely data-driven method, with a similar number of network neutrons, verifying its effectiveness. The contributions of this work mainly lie in the following aspects: a new component-level neural network structure is proposed to improve convergence and computational efficiency; a multi-objective loss function based on component co-working is proposed to direct the model to converge toward the physical thermodynamic process; a fusion training method of multiple data sources is established to train the model with good convergence and high computational accuracy

Fast Fluid Simulation via Dynamic Multi-Scale Gridding

Recent works on learning-based frameworks for Lagrangian (i.e., particle-based) fluid simulation, though bypassing iterative pressure projection via efficient convolution operators, are still time-consuming due to excessive amount of particles. To address this challenge, we propose a dynamic multi-scale gridding method to reduce the magnitude of elements that have to be processed, by observing repeated particle motion patterns within certain consistent regions. Specifically, we hierarchically generate multi-scale micelles in Euclidean space by grouping particles that share similar motion patterns/characteristics based on super-light motion and scale estimation modules. With little internal motion variation, each micelle is modeled as a single rigid body with convolution only applied to a single representative particle. In addition, a distance-based interpolation is conducted to propagate relative motion message among micelles. With our efficient design, the network produces high visual fidelity fluid simulations with the inference time to be only 4.24 ms/frame (with 6K fluid particles), hence enables real-time human-computer interaction and animation. Experimental results on multiple datasets show that our work achieves great simulation acceleration with negligible prediction error increase

Recovery of Soil-Denitrifying Community along a Chronosequence of Sand-Fixation Forest in a Semi-Arid Desertified Grassland

Revegetation on moving sand dunes is a widely used approach for restoring the degraded sandy land in northeastern China. The development of sand-fixation forest might improve the structures of soil microbial communities and affect soil N cycle. In the present study, the diversities of nitrite (nirS and nirK) and nitrous oxide (nosZ) reductase genes were investigated under a chronosequence of Caragana microphylla sand-fixation shrub forest (9- and 19-year), adjacent non-vegetated shifting sand-dune, and a natural forest dominated by C. microphylla. The dominant compositions and gene abundance were analyzed by a clone library technique and quantitative polymerase chain reaction, respectively. The compositions and dominant taxa of nirK, nirS, and nosZ communities under forest soil were all similar to those in the shifting sand-dune. However, the three gene abundances all linearly increased across forest age. Clones associated with known denitrifiers carrying nosZ, nirK, or nirS genes, such as members of Pseudomonas, Mesorhizobium, Rhizobium, Rhodopseudomonas, Azospirillum, and Cupriavidus, were detected. These denitrifiers were found to be abundant in soil and dominant in soil denitrification. Soil pH, total N, and available N affected the denitrifying communities by altering the relative abundance of dominant taxa. Overall, although soil attributes and forest age had no significant effects on the dominant constituents of nirK, nirS, and nosZ communities, revegetation on shifting sand-dunes facilitated the quantitative restoration of soil denitrifiers due to the increase in soil nutrients

New Insights in Molecular Mechanisms in Antimicrobial Resistance and Strategies in Anti-Biofilms

This topical collection, entitled “Antimicrobial resistance and anti-biofilms”, was first launched in the journal Antibiotics in November of 2020 [...

Investigation on Filtration Control of Zwitterionic Polymer AADN in High Temperature High Pressure Water-Based Drilling Fluids

With the exploration and development of high-temperature and high-salt deep oil and gas, more rigorous requirements are warranted for the performance of water-based drilling fluids (WBDFs). In this study, acrylamide, 2-acrylamide-2-methylpropanesulfonic acid, diallyl dimethyl ammonium chloride, and N-vinylpyrrolidone were synthesized by free radical copolymerization in an aqueous solution to form a temperature and salt-resistant zwitterionic polymer gel filtration loss reducer (AADN). The zwitterionic polymer had excellent adsorption and hydration groups, which could effectively combine with bentonite through hydrogen bonds and electrostatic attraction, strengthening the hydration film thickness on the surface of bentonite, and promoting the stable dispersion of drilling fluid. In addition, the reverse polyelectrolyte effect of zwitterionic polymers strengthened the drilling fluid’s ability to resist high-temperature and high-salt. The AADN-based drilling fluid showed excellent rheological and filtration control properties (FLAPI < 8 mL, FLHTHP < 29.6 mL) even after aging at high-temperature (200 °C) and high-salt (20 wt% NaCl) conditions. This study provides a new strategy for simultaneously improving the high-temperature and high-salt tolerance of WBDFs, presenting the potential for application in drilling in high-temperature and high-salt deep formations

Kinetic Hydrate Inhibition of Natural Gels in Complex Sediment Environments

Natural gels are emerging as a hotspot of global research for their greenness, environmental-friendliness, and good hydrate inhibition performance. However, previous studies mostly performed experiments for simple pure water systems and the inhibition mechanism in the sediment environment remains unclear. Given this, the inhibition performance of xanthan gum and pectin on hydrate nucleation and growth in sediment environments was evaluated via hydrate formation inhibition tests, and the inhibition internal mechanisms were revealed via a comprehensive analysis integrating various methods. Furthermore, the influences of natural gels on sediment dispersion stability and low-temperature fluid rheology were investigated. Research showed that the sediments of gas hydrate reservoirs in the South China Sea are mainly composed of micro-nano quartz and clay minerals. Xanthan gum and pectin can effectively inhibit the hydrate formation via the joint effects of the binding, disturbing, and interlayer mass transfer suppression processes. Sediments promote hydrate nucleation and yet inhibit hydrate growth. The interaction of sediments with active groups of natural gels weakens the abilities of gels to inhibit hydrate nucleation and reduce hydrate formation. Nonetheless, sediments help gels to slow down hydrate formation. Our comprehensive analysis pointed out that pectin with a concentration of 0.5 wt% can effectively inhibit the hydrate nucleation and growth while improving the dispersion stability and low-temperature rheology of sediment-containing fluids

Metabolomic and transcriptomic analyses reveal response mechanisms of juvenile flounder (Paralichthys olivaceus) to sublethal methylmercury

Although methylmercury (MeHg) has been recognized as a typical heavy metal posing huge damages to various life processes of fish, the response mechanisms of marine fish at early life stages (ELSs) to MeHg is still poorly understood. In this study, non-targeted liquid chromatography-mass spectrometry (LC-MS) based metabolomic and transcriptomic approaches were used to explore response mechanisms of juvenile flounder (Paralichthys olivaceus) to long-term sublethal MeHg exposure (0 and 1.0 mu g L-1; 30 d). After exposure, growth parameters of flounder were significantly decreased. Metabolomic and transcriptomic analyses of liver tissue showed obvious difference about biological pathways and identified biomarkers (around 2502 genes and 16 secondary metabolites). Those significantly differentially expressed genes (DEGs) and their enriched pathways were mainly related to immune response, oxidative stress, lipids metabolism, glycometabolism, amino acid and nucleotide metabolism and regulation of protein processes, while those identified secondary metabolites were mainly enriched in tryptophan metabolism, biosynthesis of unsaturated fatty acids, linoleic acid metabolism and glutathione metabolism. Additionally, multi-omic method was used to explore response mechanisms of key pathways under MeHg stress. In this regard, only 57 DEGs and 6 secondary metabolites were significantly enriched in 7 pathways to constitute an integrated regulatory network, including glutathione metabolism, thyroid hormone synthesis, linoleic acid metabolism, biosynthesis of unsaturated fatty acids, tryptophan metabolism pathway, serotonergic synapse and African trypanosomiasis. Above all, we could speculate that antioxidative function, lipids metabolism, nervous system and amino acid metabolism were the more sensitive targets in response to MeHg stress, which were conductive to deeply understand the response mechanisms of fish at ELSs under MeHg exposure. Those identified biomarkers could also be widely used for toxicological studies of pollutants and ecological risks monitoring